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Author SHA1 Message Date
ethernet ef1a933e29 change: add setup stamp 2026-06-04 22:25:40 -04:00
ethernet 1e5ae386ee fix: bad const 2026-06-04 22:09:54 -04:00
ethernet 77fef76924 fix(venv): .venv -> venv 2026-06-04 21:47:15 -04:00
ethernet 55879ebf1f fix(update): detached head update support 2026-06-04 21:33:34 -04:00
ethernet 692146939e fix(windows): Split hermes update into two-phase pull + post-pull with re-exec
_cmd_update_impl (monolith ~1350 lines) is gone. The update flow is now:

Phase 1 (_cmd_update_pull_new_version):
  - concurrent guard, backup, git pull / ZIP download+extract
  - stash handling, syntax guard, rollback, bytecode clear

Phase 2 (_cmd_update_post_pull):
  - pip install (managed-uv), node deps, web UI, desktop rebuild
  - skills sync, config migration, gateway restart, cleanup

Between phases, _reexec_for_post_pull replaces the process:
  - POSIX: os.execvp (true exec, same PID, fresh sys.modules)
  - Windows: subprocess.run relay (parent stays alive so bootstrap
    installer's child.wait() sees the real exit code)

Hidden --post-pull flag routes directly to phase 2.
--pre-update-snapshot carries snapshot ID across exec boundary.

Pip self-update is removed entirely. pip/uv/pipx installs now error
with recommended_update_command guidance, same as managed installs.

Also allow uv to come from termux.

Test updates:
  - _inline_post_pull autouse fixture in test_cmd_update,
    test_update_autostash, test_update_concurrent_quarantine
    (patches _reexec_for_post_pull to call _cmd_update_post_pull
    in-process, preventing os.execvp from nuking pytest)
  - test_uv_tool_update: removed _cmd_update_pip tests (function
    deleted), kept is_uv_tool_install + recommendation helpers
  - test_update_zip_symlink_reject: renamed imports to match
    _update_files_via_zip (now a thin delegation wrapper)
2026-06-04 21:16:16 -04:00
ethernet 27135e0e6a fix(update): don't try any git commands on windows if .git is missing 2026-06-04 20:23:52 -04:00
ethernet f774e9c6f5 feat(windows installer): recover from partial clone with no origin 2026-06-04 20:23:52 -04:00
2821 changed files with 66408 additions and 425902 deletions
-43
View File
@@ -63,46 +63,3 @@ data/
# Compose/profile runtime state (bind-mounted; avoid ownership/secret issues)
hermes-config/
runtime/
# ---------- Not needed inside the Docker image ----------
# Desktop app source (Tauri/Electron); never installed in the container.
# apps/shared is the dashboard↔desktop websocket helper and is linked from
# web/package.json as a file: workspace dep — keep it in the build context.
apps/
!apps/shared/
!apps/shared/**
# Test suite — not shipped in production images
tests/
# Documentation site (Docusaurus) and supplementary docs
website/
docs/
# Assets only used by the GitHub README
assets/
infographic/
# Plugin-level docs (hermes-achievements ships docs/ but the runtime doesn't read them)
plugins/hermes-achievements/docs/
# Nix / Homebrew / AUR packaging metadata — irrelevant to Docker
nix/
flake.nix
flake.lock
packaging/
# Design and planning documents
plans/
.plans/
# ACP registry manifest (icon + agent.json) — not consumed at runtime
acp_registry/
# Repo-level dotfiles that are git-only or dev-tooling config
.env.example
.envrc
.gitattributes
.hadolint.yaml
.mailmap
-4
View File
@@ -105,7 +105,6 @@
# Get your token at: https://huggingface.co/settings/tokens
# Required permission: "Make calls to Inference Providers"
# HF_TOKEN=
# HF_BASE_URL=https://router.huggingface.co/v1 # Override default base URL
# OPENCODE_GO_BASE_URL=https://opencode.ai/zen/go/v1 # Override default base URL
# =============================================================================
@@ -412,9 +411,6 @@ IMAGE_TOOLS_DEBUG=false
# Groq API key (free tier — used for Whisper STT in voice mode)
# GROQ_API_KEY=
# ElevenLabs API key (cloud STT/TTS — Scribe transcription)
# ELEVENLABS_API_KEY=
# =============================================================================
# STT PROVIDER SELECTION
# =============================================================================
+1 -1
View File
@@ -1,5 +1,5 @@
watch_file pyproject.toml uv.lock
watch_file package-lock.json package.json web/package.json ui-tui/package.json website/package.json apps/shared/package.json apps/desktop/package.json ui-tui/packages/hermes-ink/package.json
watch_file flake.nix flake.lock nix/devShell.nix nix/tui.nix nix/package.nix nix/python.nix nix/hermes-agent.nix nix/desktop.nix
watch_file flake.nix flake.lock nix/devShell.nix nix/tui.nix nix/package.nix nix/python.nix
use flake
-62
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@@ -1,62 +0,0 @@
name: Detect affected areas
description: >-
Classify a PR's changed files into CI work lanes (python, frontend, site,
scan, deps, mcp_catalog) so the orchestrator can conditionally call only
the sub-workflows a PR can affect. Outputs are always "true" on push/dispatch
events and fail open (everything "true") when the diff cannot be computed.
outputs:
python:
description: Run Python tests / ruff / ty / windows-footguns.
value: ${{ steps.classify.outputs.python }}
frontend:
description: Run the TypeScript typecheck matrix + desktop build.
value: ${{ steps.classify.outputs.frontend }}
docker_meta:
description: Docker setup and meta files have changed.
value: ${{ steps.classify.outputs.docker_meta }}
site:
description: Build the Docusaurus docs site.
value: ${{ steps.classify.outputs.site }}
scan:
description: Run the supply-chain critical-pattern scanner.
value: ${{ steps.classify.outputs.scan }}
deps:
description: Check pyproject.toml dependency upper bounds.
value: ${{ steps.classify.outputs.deps }}
mcp_catalog:
description: Require MCP catalog security review label.
value: ${{ steps.classify.outputs.mcp_catalog }}
runs:
using: composite
steps:
- name: Classify changed files
id: classify
shell: bash
env:
GH_TOKEN: ${{ github.token }}
REPO: ${{ github.repository }}
EVENT_NAME: ${{ github.event_name }}
BASE_SHA: ${{ github.event.pull_request.base.sha }}
HEAD_SHA: ${{ github.event.pull_request.head.sha }}
run: |
set -euo pipefail
# Only pull_request events are gated. Other events (push, release,
# dispatch) leave CHANGED empty, so the classifier fails open and every
# lane runs. Post-merge / on-demand validation is never weakened.
if [ "$EVENT_NAME" = "pull_request" ]; then
# Use the compare endpoint with the pinned base/head SHAs from the
# event payload instead of the "current PR files" endpoint. The SHAs
# are frozen at trigger time, so the file list is deterministic even
# if the PR receives a new push between trigger and detect.
CHANGED="$(gh api \
--paginate \
"repos/${REPO}/compare/${BASE_SHA}...${HEAD_SHA}" \
--jq '.files[].filename' || true)"
fi
echo "Changed files:"
printf '%s\n' "${CHANGED:-(none)}"
printf '%s\n' "${CHANGED:-}" | python3 scripts/ci/classify_changes.py
@@ -0,0 +1,50 @@
name: Hermes smoke test
description: >
Run the image's built-in entrypoint against `--help` and `dashboard --help`
to catch basic runtime regressions before publishing. Requires the image
to already be loaded into the local Docker daemon under `image`.
Works identically on amd64 and arm64 runners.
inputs:
image:
description: Fully-qualified image tag (e.g. nousresearch/hermes-agent:test)
required: true
runs:
using: composite
steps:
- name: Ensure /tmp/hermes-test is hermes-writable
shell: bash
run: |
# The image runs as the hermes user (UID 10000). GitHub Actions
# creates /tmp/hermes-test root-owned by default, which hermes
# can't write to — chown it to match the in-container UID before
# bind-mounting. Real users doing `docker run -v ~/.hermes:...`
# with their own UID hit the same issue and have their own
# remediations (HERMES_UID env var, or chown locally).
mkdir -p /tmp/hermes-test
sudo chown -R 10000:10000 /tmp/hermes-test
- name: hermes --help
shell: bash
run: |
# Use the image's real ENTRYPOINT (/init + main-wrapper.sh) so
# this exercises the actual production startup path. PR #30136
# review caught that an --entrypoint override here had been
# silently neutered by the s6-overlay migration — stage2-hook
# ignores its CMD args, so the smoke test was a no-op.
docker run --rm \
-v /tmp/hermes-test:/opt/data \
"${{ inputs.image }}" --help
- name: hermes dashboard --help
shell: bash
run: |
# Regression guard for #9153: dashboard was present in source but
# missing from the published image. If this fails, something in
# the Dockerfile is excluding the dashboard subcommand from the
# installed package.
docker run --rm \
-v /tmp/hermes-test:/opt/data \
"${{ inputs.image }}" dashboard --help
-50
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@@ -1,50 +0,0 @@
name: Retry a flaky command
description: >-
Run a shell command, retrying on non-zero exit. For dependency installs
(npm ci, uv sync) whose only failures are transient network/toolchain
flakes — a node-gyp header fetch, a registry blip — so CI self-heals
instead of needing a manual re-run.
inputs:
command:
description: Shell command to run (and retry).
required: true
attempts:
description: Max attempts before giving up.
default: "3"
delay:
description: Seconds to wait between attempts.
default: "10"
working-directory:
description: Directory to run in.
default: "."
runs:
using: composite
steps:
- shell: bash
working-directory: ${{ inputs.working-directory }}
# command goes through env, never interpolated into the script body, so
# a command with quotes/specials can't break or inject into the runner.
env:
_CMD: ${{ inputs.command }}
_ATTEMPTS: ${{ inputs.attempts }}
_DELAY: ${{ inputs.delay }}
run: |
set -uo pipefail
n=0
while :; do
n=$((n + 1))
echo "::group::attempt $n/$_ATTEMPTS: $_CMD"
if bash -c "$_CMD"; then
echo "::endgroup::"
exit 0
fi
echo "::endgroup::"
if [ "$n" -ge "$_ATTEMPTS" ]; then
echo "::error::failed after $n attempts: $_CMD"
exit 1
fi
echo "::warning::attempt $n failed; retrying in ${_DELAY}s: $_CMD"
sleep "$_DELAY"
done
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@@ -0,0 +1,100 @@
name: Build Windows Installer
on:
workflow_dispatch:
permissions:
contents: read
jobs:
# Gate: workflow_dispatch is already restricted to users with write access,
# but we want ADMIN-only. Explicitly check the triggering actor's repo
# permission via the API and fail fast for anyone below admin.
authorize:
name: Authorize (admins only)
runs-on: ubuntu-latest
timeout-minutes: 5
steps:
- name: Check actor is a repo admin
env:
GH_TOKEN: ${{ github.token }}
ACTOR: ${{ github.actor }}
run: |
set -euo pipefail
perm=$(gh api \
"repos/${{ github.repository }}/collaborators/${ACTOR}/permission" \
--jq '.permission')
echo "Actor '${ACTOR}' has permission: ${perm}"
if [ "${perm}" != "admin" ]; then
echo "::error::'${ACTOR}' is not a repo admin (permission=${perm}). Refusing to build/sign."
exit 1
fi
echo "Authorized: '${ACTOR}' is an admin."
build:
name: Hermes-Setup.exe
needs: authorize
runs-on: windows-latest
timeout-minutes: 30
permissions:
contents: read
# Required for OIDC auth to Azure (azure/login federated credentials).
id-token: write
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Setup Node.js
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
- name: Install npm dependencies
run: npm ci
- name: Setup Rust
uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable
- name: Cache Rust targets
uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
with:
workspaces: apps/bootstrap-installer/src-tauri
- name: Build installer
run: npm run tauri:build
working-directory: apps/bootstrap-installer
- name: Azure login (OIDC)
uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Sign Hermes-Setup.exe with Azure Artifact Signing
uses: azure/artifact-signing-action@c7ab2a863ab5f9a846ddb8265964877ef296ee82 # v2
with:
endpoint: ${{ vars.AZURE_SIGNING_ENDPOINT }}
signing-account-name: ${{ vars.AZURE_SIGNING_ACCOUNT_NAME }}
certificate-profile-name: ${{ vars.AZURE_SIGNING_CERTIFICATE_PROFILE }}
# Sign both the raw exe and the bundled NSIS installer.
files-folder: ${{ github.workspace }}\apps\bootstrap-installer\src-tauri\target\release
files-folder-filter: exe
files-folder-recurse: true
file-digest: SHA256
timestamp-rfc3161: http://timestamp.acs.microsoft.com
timestamp-digest: SHA256
- name: Upload NSIS installer
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: Hermes-Setup-installer
path: apps/bootstrap-installer/src-tauri/target/release/bundle/nsis/*.exe
- name: Upload raw exe
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: Hermes-Setup-exe
path: apps/bootstrap-installer/src-tauri/target/release/Hermes-Setup.exe
-231
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@@ -1,231 +0,0 @@
name: CI
# Orchestrator workflow. Runs ``detect-changes`` once, then conditionally
# calls the sub-workflows that a PR can actually affect. A final
# ``all-checks-pass`` gate job aggregates results so branch protection only
# needs to require a single check.
#
# Sub-workflows are triggered via ``workflow_call`` and keep their own job
# definitions, matrices, and concurrency settings. They no longer have
# ``push:`` / ``pull_request:`` triggers of their own — everything flows
# through this file.
on:
pull_request:
push:
branches: [main]
permissions:
contents: read
pull-requests: write # needed by lint (PR comment) + supply-chain (PR comment)
actions: read # needed by osv-scanner (SARIF upload)
security-events: write # needed by osv-scanner (SARIF upload)
packages: write # needed by docker build
concurrency:
group: ci-${{ github.ref }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
# ─────────────────────────────────────────────────────────────────────
# detect: run the classifier once. Every downstream job reads its outputs
# to decide whether to run. On push/dispatch the classifier fails open
# (all lanes true) so post-merge validation is never weakened.
# ─────────────────────────────────────────────────────────────────────
detect:
name: Detect affected areas
runs-on: ubuntu-latest
outputs:
python: ${{ steps.classify.outputs.python }}
frontend: ${{ steps.classify.outputs.frontend }}
site: ${{ steps.classify.outputs.site }}
scan: ${{ steps.classify.outputs.scan }}
deps: ${{ steps.classify.outputs.deps }}
docker_meta: ${{ steps.classify.outputs.docker_meta }}
mcp_catalog: ${{ steps.classify.outputs.mcp_catalog }}
event_name: ${{ github.event_name }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Detect affected areas
id: classify
uses: ./.github/actions/detect-changes
# ─────────────────────────────────────────────────────────────────────
# Lane-gated sub-workflows. Each runs in parallel after detect finishes.
# Skipped workflows (if condition is false) don't spin up runners.
# ─────────────────────────────────────────────────────────────────────
tests:
name: Python tests
needs: detect
if: needs.detect.outputs.python == 'true'
uses: ./.github/workflows/tests.yml
with:
slice_count: 8
lint:
name: Python lints
needs: detect
if: needs.detect.outputs.python == 'true'
uses: ./.github/workflows/lint.yml
with:
event_name: ${{ needs.detect.outputs.event_name }}
typecheck:
name: TypeScript
needs: detect
if: needs.detect.outputs.frontend == 'true'
uses: ./.github/workflows/typecheck.yml
docs-site:
name: Docs Site
needs: detect
if: needs.detect.outputs.site == 'true'
uses: ./.github/workflows/docs-site-checks.yml
history-check:
name: Deny unrelated histories
needs: detect
if: needs.detect.outputs.event_name == 'pull_request'
uses: ./.github/workflows/history-check.yml
contributor-check:
name: Check contributors
needs: detect
if: needs.detect.outputs.python == 'true'
uses: ./.github/workflows/contributor-check.yml
uv-lockfile:
name: Check uv.lock
needs: detect
uses: ./.github/workflows/uv-lockfile-check.yml
docker-lint:
name: Lint Docker scripts
needs: detect
if: needs.detect.outputs.docker_meta == 'true'
uses: ./.github/workflows/docker-lint.yml
docker:
name: Build&Test Docker image
needs: detect
if: needs.detect.outputs.python == 'true' || needs.detect.outputs.frontend == 'true' || needs.detect.outputs.docker_meta == 'true'
uses: ./.github/workflows/docker.yml
secrets: inherit
supply-chain:
name: Supply-chain scan
needs: detect
if: needs.detect.outputs.event_name == 'pull_request' && (needs.detect.outputs.scan == 'true' || needs.detect.outputs.deps == 'true' || needs.detect.outputs.mcp_catalog == 'true')
uses: ./.github/workflows/supply-chain-audit.yml
with:
event_name: ${{ needs.detect.outputs.event_name }}
scan: ${{ needs.detect.outputs.scan == 'true' }}
deps: ${{ needs.detect.outputs.deps == 'true' }}
mcp_catalog: ${{ needs.detect.outputs.mcp_catalog == 'true' }}
osv-scanner:
name: OSV scan
uses: ./.github/workflows/osv-scanner.yml
# ─────────────────────────────────────────────────────────────────────
# Gate: runs after everything. ``if: always()`` ensures it reports a
# status even when some deps were skipped. Only actual ``failure``
# results cause it to fail; ``skipped`` is treated as success.
#
# Branch protection should require ONLY this check.
# ─────────────────────────────────────────────────────────────────────
all-checks-pass:
name: All required checks pass
needs:
- tests
- lint
- typecheck
- docs-site
- history-check
- contributor-check
- uv-lockfile
- docker-lint
- supply-chain
- osv-scanner
# we don't require docker to pass rn because it's so slow lol
# - docker
if: always()
runs-on: ubuntu-latest
steps:
- name: Evaluate job results
env:
RESULTS: ${{ toJSON(needs.*.result) }}
run: |
echo "$RESULTS" | python3 -c "
import json, sys
results = json.load(sys.stdin)
failed = [r for r in results if r == 'failure']
if failed:
print(f'::error::{len(failed)} job(s) failed')
sys.exit(1)
print('All checks passed (or were skipped)')
"
# ─────────────────────────────────────────────────────────────────────
# CI timing report: collect per-job/step durations from the GitHub API,
# cache them on main (as a baseline), and on PRs generate an HTML diff
# report with a gantt chart + per-step breakdown. The report is uploaded
# as an artifact and a markdown summary is written to $GITHUB_STEP_SUMMARY.
# ─────────────────────────────────────────────────────────────────────
ci-timings:
name: CI timing report
needs: [all-checks-pass, docker]
if: always()
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Restore baseline cache (PR only)
if: github.event_name == 'pull_request'
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: ci-timings-baseline.json
# Prefix-match: exact key will never hit (run_id differs), so
# restore-keys finds the most recent baseline from main.
key: ci-timings-baseline-never-exact
restore-keys: |
ci-timings-baseline-
- name: Collect timings and generate report
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
python3 scripts/ci/timings_report.py \
--baseline ci-timings-baseline.json \
--output ci-timings-report.html \
--json-out ci-timings.json \
--summary-out ci-timings-summary.md
- name: Upload HTML report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
id: ci-timings-artifact
with:
name: ci-timings-report
path: ci-timings-report.html
retention-days: 14
archive: false
- name: Output summary
env:
REPORT_URL: ${{ steps.ci-timings-artifact.outputs.artifact-url}}
run: |
echo "# CI Timing report" >> "$GITHUB_STEP_SUMMARY"
echo "[View the full interactive report]($REPORT_URL)" >> "$GITHUB_STEP_SUMMARY"
cat ci-timings-summary.md >> "$GITHUB_STEP_SUMMARY"
- name: Save baseline cache (main only)
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
run: cp ci-timings.json ci-timings-baseline.json
- name: Upload baseline to cache (main only)
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: ci-timings-baseline.json
key: ci-timings-baseline-${{ github.run_id }}
+19 -1
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@@ -1,7 +1,11 @@
name: Contributor Attribution Check
on:
workflow_call:
pull_request:
branches: [main]
# No paths filter — the job must always run so the required check
# reports a status (path-gated workflows leave checks "pending" forever
# when no matching files change, which blocks merge).
permissions:
contents: read
@@ -14,7 +18,21 @@ jobs:
with:
fetch-depth: 0 # Full history needed for git log
- name: Check if relevant files changed
id: filter
run: |
BASE="${{ github.event.pull_request.base.sha }}"
HEAD="${{ github.event.pull_request.head.sha }}"
CHANGED=$(git diff --name-only "$BASE"..."$HEAD" -- '*.py' '**/*.py' '.github/workflows/contributor-check.yml' || true)
if [ -n "$CHANGED" ]; then
echo "run=true" >> "$GITHUB_OUTPUT"
else
echo "run=false" >> "$GITHUB_OUTPUT"
echo "No Python files changed, skipping attribution check."
fi
- name: Check for unmapped contributor emails
if: steps.filter.outputs.run == 'true'
run: |
# Get the merge base between this PR and main
MERGE_BASE=$(git merge-base origin/main HEAD)
+9 -86
View File
@@ -11,20 +11,8 @@ on:
- 'optional-skills/**'
- '.github/workflows/deploy-site.yml'
workflow_dispatch:
inputs:
skills_index_run_id:
description: 'Optional Build Skills Index run ID whose skills-index artifact should be deployed'
required: false
type: string
rebuild_skills_index:
description: 'Force a fresh multi-source crawl instead of reusing the latest healthy index'
required: false
default: false
type: boolean
permissions:
contents: read
actions: read
pages: write
id-token: write
@@ -56,7 +44,7 @@ jobs:
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
node-version: 20
cache: npm
cache-dependency-path: website/package-lock.json
@@ -67,81 +55,16 @@ jobs:
- name: Install PyYAML for skill extraction
run: pip install pyyaml==6.0.2 httpx==0.28.1
- name: Prepare skills index (unified multi-source catalog)
- name: Build skills index (unified multi-source catalog)
env:
GH_TOKEN: ${{ github.token }}
GITHUB_TOKEN: ${{ github.token }}
SKILLS_INDEX_RUN_ID: ${{ github.event.inputs.skills_index_run_id || '' }}
REBUILD_SKILLS_INDEX: ${{ github.event.inputs.rebuild_skills_index || 'false' }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
# The unified external catalog is expensive to crawl and can burn
# through the repository installation's GitHub API quota when several
# docs deploys land close together. Normal docs deploys therefore
# reuse the latest healthy catalog: first the artifact from a
# scheduled skills-index run, then the currently live index. Only a
# manual force rebuild does a fresh crawl here.
#
# If we do crawl, the build remains fatal. build_skills_index.py runs
# the health check BEFORE writing and exits non-zero on source
# collapse, keeping the last good Pages deployment live instead of
# publishing a degenerate catalog.
set -euo pipefail
INDEX_PATH="website/static/api/skills-index.json"
mkdir -p "$(dirname "$INDEX_PATH")"
validate_index() {
python3 - "$INDEX_PATH" <<'PY'
import json
import sys
from pathlib import Path
path = Path(sys.argv[1])
try:
data = json.loads(path.read_text(encoding="utf-8"))
except Exception as exc:
print(f"invalid skills index JSON: {exc}", file=sys.stderr)
sys.exit(1)
skills = data.get("skills")
if not isinstance(skills, list) or len(skills) < 1500:
count = len(skills) if isinstance(skills, list) else "missing"
print(f"skills index too small: {count}", file=sys.stderr)
sys.exit(1)
print(f"skills index ready: {len(skills)} skills")
PY
}
if [ "$REBUILD_SKILLS_INDEX" = "true" ]; then
python3 scripts/build_skills_index.py
validate_index
exit 0
fi
if [ -n "$SKILLS_INDEX_RUN_ID" ]; then
tmpdir="$(mktemp -d)"
echo "Downloading skills-index artifact from run $SKILLS_INDEX_RUN_ID"
if gh run download "$SKILLS_INDEX_RUN_ID" --name skills-index --dir "$tmpdir"; then
candidate="$(find "$tmpdir" -name skills-index.json -type f | head -n 1 || true)"
if [ -n "$candidate" ]; then
cp "$candidate" "$INDEX_PATH"
if validate_index; then
exit 0
fi
fi
fi
echo "::warning::Could not use skills-index artifact from run $SKILLS_INDEX_RUN_ID; trying live index"
fi
echo "Downloading currently live skills index"
if curl -fsSL --retry 3 --retry-delay 5 \
"https://hermes-agent.nousresearch.com/docs/api/skills-index.json" \
-o "$INDEX_PATH" && validate_index; then
exit 0
fi
echo "::warning::Live skills index unavailable or unhealthy; falling back to a fresh crawl"
rm -f "$INDEX_PATH"
python3 scripts/build_skills_index.py
validate_index
# Always rebuild — the file isn't committed (gitignored), so a
# fresh checkout starts without it and we want the freshest crawl
# in every deploy. Failure is non-fatal: extract-skills.py will
# fall back to the legacy snapshot cache and the Skills Hub page
# still renders, just without the latest community catalog.
python3 scripts/build_skills_index.py || echo "Skills index build failed (non-fatal)"
- name: Extract skill metadata for dashboard
run: python3 website/scripts/extract-skills.py
+16 -3
View File
@@ -2,16 +2,29 @@ name: Docker / shell lint
# Lints the container build inputs: Dockerfile (via hadolint) and any shell
# scripts under docker/ (via shellcheck). These catch the class of regression
# the behavioral docker smoke test can't — unquoted variable
# the behavioral docker-publish smoke test can't — unquoted variable
# expansions, silently-failing RUN commands, etc.
#
# Rules and ignores are documented in .hadolint.yaml at the repo root.
# shellcheck severity is pinned to `error` so SC1091-style "can't follow
# sourced script" info-level warnings don't fail the job — the .venv
# sourced script" info-level warnings don't fail the job — the venv
# activate script doesn't exist at lint time.
on:
workflow_call:
push:
branches: [main]
paths:
- Dockerfile
- docker/**
- .hadolint.yaml
- .github/workflows/docker-lint.yml
pull_request:
branches: [main]
paths:
- Dockerfile
- docker/**
- .hadolint.yaml
- .github/workflows/docker-lint.yml
permissions:
contents: read
+360
View File
@@ -0,0 +1,360 @@
name: Docker Build and Publish
on:
push:
branches: [main]
paths:
- '**/*.py'
- 'pyproject.toml'
- 'uv.lock'
- 'Dockerfile'
- 'docker/**'
- '.github/workflows/docker-publish.yml'
- '.github/actions/hermes-smoke-test/**'
pull_request:
branches: [main]
paths:
- '**/*.py'
- 'pyproject.toml'
- 'uv.lock'
- 'Dockerfile'
- 'docker/**'
- '.github/workflows/docker-publish.yml'
- '.github/actions/hermes-smoke-test/**'
release:
types: [published]
permissions:
contents: read
# Needed so the arm64 job can push/pull its registry-backed build cache
# to ghcr.io (cache-to/cache-from type=registry). See the build-arm64
# job for why registry cache replaced the gha cache on that arch.
packages: write
# Concurrency: push/release runs are NEVER cancelled so every merge gets
# its own image. PR runs reuse a PR-scoped group with
# cancel-in-progress: true so rapid pushes to the same PR collapse to the
# latest commit.
concurrency:
group: docker-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
env:
IMAGE_NAME: nousresearch/hermes-agent
jobs:
# ---------------------------------------------------------------------------
# Build amd64 natively. This job also runs the smoke tests (basic --help
# and the dashboard subcommand regression guard from #9153), because amd64
# is the only arch we can `load` into the local daemon on an amd64 runner.
# ---------------------------------------------------------------------------
build-amd64:
# Only run on the upstream repository, not on forks
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-latest
timeout-minutes: 45
outputs:
digest: ${{ steps.push.outputs.digest }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
# Build once, load into the local daemon for smoke testing. Cached
# to gha with a per-arch scope; the push step below reuses every
# layer from this build.
- name: Build image (amd64, smoke test)
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
load: true
platforms: linux/amd64
tags: ${{ env.IMAGE_NAME }}:test
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
cache-from: type=gha,scope=docker-amd64
cache-to: type=gha,mode=max,scope=docker-amd64
- name: Smoke test image
uses: ./.github/actions/hermes-smoke-test
with:
image: ${{ env.IMAGE_NAME }}:test
# ---------------------------------------------------------------------
# Run the docker-integration test suite against the freshly-built
# image already loaded into the local daemon (`:test`). These tests
# are excluded from the sharded `tests.yml :: test` matrix on purpose
# (see `_SKIP_PARTS` in scripts/run_tests_parallel.py) because each
# shard would otherwise reach the session-scoped ``built_image``
# fixture in ``tests/docker/conftest.py`` and start a 3-7min
# ``docker build`` under a 180s pytest-timeout cap — guaranteed to
# die in fixture setup.
#
# Piggybacking here avoids a second image build: the smoke test
# already proved the image loads + runs, so the daemon has it under
# `${IMAGE_NAME}:test` and we just point ``HERMES_TEST_IMAGE`` at
# that. The fixture's ``HERMES_TEST_IMAGE`` branch (see
# tests/docker/conftest.py:62-63) short-circuits the rebuild.
#
# Why this job and not a standalone one: the image is 5GB+; passing
# it between jobs via ``docker save``/``upload-artifact`` is slower
# than the build itself. Reusing the existing daemon state is the
# cheapest path to coverage on every PR that touches docker code.
# ---------------------------------------------------------------------
- name: Install uv (for docker tests)
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
- name: Set up Python 3.11 (for docker tests)
run: uv python install 3.11
- name: Install Python dependencies (for docker tests)
run: |
uv venv venv --python 3.11
source venv/bin/activate
# ``dev`` extra pulls in pytest, pytest-asyncio, pytest-timeout —
# everything tests/docker/ needs. We deliberately avoid ``all``
# here because the docker tests only drive the container via
# subprocess and don't import hermes_agent's optional deps.
uv pip install -e ".[dev]"
- name: Run docker integration tests
env:
# Skip rebuild; use the image already loaded by the build step.
HERMES_TEST_IMAGE: ${{ env.IMAGE_NAME }}:test
# Match the policy in tests.yml :: test job — no accidental
# real-API calls from inside the harness.
OPENROUTER_API_KEY: ""
OPENAI_API_KEY: ""
NOUS_API_KEY: ""
run: |
source venv/bin/activate
python -m pytest tests/docker/ -v --tb=short
- name: Log in to Docker Hub
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
# Push amd64 by digest only (no tag). The merge job assembles the
# tagged manifest list. `push-by-digest=true` is docker's recommended
# pattern for multi-runner multi-platform builds.
- name: Push amd64 by digest
id: push
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
platforms: linux/amd64
labels: |
org.opencontainers.image.revision=${{ github.sha }}
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
outputs: type=image,name=${{ env.IMAGE_NAME }},push-by-digest=true,name-canonical=true,push=true
cache-from: type=gha,scope=docker-amd64
cache-to: type=gha,mode=max,scope=docker-amd64
# Write the digest to a file and upload it as an artifact so the
# merge job can stitch both per-arch digests into a manifest list.
- name: Export digest
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
run: |
mkdir -p /tmp/digests
digest="${{ steps.push.outputs.digest }}"
touch "/tmp/digests/${digest#sha256:}"
- name: Upload digest artifact
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: digest-amd64
path: /tmp/digests/*
if-no-files-found: error
retention-days: 1
# ---------------------------------------------------------------------------
# Build arm64 natively on GitHub's free arm64 runner. This replaces the
# previous QEMU-emulated arm64 build, which was ~5-10x slower and shared
# a cache scope with amd64. Matches the amd64 job's shape: build+load,
# smoke test, then on push/release push by digest.
# ---------------------------------------------------------------------------
build-arm64:
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-24.04-arm
timeout-minutes: 45
outputs:
digest: ${{ steps.push.outputs.digest }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
# Log in to ghcr.io so the registry-backed build cache below can be
# read (cache-from) on every event and written (cache-to) on
# push/release. Uses the workflow's GITHUB_TOKEN, which is valid for
# the whole job — unlike the gha cache backend's short-lived Azure SAS
# token, which expired mid-build on slow cold-cache arm64 runs and
# crashed the build before the smoke test (the reason the gha cache
# was removed from arm64 PRs in the first place).
- name: Log in to ghcr.io (build cache)
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
# Build once, load into the local daemon for smoke testing.
#
# PR builds use the registry-backed cache READ-ONLY (cache-from only):
# they pull warm layers pushed by the most recent main build but never
# write, so rapid PR pushes don't race on cache writes or pollute the
# cache ref. This restores warm-cache speed to arm64 PR builds (which
# were running fully uncached and were ~45% slower than amd64, making
# them the job most often cancelled on supersede).
#
# Registry cache (type=registry on ghcr.io) is used instead of the gha
# cache that previously broke here: its credential is the job-lifetime
# GITHUB_TOKEN, not a short-lived SAS token, so the cold-build-outlives-
# token failure mode cannot recur.
- name: Build image (arm64, smoke test, cache read-only PR)
if: github.event_name == 'pull_request'
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
load: true
platforms: linux/arm64
tags: ${{ env.IMAGE_NAME }}:test
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
cache-from: type=registry,ref=ghcr.io/nousresearch/hermes-agent:buildcache-arm64
# Main/release builds read AND write the registry cache so the digest
# push below reuses layers from this smoke-test build, and so the next
# PR/main build starts warm.
- name: Build image (arm64, smoke test, cached publish)
if: github.event_name != 'pull_request'
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
load: true
platforms: linux/arm64
tags: ${{ env.IMAGE_NAME }}:test
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
cache-from: type=registry,ref=ghcr.io/nousresearch/hermes-agent:buildcache-arm64
cache-to: type=registry,ref=ghcr.io/nousresearch/hermes-agent:buildcache-arm64,mode=max
- name: Smoke test image
uses: ./.github/actions/hermes-smoke-test
with:
image: ${{ env.IMAGE_NAME }}:test
- name: Log in to Docker Hub
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Push arm64 by digest
id: push
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
platforms: linux/arm64
labels: |
org.opencontainers.image.revision=${{ github.sha }}
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
outputs: type=image,name=${{ env.IMAGE_NAME }},push-by-digest=true,name-canonical=true,push=true
cache-from: type=registry,ref=ghcr.io/nousresearch/hermes-agent:buildcache-arm64
cache-to: type=registry,ref=ghcr.io/nousresearch/hermes-agent:buildcache-arm64,mode=max
- name: Export digest
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
run: |
mkdir -p /tmp/digests
digest="${{ steps.push.outputs.digest }}"
touch "/tmp/digests/${digest#sha256:}"
- name: Upload digest artifact
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: digest-arm64
path: /tmp/digests/*
if-no-files-found: error
retention-days: 1
# ---------------------------------------------------------------------------
# Stitch both per-arch digests into a single tagged multi-arch manifest.
# This is a registry-side operation — no building, no layer re-push —
# so it runs in ~30 seconds.
#
# On main pushes: tags both :main and :latest.
# On releases: tags :<release_tag_name>.
# ---------------------------------------------------------------------------
merge:
if: github.repository == 'NousResearch/hermes-agent' && (github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release')
runs-on: ubuntu-latest
needs: [build-amd64, build-arm64]
timeout-minutes: 10
steps:
- name: Download digests
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
path: /tmp/digests
pattern: digest-*
merge-multiple: true
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
- name: Log in to Docker Hub
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Create manifest list and push
working-directory: /tmp/digests
run: |
set -euo pipefail
args=()
for digest_file in *; do
args+=("${IMAGE_NAME}@sha256:${digest_file}")
done
if [ "${{ github.event_name }}" = "release" ]; then
TAG="${{ github.event.release.tag_name }}"
docker buildx imagetools create \
-t "${IMAGE_NAME}:${TAG}" \
"${args[@]}"
else
docker buildx imagetools create \
-t "${IMAGE_NAME}:main" \
-t "${IMAGE_NAME}:latest" \
"${args[@]}"
fi
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
- name: Inspect image
run: |
if [ "${{ github.event_name }}" = "release" ]; then
docker buildx imagetools inspect "${IMAGE_NAME}:${{ github.event.release.tag_name }}"
else
docker buildx imagetools inspect "${IMAGE_NAME}:main"
fi
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
-209
View File
@@ -1,209 +0,0 @@
name: Docker Build, Test, and Publish
on:
release:
types: [published]
workflow_call:
permissions:
contents: read
# Concurrency: push/release runs are NEVER cancelled so every merge gets
# its own image. PR runs reuse a PR-scoped group with
# cancel-in-progress: true so rapid pushes to the same PR collapse to
# the latest commit.
concurrency:
group: docker-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
env:
IMAGE_NAME: nousresearch/hermes-agent
jobs:
# Build, test, and optionally push the image for each architecture.
build:
if: github.repository == 'NousResearch/hermes-agent'
strategy:
fail-fast: false
matrix:
include:
- arch: amd64
runner: ubuntu-latest
platform: linux/amd64
cache-from: type=gha,scope=docker-amd64
cache-to: type=gha,mode=max,scope=docker-amd64
- arch: arm64
runner: ubuntu-24.04-arm
platform: linux/arm64
cache-from: type=gha,scope=docker-arm64
cache-to: type=gha,mode=max,scope=docker-arm64
runs-on: ${{ matrix.runner }}
timeout-minutes: 45
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
# Build once, load into the local daemon for testing. Cached
# per-arch; the push step below reuses every layer from this build.
- name: Build image (${{ matrix.arch }})
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
load: true
platforms: ${{ matrix.platform }}
tags: ${{ env.IMAGE_NAME }}:test
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
cache-from: ${{ matrix.cache-from }}
cache-to: ${{ (github.event_name != 'pull_request') && matrix.cache-to || '' }}
- name: Log in to Docker Hub
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
# Push by digest only (no tag). The merge job assembles the
# tagged manifest list. `push-by-digest=true` is docker's recommended
# pattern for multi-runner multi-platform builds.
- name: Push ${{ matrix.arch }} by digest
id: push
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: docker/build-push-action@bcafcacb16a39f128d818304e6c9c0c18556b85f # v7.1.0
with:
context: .
file: Dockerfile
platforms: ${{ matrix.platform }}
labels: |
org.opencontainers.image.revision=${{ github.sha }}
build-args: |
HERMES_GIT_SHA=${{ github.sha }}
outputs: type=image,name=${{ env.IMAGE_NAME }},push-by-digest=true,name-canonical=true,push=true
cache-from: ${{ matrix.cache-from }}
cache-to: ${{ matrix.cache-to }}
# Write the digest to a file and upload it as an artifact so the
# merge job can stitch both per-arch digests into a manifest list.
- name: Export digest
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
run: |
mkdir -p /tmp/digests
digest="${{ steps.push.outputs.digest }}"
touch "/tmp/digests/${digest#sha256:}"
- name: Upload digest artifact
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release'
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
with:
name: digest-${{ matrix.arch }}
path: /tmp/digests/*
if-no-files-found: error
retention-days: 1
# Run the docker-integration test suite against the freshly-built
# image already loaded into the local daemon (`:test`).
#
# Piggybacking here avoids a second image build: the build step
# already loaded the image into the daemon under
# `${IMAGE_NAME}:test`, so we just point ``HERMES_TEST_IMAGE`` at
# that. The fixture's ``HERMES_TEST_IMAGE`` branch (see
# tests/docker/conftest.py:62-63) short-circuits the rebuild.
#
# Why this job and not a standalone one: the image is 5GB+; passing
# it between jobs via ``docker save``/``upload-artifact`` is slower
# than the build itself. Reusing the existing daemon state is the
# cheapest path to coverage on every PR that touches docker code.
# ---------------------------------------------------------------------
- name: Install uv (for docker tests)
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
- name: Set up Python 3.11 (for docker tests)
run: uv python install 3.11
- name: Install Python dependencies (for docker tests)
run: |
# ``dev`` extra pulls in pytest, pytest-asyncio —
# everything tests/docker/ needs. We deliberately avoid ``all``
# here because the docker tests only drive the container via
# subprocess and don't import hermes_agent's optional deps.
uv sync --locked --python 3.11 --extra dev
- name: Run docker integration tests
env:
# Skip rebuild; use the image already loaded by the build step.
HERMES_TEST_IMAGE: ${{ env.IMAGE_NAME }}:test
# Match the policy in tests.yml :: test job — no accidental
# real-API calls from inside the harness.
OPENROUTER_API_KEY: ""
OPENAI_API_KEY: ""
NOUS_API_KEY: ""
run: |
scripts/run_tests.sh tests/docker/ --file-timeout 600
# ---------------------------------------------------------------------------
# Stitch both per-arch digests into a single tagged multi-arch manifest.
# This is a registry-side operation — no building, no layer re-push —
# so it runs in ~30 seconds.
#
# On main pushes: tags both :main and :latest.
# On releases: tags :<release_tag_name>.
# ---------------------------------------------------------------------------
merge:
if: github.repository == 'NousResearch/hermes-agent' && (github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'release')
runs-on: ubuntu-latest
needs: [build]
timeout-minutes: 10
steps:
- name: Download digests
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
path: /tmp/digests
pattern: digest-*
merge-multiple: true
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
- name: Log in to Docker Hub
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Create manifest list and push
working-directory: /tmp/digests
run: |
set -euo pipefail
args=()
for digest_file in *; do
args+=("${IMAGE_NAME}@sha256:${digest_file}")
done
if [ "${{ github.event_name }}" = "release" ]; then
TAG="${{ github.event.release.tag_name }}"
docker buildx imagetools create \
-t "${IMAGE_NAME}:${TAG}" \
"${args[@]}"
else
docker buildx imagetools create \
-t "${IMAGE_NAME}:main" \
-t "${IMAGE_NAME}:latest" \
"${args[@]}"
fi
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
- name: Inspect image
run: |
if [ "${{ github.event_name }}" = "release" ]; then
docker buildx imagetools inspect "${IMAGE_NAME}:${{ github.event.release.tag_name }}"
else
docker buildx imagetools inspect "${IMAGE_NAME}:main"
fi
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
+13 -13
View File
@@ -1,7 +1,11 @@
name: Docs Site Checks
on:
workflow_call:
pull_request:
paths:
- 'website/**'
- '.github/workflows/docs-site-checks.yml'
workflow_dispatch:
permissions:
contents: read
@@ -10,28 +14,24 @@ jobs:
docs-site-checks:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
node-version: 20
cache: npm
cache-dependency-path: website/package-lock.json
- name: Install website dependencies
uses: ./.github/actions/retry
with:
command: npm ci
working-directory: website
run: npm ci
working-directory: website
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
python-version: '3.11'
- name: Install ascii-guard
uses: ./.github/actions/retry
with:
command: python -m pip install ascii-guard==2.3.0 pyyaml==6.0.3
run: python -m pip install ascii-guard==2.3.0 pyyaml==6.0.3
- name: Extract skill metadata for dashboard
run: python3 website/scripts/extract-skills.py
+4 -3
View File
@@ -14,7 +14,8 @@ name: History Check
# the PR head and main to be non-empty.
on:
workflow_call:
pull_request:
branches: [main]
permissions:
contents: read
@@ -23,9 +24,9 @@ jobs:
check-common-ancestor:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0 # full history both sides for merge-base
fetch-depth: 0 # full history both sides for merge-base
- name: Reject PRs with no common ancestor on main
run: |
+61 -17
View File
@@ -9,12 +9,18 @@ name: Lint (ruff + ty)
# enforcement fails.
on:
workflow_call:
inputs:
event_name:
description: The event name from the calling orchestrator (pull_request or push).
type: string
required: true
push:
branches: [main]
paths-ignore:
- "**/*.md"
- "docs/**"
- "website/**"
pull_request:
branches: [main]
paths-ignore:
- "**/*.md"
- "docs/**"
- "website/**"
permissions:
contents: read
@@ -27,7 +33,6 @@ concurrency:
jobs:
lint-diff:
name: ruff + ty diff
if: inputs.event_name == 'pull_request'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
@@ -37,19 +42,19 @@ jobs:
fetch-depth: 0 # need full history for merge-base + worktree
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
- name: Install ruff + ty
uses: ./.github/actions/retry
with:
command: uv tool install ruff && uv tool install ty
run: |
uv tool install ruff
uv tool install ty
- name: Determine base ref
id: base
run: |
# For PRs, diff against the merge base with the target branch.
# For pushes to main, diff against the previous commit on main.
if [ "${{ inputs.event_name }}" = "pull_request" ]; then
if [ "${{ github.event_name }}" = "pull_request" ]; then
BASE_SHA=$(git merge-base "origin/${{ github.base_ref }}" HEAD)
BASE_REF="origin/${{ github.base_ref }}"
else
@@ -105,10 +110,51 @@ jobs:
--base-ty .lint-reports/base/ty.json \
--head-ty .lint-reports/head/ty.json \
--base-ref "${{ steps.base.outputs.ref }}" \
--head-ref "${{ inputs.event_name == 'pull_request' && github.head_ref || github.ref_name }}" \
--head-ref "${{ github.event_name == 'pull_request' && github.head_ref || github.ref_name }}" \
--output .lint-reports/summary.md
cat .lint-reports/summary.md >> "$GITHUB_STEP_SUMMARY"
- name: Upload reports as artifact
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: lint-reports
path: .lint-reports/
retention-days: 14
- name: Post / update PR comment
if: github.event_name == 'pull_request' && github.event.pull_request.head.repo.full_name == github.repository
continue-on-error: true
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7
with:
script: |
const fs = require('fs');
const body = fs.readFileSync('.lint-reports/summary.md', 'utf8');
const marker = '<!-- lint-diff-summary -->';
const fullBody = marker + '\n' + body;
const { data: comments } = await github.rest.issues.listComments({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
});
const existing = comments.find(c => c.body && c.body.includes(marker));
if (existing) {
await github.rest.issues.updateComment({
owner: context.repo.owner,
repo: context.repo.repo,
comment_id: existing.id,
body: fullBody,
});
} else {
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: fullBody,
});
}
ruff-blocking:
# Enforce the rules in pyproject.toml [tool.ruff.lint.select]. Currently
# PLW1514 (unspecified-encoding) — catches bare ``open()`` /
@@ -124,12 +170,10 @@ jobs:
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
- name: Install ruff
uses: ./.github/actions/retry
with:
command: uv tool install ruff
run: uv tool install ruff
- name: ruff check .
# No --exit-zero, no || true. Exit code propagates to the job,
+254
View File
@@ -0,0 +1,254 @@
name: Nix Lockfile Fix
on:
push:
branches: [main]
paths:
- 'package-lock.json'
- 'package.json'
- 'ui-tui/package.json'
- 'apps/desktop/package.json'
workflow_dispatch:
inputs:
pr_number:
description: 'PR number to fix (leave empty to run on the selected branch)'
required: false
type: string
issue_comment:
types: [edited]
permissions:
contents: write
pull-requests: write
concurrency:
group: nix-lockfile-fix-${{ github.event.issue.number || github.event.inputs.pr_number || github.ref }}
cancel-in-progress: false
jobs:
# ── Auto-fix on main ───────────────────────────────────────────────
# Fires when a push to main touches package.json or package-lock.json.
# Runs fix-lockfiles and pushes the hash update commit directly to main
# so Nix builds never stay broken.
#
# Safety invariants:
# 1. The fix commit only touches nix/*.nix files, which are NOT in
# the paths filter above, so this cannot re-trigger itself.
# 2. An explicit file-whitelist check before commit aborts if
# fix-lockfiles ever modifies unexpected files.
# 3. Job-level concurrency with cancel-in-progress: true ensures
# back-to-back pushes collapse to the newest; ref: main checkout
# always operates on the latest branch state.
# 4. Uses a GitHub App token (not GITHUB_TOKEN) so the fix commit
# triggers downstream nix.yml verification.
auto-fix-main:
if: github.event_name == 'push'
runs-on: ubuntu-latest
timeout-minutes: 25
concurrency:
group: auto-fix-main
cancel-in-progress: true
steps:
- name: Generate GitHub App token
id: app-token
uses: actions/create-github-app-token@7bfa3a4717ef143a604ee0a99d859b8886a96d00 # v1.9.3
with:
app-id: ${{ secrets.APP_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
ref: main
token: ${{ steps.app-token.outputs.token }}
- uses: ./.github/actions/nix-setup
with:
cachix-auth-token: ${{ secrets.CACHIX_AUTH_TOKEN }}
- name: Apply lockfile hashes
id: apply
run: nix run .#fix-lockfiles -- --apply
- name: Commit & push
if: steps.apply.outputs.changed == 'true'
shell: bash
run: |
set -euo pipefail
# Ensure only nix files were modified — prevents accidental
# self-triggering if fix-lockfiles ever touches package files.
unexpected="$(git diff --name-only | grep -Ev '^nix/(tui|web)\.nix$' || true)"
if [ -n "$unexpected" ]; then
echo "::error::Unexpected modified files: $unexpected"
exit 1
fi
# Record the base SHA before committing — used to detect package
# file changes if we need to rebase after a non-fast-forward push.
BASE_SHA="$(git rev-parse HEAD)"
git config user.name 'github-actions[bot]'
git config user.email '41898282+github-actions[bot]@users.noreply.github.com'
git add nix/tui.nix nix/web.nix
git commit -m "fix(nix): auto-refresh npm lockfile hashes" \
-m "Source: $GITHUB_SHA" \
-m "Run: $GITHUB_SERVER_URL/$GITHUB_REPOSITORY/actions/runs/$GITHUB_RUN_ID"
# Retry push with rebase in case main advanced with an unrelated
# commit during the nix build. Without this, a non-fast-forward
# rejection silently loses the fix. If package files changed during
# the rebase, abort — a fresh auto-fix run will handle the new state.
for attempt in 1 2 3; do
if git push origin HEAD:main; then
exit 0
fi
echo "::warning::Push attempt $attempt failed (non-fast-forward?), rebasing…"
git fetch origin main
# If package files changed between our base and the new main,
# our computed hashes are stale. Abort and let the next triggered
# run recompute from the correct package-lock state.
pkg_changed="$(git diff --name-only "$BASE_SHA"..origin/main -- \
'package-lock.json' 'package.json' \
'ui-tui/package.json' 'apps/desktop/package.json' || true)"
if [ -n "$pkg_changed" ]; then
echo "::warning::Package files changed since hash computation — aborting; a fresh run will recompute"
exit 0
fi
git rebase origin/main
done
echo "::error::Failed to push after 3 rebase attempts"
exit 1
# ── PR fix (manual / checkbox) ─────────────────────────────────────
# Existing behavior: run on manual dispatch OR when a task-list
# checkbox in the sticky lockfile-check comment flips from [ ] to [x].
fix:
if: |
github.event_name == 'workflow_dispatch' ||
(github.event_name == 'issue_comment'
&& github.event.issue.pull_request != null
&& contains(github.event.comment.body, '[x] **Apply lockfile fix**')
&& !contains(github.event.changes.body.from, '[x] **Apply lockfile fix**'))
runs-on: ubuntu-latest
timeout-minutes: 25
steps:
- name: Authorize & resolve PR
id: resolve
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
script: |
// 1. Verify the actor has write access — applies to both checkbox
// clicks and manual dispatch.
const { data: perm } =
await github.rest.repos.getCollaboratorPermissionLevel({
owner: context.repo.owner,
repo: context.repo.repo,
username: context.actor,
});
if (!['admin', 'write', 'maintain'].includes(perm.permission)) {
core.setFailed(
`${context.actor} lacks write access (has: ${perm.permission})`
);
return;
}
// 2. Resolve which ref to check out.
let prNumber = '';
if (context.eventName === 'issue_comment') {
prNumber = String(context.payload.issue.number);
} else if (context.eventName === 'workflow_dispatch') {
prNumber = context.payload.inputs.pr_number || '';
}
if (!prNumber) {
core.setOutput('ref', context.ref.replace(/^refs\/heads\//, ''));
core.setOutput('repo', context.repo.repo);
core.setOutput('owner', context.repo.owner);
core.setOutput('pr', '');
return;
}
const { data: pr } = await github.rest.pulls.get({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: Number(prNumber),
});
core.setOutput('ref', pr.head.ref);
core.setOutput('repo', pr.head.repo.name);
core.setOutput('owner', pr.head.repo.owner.login);
core.setOutput('pr', String(pr.number));
# Wipe the sticky lockfile-check comment to a "running" state as soon
# as the job is authorized, so the user sees their click was picked up
# before the ~minute of nix build work.
- name: Mark sticky as running
if: steps.resolve.outputs.pr != ''
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
number: ${{ steps.resolve.outputs.pr }}
message: |
### 🔄 Applying lockfile fix…
Triggered by @${{ github.actor }} — [workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}).
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
repository: ${{ steps.resolve.outputs.owner }}/${{ steps.resolve.outputs.repo }}
ref: ${{ steps.resolve.outputs.ref }}
token: ${{ secrets.GITHUB_TOKEN }}
fetch-depth: 0
- uses: ./.github/actions/nix-setup
with:
cachix-auth-token: ${{ secrets.CACHIX_AUTH_TOKEN }}
- name: Apply lockfile hashes
id: apply
run: nix run .#fix-lockfiles
- name: Commit & push
if: steps.apply.outputs.changed == 'true'
shell: bash
run: |
set -euo pipefail
git config user.name 'github-actions[bot]'
git config user.email '41898282+github-actions[bot]@users.noreply.github.com'
git add nix/tui.nix nix/web.nix
git commit -m "fix(nix): refresh npm lockfile hashes"
git push
- name: Update sticky (applied)
if: steps.apply.outputs.changed == 'true' && steps.resolve.outputs.pr != ''
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
number: ${{ steps.resolve.outputs.pr }}
message: |
### ✅ Lockfile fix applied
Pushed a commit refreshing the npm lockfile hashes — [workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}).
- name: Update sticky (already current)
if: steps.apply.outputs.changed == 'false' && steps.resolve.outputs.pr != ''
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
number: ${{ steps.resolve.outputs.pr }}
message: |
### ✅ Lockfile hashes already current
Nothing to commit — [workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}).
- name: Update sticky (failed)
if: failure() && steps.resolve.outputs.pr != ''
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
number: ${{ steps.resolve.outputs.pr }}
message: |
### ❌ Lockfile fix failed
See the [workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}) for logs.
+105
View File
@@ -0,0 +1,105 @@
name: Nix
on:
push:
branches: [main]
pull_request:
permissions:
contents: read
pull-requests: write
concurrency:
group: nix-${{ github.ref }}
cancel-in-progress: true
jobs:
nix:
strategy:
matrix:
os: [ubuntu-latest, macos-latest]
runs-on: ${{ matrix.os }}
timeout-minutes: 30
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: ./.github/actions/nix-setup
with:
cachix-auth-token: ${{ secrets.CACHIX_AUTH_TOKEN }}
- name: Resolve head SHA
if: github.event_name == 'pull_request'
id: sha
shell: bash
run: |
FULL="${{ github.event.pull_request.head.sha || github.sha }}"
echo "full=$FULL" >> "$GITHUB_OUTPUT"
echo "short=${FULL:0:7}" >> "$GITHUB_OUTPUT"
- name: Check flake
id: flake
continue-on-error: true
run: nix flake check --print-build-logs
# When the flake check fails, run a targeted diagnostic to see if
# the failure is specifically a stale npm lockfile hash in one of the
# known npm subpackages (tui / web). This avoids surfacing a generic
# "build failed" message when the fix is a single known command.
- name: Diagnose npm lockfile hashes
id: hash_check
if: steps.flake.outcome == 'failure' && runner.os == 'Linux'
continue-on-error: true
env:
LINK_SHA: ${{ steps.sha.outputs.full }}
run: nix run .#fix-lockfiles -- --check
# If fix-lockfiles itself crashes (infrastructure blip, cache throttle,
# etc.) it won't set stale=true/false. Treat that as a distinct failure
# mode rather than silently ignoring it.
- name: Fail if hash check crashed without reporting
if: steps.hash_check.outcome == 'failure' && steps.hash_check.outputs.stale != 'true' && steps.hash_check.outputs.stale != 'false'
run: |
echo "::error::fix-lockfiles exited without reporting stale status — likely an infrastructure or script failure"
exit 1
- name: Post sticky PR comment (stale hashes)
if: steps.hash_check.outputs.stale == 'true' && github.event_name == 'pull_request'
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
message: |
### ⚠️ npm lockfile hash out of date
Checked against commit [`${{ steps.sha.outputs.short }}`](${{ github.server_url }}/${{ github.repository }}/commit/${{ steps.sha.outputs.full }}) (PR head at check time).
The `hash = "sha256-..."` line in these nix files no longer matches the committed `package-lock.json`:
${{ steps.hash_check.outputs.report }}
#### Apply the fix
- [ ] **Apply lockfile fix** — tick to push a commit with the correct hashes to this PR branch
- Or [run the Nix Lockfile Fix workflow](${{ github.server_url }}/${{ github.repository }}/actions/workflows/nix-lockfile-fix.yml) manually (pass PR `#${{ github.event.pull_request.number }}`)
- Or locally: `nix run .#fix-lockfiles` and commit the diff
# Clear the sticky comment when either the flake check passed outright (no
# hash check needed) or the hash check explicitly returned stale=false
# (check failed for a non-hash reason).
- name: Clear sticky PR comment (resolved)
if: |
github.event_name == 'pull_request' &&
(steps.hash_check.outputs.stale == 'false' ||
steps.flake.outcome == 'success')
uses: marocchino/sticky-pull-request-comment@52423e01640425a022ef5fd42c6fb5f633a02728 # v2.9.1
with:
header: nix-lockfile-check
delete: true
- name: Final fail if flake check failed
if: steps.flake.outcome == 'failure'
run: |
if [ "${{ steps.hash_check.outputs.stale }}" == "true" ]; then
echo "::error::Nix build failed due to stale npm lockfile hash. Run: nix run .#fix-lockfiles"
else
echo "::error::Nix flake check failed. See logs above."
fi
exit 1
+26 -8
View File
@@ -1,8 +1,8 @@
name: OSV-Scanner
# Scans lockfiles (uv.lock, package-lock.json) against the OSV vulnerability
# database. Runs on every PR/push (via the ci.yml orchestrator's workflow_call)
# and on a weekly schedule against main.
# database. Runs on every PR that touches a lockfile and on a weekly schedule
# against main.
#
# This is detection-only — OSV-Scanner does NOT open PRs or modify pins.
# It reports known CVEs in currently-pinned dependency versions so we can
@@ -10,9 +10,9 @@ name: OSV-Scanner
# (full SHA / exact version) is preserved; only the notification signal
# is added.
#
# Complements the supply-chain-audit.yml workflow (which scans for malicious
# code patterns in PR diffs) by covering the orthogonal "currently-pinned
# dep became known-vulnerable" case.
# Complements the existing supply-chain-audit.yml workflow (which scans
# for malicious code patterns in PR diffs) by covering the orthogonal
# "currently-pinned dep became known-vulnerable" case.
#
# Uses Google's officially-recommended reusable workflow, pinned by SHA.
# Findings land in the repo's Security tab (Code Scanning > OSV-Scanner).
@@ -20,11 +20,29 @@ name: OSV-Scanner
# vulnerabilities in pinned deps that we may need to patch deliberately.
on:
workflow_call:
pull_request:
branches: [main]
paths:
- 'uv.lock'
- 'pyproject.toml'
- 'package.json'
- 'package-lock.json'
- 'ui-tui/package.json'
- 'website/package.json'
- 'website/package-lock.json'
- '.github/workflows/osv-scanner.yml'
push:
branches: [main]
paths:
- 'uv.lock'
- 'pyproject.toml'
- 'package.json'
- 'package-lock.json'
- 'website/package-lock.json'
schedule:
# Weekly scan against main — catches CVEs published after merge for
# deps that haven't changed since.
- cron: "0 9 * * 1"
- cron: '0 9 * * 1'
workflow_dispatch:
permissions:
@@ -36,7 +54,7 @@ permissions:
jobs:
scan:
name: Scan lockfiles
uses: google/osv-scanner-action/.github/workflows/osv-scanner-reusable.yml@9a498708959aeaef5ef730655706c5a1df1edbc2 # v2.3.8
uses: google/osv-scanner-action/.github/workflows/osv-scanner-reusable.yml@9a498708959aeaef5ef730655706c5a1df1edbc2 # v2.3.8
with:
# Scan explicit lockfiles rather than recursing, so we only look at
# the three sources of truth and skip vendored / test / worktree dirs.
+10 -10
View File
@@ -3,17 +3,17 @@ name: Build Skills Index
on:
schedule:
# Run twice daily: 6 AM and 6 PM UTC
- cron: "0 6,18 * * *"
workflow_dispatch: # Manual trigger
- cron: '0 6,18 * * *'
workflow_dispatch: # Manual trigger
push:
branches: [main]
paths:
- "scripts/build_skills_index.py"
- ".github/workflows/skills-index.yml"
- 'scripts/build_skills_index.py'
- '.github/workflows/skills-index.yml'
permissions:
contents: read
actions: write # to trigger deploy-site.yml on schedule
actions: write # to trigger deploy-site.yml on schedule
jobs:
build-index:
@@ -21,11 +21,11 @@ jobs:
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
python-version: '3.11'
- name: Install dependencies
run: pip install httpx==0.28.1 pyyaml==6.0.2
@@ -36,7 +36,7 @@ jobs:
run: python scripts/build_skills_index.py
- name: Upload index artifact
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: skills-index
path: website/static/api/skills-index.json
@@ -53,4 +53,4 @@ jobs:
- name: Trigger Deploy Site workflow
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: gh workflow run deploy-site.yml --repo ${{ github.repository }} -f skills_index_run_id=${{ github.run_id }}
run: gh workflow run deploy-site.yml --repo ${{ github.repository }}
+77 -71
View File
@@ -1,5 +1,16 @@
name: Supply Chain Audit
on:
pull_request:
types: [opened, synchronize, reopened]
# No paths filter — the jobs must always run so required checks
# report a status (path-gated workflows leave checks "pending" forever
# when no matching files change, which blocks merge).
permissions:
pull-requests: write
contents: read
# Narrow, high-signal scanner. Only fires on critical indicators of supply
# chain attacks (e.g. the litellm-style payloads). Low-signal heuristics
# (plain base64, plain exec/eval, dependency/Dockerfile/workflow edits,
@@ -8,44 +19,50 @@ name: Supply Chain Audit
# the scanner. Keep this file's checks ruthlessly narrow: if you find
# yourself adding WARNING-tier patterns here again, make a separate
# advisory-only workflow instead.
#
# Path-gating is handled centrally by the ``ci.yml`` orchestrator's
# ``detect`` job. The orchestrator passes ``scan`` / ``deps`` /
# ``mcp_catalog`` booleans as inputs; this workflow's jobs gate on those
# inputs instead of re-computing the diff.
on:
workflow_call:
inputs:
event_name:
description: The event name from the calling orchestrator.
type: string
required: true
scan:
description: Whether supply-chain-relevant files changed.
type: boolean
required: true
deps:
description: Whether pyproject.toml changed.
type: boolean
required: true
mcp_catalog:
description: Whether the MCP catalog / installer changed.
type: boolean
required: true
permissions:
pull-requests: write
contents: read
jobs:
# ── Path filter (shared by both scan and dep-bounds) ───────────────
changes:
runs-on: ubuntu-latest
outputs:
# True when any file the scanner cares about changed in this PR
scan: ${{ steps.filter.outputs.scan }}
# True when pyproject.toml changed in this PR
deps: ${{ steps.filter.outputs.deps }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
- name: Check for relevant file changes
id: filter
run: |
BASE="${{ github.event.pull_request.base.sha }}"
HEAD="${{ github.event.pull_request.head.sha }}"
SCAN_FILES=$(git diff --name-only "$BASE"..."$HEAD" -- \
'*.py' '**/*.py' '*.pth' '**/*.pth' \
'setup.py' 'setup.cfg' \
'sitecustomize.py' 'usercustomize.py' '__init__.pth' \
'pyproject.toml' || true)
if [ -n "$SCAN_FILES" ]; then
echo "scan=true" >> "$GITHUB_OUTPUT"
else
echo "scan=false" >> "$GITHUB_OUTPUT"
fi
DEPS_FILES=$(git diff --name-only "$BASE"..."$HEAD" -- 'pyproject.toml' || true)
if [ -n "$DEPS_FILES" ]; then
echo "deps=true" >> "$GITHUB_OUTPUT"
else
echo "deps=false" >> "$GITHUB_OUTPUT"
fi
scan:
name: Scan PR for critical supply chain risks
if: inputs.scan
needs: changes
if: needs.changes.outputs.scan == 'true'
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
@@ -84,7 +101,7 @@ jobs:
fi
# --- base64 decode + exec/eval on the same line (the litellm attack pattern) ---
B64_EXEC_HITS=$(echo "$DIFF" | grep -n '^+' | grep -iE 'base64\.(b64decode|decodebytes|urlsafe_b64decode)' | grep -iE 'exec\(|eval\(' | head -10 || true)
B64_EXEC_HITS=$(echo "$DIFF" | grep -n '^\+' | grep -iE 'base64\.(b64decode|decodebytes|urlsafe_b64decode)' | grep -iE 'exec\(|eval\(' | head -10 || true)
if [ -n "$B64_EXEC_HITS" ]; then
FINDINGS="${FINDINGS}
### 🚨 CRITICAL: base64 decode + exec/eval combo
@@ -98,7 +115,7 @@ jobs:
fi
# --- subprocess with encoded/obfuscated command argument ---
PROC_HITS=$(echo "$DIFF" | grep -n '^+' | grep -E 'subprocess\.(Popen|call|run)\s*\(' | grep -iE 'base64|\\x[0-9a-f]{2}|chr\(' | head -10 || true)
PROC_HITS=$(echo "$DIFF" | grep -n '^\+' | grep -E 'subprocess\.(Popen|call|run)\s*\(' | grep -iE 'base64|\\x[0-9a-f]{2}|chr\(' | head -10 || true)
if [ -n "$PROC_HITS" ]; then
FINDINGS="${FINDINGS}
### 🚨 CRITICAL: subprocess with encoded/obfuscated command
@@ -160,13 +177,27 @@ jobs:
echo "::error::CRITICAL supply chain risk patterns detected in this PR. See the PR comment for details."
exit 1
# Gate: reports success when scan was skipped (no relevant files changed).
# This ensures the required check always gets a status.
scan-gate:
name: Scan PR for critical supply chain risks
needs: changes
# always() so the gate still reports SUCCESS even if `changes` fails/is
# skipped — without it, a failed dependency would leave the required
# check unreported (i.e. "pending"), the exact failure mode this fixes.
if: always() && needs.changes.outputs.scan != 'true'
runs-on: ubuntu-latest
steps:
- run: echo "No supply-chain-relevant files changed, skipping scan."
dep-bounds:
name: Check PyPI dependency upper bounds
if: inputs.deps
needs: changes
if: needs.changes.outputs.deps == 'true'
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
@@ -212,7 +243,7 @@ jobs:
$(cat /tmp/unbounded.txt)
\`\`\`
**Fix:** Add an upper bound, e.g. \`"package>=1.2.0,<2"\`
**Fix:** Add an upper bound, e.g. \`\"package>=1.2.0,<2\"\`
---
*See PR #2810 and CONTRIBUTING.md for the full policy rationale.*"
@@ -225,40 +256,15 @@ jobs:
echo "::error::PyPI dependencies without upper bounds detected. Add <next_major ceiling per CONTRIBUTING.md policy."
exit 1
mcp-catalog-review:
name: MCP catalog security review
if: inputs.mcp_catalog
# Gate: reports success when dep-bounds was skipped (no pyproject.toml changed).
# This ensures the required check always gets a status.
dep-bounds-gate:
name: Check PyPI dependency upper bounds
needs: changes
# always() so the gate still reports SUCCESS even if `changes` fails/is
# skipped — without it, a failed dependency would leave the required
# check unreported (i.e. "pending"), the exact failure mode this fixes.
if: always() && needs.changes.outputs.deps != 'true'
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
- name: Require explicit MCP catalog review label
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
set -euo pipefail
PR="${{ github.event.pull_request.number }}"
LABELS=$(gh pr view "$PR" --json labels --jq '.labels[].name' || true)
if echo "$LABELS" | grep -Fxq 'mcp-catalog-reviewed'; then
echo "MCP catalog review label present."
exit 0
fi
BODY="## ⚠️ MCP catalog security review required
This PR changes the bundled MCP catalog or MCP catalog installer code. MCP entries can define local commands that users later install into \`mcp_servers\`, so this needs explicit maintainer review before merge.
A maintainer should verify:
- any new/changed \`optional-mcps/**/manifest.yaml\` command and args are expected,
- stdio transports do not use shell+egress/exfiltration payloads,
- git install refs are pinned and bootstrap commands are minimal,
- requested env vars/secrets match the upstream MCP's documented needs.
After review, add the \`mcp-catalog-reviewed\` label and re-run this check."
gh pr comment "$PR" --body "$BODY" || echo "::warning::Could not post PR comment (expected for fork PRs)"
echo "::error::MCP catalog changes require the mcp-catalog-reviewed label."
exit 1
- run: echo "No pyproject.toml changes, skipping dependency bounds check."
+66 -99
View File
@@ -1,54 +1,43 @@
name: Tests
on:
workflow_call:
inputs:
slice_count:
description: Number of parallel test slices
type: number
default: 8
push:
branches: [main]
paths-ignore:
- '**/*.md'
- 'docs/**'
pull_request:
branches: [main]
paths-ignore:
- '**/*.md'
- 'docs/**'
permissions:
contents: read
# Cancel in-progress runs for the same ref
# Cancel in-progress runs for the same PR/branch
concurrency:
group: tests-${{ github.ref }}
cancel-in-progress: true
jobs:
generate:
name: "Generate slices"
runs-on: ubuntu-latest
outputs:
matrix: ${{ steps.matrix.outputs.matrix }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Restore duration cache
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: test_durations.json
key: test-durations
- name: Generate test slices
id: matrix
run: |
MATRIX=$(python3 scripts/run_tests_parallel.py --generate-slices ${{ inputs.slice_count }})
echo "matrix=$MATRIX" >> "$GITHUB_OUTPUT"
test:
name: Run tests slice ${{ matrix.slice.index }}/${{ inputs.slice_count }}
needs: generate
runs-on: ubuntu-latest
timeout-minutes: 30
strategy:
fail-fast: false
matrix: ${{ fromJSON(needs.generate.outputs.matrix) }}
matrix:
slice: [1, 2, 3, 4, 5, 6]
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Restore duration cache
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: test_durations.json
# Single stable key. main always overwrites, PRs always find it.
key: test-durations
- name: Install ripgrep (prebuilt binary)
run: |
@@ -56,7 +45,7 @@ jobs:
RG_VERSION=15.1.0
RG_SHA256=1c9297be4a084eea7ecaedf93eb03d058d6faae29bbc57ecdaf5063921491599
RG_TARBALL=ripgrep-${RG_VERSION}-x86_64-unknown-linux-musl.tar.gz
curl -sSfL --retry 3 --retry-delay 5 -o "$RG_TARBALL" \
curl -sSfL -o "$RG_TARBALL" \
"https://github.com/BurntSushi/ripgrep/releases/download/${RG_VERSION}/${RG_TARBALL}"
echo "${RG_SHA256} ${RG_TARBALL}" | sha256sum -c -
tar -xzf "$RG_TARBALL"
@@ -65,48 +54,44 @@ jobs:
rg --version
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
with:
# Persist uv's download/wheel cache (~/.cache/uv) across runs.
# Keyed on the dependency manifests, so the cache is reused until
# pyproject.toml or uv.lock changes. `uv sync` still runs every
# time, but resolves from the warm cache instead of re-downloading
# and re-building wheels.
enable-cache: true
cache-dependency-glob: |
pyproject.toml
uv.lock
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
- name: Set up Python 3.11
run: uv python install 3.11
- name: Install dependencies
# `uv sync --locked` installs the exact pinned set from uv.lock (and
# fails if the lock is out of sync with pyproject.toml), giving a
# reproducible env. It also creates .venv itself, so no separate
# `uv venv` step is needed.
uses: ./.github/actions/retry
with:
command: uv sync --locked --python 3.11 --extra all --extra dev
run: |
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
- name: Minimize uv cache
# Optimized for CI: prunes pre-built wheels that are cheap to
# re-download, keeping the persisted cache small and fast to restore.
run: uv cache prune --ci
- name: Run tests (slice ${{ matrix.slice.index }}/${{ inputs.slice_count }})
# Per-file isolation via scripts/run_tests.sh: each test file runs
# in its own freshly-spawned `python -m pytest <file>` subprocess
- name: Run tests (slice ${{ matrix.slice }}/6)
# Per-file isolation via scripts/run_tests_parallel.py: discovers
# every test_*.py file under tests/ (excluding integration/ + e2e/),
# then runs `python -m pytest <file>` in a freshly-spawned subprocess
# with bounded parallelism. No xdist, no shared workers, no
# module-level state leakage between files.
#
# File list is pre-computed by the generate job (--generate-slices)
# which runs LPT distribution once and passes the file list to each
# matrix job via --files. Previously each job re-discovered files and
# re-ran LPT independently — redundant N times.
# Why per-file (not per-test): per-test spawn cost (~250ms × 17k
# tests = 70min CPU minimum) blew the wall-clock budget. Per-file
# spawn (~250ms × ~850 files = ~3.5min) fits while still giving
# every file a fresh interpreter — the only isolation boundary
# that matters in practice (cross-file leakage was the original
# flake source; intra-file is the test author's responsibility).
#
# Why drop xdist entirely: xdist's persistent workers accumulate
# state across files, which is exactly the leakage we wanted to
# fix. ThreadPoolExecutor + subprocess.run is ~60 lines and does
# the job with cleaner semantics.
#
# Matrix slicing (--slice I/N): files are distributed across 6
# jobs by cached duration (LPT algorithm) so each job gets
# roughly equal wall time. Without a cache, files default to 2s
# estimate and get split roughly evenly by count — still correct,
# just not perfectly balanced.
run: |
source .venv/bin/activate
scripts/run_tests.sh --files '${{ matrix.slice.files }}'
source venv/bin/activate
python scripts/run_tests_parallel.py --slice ${{ matrix.slice }}/6
env:
# Ensure tests don't accidentally call real APIs
OPENROUTER_API_KEY: ""
@@ -114,9 +99,9 @@ jobs:
NOUS_API_KEY: ""
- name: Upload per-slice durations
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: test-durations-slice-${{ matrix.slice.index }}
name: test-durations-slice-${{ matrix.slice }}
path: test_durations.json
retention-days: 1
@@ -124,11 +109,11 @@ jobs:
# (including PRs) get balanced slicing.
save-durations:
needs: test
if: needs.test.result == 'success' && github.ref == 'refs/heads/main'
if: always() && github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
steps:
- name: Download all slice durations
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
with:
pattern: test-durations-slice-*
path: durations
@@ -148,17 +133,17 @@ jobs:
"
- name: Save merged duration cache
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: test_durations.json
key: test-durations-${{ github.run_id }}
key: test-durations
e2e:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Install ripgrep (prebuilt binary)
run: |
@@ -166,7 +151,7 @@ jobs:
RG_VERSION=15.1.0
RG_SHA256=1c9297be4a084eea7ecaedf93eb03d058d6faae29bbc57ecdaf5063921491599
RG_TARBALL=ripgrep-${RG_VERSION}-x86_64-unknown-linux-musl.tar.gz
curl -sSfL --retry 3 --retry-delay 5 -o "$RG_TARBALL" \
curl -sSfL -o "$RG_TARBALL" \
"https://github.com/BurntSushi/ripgrep/releases/download/${RG_VERSION}/${RG_TARBALL}"
echo "${RG_SHA256} ${RG_TARBALL}" | sha256sum -c -
tar -xzf "$RG_TARBALL"
@@ -175,45 +160,27 @@ jobs:
rg --version
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
with:
# Persist uv's download/wheel cache (~/.cache/uv) across runs.
# Keyed on the dependency manifests, so the cache is reused until
# pyproject.toml or uv.lock changes. `uv sync` still runs every
# time, but resolves from the warm cache instead of re-downloading
# and re-building wheels.
enable-cache: true
cache-dependency-glob: |
pyproject.toml
uv.lock
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
- name: Set up Python 3.11
run: uv python install 3.11
- name: Install dependencies
# `uv sync --locked` installs the exact pinned set from uv.lock (and
# fails if the lock is out of sync with pyproject.toml), giving a
# reproducible env. It also creates .venv itself, so no separate
# `uv venv` step is needed.
uses: ./.github/actions/retry
with:
command: uv sync --locked --python 3.11 --extra all --extra dev
- name: Minimize uv cache
# Optimized for CI: prunes pre-built wheels that are cheap to
# re-download, keeping the persisted cache small and fast to restore.
run: uv cache prune --ci
run: |
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
- name: Packaged-wheel i18n smoke test
run: |
source .venv/bin/activate
source venv/bin/activate
python -m pytest -m integration tests/test_wheel_locales_e2e.py -v
- name: Run e2e tests
run: |
source .venv/bin/activate
source venv/bin/activate
python -m pytest tests/e2e/ -v --tb=short
env:
OPENROUTER_API_KEY: ""
OPENAI_API_KEY: ""
NOUS_API_KEY: ""
NOUS_API_KEY: ""
-51
View File
@@ -1,51 +0,0 @@
# .github/workflows/typecheck.yml
name: Typecheck
on:
workflow_call:
jobs:
typecheck:
name: Check TypeScript
runs-on: ubuntu-latest
strategy:
matrix:
package:
[ui-tui, web, apps/bootstrap-installer, apps/desktop, apps/shared]
fail-fast: false # report all failures, not just the first one
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
# --ignore-scripts: typecheck only needs the TS sources + type defs, not
# native builds. Skipping install scripts drops node-pty's node-gyp
# header fetch — the transient flake that killed this job pre-`tsc` — and
# is faster. retry covers the remaining registry blips.
- uses: ./.github/actions/retry
with:
command: npm ci --ignore-scripts
- run: npm run --prefix ${{ matrix.package }} typecheck
# Production build of the desktop renderer. `typecheck` runs `tsc` only,
# which does NOT exercise Vite/Rolldown module resolution — so an
# unresolvable package export (e.g. a transitive @assistant-ui/tap that no
# longer exports "./react-shim") slips past typecheck and only explodes when
# users build apps/desktop from source on install/update. Run the real
# `vite build` here so that class of break fails in CI instead.
desktop-build:
name: Build desktop app
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
# Keep install scripts here: the production build may need node-pty's
# native binary. retry handles the transient install-time fetch flakes.
- uses: ./.github/actions/retry
with:
command: npm ci
- run: npm run --prefix apps/desktop build
+16 -16
View File
@@ -5,11 +5,11 @@ name: Publish to PyPI
on:
push:
tags:
- "v20*" # CalVer tags: v2026.5.15, v2026.5.15.2, etc.
- 'v20*' # CalVer tags: v2026.5.15, v2026.5.15.2, etc.
workflow_dispatch:
inputs:
confirm_tag:
description: "Tag to publish (e.g. v2026.5.15). Must already exist."
description: 'Tag to publish (e.g. v2026.5.15). Must already exist.'
required: true
type: string
@@ -27,7 +27,7 @@ jobs:
name: Build distribution 📦
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
# On workflow_dispatch, check out the confirmed tag.
@@ -43,17 +43,17 @@ jobs:
fi
- name: Set up Python
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.13"
python-version: '3.13'
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
- name: Set up Node.js
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: "22"
node-version: '22'
- name: Build web dashboard
run: cd web && npm ci && npm run build
@@ -81,7 +81,7 @@ jobs:
run: uv build --sdist --wheel
- name: Upload distribution artifacts
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: python-package-distributions
path: dist/
@@ -94,17 +94,17 @@ jobs:
name: pypi
url: https://pypi.org/p/hermes-agent
permissions:
id-token: write # OIDC trusted publishing
id-token: write # OIDC trusted publishing
steps:
- name: Download distribution artifacts
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
name: python-package-distributions
path: dist/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
with:
skip-existing: true
@@ -116,12 +116,12 @@ jobs:
needs: publish
runs-on: ubuntu-latest
permissions:
contents: write # attach assets to the existing release
id-token: write # sigstore signing
contents: write # attach assets to the existing release
id-token: write # sigstore signing
steps:
- name: Download distribution artifacts
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
name: python-package-distributions
path: dist/
@@ -145,7 +145,7 @@ jobs:
- name: Sign with Sigstore
if: env.skip_sign != 'true'
uses: sigstore/gh-action-sigstore-python@04cffa1d795717b140764e8b640de88853c92acc # v3.3.0
uses: sigstore/gh-action-sigstore-python@04cffa1d795717b140764e8b640de88853c92acc # v3.3.0
with:
inputs: >-
./dist/*.tar.gz
+17 -6
View File
@@ -4,7 +4,7 @@ name: uv.lock check
# that modify pyproject.toml without regenerating uv.lock (or vice versa)
# must not merge, because the Docker build's `uv sync --frozen` step will
# fail on a stale lockfile and we'd rather catch it here than in the
# docker workflow on main.
# docker-publish workflow on main.
#
# ─────────────────────────────────────────────────────────────────────────
# IMPORTANT: this check runs against the MERGED state, not just your branch
@@ -44,14 +44,25 @@ name: uv.lock check
# the same way. Better to catch it here than after merge.
on:
workflow_call:
push:
branches: [main]
paths:
- 'pyproject.toml'
- 'uv.lock'
- '.github/workflows/uv-lockfile-check.yml'
pull_request:
branches: [main]
paths:
- 'pyproject.toml'
- 'uv.lock'
- '.github/workflows/uv-lockfile-check.yml'
permissions:
contents: read
concurrency:
group: uv-lockfile-check-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
check:
@@ -60,10 +71,10 @@ jobs:
timeout-minutes: 5
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
# `uv lock --check` re-resolves the project from pyproject.toml and
# compares the result to uv.lock, exiting non-zero if they disagree.
@@ -100,7 +111,7 @@ jobs:
This check is blocking because the Docker image build uses
`uv sync --frozen --extra all`, which rejects stale lockfiles
— catching it here avoids a ~15 min failed docker run
— catching it here avoids a ~15 min failed docker-publish run
on `main` post-merge.
EOF
echo "::error title=uv.lock out of sync::Run \`uv lock\` locally and commit the result. If on a PR, sync with main first."
+5 -25
View File
@@ -5,7 +5,6 @@
*.pyc*
__pycache__/
.venv/
.venv
.vscode/
.env
.env.local
@@ -90,9 +89,6 @@ website/static/api/skills-index.json
# every build).
website/static/api/skills.json
website/static/api/skills-meta.json
# automation-blueprints-index.json is a build artifact emitted by
# website/scripts/extract-automation-blueprints.py during prebuild.
website/static/api/automation-blueprints-index.json
models-dev-upstream/
# Local editor / agent tooling (machine-specific; keep in global config, not the repo)
@@ -112,18 +108,6 @@ docs/superpowers/*
# logs, and per-session caches are never artifacts of the codebase.
.hermes/
# Desktop/bootstrap install marker written into the managed checkout root by the
# bootstrap installer. It is Hermes-managed runtime state, never a code change —
# ignore it so `hermes update`'s `git stash push --include-untracked` does not
# treat it as a local edit and autostash it on every run (#38529).
.hermes-bootstrap-complete
# Interrupted-update breadcrumb + recovery lock written next to the shared venv
# by `hermes update` / launch-time self-heal. Runtime state, never a code change
# — ignore so `git status` stays clean and update's autostash skips them.
.update-incomplete
.update-incomplete.lock
# Tool Search live-test harness output — non-deterministic model transcripts,
# regenerated by scripts/tool_search_livetest.py. Never an artifact of the repo.
scripts/out/
@@ -134,12 +118,8 @@ scripts/out/
# committed to the repo root. See the hermes-release skill.
RELEASE_v*.md
# Desktop demo-run scratch output (hermes writes demo/*.txt during recorded
# walkthroughs). Throwaway artifacts, never part of the app.
apps/desktop/demo/
# PR infographics are rendered locally and embedded in PR descriptions via the
# image-provider (fal.media) URL — they are NEVER committed to the repo. The
# PR body is the archive. See the hermes-agent-dev skill's
# pr-infographic-workflow reference (storage rule + lapse #8 / #COMMIT-1).
infographic/
# Post-pull stamp written by `hermes update`'s post-pull phase into the install
# dir (the repo root for source checkouts). Records the commit the post-pull
# steps last ran for; the per-launch gate compares it against live HEAD. Local
# runtime state, never a repo artifact.
.post_pull_stamp
+143 -294
View File
@@ -4,220 +4,14 @@ Instructions for AI coding assistants and developers working on the hermes-agent
**Never give up on the right solution.**
## What Hermes Is
Hermes is a personal AI agent that runs the same agent core across a CLI, a
messaging gateway (Telegram, Discord, Slack, and ~20 other platforms), a TUI,
and an Electron desktop app. It learns across sessions (memory + skills),
delegates to subagents, runs scheduled jobs, and drives a real terminal and
browser. It is extended primarily through **plugins and skills**, not by
growing the core.
Two properties shape almost every design decision and are the lens for
reviewing any change:
- **Per-conversation prompt caching is sacred.** A long-lived conversation
reuses a cached prefix every turn. Anything that mutates past context,
swaps toolsets, or rebuilds the system prompt mid-conversation invalidates
that cache and multiplies the user's cost. We do not do it (the one
exception is context compression).
- **The core is a narrow waist; capability lives at the edges.** Every model
tool we add is sent on every API call, so the bar for a new *core* tool is
high. Most new capability should arrive as a CLI command + skill, a
service-gated tool, or a plugin — not as core surface.
## Contribution Rubric — What We Want / What We Don't
This is the project's intent layer. Use it two ways:
1. **For humans and for your own work** — what gets merged and what gets
rejected, so a contribution aims at the target.
2. **For automated review (the triage sweeper)** — guidance on when a PR is
safe to close on the three allowed reasons (`implemented_on_main`,
`cannot_reproduce`, `incoherent`) and, just as important, **when NOT to
close** one. Taste-based "we don't want this / out of scope" closes are NOT
an automated decision — those stay with a human maintainer. The sweeper's
job here is to recognize design intent and *avoid wrongly closing a
legitimate contribution*, not to make the won't-implement call itself.
Read the balance right: Hermes ships a **lot** — most merges are bug fixes to
real reported behavior, and the product surface (platforms, channels,
providers, models, desktop/TUI features) expands aggressively and on purpose.
The restraint below is aimed squarely at the **core agent + the model tool
schema**, the one place where every addition is paid for on every API call.
"Smallest footprint" governs *how a capability is wired into the core*, NOT
whether the product is allowed to grow. We are expansive at the edges and
conservative at the waist.
### What we want
- **Fix real bugs, well.** The bulk of what lands is `fix(...)` against an
actual reported symptom. A good fix reproduces the symptom on current
`main`, points to the exact line where it manifests, and fixes the whole bug
class — sibling call paths included — not just the one site the reporter hit.
- **Expand reach at the edges.** New platform adapters, channels, providers,
models, and desktop/TUI/dashboard features are welcome and land routinely,
including large ones (a new messaging channel, a session-cap feature, a
Windows PTY bridge). Breadth in the product is a goal, not a footprint
concern — as long as it integrates with the existing setup/config UX
(`hermes tools`, `hermes setup`, auto-install) rather than bolting on a raw
env var.
- **Refactor god-files into clean modules.** Extracting a multi-thousand-line
cluster out of `cli.py` / `run_agent.py` / `gateway/run.py` into a focused
mixin or module is wanted work, even when the diff is huge and mechanical
(large `+N/-N` refactors merge regularly). The "every line traces to the
request" test applies to *feature* PRs; a declared refactor's request IS the
extraction.
- **Keep the core narrow.** New *model tools* are the expensive exception —
every tool ships on every API call. Prefer, in order: extend existing code →
CLI command + skill → service-gated tool (`check_fn`) → plugin → MCP server
in the catalog → new core tool (last resort). See "The Footprint Ladder."
- **Extend, don't duplicate.** Before adding a module/manager/hook, check
whether existing infrastructure already covers the use case. When several PRs
integrate the same *category*, design one shared interface instead of merging
them one at a time (see the ABC + orchestrator note under the Footprint
Ladder).
- **Behavior contracts over snapshots.** Tests should assert how two pieces of
data must relate (invariants), not freeze a current value (model lists,
config version literals, enumeration counts). See "Don't write
change-detector tests."
- **E2E validation, not just green unit mocks.** For anything touching
resolution chains, config propagation, security boundaries, remote
backends, or file/network I/O, exercise the real path with real imports
against a temp `HERMES_HOME`. Mocks hide integration bugs.
- **Cache-, alternation-, and invariant-safe.** Preserve prompt caching, strict
message role alternation (never two same-role messages in a row; never a
synthetic user message injected mid-loop), and a system prompt that is
byte-stable for the life of a conversation.
- **Contributor credit preserved.** Salvage external work by cherry-picking
(rebase-merge) so authorship survives in git history; don't reimplement from
scratch when you can build on top.
### What we don't want (rejected even when well-built)
- **Speculative infrastructure.** Hooks, callbacks, or extension points with no
concrete consumer. Adding a hook is easy; removing one after plugins depend
on it is hard. A hook is NOT speculative if a contributor has a real, stated
use case — even if the consumer ships separately.
- **New `HERMES_*` env vars for non-secret config.** `.env` is for secrets
only (API keys, tokens, passwords). All behavioral settings — timeouts,
thresholds, feature flags, display prefs — go in `config.yaml`. Bridge to an
internal env var if the mechanism needs one, but user-facing docs point to
`config.yaml`. Reject PRs that tell users to "set X in your .env" unless X
is a credential.
- **A new core tool when terminal + file already do the job, or when a skill
would.** If the only barrier is file visibility on a remote backend, fix the
mount, not the toolset.
- **Lazy-reading escape hatches on instructional tools.** No `offset`/`limit`
pagination on tools that load content the agent must read fully (skills,
prompts, playbooks). Models will read page 1 and skip the rest.
- **"Fixes" that destroy the feature they secure.** A mitigation that kills the
feature's purpose is the wrong mitigation. Read the original commit's intent
(`git log -p -S`) before restricting behavior; find a fix that preserves the
feature.
- **Outbound telemetry / usage attribution without opt-in gating.** No new
analytics, third-party identifier tagging, or attribution tags until a
generic user-facing opt-in (config gate + setup prompt + `hermes tools`
toggle) exists. Park behind a label, do not merge.
- **Change-detector tests, cache-breaking mid-conversation, dead code wired in
without E2E proof, and plugins that touch core files.** Plugins live in their
own directory and work within the ABCs/hooks we provide; if a plugin needs
more, widen the generic plugin surface, don't special-case it in core.
- **Third-party products / other people's projects integrated into the core
tree.** Observability backends, vendor SaaS integrations, analytics dashboards,
and similar "someone else's product" plugins do NOT land under `plugins/` in
this repo. They place an ongoing maintenance burden on us to keep them working
against a fast-moving core, for a backend we don't own. Ship them as a
**standalone plugin repo** users install into `~/.hermes/plugins/` (or via a
pip entry point), and promote them in the Nous Research Discord
(`#plugins-skills-and-skins`). This is a coupling-and-maintenance decision, not
a quality bar — the plugin can be excellent and still be a close. PRs that add
such a directory to the tree are closed with a pointer to publish it as its own
repo.
### Before you call it a bug — verify the premise (and when NOT to close)
The most common reason a well-written PR gets closed is not code quality — it
is that the change is built on a **wrong premise**, or it treats an
**intentional design as a gap**. These patterns cut both ways: they tell a
human reviewer what to scrutinize, and they tell the automated sweeper when a
PR is NOT safe to close as `implemented_on_main` / `cannot_reproduce` (when in
doubt, leave it open for a human). They are distilled from real closes.
- **"Intentional design, not a gap."** A limitation that looks like an
oversight is often deliberate. Before "fixing" a missing link or a
restriction, ask whether the isolation IS the design. Example: profiles are
independent islands on purpose — a PR adding live config inheritance from the
default profile was closed because coupling profiles together is exactly what
the design prevents (the copy-at-creation `--clone` path already covers the
legitimate "start from my default" case). Read the original commit's intent
(`git log -p -S "<symbol>"`) before assuming something is unfinished.
- **"The premise doesn't hold against how X actually works."** A PR's
justification frequently rests on a wrong mental model of an existing
mechanism. Trace the real code/runtime before accepting the rationale. Two
real closes: a rate-limit "re-probe during cooldown" PR (the breaker only
trips on a *confirmed-empty* account bucket, so re-probing just hammers a
bucket we've already proven empty); a usage-accumulation fix whose new branch
**never executes at runtime** because an earlier guard already popped the
state it depended on. If you can't point to the exact line where the bug
manifests AND show the fix changes that line's behavior, you haven't verified
the premise.
- **"This fix was wrong — the absence/omission was deliberate."** Adding the
obvious-looking missing piece can break things the omission was protecting.
Example: restoring "missing" `__init__.py` files made a test tree importable
as a dotted package that shadowed the real plugin, deleting its `register()`
at import time. The absence was load-bearing.
- **"Overreached / resurrected an approach we'd moved past."** Scope creep that
supersedes an agreed-on base, or revives a direction the maintainers
deliberately closed, gets rejected even when the code works. Keep the change
to the narrow piece that was actually agreed; offer the rest as a focused
follow-up.
The throughline: **verify the claim AND the intent against the codebase before
writing or merging a fix.** A confirmed reproduction on current `main` plus a
line-level account of where the fix acts beats a plausible-sounding rationale
every time. When in doubt about intent, it is cheaper to ask than to ship a
fix that fights the design.
### The Footprint Ladder (new capability decision)
Each rung adds more permanent surface than the one above. Choose the highest
(least-footprint) rung that correctly solves the problem:
1. **Extend existing code** — the capability is a variation of something that
already exists. Zero new surface.
2. **CLI command + skill** — manages config/state/infra expressible as shell
commands. The agent runs `hermes <subcommand>` guided by a skill. Zero
model-tool footprint. Default choice for subscriptions, scheduled tasks,
service setup. Examples: `hermes webhook`, `hermes cron`, `hermes tools`.
3. **Service-gated tool (`check_fn`)** — needs structured params/returns AND
only appears when a prerequisite is configured. Zero footprint otherwise.
Examples: Home Assistant tools (gated on token), memory-provider tools.
4. **Plugin** — third-party/niche/user-specific capability that doesn't ship in
core. Lives in `~/.hermes/plugins/` or a pip package, discovered at runtime.
5. **MCP server (in the catalog)** — if the capability genuinely needs to be a
tool (structured I/O the agent invokes) but isn't core-fundamental, prefer
building it as an MCP server and adding it to the MCP catalog over growing
the core toolset. The agent connects to it through the built-in MCP client;
zero permanent core-schema footprint, and it's reusable by any MCP host.
6. **New core tool** — only when the capability is fundamental, broadly useful
to nearly every user, and unreachable via terminal + file (or an MCP server).
Examples of correct core tools: terminal, read_file, web_search,
browser_navigate.
When 3+ open PRs try to integrate the same *category* of thing (memory
backends, providers, notifiers), don't merge them one at a time — design an
ABC + orchestrator, wrap the existing built-in as the first provider, and turn
the competing PRs into plugins against that interface.
## Development Environment
```bash
# Prefer .venv; fall back to venv if that's what your checkout has.
source .venv/bin/activate # or: source venv/bin/activate
# Prefer venv; fall back to .venv if that's what your checkout has.
source venv/bin/activate # or: source .venv/bin/activate
```
`scripts/run_tests.sh` probes `.venv` first, then `venv`, then
`scripts/run_tests.sh` probes `venv` first, then `.venv`, then
`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the
main checkout).
@@ -395,23 +189,30 @@ All slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandD
### Adding a Slash Command
1. Add a `CommandDef` entry to `COMMAND_REGISTRY` in `hermes_cli/commands.py`:
```python
CommandDef("mycommand", "Description of what it does", "Session",
aliases=("mc",), args_hint="[arg]"),
```
2. Add handler in `HermesCLI.process_command()` in `cli.py`:
```python
elif canonical == "mycommand":
self._handle_mycommand(cmd_original)
```
3. If the command is available in the gateway, add a handler in `gateway/run.py`:
```python
if canonical == "mycommand":
return await self._handle_mycommand(event)
```
4. For persistent settings, use `save_config_value()` in `cli.py`
**CommandDef fields:**
- `name` — canonical name without slash (e.g. `"background"`)
- `description` — human-readable description
- `category` — one of `"Session"`, `"Configuration"`, `"Tools & Skills"`, `"Info"`, `"Exit"`
@@ -446,16 +247,16 @@ Newline-delimited JSON-RPC over stdio. Requests from Ink, events from Python. Se
### Key Surfaces
| Surface | Ink component | Gateway method |
|---------|---------------|----------------|
| Chat streaming | `app.tsx` + `messageLine.tsx` | `prompt.submit` → `message.delta/complete` |
| Tool activity | `thinking.tsx` | `tool.start/progress/complete` |
| Approvals | `prompts.tsx` | `approval.respond` ← `approval.request` |
| Clarify/sudo/secret | `prompts.tsx`, `maskedPrompt.tsx` | `clarify/sudo/secret.respond` |
| Session picker | `sessionPicker.tsx` | `session.list/resume` |
| Slash commands | Local handler + fallthrough | `slash.exec` → `_SlashWorker`, `command.dispatch` |
| Completions | `useCompletion` hook | `complete.slash`, `complete.path` |
| Theming | `theme.ts` + `branding.tsx` | `gateway.ready` with skin data |
| Surface | Ink component | Gateway method |
| ------------------- | --------------------------------- | ------------------------------------------------- |
| Chat streaming | `app.tsx` + `messageLine.tsx` | `prompt.submit` → `message.delta/complete` |
| Tool activity | `thinking.tsx` | `tool.start/progress/complete` |
| Approvals | `prompts.tsx` | `approval.respond` ← `approval.request` |
| Clarify/sudo/secret | `prompts.tsx`, `maskedPrompt.tsx` | `clarify/sudo/secret.respond` |
| Session picker | `sessionPicker.tsx` | `session.list/resume` |
| Slash commands | Local handler + fallthrough | `slash.exec` → `_SlashWorker`, `command.dispatch` |
| Completions | `useCompletion` hook | `complete.slash`, `complete.path` |
| Theming | `theme.ts` + `branding.tsx` | `gateway.ready` with skin data |
### Slash Command Flow
@@ -470,7 +271,7 @@ npm install # first time
npm run dev # watch mode (rebuilds hermes-ink + tsx --watch)
npm start # production
npm run build # full build (hermes-ink + tsc)
npm run typecheck # typecheck only (tsc --noEmit)
npm run type-check # typecheck only (tsc --noEmit)
npm run lint # eslint
npm run fmt # prettier
npm test # vitest
@@ -478,7 +279,7 @@ npm test # vitest
### TUI in the Dashboard (`hermes dashboard` → `/chat`)
The dashboard embeds the real `hermes --tui` — **not** a rewrite. See `hermes_cli/pty_bridge.py` + the `@app.websocket("/api/pty")` endpoint in `hermes_cli/web_server.py`.
The dashboard embeds the real `hermes --tui` — **not** a rewrite. See `hermes_cli/pty_bridge.py` + the `@app.websocket("/api/pty")` endpoint in `hermes_cli/web_server.py`.
- Browser loads `web/src/pages/ChatPage.tsx`, which mounts xterm.js's `Terminal` with the WebGL renderer, `@xterm/addon-fit` for container-driven resize, and `@xterm/addon-unicode11` for modern wide-character widths.
- `/api/pty?token=…` upgrades to a WebSocket; auth uses the same ephemeral `_SESSION_TOKEN` as REST, via query param (browsers can't set `Authorization` on WS upgrade).
@@ -491,7 +292,7 @@ The dashboard embeds the real `hermes --tui` — **not** a rewrite. See `hermes
### Electron Desktop Chat App (`apps/desktop/`)
A **separate** chat surface from both the classic CLI and the dashboard's embedded TUI. It is an Electron + React + nanostore renderer (`@assistant-ui/react`) that talks to a `tui_gateway` backend over JSON-RPC (`requestGateway(method, params)`). The WebSocket/JSON-RPC transport lives in the framework-agnostic `apps/shared` package (`@hermes/shared` — `JsonRpcGatewayClient` + WS URL helpers), which the web dashboard (`web/`) also consumes; **desktop has no build/runtime dependency on the dashboard frontend** — it spawns a headless `hermes serve` backend server (the same gateway `dashboard` serves, minus the browser UI). `dashboard` and `serve` share `cmd_dashboard`/`start_server` but are independent surfaces — neither launches the other. The one exception is a backward-compat *fallback*: `serve` is newer, so the desktop spawn (`electron/backend-command.cjs` + `backendSupportsServe()` in `main.cjs`) detects whether the resolved runtime registers `serve` and, only when it does not (an older managed install / PATH `hermes` the app hasn't updated yet), rewrites the argv to the legacy `dashboard --no-open`. Without that, a new app against an un-upgraded runtime would crash on an unknown subcommand and brick every mid-upgrade user. It does NOT embed `hermes --tui` — it has its own composer, transcript, and slash-command pipeline. Route desktop bugs to the `hermes-desktop-app-work` skill, not `hermes-dashboard-work`.
A **separate** chat surface from both the classic CLI and the dashboard's embedded TUI. It is an Electron + React + nanostore renderer (`@assistant-ui/react`) that talks to a `tui_gateway` backend over JSON-RPC (`requestGateway(method, params)`). It does NOT embed `hermes --tui` — it has its own composer, transcript, and slash-command pipeline. Route desktop bugs to the `hermes-desktop-app-work` skill, not `hermes-dashboard-work`.
**Slash commands in the desktop app are curated client-side, then dispatched to the backend.** The pipeline:
@@ -508,11 +309,9 @@ A **separate** chat surface from both the classic CLI and the dashboard's embedd
## Adding New Tools
Before adding any tool, settle the footprint question first (see "The
Footprint Ladder" in the Contribution Rubric): most capabilities should NOT
be core tools. For custom or local-only tools, do **not** edit Hermes core.
Use the plugin route instead: create `~/.hermes/plugins/<name>/plugin.yaml`
and `~/.hermes/plugins/<name>/__init__.py`, then register tools with
For most custom or local-only tools, do **not** edit Hermes core. Use the plugin
route instead: create `~/.hermes/plugins/<name>/plugin.yaml` and
`~/.hermes/plugins/<name>/__init__.py`, then register tools with
`ctx.register_tool(...)`. Plugin toolsets are discovered automatically and can be
enabled or disabled without touching `tools/` or `toolsets.py`.
@@ -522,6 +321,7 @@ core Hermes tool that should ship in the base system.
Built-in/core tools require changes in **2 files**:
**1. Create `tools/your_tool.py`:**
```python
import json, os
from tools.registry import registry
@@ -542,7 +342,7 @@ registry.register(
)
```
**2. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset. **This step is required:** auto-discovery imports the tool and registers its schema, but the tool is only *exposed to an agent* if its name appears in a toolset. `_HERMES_CORE_TOOLS` is not dead code — it's the default bundle every platform's base toolset inherits from.
**2. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset. **This step is required:** auto-discovery imports the tool and registers its schema, but the tool is only _exposed to an agent_ if its name appears in a toolset. `_HERMES_CORE_TOOLS` is not dead code — it's the default bundle every platform's base toolset inherits from.
Auto-discovery: any `tools/*.py` file with a top-level `registry.register()` call is imported automatically — no manual import list to maintain. Wiring into a toolset is still a deliberate, manual step.
@@ -562,14 +362,15 @@ All dependencies must have upper bounds to limit supply-chain attack surface.
This policy was established after the litellm compromise (PR #2796, #2810) and
reinforced after the Mini Shai-Hulud worm campaign (May 2026).
| Source type | Treatment | Example |
|---|---|---|
| PyPI package | `>=floor,<next_major` | `"httpx>=0.28.1,<1"` |
| Git URL | Commit SHA | `git+https://...@<40-char-sha>` |
| GitHub Actions | Commit SHA + comment | `uses: actions/checkout@<sha> # v4` |
| CI-only pip | `==exact` | `pyyaml==6.0.2` |
| Source type | Treatment | Example |
| -------------- | --------------------- | ------------------------------------ |
| PyPI package | `>=floor,<next_major` | `"httpx>=0.28.1,<1"` |
| Git URL | Commit SHA | `git+https://...@<40-char-sha>` |
| GitHub Actions | Commit SHA + comment | `uses: actions/checkout@<sha> # v4` |
| CI-only pip | `==exact` | `pyyaml==6.0.2` |
**When adding a new dependency to `pyproject.toml`:**
1. Pin to `>=current_version,<next_major` for post-1.0 (e.g. `>=1.5.0,<2`).
2. For pre-1.0 packages, use `<0.(current_minor + 2)` (e.g. `>=0.29,<0.32`).
3. Never commit a bare `>=X.Y.Z` without a ceiling — CI and reviewers will reject it.
@@ -582,6 +383,7 @@ Reference: #2810 (bounds pass), #9801 (SHA pinning + audit CI).
## Adding Configuration
### config.yaml options:
1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
2. Bump `_config_version` (check the current value at the top of `DEFAULT_CONFIG`)
ONLY if you need to actively migrate/transform existing user config
@@ -606,7 +408,9 @@ its own provider/model/base_url/max_tokens/reasoning_effort. See
`archive_after_days`, `backup` (nested).
### .env variables (SECRETS ONLY — API keys, tokens, passwords):
1. Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` with metadata:
```python
"NEW_API_KEY": {
"description": "What it's for",
@@ -624,16 +428,17 @@ the env var in code (see `gateway_timeout`, `terminal.cwd` → `TERMINAL_CWD`).
### Config loaders (three paths — know which one you're in):
| Loader | Used by | Location |
|--------|---------|----------|
| `load_cli_config()` | CLI mode | `cli.py` — merges CLI-specific defaults + user YAML |
| `load_config()` | `hermes tools`, `hermes setup`, most CLI subcommands | `hermes_cli/config.py` — merges `DEFAULT_CONFIG` + user YAML |
| Direct YAML load | Gateway runtime | `gateway/run.py` + `gateway/config.py` — reads user YAML raw |
| Loader | Used by | Location |
| ------------------- | ---------------------------------------------------- | ------------------------------------------------------------ |
| `load_cli_config()` | CLI mode | `cli.py` — merges CLI-specific defaults + user YAML |
| `load_config()` | `hermes tools`, `hermes setup`, most CLI subcommands | `hermes_cli/config.py` — merges `DEFAULT_CONFIG` + user YAML |
| Direct YAML load | Gateway runtime | `gateway/run.py` + `gateway/config.py` — reads user YAML raw |
If you add a new key and the CLI sees it but the gateway doesn't (or vice
versa), you're on the wrong loader. Check `DEFAULT_CONFIG` coverage.
### Working directory:
- **CLI** — uses the process's current directory (`os.getcwd()`).
- **Messaging** — uses `terminal.cwd` from `config.yaml`. The gateway bridges this
to the `TERMINAL_CWD` env var for child tools. **`MESSAGING_CWD` has been
@@ -662,24 +467,24 @@ hermes_cli/skin_engine.py # SkinConfig dataclass, built-in skins, YAML loader
### What skins customize
| Element | Skin Key | Used By |
|---------|----------|---------|
| Banner panel border | `colors.banner_border` | `banner.py` |
| Banner panel title | `colors.banner_title` | `banner.py` |
| Banner section headers | `colors.banner_accent` | `banner.py` |
| Banner dim text | `colors.banner_dim` | `banner.py` |
| Banner body text | `colors.banner_text` | `banner.py` |
| Response box border | `colors.response_border` | `cli.py` |
| Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
| Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
| Spinner verbs | `spinner.thinking_verbs` | `display.py` |
| Spinner wings (optional) | `spinner.wings` | `display.py` |
| Tool output prefix | `tool_prefix` | `display.py` |
| Per-tool emojis | `tool_emojis` | `display.py` → `get_tool_emoji()` |
| Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
| Welcome message | `branding.welcome` | `cli.py` |
| Response box label | `branding.response_label` | `cli.py` |
| Prompt symbol | `branding.prompt_symbol` | `cli.py` |
| Element | Skin Key | Used By |
| ------------------------ | ------------------------- | --------------------------------- |
| Banner panel border | `colors.banner_border` | `banner.py` |
| Banner panel title | `colors.banner_title` | `banner.py` |
| Banner section headers | `colors.banner_accent` | `banner.py` |
| Banner dim text | `colors.banner_dim` | `banner.py` |
| Banner body text | `colors.banner_text` | `banner.py` |
| Response box border | `colors.response_border` | `cli.py` |
| Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
| Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
| Spinner verbs | `spinner.thinking_verbs` | `display.py` |
| Spinner wings (optional) | `spinner.wings` | `display.py` |
| Tool output prefix | `tool_prefix` | `display.py` |
| Per-tool emojis | `tool_emojis` | `display.py` → `get_tool_emoji()` |
| Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
| Welcome message | `branding.welcome` | `cli.py` |
| Response box label | `branding.response_label` | `cli.py` |
| Prompt symbol | `branding.prompt_symbol` | `cli.py` |
### Built-in skins
@@ -794,24 +599,6 @@ landing in this tree. PRs that add a new directory under
provider as its own repo. Existing in-tree providers stay; bug fixes
to them are welcome.
**No new third-party-product plugins in-tree (policy, June 2026):** the
same rule applies beyond memory providers. Plugins that integrate
someone else's product or project — observability/metrics backends,
vendor SaaS connectors, analytics dashboards, paid-service tie-ins —
must ship as **standalone plugin repos** that users install into
`~/.hermes/plugins/` (or via pip entry points). They register through
the existing plugin discovery path and use the ABCs/hooks/ctx surface
we expose; nothing special is needed in core. The reason is
maintenance load: every product we absorb into the tree becomes our
burden to keep working against a fast-moving core, for a backend we
don't own. Promote standalone plugins in the Nous Research Discord
(`#plugins-skills-and-skins`). PRs that add such a directory under
`plugins/` are closed with a pointer to publish it as its own repo —
this is a coupling decision, not a quality judgment. (The
`observability/`, `kanban/`, `disk-cleanup/`, etc. directories already
in the tree are existing precedent, not an invitation to add more
third-party-product plugins alongside them.)
### Model-provider plugins (`plugins/model-providers/<name>/`)
Every inference backend (openrouter, anthropic, gmi, deepseek, nvidia, …)
@@ -822,6 +609,7 @@ discovery system** — scanned on first `get_provider_profile()` or
`list_providers()` call, NOT by the general PluginManager.
Scan order:
1. Bundled: `<repo>/plugins/model-providers/<name>/`
2. User: `$HERMES_HOME/plugins/model-providers/<name>/`
3. Legacy: `<repo>/providers/<name>.py` (back-compat)
@@ -891,6 +679,7 @@ violate them.
the implementation. No marketing words ("powerful",
"comprehensive", "seamless", "advanced"). Don't repeat the skill
name. Verify with:
```python
import re, pathlib
m = re.search(r'^description: (.*)$',
@@ -983,10 +772,9 @@ Enable/disable per platform via `hermes tools` (the curses UI) or the
## Delegation (`delegate_task`)
`tools/delegate_tool.py` spawns a subagent with an isolated
context + terminal session. By default the parent waits for the
child's summary before continuing its own loop. With `background=true`,
Hermes returns a delegation id immediately and the result re-enters the
conversation later through the async-delegation completion queue.
context + terminal session. Synchronous: the parent waits for the
child's summary before continuing its own loop — if the parent is
interrupted, the child is cancelled.
Two shapes:
@@ -1008,9 +796,9 @@ Key config knobs (under `delegation:` in `config.yaml`):
`orchestrator_enabled`, `subagent_auto_approve`, `inherit_mcp_toolsets`,
`max_iterations`.
Durability rule: background `delegate_task` is detached from the current
turn but still process-local. For work that must survive process restart, use
`cronjob` or `terminal(background=True, notify_on_complete=True)` instead.
Synchronicity rule: delegate_task is **not** durable. For long-running
work that must outlive the current turn, use `cronjob` or
`terminal(background=True, notify_on_complete=True)` instead.
---
@@ -1031,6 +819,7 @@ go to `~/.hermes/skills/.archive/` and are restorable.
archived), `pinned`.
Invariants:
- Curator only touches skills with `created_by: "agent"` provenance —
bundled + hub-installed skills are off-limits.
- Never deletes; max destructive action is archive.
@@ -1056,6 +845,7 @@ schedule jobs via the `cronjob` tool; users via `hermes cron <verb>`
`/cron` slash command.
Supported schedule formats:
- Duration: `"30m"`, `"2h"`, `"1d"`
- "every" phrase: `"every 2h"`, `"every monday 9am"`
- 5-field cron expression: `"0 9 * * *"`
@@ -1069,6 +859,7 @@ job B's prompt), `workdir` (run in a specific directory with its
`AGENTS.md`/`CLAUDE.md` loaded), and multi-platform delivery.
Hardening invariants:
- **3-minute hard interrupt** on cron sessions — runaway agent loops
cannot monopolize the scheduler.
- Catchup window: half the job's period, clamped to 120s–2h.
@@ -1111,10 +902,11 @@ kanban task.
standalone dispatcher deployment).
Isolation model:
- **Board** is the hard boundary — workers are spawned with
`HERMES_KANBAN_BOARD` pinned in their env so they can't see other
boards.
- **Tenant** is a soft namespace *within* a board — one specialist
- **Tenant** is a soft namespace _within_ a board — one specialist
fleet can serve multiple businesses with workspace-path + memory-key
isolation.
- After `kanban.failure_limit` consecutive non-success attempts on the
@@ -1130,6 +922,7 @@ Full user-facing docs: `website/docs/user-guide/features/kanban.md`.
### Prompt Caching Must Not Break
Hermes-Agent ensures caching remains valid throughout a conversation. **Do NOT implement changes that would:**
- Alter past context mid-conversation
- Change toolsets mid-conversation
- Reload memories or rebuild system prompts mid-conversation
@@ -1168,6 +961,7 @@ automatically scope to the active profile.
1. **Use `get_hermes_home()` for all HERMES_HOME paths.** Import from `hermes_constants`.
NEVER hardcode `~/.hermes` or `Path.home() / ".hermes"` in code that reads/writes state.
```python
# GOOD
from hermes_constants import get_hermes_home
@@ -1179,6 +973,7 @@ automatically scope to the active profile.
2. **Use `display_hermes_home()` for user-facing messages.** Import from `hermes_constants`.
This returns `~/.hermes` for default or `~/.hermes/profiles/<name>` for profiles.
```python
# GOOD
from hermes_constants import display_hermes_home
@@ -1194,6 +989,7 @@ automatically scope to the active profile.
4. **Tests that mock `Path.home()` must also set `HERMES_HOME`** — since code now uses
`get_hermes_home()` (reads env var), not `Path.home() / ".hermes"`:
```python
with patch.object(Path, "home", return_value=tmp_path), \
patch.dict(os.environ, {"HERMES_HOME": str(tmp_path / ".hermes")}):
@@ -1204,7 +1000,7 @@ automatically scope to the active profile.
a unique credential (bot token, API key), call `acquire_scoped_lock()` from
`gateway.status` in the `connect()`/`start()` method and `release_scoped_lock()` in
`disconnect()`/`stop()`. This prevents two profiles from using the same credential.
See `plugins/platforms/irc/adapter.py` for the canonical pattern.
See `gateway/platforms/telegram.py` for the canonical pattern.
6. **Profile operations are HOME-anchored, not HERMES_HOME-anchored** — `_get_profiles_root()`
returns `Path.home() / ".hermes" / "profiles"`, NOT `get_hermes_home() / "profiles"`.
@@ -1214,11 +1010,13 @@ automatically scope to the active profile.
## Known Pitfalls
### DO NOT hardcode `~/.hermes` paths
Use `get_hermes_home()` from `hermes_constants` for code paths. Use `display_hermes_home()`
for user-facing print/log messages. Hardcoding `~/.hermes` breaks profiles — each profile
has its own `HERMES_HOME` directory. This was the source of 5 bugs fixed in PR #3575.
### DO NOT introduce new `simple_term_menu` usage
Existing call sites in `hermes_cli/main.py` remain for legacy fallback only;
the preferred UI is curses (stdlib) because `simple_term_menu` has
ghost-duplication rendering bugs in tmux/iTerm2 with arrow keys. New
@@ -1226,15 +1024,19 @@ interactive menus must use `hermes_cli/curses_ui.py` — see
`hermes_cli/tools_config.py` for the canonical pattern.
### DO NOT use `\033[K` (ANSI erase-to-EOL) in spinner/display code
Leaks as literal `?[K` text under `prompt_toolkit`'s `patch_stdout`. Use space-padding: `f"\r{line}{' ' * pad}"`.
### `_last_resolved_tool_names` is a process-global in `model_tools.py`
`_run_single_child()` in `delegate_tool.py` saves and restores this global around subagent execution. If you add new code that reads this global, be aware it may be temporarily stale during child agent runs.
### DO NOT hardcode cross-tool references in schema descriptions
Tool schema descriptions must not mention tools from other toolsets by name (e.g., `browser_navigate` saying "prefer web_search"). Those tools may be unavailable (missing API keys, disabled toolset), causing the model to hallucinate calls to non-existent tools. If a cross-reference is needed, add it dynamically in `get_tool_definitions()` in `model_tools.py` — see the `browser_navigate` / `execute_code` post-processing blocks for the pattern.
### The gateway has TWO message guards — both must bypass approval/control commands
When an agent is running, messages pass through two sequential guards:
(1) **base adapter** (`gateway/platforms/base.py`) queues messages in
`_pending_messages` when `session_key in self._active_sessions`, and
@@ -1246,6 +1048,7 @@ guards and be dispatched inline, not via `_process_message_background()`
(which races session lifecycle).
### Squash merges from stale branches silently revert recent fixes
Before squash-merging a PR, ensure the branch is up to date with `main`
(`git fetch origin main && git reset --hard origin/main` in the worktree,
then re-apply the PR's commits). A stale branch's version of an unrelated
@@ -1254,16 +1057,19 @@ with `git diff HEAD~1..HEAD` after merging — unexpected deletions are a
red flag.
### Don't wire in dead code without E2E validation
Unused code that was never shipped was dead for a reason. Before wiring an
unused module into a live code path, E2E test the real resolution chain
with actual imports (not mocks) against a temp `HERMES_HOME`.
### Tests must not write to `~/.hermes/`
The `_isolate_hermes_home` autouse fixture in `tests/conftest.py` redirects `HERMES_HOME` to a temp dir. Never hardcode `~/.hermes/` paths in tests.
**Profile tests**: When testing profile features, also mock `Path.home()` so that
`_get_profiles_root()` and `_get_default_hermes_home()` resolve within the temp dir.
Use the pattern from `tests/hermes_cli/test_profiles.py`:
```python
@pytest.fixture
def profile_env(tmp_path, monkeypatch):
@@ -1289,22 +1095,65 @@ scripts/run_tests.sh # full suite, CI-parity
scripts/run_tests.sh tests/gateway/ # one directory
scripts/run_tests.sh tests/agent/test_foo.py::test_x # one test
scripts/run_tests.sh -v --tb=long # pass-through pytest flags
scripts/run_tests.sh --no-isolate tests/foo/ # disable subprocess isolation (faster, for debugging)
```
### Subprocess-per-test-file isolation
### Subprocess-per-test isolation
Every test file runs in a freshly-spawned Python subprocess via `run_tests_parallel.py`. This means module-level dicts/sets and
ContextVars from one test file cannot leak into the next.
Every test runs in a freshly-spawned Python subprocess via the in-tree plugin
at `tests/_isolate_plugin.py`. This means module-level dicts/sets and
ContextVars from one test cannot leak into the next — the historic
`_reset_module_state` autouse fixture is gone.
### Why the wrapper
Implementation notes:
| | Without wrapper | With wrapper |
| ------------------- | ------------------------------------------- | ----------------------------------------- |
| Provider API keys | Whatever is in your env (auto-detects pool) | All env vars except a specific few unset. |
| HOME / `~/.hermes/` | Your real config+auth.json | Temp dir per test |
| Timezone | Local TZ (PDT etc.) | UTC |
| Locale | Whatever is set | C.UTF-8 |
- The plugin uses `multiprocessing.get_context("spawn")`, which works on
Linux, macOS, and Windows alike (POSIX `fork` is not used).
- Per-test overhead is ~0.5–1.0s (Python startup + pytest collection). xdist
parallelism amortizes this across cores; on a 20-core box the full suite
finishes in roughly the same wall time as before, but flake-free.
- `isolate_timeout` (configured in `pyproject.toml`) caps each test at 30s.
Hangs are killed and surfaced as a failure report.
- Pass `--no-isolate` to disable isolation — useful when debugging a single
test interactively, or when you specifically want to verify state leakage.
- The plugin disables itself in child processes (sentinel envvar
`HERMES_ISOLATE_CHILD=1`), so there's no fork-bomb risk.
### Why the wrapper (and why the old "just call pytest" doesn't work)
Five real sources of local-vs-CI drift the script closes:
| | Without wrapper | With wrapper |
| ------------------- | ------------------------------------------- | -------------------------------------------------------------------- |
| Provider API keys | Whatever is in your env (auto-detects pool) | All `*_API_KEY`/`*_TOKEN`/etc. unset |
| HOME / `~/.hermes/` | Your real config+auth.json | Temp dir per test |
| Timezone | Local TZ (PDT etc.) | UTC |
| Locale | Whatever is set | C.UTF-8 |
| xdist workers | `-n auto` = all cores | `-n auto` (safe — subprocess isolation prevents cross-worker flakes) |
`tests/conftest.py` also enforces points 1-4 as an autouse fixture so ANY pytest
invocation (including IDE integrations) gets hermetic behavior — but the wrapper
is belt-and-suspenders.
### Running without the wrapper (only if you must)
If you can't use the wrapper (e.g. inside an IDE that shells pytest directly),
at minimum activate the venv. The isolation plugin loads automatically from
`addopts` in `pyproject.toml`, so you get the same per-test process isolation
either way.
```bash
source .venv/bin/activate # or: source venv/bin/activate
python -m pytest tests/ -q
```
If you need to bypass isolation for fast feedback while debugging:
```bash
python -m pytest tests/agent/test_foo.py -q --no-isolate
```
Always run the full suite before pushing changes.
### Don't write change-detector tests
-602
View File
@@ -1,602 +0,0 @@
# Contribuir a Hermes Agent
¡Gracias por contribuir a Hermes Agent! Esta guía cubre todo lo que necesitas: configurar tu entorno de desarrollo, entender la arquitectura, decidir qué construir y conseguir que tu PR sea aceptado.
---
## Prioridades de Contribución
Valoramos las contribuciones en este orden:
1. **Correcciones de errores** — bloqueos, comportamiento incorrecto, pérdida de datos. Siempre la máxima prioridad.
2. **Compatibilidad entre plataformas** — macOS, diferentes distribuciones de Linux y WSL2 en Windows. Queremos que Hermes funcione en todas partes.
3. **Fortalecimiento de seguridad** — inyección de shell, inyección de prompts, traversal de rutas, escalada de privilegios. Ver [Consideraciones de Seguridad](#consideraciones-de-seguridad).
4. **Rendimiento y robustez** — lógica de reintento, manejo de errores, degradación elegante.
5. **Nuevas habilidades** — pero solo las ampliamente útiles. Ver [¿Debería ser una Habilidad o una Herramienta?](#debería-ser-una-habilidad-o-una-herramienta)
6. **Nuevas herramientas** — raramente necesarias. La mayoría de las capacidades deberían ser habilidades. Ver más abajo.
7. **Documentación** — correcciones, aclaraciones, nuevos ejemplos.
---
## ¿Debería ser una Habilidad o una Herramienta?
Esta es la pregunta más común para los nuevos colaboradores. La respuesta casi siempre es **habilidad**.
### Hazlo una Habilidad cuando:
- La capacidad se puede expresar como instrucciones + comandos de shell + herramientas existentes
- Envuelve una CLI externa o API que el agente puede llamar a través de `terminal` o `web_extract`
- No necesita integración personalizada de Python ni gestión de claves API integrada en el agente
- Ejemplos: búsqueda en arXiv, flujos de trabajo de git, gestión de Docker, procesamiento de PDF, email a través de herramientas CLI
### Hazlo una Herramienta cuando:
- Requiere integración de extremo a extremo con claves API, flujos de autenticación o configuración de múltiples componentes gestionada por el harness del agente
- Necesita lógica de procesamiento personalizada que debe ejecutarse con precisión en cada ocasión (no "mejor esfuerzo" de la interpretación del LLM)
- Maneja datos binarios, streaming o eventos en tiempo real que no pueden pasar por el terminal
- Ejemplos: automatización de navegador (gestión de sesiones Browserbase), TTS (codificación de audio + entrega en plataforma), análisis de visión (manejo de imágenes base64)
### ¿Debería la Habilidad estar incluida?
Las habilidades incluidas (en `skills/`) se envían con cada instalación de Hermes. Deben ser **ampliamente útiles para la mayoría de los usuarios**:
- Manejo de documentos, investigación web, flujos de trabajo de desarrollo comunes, administración de sistemas
- Usadas regularmente por una amplia gama de personas
Si tu habilidad es oficial y útil pero no universalmente necesaria (ej., una integración de servicio de pago, una dependencia pesada), ponla en **`optional-skills/`** — se envía con el repositorio pero no está activada por defecto. Los usuarios pueden descubrirla a través de `hermes skills browse` (etiquetada como "oficial") e instalarla con `hermes skills install` (sin advertencia de terceros, confianza integrada).
Si tu habilidad es especializada, contribuida por la comunidad o de nicho, es mejor para un **Skills Hub** — súbela a un registro de habilidades y compártela en el [Discord de Nous Research](https://discord.gg/NousResearch). Los usuarios pueden instalarla con `hermes skills install`.
---
## Proveedores de Memoria: Publicar como Plugin Independiente
**Ya no aceptamos nuevos proveedores de memoria en este repositorio.** El conjunto de proveedores integrados en `plugins/memory/` (honcho, mem0, supermemory, byterover, hindsight, holographic, openviking, retaindb) está cerrado. Si quieres añadir un nuevo backend de memoria, publícalo como un **repositorio de plugin independiente** que los usuarios instalen en `~/.hermes/plugins/` (o a través de un entry point de pip).
Los plugins de memoria independientes:
- Implementan el mismo ABC `MemoryProvider` (`agent/memory_provider.py`) — `sync_turn`, `prefetch`, `shutdown` y opcionalmente `post_setup(hermes_home, config)` para integración con el asistente de configuración
- Usan el mismo sistema de descubrimiento — `discover_memory_providers()` los recoge desde directorios de plugins de usuario/proyecto y entry points de pip
- Se integran con `hermes memory setup` a través de `post_setup()` — sin necesidad de tocar el código base
- Pueden registrar sus propios subcomandos CLI a través de `register_cli(subparser)` en un archivo `cli.py`
- Obtienen todos los mismos hooks de ciclo de vida y plomería de configuración que los proveedores incluidos en el árbol
Los PRs que añadan un nuevo directorio bajo `plugins/memory/` serán cerrados con un puntero para publicar el proveedor como su propio repositorio. Los proveedores en árbol existentes se mantienen; las correcciones de errores para ellos son bienvenidas.
Esto no es una barra de calidad — es una decisión de acoplamiento y mantenimiento. Los proveedores de memoria son el tipo de plugin más común y no deberían vivir todos en este árbol.
---
## Configuración del Desarrollo
### Prerequisitos
| Requisito | Notas |
|-----------|-------|
| **Git** | Con la extensión `git-lfs` instalada |
| **Python 3.11+** | uv lo instalará si falta |
| **uv** | Gestor de paquetes Python rápido ([instalar](https://docs.astral.sh/uv/)) |
| **Node.js 20+** | Opcional — necesario para herramientas de navegador y puente WhatsApp (coincide con los engines de `package.json` raíz) |
### Clonar e instalar
```bash
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
# Crear venv con Python 3.11
uv venv venv --python 3.11
export VIRTUAL_ENV="$(pwd)/venv"
# Instalar con todos los extras (mensajería, cron, menús CLI, herramientas de desarrollo)
uv pip install -e ".[all,dev]"
# Opcional: herramientas de navegador
npm install
```
### Configurar para desarrollo
```bash
mkdir -p ~/.hermes/{cron,sessions,logs,memories,skills}
cp cli-config.yaml.example ~/.hermes/config.yaml
touch ~/.hermes/.env
# Añadir al menos una clave de proveedor LLM:
echo "OPENROUTER_API_KEY=***" >> ~/.hermes/.env
```
### Ejecutar
```bash
# Enlace simbólico para acceso global
mkdir -p ~/.local/bin
ln -sf "$(pwd)/venv/bin/hermes" ~/.local/bin/hermes
# Verificar
hermes doctor
hermes chat -q "Hola"
```
### Ejecutar tests
```bash
# Preferido — coincide con CI (entorno hermético, 4 workers xdist); ver AGENTS.md
scripts/run_tests.sh
# Alternativa (activa el venv primero). El wrapper sigue recomendándose
# para paridad con GitHub Actions antes de abrir un PR:
pytest tests/ -v
```
---
## Estructura del Proyecto
```
hermes-agent/
├── run_agent.py # Clase AIAgent — bucle de conversación central, despacho de herramientas, persistencia de sesión
├── cli.py # Clase HermesCLI — TUI interactiva, integración prompt_toolkit
├── model_tools.py # Orquestación de herramientas (capa delgada sobre tools/registry.py)
├── toolsets.py # Agrupaciones y presets de herramientas (hermes-cli, hermes-telegram, etc.)
├── hermes_state.py # Base de datos de sesiones SQLite con búsqueda de texto completo FTS5, títulos de sesión
├── batch_runner.py # Procesamiento en lote paralelo para generación de trayectorias
│
├── agent/ # Internos del agente (módulos extraídos)
│ ├── prompt_builder.py # Ensamblaje del prompt del sistema (identidad, habilidades, archivos de contexto, memoria)
│ ├── context_compressor.py # Auto-resumición al acercarse a los límites de contexto
│ ├── auxiliary_client.py # Resuelve clientes OpenAI auxiliares (resumición, visión)
│ ├── display.py # KawaiiSpinner, formateo del progreso de herramientas
│ ├── model_metadata.py # Longitudes de contexto del modelo, estimación de tokens
│ └── trajectory.py # Ayudantes para guardar trayectorias
│
├── hermes_cli/ # Implementaciones de comandos CLI
│ ├── main.py # Punto de entrada, análisis de argumentos, despacho de comandos
│ ├── config.py # Gestión de configuración, migración, definiciones de variables de entorno
│ ├── setup.py # Asistente de configuración interactivo
│ ├── auth.py # Resolución de proveedor, OAuth, Nous Portal
│ ├── models.py # Listas de selección de modelos de OpenRouter
│ ├── banner.py # Banner de bienvenida, arte ASCII
│ ├── commands.py # Registro central de comandos de barra (CommandDef), autocompletado, ayudantes del gateway
│ ├── callbacks.py # Callbacks interactivos (aclarar, sudo, aprobación)
│ ├── doctor.py # Diagnósticos
│ ├── skills_hub.py # CLI del Skills Hub + comando de barra /skills
│ └── skin_engine.py # Motor de skins/temas — personalización visual de CLI basada en datos
│
├── tools/ # Implementaciones de herramientas (auto-registradas)
│ ├── registry.py # Registro central de herramientas (esquemas, manejadores, despacho)
│ ├── approval.py # Detección de comandos peligrosos + aprobación por sesión
│ ├── terminal_tool.py # Orquestación del terminal (sudo, ciclo de vida del entorno, backends)
│ ├── file_operations.py # read_file, write_file, búsqueda, patch, etc.
│ ├── web_tools.py # web_search, web_extract (Paralelo/Firecrawl + resumición Gemini)
│ ├── vision_tools.py # Análisis de imágenes a través de modelos multimodales
│ ├── delegate_tool.py # Lanzamiento de subagentes y ejecución paralela de tareas
│ ├── code_execution_tool.py # Python sandboxado con acceso a herramientas vía RPC
│ ├── session_search_tool.py # Búsqueda en conversaciones pasadas con FTS5 + ventanas ancladas
│ ├── cronjob_tools.py # Gestión de tareas programadas
│ ├── skill_tools.py # Búsqueda, carga y gestión de habilidades
│ └── environments/ # Backends de ejecución del terminal
│ ├── base.py # ABC BaseEnvironment
│ ├── local.py, docker.py, ssh.py, singularity.py, modal.py, daytona.py
│
├── gateway/ # Gateway de mensajería
│ ├── run.py # GatewayRunner — ciclo de vida de plataformas, enrutamiento de mensajes, cron
│ ├── config.py # Resolución de configuración de plataformas
│ ├── session.py # Almacén de sesiones, prompts de contexto, políticas de reset
│ └── platforms/ # Adaptadores de plataformas
│ ├── telegram.py, discord_adapter.py, slack.py, whatsapp.py
│
├── scripts/ # Scripts del instalador y puente
│ ├── install.sh # Instalador Linux/macOS
│ ├── install.ps1 # Instalador Windows PowerShell
│ └── whatsapp-bridge/ # Puente WhatsApp Node.js (Baileys)
│
├── skills/ # Habilidades incluidas (copiadas a ~/.hermes/skills/ en la instalación)
├── optional-skills/ # Habilidades opcionales oficiales (descubribles vía hub, no activadas por defecto)
├── tests/ # Suite de tests
├── website/ # Sitio de documentación (hermes-agent.nousresearch.com)
│
├── cli-config.yaml.example # Configuración de ejemplo (copiada a ~/.hermes/config.yaml)
└── AGENTS.md # Guía de desarrollo para asistentes de codificación IA
```
### Configuración del usuario (almacenada en `~/.hermes/`)
| Ruta | Propósito |
|------|-----------|
| `~/.hermes/config.yaml` | Configuración (modelo, terminal, toolsets, compresión, etc.) |
| `~/.hermes/.env` | Claves API y secretos |
| `~/.hermes/auth.json` | Credenciales OAuth (Nous Portal) |
| `~/.hermes/skills/` | Todas las habilidades activas (incluidas + instaladas desde hub + creadas por el agente) |
| `~/.hermes/memories/` | Memoria persistente (MEMORY.md, USER.md) |
| `~/.hermes/state.db` | Base de datos de sesiones SQLite |
| `~/.hermes/sessions/` | Índice de enrutamiento del gateway (`sessions.json`), migas de pan de solicitudes, transcripciones `*.jsonl` del gateway y (opcionalmente) snapshots JSON por sesión cuando `sessions.write_json_snapshots: true` está configurado. Los snapshots por sesión están desactivados por defecto; state.db es canónica. |
| `~/.hermes/cron/` | Datos de trabajos programados |
| `~/.hermes/whatsapp/session/` | Credenciales del puente WhatsApp |
---
## Descripción General de la Arquitectura
### Bucle Central
```
Mensaje del usuario → AIAgent._run_agent_loop()
├── Construir prompt del sistema (prompt_builder.py)
├── Construir kwargs de API (modelo, mensajes, herramientas, configuración de razonamiento)
├── Llamar al LLM (API compatible con OpenAI)
├── Si tool_calls en la respuesta:
│ ├── Ejecutar cada herramienta a través del despacho del registro
│ ├── Añadir resultados de herramientas a la conversación
│ └── Volver a la llamada al LLM
├── Si respuesta de texto:
│ ├── Persistir sesión en DB
│ └── Devolver final_response
└── Compresión de contexto si se acerca al límite de tokens
```
### Patrones de Diseño Clave
- **Herramientas auto-registradas**: Cada archivo de herramienta llama a `registry.register()` en el momento de importación. `model_tools.py` activa el descubrimiento importando todos los módulos de herramientas.
- **Agrupación en toolsets**: Las herramientas se agrupan en toolsets (`web`, `terminal`, `file`, `browser`, etc.) que pueden habilitarse/deshabilitarse por plataforma.
- **Persistencia de sesión**: Todas las conversaciones se almacenan en SQLite (`hermes_state.py`) con búsqueda de texto completo y títulos de sesión únicos.
- **Inyección efímera**: Los prompts del sistema y los mensajes de relleno se inyectan en el momento de la llamada API, nunca se persisten en la base de datos ni en los logs.
- **Abstracción de proveedor**: El agente funciona con cualquier API compatible con OpenAI. La resolución del proveedor ocurre en el momento de la inicialización.
- **Enrutamiento de proveedor**: Al usar OpenRouter, `provider_routing` en config.yaml controla la selección del proveedor.
---
## Estilo de Código
- **PEP 8** con excepciones prácticas (no imponemos longitud de línea estricta)
- **Comentarios**: Solo cuando se explica la intención no obvia, compromisos o peculiaridades de API. No narres lo que hace el código
- **Manejo de errores**: Captura excepciones específicas. Registra con `logger.warning()`/`logger.error()` — usa `exc_info=True` para errores inesperados
- **Multiplataforma**: Nunca asumas Unix. Ver [Compatibilidad Multiplataforma](#compatibilidad-multiplataforma)
---
## Añadir una Nueva Herramienta
Antes de escribir una herramienta, pregúntate: [¿debería ser una habilidad en su lugar?](#debería-ser-una-habilidad-o-una-herramienta)
Las herramientas se auto-registran en el registro central. Cada archivo de herramienta co-localiza su esquema, manejador y registro:
```python
"""my_tool — Breve descripción de lo que hace esta herramienta."""
import json
from tools.registry import registry
def my_tool(param1: str, param2: int = 10, **kwargs) -> str:
"""Manejador. Devuelve un resultado en cadena (a menudo JSON)."""
result = do_work(param1, param2)
return json.dumps(result)
MY_TOOL_SCHEMA = {
"type": "function",
"function": {
"name": "my_tool",
"description": "Qué hace esta herramienta y cuándo debería usarla el agente.",
"parameters": {
"type": "object",
"properties": {
"param1": {"type": "string", "description": "Qué es param1"},
"param2": {"type": "integer", "description": "Qué es param2", "default": 10},
},
"required": ["param1"],
},
},
}
def _check_requirements() -> bool:
"""Devuelve True si las dependencias de esta herramienta están disponibles."""
return True
registry.register(
name="my_tool",
toolset="my_toolset",
schema=MY_TOOL_SCHEMA,
handler=lambda args, **kw: my_tool(**args, **kw),
check_fn=_check_requirements,
)
```
**Conectar a un toolset (requerido):** Las herramientas integradas se auto-descubren: cualquier
archivo `tools/*.py` que contenga una llamada de nivel superior `registry.register(...)` es
importado por `discover_builtin_tools()` en `tools/registry.py` cuando `model_tools`
se carga. **No** hay una lista de importaciones manual en `model_tools.py` que mantener.
Todavía debes añadir el nombre de la herramienta a la lista apropiada en `toolsets.py`
(por ejemplo `_HERMES_CORE_TOOLS` o un toolset dedicado); de lo contrario la herramienta
se registra pero nunca se expone al agente.
Consulta `AGENTS.md` (sección **Adding New Tools**) para rutas conscientes del perfil y
orientación sobre plugins vs. núcleo.
---
## Añadir una Habilidad
Las habilidades incluidas viven en `skills/` organizadas por categoría. Las habilidades opcionales oficiales usan la misma estructura en `optional-skills/`:
```
skills/
├── research/
│ └── arxiv/
│ ├── SKILL.md # Requerido: instrucciones principales
│ └── scripts/ # Opcional: scripts auxiliares
│ └── search_arxiv.py
├── productivity/
│ └── ocr-and-documents/
│ ├── SKILL.md
│ ├── scripts/
│ └── references/
└── ...
```
### Formato de SKILL.md
```markdown
---
name: my-skill
description: Breve descripción (mostrada en los resultados de búsqueda de habilidades)
version: 1.0.0
author: Tu Nombre
license: MIT
platforms: [macos, linux] # Opcional — restringir a plataformas de SO específicas
required_environment_variables: # Opcional — metadatos de configuración segura al cargar
- name: MY_API_KEY
prompt: Clave API
help: Dónde obtenerla
required_for: funcionalidad completa
prerequisites: # Requisitos de tiempo de ejecución heredados opcionales
env_vars: [MY_API_KEY]
commands: [curl, jq]
metadata:
hermes:
tags: [Categoría, Subcategoría, Palabras clave]
related_skills: [other-skill-name]
fallback_for_toolsets: [web]
requires_toolsets: [terminal]
---
# Título de la Habilidad
Introducción breve.
## Cuándo Usar
Condiciones de activación — ¿cuándo debería el agente cargar esta habilidad?
## Referencia Rápida
Tabla de comandos o llamadas API comunes.
## Procedimiento
Instrucciones paso a paso que el agente sigue.
## Problemas Conocidos
Modos de fallo conocidos y cómo manejarlos.
## Verificación
Cómo confirma el agente que funcionó.
```
### Estándares de autoría de habilidades (OBLIGATORIOS)
Todo skill nuevo o modernizado — incluido, opcional o contribuido — debe cumplir estos estándares antes del merge:
1. **`description` ≤ 60 caracteres, una oración, termina con punto.** Las descripciones largas saturan la UI de listado de habilidades. Indica la capacidad, no la implementación. Sin palabras de marketing ("potente", "completo", "fluido", "avanzado").
2. **Las herramientas referenciadas en el cuerpo de SKILL.md deben ser herramientas nativas de Hermes o servidores MCP que la habilidad espere explícitamente.** Usa los nombres de herramientas en comillas invertidas: `` `terminal` ``, `` `web_extract` ``, `` `web_search` ``, `` `read_file` ``, `` `write_file` ``, etc.
3. **El campo `platforms:` auditado contra las importaciones reales del script.** Las habilidades que usen primitivos solo de POSIX deben declarar sus plataformas soportadas.
4. **`author` da crédito primero al colaborador humano.**
5. **El cuerpo de SKILL.md usa el orden moderno de secciones:** título, intro de 2-3 oraciones, luego: `## Cuándo Usar`, `## Prerequisitos`, `## Cómo Ejecutar`, `## Referencia Rápida`, `## Procedimiento`, `## Problemas Conocidos`, `## Verificación`.
6. **Los scripts van en `scripts/`, las referencias en `references/`, las plantillas en `templates/`.**
7. **Los tests viven en `tests/skills/test_<skill>_skill.py`** y usan solo stdlib + pytest + `unittest.mock`. Sin llamadas de red en vivo.
8. **Las adiciones a `.env.example` están aisladas en un bloque claramente delimitado.**
---
## Añadir una Skin / Tema
Hermes usa un sistema de skins basado en datos — no se necesitan cambios de código para añadir una nueva skin.
**Opción A: Skin de usuario (archivo YAML)**
Crea `~/.hermes/skins/<nombre>.yaml`:
```yaml
name: mitema
description: Breve descripción del tema
colors:
banner_border: "#HEX"
banner_title: "#HEX"
banner_accent: "#HEX"
banner_dim: "#HEX"
banner_text: "#HEX"
response_border: "#HEX"
spinner:
waiting_faces: ["(⚔)", "(⛨)"]
thinking_faces: ["(⚔)", "(⌁)"]
thinking_verbs: ["forjando", "planeando"]
branding:
agent_name: "Mi Agente"
welcome: "Mensaje de bienvenida"
response_label: " ⚔ Agente "
prompt_symbol: "⚔"
tool_prefix: "╎"
```
Todos los campos son opcionales — los valores faltantes se heredan de la skin predeterminada.
**Opción B: Skin integrada**
Añade al dict `_BUILTIN_SKINS` en `hermes_cli/skin_engine.py`. Usa el mismo esquema que arriba pero como dict de Python.
**Activar:**
- CLI: `/skin mitema` o establece `display.skin: mitema` en config.yaml
---
## Compatibilidad Multiplataforma
Hermes se ejecuta en Linux, macOS y Windows nativo (además de WSL2). Al escribir código
que toca el SO, asume que *cualquier* plataforma puede alcanzar tu ruta de código.
> **Antes de hacer PR:** ejecuta `scripts/check-windows-footguns.py` para detectar
> los patrones inseguros comunes de Windows en tu diff. Es basado en grep y barato;
> CI también lo ejecuta en cada PR.
### Reglas críticas
1. **Nunca llames `os.kill(pid, 0)` para comprobaciones de liveness.** En Windows **NO es una operación sin efecto**. Usa `psutil.pid_exists(pid)` en su lugar.
2. **Usa `shutil.which()` antes de hacer shell — no asumas que Windows tiene las herramientas que tiene Linux.** `ps`, `kill`, `grep`, `awk`, etc. simplemente no existen en Windows.
3. **`termios` y `fcntl` son solo de Unix.** Siempre captura tanto `ImportError` como `NotImplementedError`.
4. **Codificación de archivos.** Windows puede guardar archivos `.env` en `cp1252`. Siempre maneja errores de codificación.
5. **Gestión de procesos.** `os.setsid()`, `os.killpg()`, `os.fork()`, `os.getuid()` y el manejo de señales POSIX difieren en Windows.
6. **Señales que no existen en Windows:** `SIGALRM`, `SIGCHLD`, `SIGHUP`, `SIGUSR1`, `SIGUSR2`, etc.
7. **Separadores de ruta.** Usa `pathlib.Path` en lugar de concatenación de cadenas con `/`.
8. **Los enlaces simbólicos necesitan privilegios elevados en Windows** (a menos que el Modo Desarrollador esté activado).
9. **Los modos de archivo POSIX (0o600, 0o644, etc.) NO se aplican en NTFS** por defecto.
10. **Los daemons de fondo desacoplados en Windows necesitan `pythonw.exe`, NO `python.exe`.**
---
## Consideraciones de Seguridad
Hermes tiene acceso al terminal. La seguridad importa.
### Protecciones existentes
| Capa | Implementación |
|------|---------------|
| **Piping de contraseña sudo** | Usa `shlex.quote()` para prevenir inyección de shell |
| **Detección de comandos peligrosos** | Patrones regex en `tools/approval.py` con flujo de aprobación del usuario |
| **Inyección de prompts en cron** | Escáner en `tools/cronjob_tools.py` bloquea patrones de anulación de instrucciones |
| **Lista de denegación de escritura** | Rutas protegidas resueltas a través de `os.path.realpath()` para prevenir bypass de enlaces simbólicos |
| **Skills Guard** | Escáner de seguridad para habilidades instaladas desde el hub (`tools/skills_guard.py`) |
| **Sandbox de ejecución de código** | El proceso hijo `execute_code` se ejecuta con claves API eliminadas del entorno |
| **Fortalecimiento de contenedor** | Docker: todas las capacidades eliminadas, sin escalada de privilegios, límites de PID, tmpfs de tamaño limitado |
### Al contribuir código sensible a la seguridad
- **Siempre usa `shlex.quote()`** al interpolar entrada del usuario en comandos de shell
- **Resuelve enlaces simbólicos** con `os.path.realpath()` antes de comprobaciones de control de acceso basadas en rutas
- **No registres secretos.** Las claves API, tokens y contraseñas nunca deben aparecer en la salida de log
- **Captura excepciones amplias** alrededor de la ejecución de herramientas para que un solo fallo no bloquee el bucle del agente
- **Prueba en todas las plataformas** si tu cambio toca rutas de archivos, gestión de procesos o comandos de shell
### Política de fijación de dependencias (fortalecimiento de la cadena de suministro)
Tras el [compromiso de la cadena de suministro de litellm](https://github.com/BerriAI/litellm/issues/24512) en marzo de 2026 y la [campaña del gusano Mini Shai-Hulud](https://socket.dev/blog/tanstack-npm-packages-compromised-mini-shai-hulud-supply-chain-attack) en mayo de 2026, todas las dependencias deben seguir estas reglas:
| Tipo de fuente | Tratamiento requerido | Justificación |
|---|---|---|
| **Paquete PyPI** | `>=suelo,<siguiente_mayor` | Las versiones de PyPI son inmutables una vez publicadas, pero pueden empujarse nuevas versiones en tu rango. |
| **URL de Git** | SHA completo del commit | Las ramas y etiquetas son refs mutables; el SHA está direccionado por contenido. |
| **GitHub Actions** | SHA completo del commit + comentario de versión | Las etiquetas de acción son refs mutables. Fija como `uses: owner/action@<sha> # vX.Y.Z` |
| **Instalaciones pip solo de CI** | `==exacto` | Builds de CI herméticos; el cambio es aceptable. |
**Cada nueva dependencia de PyPI en un PR debe tener un límite superior `<siguiente_mayor`.** Los PRs que añadan especificaciones `>=X.Y.Z` sin límite superior serán rechazados.
---
## Proceso de Pull Request
### Nomenclatura de ramas
```
fix/descripcion # Correcciones de errores
feat/descripcion # Nuevas funcionalidades
docs/descripcion # Documentación
test/descripcion # Tests
refactor/descripcion # Reestructuración de código
```
### Antes de enviar
1. **Ejecutar tests**: `scripts/run_tests.sh` (recomendado; igual que CI) o `pytest tests/ -v` con el venv del proyecto activado
2. **Probar manualmente**: Ejecuta `hermes` y ejercita la ruta de código que cambiaste
3. **Verificar impacto multiplataforma**: Si tocas E/S de archivos, gestión de procesos o manejo del terminal, considera macOS, Linux y WSL2
4. **Mantén los PRs enfocados**: Un cambio lógico por PR. No mezcles una corrección de error con una refactorización con una nueva funcionalidad.
### Descripción del PR
Incluye:
- **Qué** cambió y **por qué**
- **Cómo probarlo** (pasos de reproducción para errores, ejemplos de uso para funcionalidades)
- **Qué plataformas** probaste
- Referencia cualquier issue relacionado
### Mensajes de commit
Usamos [Conventional Commits](https://www.conventionalcommits.org/):
```
<tipo>(<alcance>): <descripción>
```
| Tipo | Usar para |
|------|-----------|
| `fix` | Correcciones de errores |
| `feat` | Nuevas funcionalidades |
| `docs` | Documentación |
| `test` | Tests |
| `refactor` | Reestructuración de código (sin cambio de comportamiento) |
| `chore` | Build, CI, actualizaciones de dependencias |
Alcances: `cli`, `gateway`, `tools`, `skills`, `agent`, `install`, `whatsapp`, `security`, etc.
Ejemplos:
```
fix(cli): prevenir bloqueo en save_config_value cuando el modelo es una cadena
feat(gateway): añadir aislamiento de sesión multi-usuario de WhatsApp
fix(security): prevenir inyección de shell en el piping de contraseña sudo
test(tools): añadir tests unitarios para file_operations
```
---
## Reportar Issues
- Usa [GitHub Issues](https://github.com/NousResearch/hermes-agent/issues)
- Incluye: SO, versión de Python, versión de Hermes (`hermes version`), traza de error completa
- Incluye pasos para reproducir
- Verifica los issues existentes antes de crear duplicados
- Para vulnerabilidades de seguridad, por favor reporta de forma privada
---
## Comunidad
- **Discord**: [discord.gg/NousResearch](https://discord.gg/NousResearch) — para preguntas, mostrar proyectos y compartir habilidades
- **GitHub Discussions**: Para propuestas de diseño y discusiones de arquitectura
- **Skills Hub**: Sube habilidades especializadas a un registro y compártelas con la comunidad
---
## Licencia
Al contribuir, aceptas que tus contribuciones serán licenciadas bajo la [Licencia MIT](LICENSE).
+9 -95
View File
@@ -18,24 +18,6 @@ We value contributions in this order:
---
## Before You Start: Search First
A quick search before you build saves your time and keeps the PR queue clean — duplicates are common here, so it's worth a minute up front.
- **Search both open *and* merged PRs and issues** for your topic or error symptom — the duplicate-check in the PR template fires at review time, after you've already done the work:
```bash
gh search issues --repo NousResearch/hermes-agent "<your terms>"
gh search prs --repo NousResearch/hermes-agent --state all "<your terms>"
```
Or use the web UI: [issues](https://github.com/NousResearch/hermes-agent/issues?q=) · [PRs (all states)](https://github.com/NousResearch/hermes-agent/pulls?q=is%3Apr).
- **The issue tracker can lag the code.** Many requested features are already implemented in-tree, so also search the source (`search_files`, or your editor's grep) for the capability before proposing it.
- **If an open PR already addresses it**, consider reviewing or improving that one instead of opening a competing duplicate.
- **For larger work**, comment on the issue to signal you're working on it, so others don't start the same thing.
Related: #38284 covers the agent-side analog — Hermes itself checking existing issues and PRs before deep self-troubleshooting. This section is the human-contributor complement.
---
## Should it be a Skill or a Tool?
This is the most common question for new contributors. The answer is almost always **skill**.
@@ -85,23 +67,6 @@ This isn't a quality bar — it's a coupling-and-maintenance decision. Memory pr
---
## Third-Party Product Integrations: Ship as a Standalone Plugin
The same rule extends to **any plugin that integrates someone else's product or project** — observability/metrics backends, vendor SaaS connectors, analytics dashboards, paid-service tie-ins, and similar third-party integrations. **These do not land in this repo.**
The reason is maintenance load, not quality. Every external product absorbed into the core tree becomes ours to keep working against a fast-moving codebase, for a backend we don't own and can't control. Hermes ships a lot and the core moves quickly; coupling third-party products into it creates an open-ended burden on the maintainers.
Publish these as a **standalone plugin repo** instead:
- Implement the relevant ABC and use the existing plugin discovery path (`~/.hermes/plugins/`, project `.hermes/plugins/`, or a pip entry point) — see [Build a Hermes Plugin](https://hermes-agent.nousresearch.com/docs/guides/build-a-hermes-plugin)
- Register lifecycle hooks (`pre_tool_call`, `post_tool_call`, `pre_llm_call`, `post_llm_call`, `on_session_start`, `on_session_end`), tools (`ctx.register_tool`), and CLI subcommands (`ctx.register_cli_command`) through the surface we already expose — no core changes needed
- If your plugin needs a capability the framework doesn't expose, that's a feature request to **widen the generic plugin surface** (a new hook or `ctx` method) — never special-case your plugin in core
- Promote it in the [Nous Research Discord](https://discord.gg/NousResearch) `#plugins-skills-and-skins` channel so users can find and install it
A well-built third-party-product plugin can clear automated review and still be closed for this reason — it's a placement decision, not a verdict on the code. PRs that add such a directory under `plugins/` will be closed with a pointer to publish it as its own repo.
---
## Development Setup
### Prerequisites
@@ -113,56 +78,15 @@ A well-built third-party-product plugin can clear automated review and still be
| **uv** | Fast Python package manager ([install](https://docs.astral.sh/uv/)) |
| **Node.js 20+** | Optional — needed for browser tools and WhatsApp bridge (matches root `package.json` engines) |
### Install with the standard installer
For most contributors, the best development bootstrap is the same path users
take: run the standard installer, then work inside the repository it cloned.
The installer creates the Hermes venv, wires the `hermes` command, stamps the
install method for `hermes update`, and clones the full git project into
`$HERMES_HOME/hermes-agent` (usually `~/.hermes/hermes-agent`). That keeps your
development environment on the same layout the CLI, updater, lazy dependency
installer, gateway, and docs assume.
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
cd "${HERMES_HOME:-$HOME/.hermes}/hermes-agent"
# Add dev/test extras on top of the standard install.
uv pip install -e ".[all,dev]"
# Optional: browser tools / docs site dependencies.
npm install
```
After that, create branches and run tests from that checkout:
```bash
git checkout -b fix/description
scripts/run_tests.sh
```
### Manual clone fallback
Use this only if you intentionally do not want Hermes' managed install layout
(for example, a throwaway clone inside a container or CI job). If you install
this way, make sure you run the `hermes` entrypoint from this venv; running the
system `python3 -m hermes_cli.main` can pick up unrelated system Python
packages.
Create the venv **outside** the cloned source tree. A venv that lives inside
the directory the agent operates from can be wiped by a relative-path command
the agent runs against its own checkout (`rm -rf venv`, `uv venv venv`, etc.),
which silently destroys the running runtime mid-session. Keeping it outside the
tree means no relative path from the workspace resolves to it.
### Clone and install
```bash
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
# Create venv with Python 3.11, OUTSIDE the source tree
uv venv ~/.hermes/venvs/hermes-dev --python 3.11
export VIRTUAL_ENV="$HOME/.hermes/venvs/hermes-dev"
export PATH="$VIRTUAL_ENV/bin:$PATH"
# Create venv with Python 3.11
uv venv venv --python 3.11
export VIRTUAL_ENV="$(pwd)/venv"
# Install with all extras (messaging, cron, CLI menus, dev tools)
uv pip install -e ".[all,dev]"
@@ -185,17 +109,13 @@ echo "OPENROUTER_API_KEY=***" >> ~/.hermes/.env
### Run
```bash
# The standard installer already put `hermes` on PATH.
hermes doctor
hermes chat -q "Hello"
```
If you used the manual clone fallback, run `./hermes` from the checkout or
symlink this clone's venv explicitly:
```bash
# Symlink for global access
mkdir -p ~/.local/bin
ln -sf "$(pwd)/venv/bin/hermes" ~/.local/bin/hermes
# Verify
hermes doctor
hermes chat -q "Hello"
```
### Run tests
@@ -454,12 +374,6 @@ Brief intro.
## When to Use
Trigger conditions — when should the agent load this skill?
## Prerequisites
Env vars, install steps, MCP setup, API key sourcing.
## How to Run
Canonical invocation through the `terminal` tool.
## Quick Reference
Table of common commands or API calls.
+39 -74
View File
@@ -9,11 +9,8 @@ FROM ghcr.io/astral-sh/uv:0.11.6-python3.13-trixie@sha256:b3c543b6c4f23a5f2df228
FROM node:22-bookworm-slim@sha256:7af03b14a13c8cdd38e45058fd957bf00a72bbe17feac43b1c15a689c029c732 AS node_source
FROM debian:13.4
# Disable Python stdout buffering to ensure logs are printed immediately.
# Do not write .pyc files at runtime: /opt/hermes is immutable in the
# published container and writable state belongs under /opt/data.
# Disable Python stdout buffering to ensure logs are printed immediately
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
# Store Playwright browsers outside the volume mount so the build-time
# install survives the /opt/data volume overlay at runtime.
@@ -28,7 +25,7 @@ ENV PLAYWRIGHT_BROWSERS_PATH=/opt/hermes/.playwright
# hermes process, the dashboard, and per-profile gateways.
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates curl iputils-ping python3 python-is-python3 ripgrep ffmpeg gcc g++ make cmake python3-dev python3-venv libffi-dev libolm-dev procps git openssh-client docker-cli xz-utils && \
ca-certificates curl iputils-ping python3 python-is-python3 ripgrep ffmpeg gcc python3-dev python3-venv libffi-dev libolm-dev procps git openssh-client docker-cli xz-utils && \
rm -rf /var/lib/apt/lists/*
# ---------- s6-overlay install ----------
@@ -119,9 +116,6 @@ COPY package.json package-lock.json ./
COPY web/package.json web/
COPY ui-tui/package.json ui-tui/
COPY ui-tui/packages/hermes-ink/ ui-tui/packages/hermes-ink/
# apps/shared/ is copied IN FULL because web/package.json references it as a
# `file:` workspace dependency (same pattern as hermes-ink above).
COPY apps/shared/ apps/shared/
# `npm_config_install_links=false` forces npm to install `file:` deps as
# symlinks instead of copies. This is the default since npm 10+, which is
@@ -152,9 +146,9 @@ RUN npm install --prefer-offline --no-audit && \
#
# `uv sync --frozen --no-install-project --extra all --extra messaging`
# installs the deps reachable through the composite `[all]` extra
# (handpicked set intended for the production image — excludes `[dev]`),
# plus gateway messaging adapters that should work in the published image
# without a first-boot lazy install. We do NOT use `--all-extras`:
# (handpicked set intended for the production image), plus gateway
# messaging adapters that should work in the published image without a
# first-boot lazy install. We do NOT use `--all-extras`:
# that would pull in `[rl]` (atroposlib + tinker + torch + wandb from
# git), `[yc-bench]` (another git dep), and `[termux-all]` (Android
# redundancy), none of which belong in the published container.
@@ -164,69 +158,53 @@ RUN npm install --prefer-offline --no-audit && \
# lazy-install access to PyPI (often blocked in containerized envs).
#
# The hindsight memory provider's client (hindsight-client) is baked in
# for the same reason: it lazy-installs into /opt/hermes/.venv at first
# for the same reason: it lazy-installs into /opt/hermes/venv at first
# use, which lives inside the (immutable) image layer rather than the
# mounted /opt/data volume, so it is lost on every container recreate /
# image update and recall/retain then fails with
# `ModuleNotFoundError: No module named 'hindsight_client'` (#38128).
#
# The Matrix gateway's deps ([matrix] extra) are baked in because
# python-olm (transitive via mautrix[encryption]) builds from source on
# Python/image combinations without usable wheels. The Docker image is
# Linux-only, so keeping the native libolm/build-toolchain packages here
# avoids the cross-platform failures that kept [matrix] out of [all]
# while still making Matrix work in the published container. Fixes #30399.
#
# The editable link is created after the source copy below.
COPY pyproject.toml uv.lock ./
RUN touch ./README.md
RUN uv sync --frozen --no-install-project --extra all --extra messaging --extra anthropic --extra bedrock --extra azure-identity --extra hindsight --extra matrix
# ---------- Frontend build (cached independently from Python source) ----------
# Copy only the frontend source trees first so that Python-only changes don't
# invalidate the (relatively slow) web + ui-tui build layer.
COPY web/ web/
COPY ui-tui/ ui-tui/
COPY apps/shared/ apps/shared/
RUN cd web && npm run build && \
cd ../ui-tui && npm run build
RUN uv sync --frozen --no-install-project --extra all --extra messaging --extra anthropic --extra bedrock --extra azure-identity --extra hindsight
# ---------- Source code ----------
# .dockerignore excludes node_modules, so the installs above survive.
# --link decouples this layer from parents for cache purposes; --chmod bakes
# the final read-only permissions at copy time so we skip the separate
# `chmod -R` pass that previously walked ~30k files across the venv +
# node_modules + source (21s amd64 / 222s arm64 — #49113). `a+rX,go-w`
# gives the non-root hermes user read + traverse but no write; root retains
# write so the build steps below don't need chmod u+w dances.
COPY --link --chmod=a+rX,go-w . .
COPY --chown=hermes:hermes . .
# Build browser dashboard and terminal UI assets.
RUN cd web && npm run build && \
cd ../ui-tui && npm run build
# ---------- Permissions ----------
# Link hermes-agent itself (editable). Deps are already installed in the
# cached layer above; `--no-deps` makes this a fast egg-link creation with no
# resolution or downloads.
RUN uv pip install --no-cache-dir --no-deps -e "."
# Wire the exec shim and install-method stamp. Files under /opt/hermes are
# already root-owned (COPY, uv sync, npm install all run as root) and
# read-only for the hermes user (go-w from the --chmod above).
# Make install dir world-readable so any HERMES_UID can read it at runtime.
# The venv needs to be traversable too.
# node_modules trees additionally need to be writable by the hermes user
# so the runtime `npm install` triggered by _tui_need_npm_install() in
# hermes_cli/main.py succeeds (see #18800). /opt/hermes/web is build-time
# only (HERMES_WEB_DIST points at hermes_cli/web_dist) and is intentionally
# not chowned here.
# /opt/hermes/gateway is runtime-writable: Python may create __pycache__ and
# gateway state artifacts beneath the package after services drop privileges,
# especially when the hermes UID is remapped at boot (#27221).
# The venv MUST remain hermes-writable so lazy_deps.py can install
# remaining optional platform packages and future pin bumps at first use.
# Without this, `uv pip install` fails with EACCES and adapters silently
# fail to load. See tools/lazy_deps.py.
USER root
RUN mkdir -p /opt/hermes/bin && \
cp /opt/hermes/docker/hermes-exec-shim.sh /opt/hermes/bin/hermes && \
chmod 0755 /opt/hermes/bin/hermes && \
printf 'docker\n' > /opt/hermes/.install_method
# The ``.install_method`` stamp is baked next to the running code (the install
# tree), NOT into $HERMES_HOME. $HERMES_HOME (/opt/data) is a shared data
# volume that is commonly bind-mounted from the host and even shared with a
# host-side Desktop/CLI install; stamping it at boot used to clobber that
# host install's marker and wrongly block its ``hermes update``. A code-scoped
# stamp is read first by detect_install_method() and is immune to the share.
RUN chmod -R a+rX /opt/hermes && \
chown -R hermes:hermes /opt/hermes/venv /opt/hermes/ui-tui /opt/hermes/gateway /opt/hermes/node_modules
# Start as root so the s6-overlay stage2 hook can usermod/groupmod and chown
# the data volume. Each supervised service then drops to the hermes user via
# `s6-setuidgid hermes` in its run script. If HERMES_UID is unset, services
# run as the default hermes user (UID 10000).
# ---------- Link hermes-agent itself (editable) ----------
# Deps are already installed in the cached layer above; `--no-deps` makes
# this a fast (~1s) egg-link creation with no resolution or downloads.
RUN uv pip install --no-cache-dir --no-deps -e "."
# ---------- Bake build-time git revision ----------
# .dockerignore excludes .git, so `git rev-parse HEAD` from inside the
# container always returns nothing — meaning `hermes dump` reports
@@ -242,11 +220,12 @@ RUN mkdir -p /opt/hermes/bin && \
#
# The arg is optional — local `docker build` without --build-arg simply
# omits the file, and the runtime falls back to live-git lookup. CI
# (.github/workflows/docker.yml) passes ${{ github.sha }} so
# (.github/workflows/docker-publish.yml) passes ${{ github.sha }} so
# every published image has it.
ARG HERMES_GIT_SHA=
RUN if [ -n "${HERMES_GIT_SHA}" ]; then \
printf '%s\n' "${HERMES_GIT_SHA}" > /opt/hermes/.hermes_build_sha; \
printf '%s\n' "${HERMES_GIT_SHA}" > /opt/hermes/.hermes_build_sha && \
chown hermes:hermes /opt/hermes/.hermes_build_sha; \
fi
# ---------- s6-overlay service wiring ----------
@@ -292,21 +271,6 @@ ENV HERMES_WEB_DIST=/opt/hermes/hermes_cli/web_dist
# check. (A separate launcher hardening is tracked independently.)
ENV HERMES_TUI_DIR=/opt/hermes/ui-tui
ENV HERMES_HOME=/opt/data
ENV HERMES_WRITE_SAFE_ROOT=/opt/data
ENV HERMES_DISABLE_LAZY_INSTALLS=1
# The published image seals /opt/hermes (root-owned, read-only) so a runtime
# lazy install can't mutate the agent's own venv and brick it. But opt-in
# backends (Firecrawl web search, Exa, Feishu, …) keep their SDKs in
# tools/lazy_deps.py — deliberately NOT baked into [all] (see pyproject.toml
# policy 2026-05-12: one quarantined release must not break every install).
# Redirect those lazy installs to a writable dir on the durable data volume.
# lazy_deps appends this dir to the END of sys.path, so a package installed
# here can only ADD modules — it can never shadow or downgrade a core module,
# so the sealed-venv guarantee holds even with installs re-enabled. The dir
# is seeded + chowned to the hermes user by docker/stage2-hook.sh and lives
# on the /opt/data volume, so it persists across container recreates / image
# updates (an ABI stamp invalidates it if a rebuild bumps the interpreter).
ENV HERMES_LAZY_INSTALL_TARGET=/opt/data/lazy-packages
# `docker exec` privilege-drop shim. When operators run
# `docker exec <c> hermes ...` they default to root, and any file the
@@ -319,8 +283,9 @@ ENV HERMES_LAZY_INSTALL_TARGET=/opt/data/lazy-packages
# Recursion is impossible because the shim exec's the venv binary by
# absolute path (/opt/hermes/.venv/bin/hermes). See the shim source for
# the opt-out env var (HERMES_DOCKER_EXEC_AS_ROOT=1).
COPY --chmod=0755 docker/hermes-exec-shim.sh /opt/hermes/bin/hermes
# Pre-s6 entrypoint.sh did `source .venv/bin/activate` which exported
# Pre-s6 entrypoint.sh did `source venv/bin/activate` which exported
# the venv bin onto PATH; Architecture B's main-wrapper.sh does the
# same for the container's main process, but `docker exec` and our
# cont-init.d scripts don't pass through the wrapper. Expose the venv
@@ -331,7 +296,7 @@ ENV HERMES_LAZY_INSTALL_TARGET=/opt/data/lazy-packages
# shim wins PATH resolution. The shim's last act is to exec the venv
# binary by absolute path, so this PATH ordering is transparent to
# every other consumer.
ENV PATH="/opt/hermes/bin:/opt/hermes/.venv/bin:/opt/data/.local/bin:${PATH}"
ENV PATH="/opt/hermes/bin:/opt/hermes/venv/bin:/opt/data/.local/bin:${PATH}"
RUN mkdir -p /opt/data
VOLUME [ "/opt/data" ]
-1
View File
@@ -1,6 +1,5 @@
graft skills
graft optional-skills
graft optional-mcps
graft locales
# Bundled plugin manifests (plugin.yaml / plugin.yml). Without these the
# PluginManager scan (hermes_cli/plugins.py) finds zero plugins on installs
+315
View File
@@ -0,0 +1,315 @@
# Plan: Split `_cmd_update_impl` into pull + post-pull phases
## Problem
After `git pull` (or ZIP extraction) overwrites source files on disk, the
running Python process still has the **old** code in `sys.modules` and
`__pycache__`. The current monolith runs post-pull steps (pip install, node
deps, skills sync, config migration, gateway restart) under stale bytecode,
which causes `ImportError` on gateway restart and subtle drift bugs.
## Solution: Two-phase update with re-exec
Split `_cmd_update_impl` into:
1. **`_cmd_update_pull_new_version`** — download new code onto disk
2. **Re-exec** into a fresh Python process
3. **`_cmd_update_post_pull`** — run all post-pull steps under the new code
The re-exec guarantees a clean `sys.modules` / `__pycache__` for phase 2.
---
## Phase 1: `_cmd_update_pull_new_version(args, gateway_mode)`
Everything up to and including "code is on disk, stash is restored":
1. Windows concurrent-hermes guard (exit 2 if locked)
2. Pre-update backup (`_run_pre_update_backup`)
3. Route detection:
- No `.git` + Windows → `_update_files_via_zip` (download + extract only)
- No `.git` + non-Windows + pip → **error** (pip self-update ripped out)
- No `.git` + non-Windows → reinstall hint + exit 1
- `.git` exists → git flow (below)
4. Git config setup (autocrlf, appendAtomically)
5. Discard lockfile churn
6. Fork detection + origin URL check
7. Fetch + branch logic + stash
8. Check for new commits (if 0, restore + "already up to date" + return)
9. Pre-update snapshot (`create_quick_snapshot`)
10. `git pull --ff-only` (or `reset --hard` on divergence)
11. Post-pull syntax guard (`_validate_critical_files_syntax`) + rollback on failure
12. Restore stashed changes
13. `_invalidate_update_cache()`
14. `_clear_bytecode_cache()`
At this point new code is on disk and the working tree is clean.
**Return `True`** to signal "re-exec needed".
### ZIP path change
`_update_files_via_zip` currently does the full pipeline (download, extract,
clear bytecode, pip install, node deps, skills sync, curator notices, kill
dashboard). We split it:
- **`_update_files_via_zip`** → rename to `_download_and_extract_zip(args)`:
downloads + extracts + clears bytecode. Returns `True` when new code landed.
- The pip install / node deps / skills sync / etc moves to
`_cmd_update_post_pull` (shared by both git and zip).
### Already-up-to-date fast path
If git reports 0 new commits (or zip extraction shows no changes), we return
`False` from pull — no re-exec needed. The caller prints "already up to date"
and exits cleanly.
---
## Re-exec mechanism
After `_cmd_update_pull_new_version` returns `True`:
```python
def _reexec_for_post_pull(args, gateway_mode: bool):
"""Replace this process with a fresh Python running --post-pull."""
argv = list(sys.argv)
# Insert --post-pull before any positional args (there shouldn't be any)
argv.append("--post-pull")
if gateway_mode:
argv.append("--gateway")
# Pass pre-update snapshot ID if we have one
snapshot_id = getattr(args, "_pre_update_snapshot_id", None)
if snapshot_id:
argv.extend(["--pre-update-snapshot", snapshot_id])
if sys.platform == "win32":
# Windows: os.execvp internally does spawn+exit (PID changes).
# The bootstrap installer watches the original PID via run_streamed().
# If we execvp, the installer sees exit-0 before post-pull finishes.
# Instead: relay through a subprocess so the parent PID stays alive
# until the child completes. The installer's child.wait() sees the
# real exit code.
result = subprocess.run([sys.executable, "-m", "hermes_cli.main"] + argv)
sys.exit(result.returncode)
else:
# POSIX: true exec — same PID, no stale modules.
os.execvp(sys.executable, [sys.executable, "-m", "hermes_cli.main"] + argv)
```
### Why subprocess.run on Windows (not Popen+exit)
`Popen` + `sys.exit` has a race: if the parent exits before the child's
stdout pipe is drained, the bootstrap installer's `BufReader` gets a broken
pipe and the child may get SIGPIPE. `subprocess.run` waits for the child to
finish and properly reaps it, then we forward the exit code. This is
functionally identical to what the bootstrap installer already does when it
spawns `hermes update` directly.
### Gateway output file continuity
In gateway mode, `hermes update --gateway` writes to an output file that the
gateway watches. On POSIX, `os.execvp` inherits FDs so the output file
continues being written. On Windows, the relay subprocess inherits the
same FDs (subprocess.run passes them through). The `--gateway` flag is
passed to the child so `_install_hangup_protection` / `_finalize_update_output`
work correctly.
**However**, the gateway's spawn path (gateway/run.py) launches the update
detached with its own output redirection. The output file is opened by the
*child*, not inherited from the parent. So on the re-exec path the child
re-opens the same output path via `_install_hangup_protection` (which
already sets up the update.log mirror). No special handling needed.
### Bootstrap installer interaction
The Windows bootstrap installer (`update.rs`) calls `hermes update --yes
--gateway` via `run_streamed()`, which:
1. Spawns the hermes binary as a child process
2. Reads stdout/stderr line-by-line
3. Waits for exit via `child.wait().await`
4. Checks exit code (0 = success, 2 = concurrent lock)
Our Windows relay pattern is safe because:
- The **parent** (original `hermes update` PID) stays alive until the
**child** (post-pull phase) exits
- `child.wait()` in the installer sees the parent exit only after the
child finishes → correct exit code propagation
- The parent's stdout/stderr are piped to the installer's `BufReader`;
the child inherits those FDs, so output streams continue seamlessly
---
## Phase 2: `_cmd_update_post_pull(args, gateway_mode)`
All post-pull steps, running under freshly-exec'd Python with clean
`sys.modules`. Steps are identical regardless of whether code arrived via
git or zip:
1. `_refresh_active_lazy_features()`
2. Python dependency install (uv/pip — the entire `ensure_uv` +
`_install_python_dependencies_with_optional_fallback` block)
3. `_update_node_dependencies()`
4. `_build_web_ui`
5. Desktop app rebuild check + build
6. Skills sync (`sync_skills` + profile seed)
7. Honcho profile sync
8. Config migration (missing env, missing config, version bump, interactive prompts)
9. Cron jobs safety-net restore
10. Curator notices (first-run + recent-run)
11. FHS PATH guard
12. cua-driver refresh (macOS)
13. Gateway restart (systemd, launchd, manual processes, survivor sweep)
14. Legacy unit warning
15. Kill stale dashboard processes
16. Print "Update complete!" + tip
---
## Argparse changes
Add to `update_parser`:
```python
update_parser.add_argument(
"--post-pull",
action="store_true",
default=False,
help=argparse.SUPPRESS, # internal flag — not for users
)
update_parser.add_argument(
"--pre-update-snapshot",
default=None,
metavar="ID",
help=argparse.SUPPRESS, # carries snapshot ID across re-exec
)
```
## `cmd_update` dispatch changes
```python
def cmd_update(args):
# ... existing managed/docker/check guards ...
_update_io_state = _install_hangup_protection(gateway_mode=gateway_mode)
try:
if getattr(args, "post_pull", False):
_cmd_update_post_pull(args, gateway_mode=gateway_mode)
else:
needs_reexec = _cmd_update_pull_new_version(args, gateway_mode=gateway_mode)
if needs_reexec:
_reexec_for_post_pull(args, gateway_mode=gateway_mode)
# if not needs_reexec, we're already done (already up to date)
finally:
_finalize_update_output(_update_io_state)
```
**Important**: `_finalize_update_output` runs in the parent on the
non-reexec path. On the re-exec path, it runs in the parent *before*
the exec replaces the process. On Windows relay, it runs before
`subprocess.run`. The post-pull child has its own
`_install_hangup_protection` / `_finalize_update_output` cycle because
`cmd_update` is re-entered via the fresh process.
Wait — actually on the re-exec path the parent process is *replaced* (POSIX)
or *exits* (Windows relay). So `_finalize_update_output` would run in the
`finally` block *after* `_reexec_for_post_pull` returns. But on POSIX,
`os.execvp` never returns (or raises on failure). On Windows,
`sys.exit(result.returncode)` never returns. So the `finally` block only
runs if `_reexec_for_post_pull` raises (e.g. execvp fails). That's the
right behavior — cleanup only on failure.
On the post-pull path, `cmd_update` is called fresh in the new process,
so `_install_hangup_protection` + `_finalize_update_output` wrap the
post-pull phase correctly.
---
## Pip install path: RIP
`_cmd_update_pip` is removed. `cmd_update` already checks for
`is_managed()` and `detect_install_method() == "docker"` before entering
the impl. We add pip to the early-exit guards:
```python
def cmd_update(args):
# ... existing is_managed() check ...
if detect_install_method(PROJECT_ROOT) == "docker":
print(format_docker_update_message())
sys.exit(1)
if detect_install_method(PROJECT_ROOT) == "pip":
from hermes_cli.config import recommended_update_command
print("✗ Self-update is not supported for pip/uv-tool installs.")
print(f" Run '{recommended_update_command()}' instead.")
sys.exit(1)
# ... --check and --post-pull dispatch ...
```
Also update `_cmd_update_check` to keep its existing pip check path
(it just checks PyPI — no code mutation, no re-exec needed).
---
## Cross-phase state
| State | Mechanism |
|-------|-----------|
| `gateway_mode` | Passed via `--gateway` flag (already exists) |
| `assume_yes` | Passed via `--yes` flag (already exists) |
| `pre_update_snapshot_id` | New `--pre-update-snapshot` arg |
| `auto_stash_ref` | NOT needed — stash is restored in phase 1 |
| `branch` | Passed via `--branch` (already exists) |
| `force` | Passed via `--force` (already exists) |
| `backup` / `no_backup` | Passed via `--backup` / `--no-backup` (already exists) |
The only new cross-phase arg is `--pre-update-snapshot`, because the
snapshot is created in phase 1 but consumed in phase 2 (cron safety net).
---
## `_update_files_via_zip` refactor
Rename current function to `_download_and_extract_zip(args) -> bool`:
- Downloads ZIP, extracts, clears bytecode
- Returns `True` if new code was written
- Does NOT do pip install, node deps, skills sync, etc.
The old post-extraction code moves to `_cmd_update_post_pull`.
---
## Test impact
| Test file | Change needed |
|-----------|--------------|
| `test_cmd_update.py` | Update mocks for two-phase flow; test `--post-pull` dispatch |
| `test_cmd_update_docker.py` | Minimal — docker bailout is unchanged |
| `test_update_autostash.py` | Stash logic stays in phase 1 — should work as-is |
| `test_update_concurrent_quarantine.py` | Concurrent check stays in phase 1 — should work as-is |
| `test_managed_installs.py` | Add pip install error test (was previously allowed) |
| `test_uv_tool_update.py` | Remove — pip/uv-tool self-update is now an error |
---
## Execution order summary
```
hermes update
└─ cmd_update()
├─ is_managed? → error
├─ docker? → error + hint
├─ pip? → error + hint (NEW — was _cmd_update_pip)
├─ --check? → _cmd_update_check() (unchanged)
├─ --post-pull? → _cmd_update_post_pull() (phase 2)
└─ else → _cmd_update_pull_new_version() (phase 1)
├─ download code (git pull or zip)
├─ validate + stash restore + clear bytecode
└─ if new code: _reexec_for_post_pull()
├─ POSIX: os.execvp → same PID, fresh Python
└─ Windows: subprocess.run + sys.exit → relay
```
-220
View File
@@ -1,220 +0,0 @@
<p align="center">
<img src="assets/banner.png" alt="Hermes Agent" width="100%">
</p>
# Hermes Agent ☤
<p align="center">
<a href="https://hermes-agent.nousresearch.com/">Hermes Agent</a> | <a href="https://hermes-agent.nousresearch.com/">Hermes Desktop</a>
</p>
<p align="center">
<a href="https://hermes-agent.nousresearch.com/docs/"><img src="https://img.shields.io/badge/Docs-hermes--agent.nousresearch.com-FFD700?style=for-the-badge" alt="Documentación"></a>
<a href="https://discord.gg/NousResearch"><img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
<a href="https://github.com/NousResearch/hermes-agent/blob/main/LICENSE"><img src="https://img.shields.io/badge/Licencia-MIT-green?style=for-the-badge" alt="Licencia: MIT"></a>
<a href="https://nousresearch.com"><img src="https://img.shields.io/badge/Creado%20por-Nous%20Research-blueviolet?style=for-the-badge" alt="Creado por Nous Research"></a>
<a href="README.md"><img src="https://img.shields.io/badge/Lang-English-blue?style=for-the-badge" alt="English"></a>
<a href="README.zh-CN.md"><img src="https://img.shields.io/badge/Lang-中文-red?style=for-the-badge" alt="中文"></a>
<a href="README.ur-pk.md"><img src="https://img.shields.io/badge/Lang-اردو-green?style=for-the-badge" alt="اردو"></a>
</p>
**El agente de IA con mejora continua creado por [Nous Research](https://nousresearch.com).** Es el único agente con un bucle de aprendizaje integrado: crea habilidades a partir de la experiencia, las mejora durante el uso, se impulsa a sí mismo a persistir el conocimiento, busca en sus propias conversaciones pasadas y construye un modelo cada vez más profundo de quién eres a lo largo de las sesiones. Ejecútalo en un VPS de $5, un clúster de GPUs o infraestructura sin servidor que cuesta casi nada cuando está inactivo. No está atado a tu laptop — habla con él desde Telegram mientras trabaja en una VM en la nube.
Usa cualquier modelo que quieras — [Nous Portal](https://portal.nousresearch.com), [OpenRouter](https://openrouter.ai) (más de 200 modelos), [NovitaAI](https://novita.ai), [NVIDIA NIM](https://build.nvidia.com) (Nemotron), [Xiaomi MiMo](https://platform.xiaomimimo.com), [z.ai/GLM](https://z.ai), [Kimi/Moonshot](https://platform.moonshot.ai), [MiniMax](https://www.minimax.io), [Hugging Face](https://huggingface.co), OpenAI, o tu propio endpoint. Cambia con `hermes model` — sin cambios de código, sin dependencias.
<table>
<tr><td><b>Una interfaz de terminal real</b></td><td>TUI completa con edición multilínea, autocompletado de comandos, historial de conversaciones, interrupción y redirección, y salida de herramientas en streaming.</td></tr>
<tr><td><b>Vive donde tú vives</b></td><td>Telegram, Discord, Slack, WhatsApp, Signal y CLI — todo desde un único proceso gateway. Transcripción de notas de voz, continuidad de conversación entre plataformas.</td></tr>
<tr><td><b>Un bucle de aprendizaje cerrado</b></td><td>Memoria curada por el agente con recordatorios periódicos. Creación autónoma de habilidades tras tareas complejas. Las habilidades mejoran solas durante el uso. Búsqueda FTS5 de sesiones con resumención por LLM para recuperación entre sesiones. Modelado de usuario dialéctico <a href="https://github.com/plastic-labs/honcho">Honcho</a>. Compatible con el estándar abierto de <a href="https://agentskills.io">agentskills.io</a>.</td></tr>
<tr><td><b>Automatizaciones programadas</b></td><td>Planificador cron integrado con entrega a cualquier plataforma. Informes diarios, copias de seguridad nocturnas, auditorías semanales — todo en lenguaje natural, ejecutándose de forma autónoma.</td></tr>
<tr><td><b>Delega y paraleliza</b></td><td>Lanza subagentes aislados para flujos de trabajo paralelos. Escribe scripts de Python que llaman a herramientas vía RPC, convirtiendo pipelines de múltiples pasos en turnos de coste cero de contexto.</td></tr>
<tr><td><b>Funciona en cualquier lugar, no solo en tu laptop</b></td><td>Seis backends de terminal — local, Docker, SSH, Singularity, Modal y Daytona. Daytona y Modal ofrecen persistencia sin servidor — el entorno de tu agente hiberna cuando está inactivo y se activa bajo demanda, costando casi nada entre sesiones. Ejecútalo en un VPS de $5 o un clúster de GPUs.</td></tr>
<tr><td><b>Listo para investigación</b></td><td>Generación de trayectorias en lote, compresión de trayectorias para entrenar la próxima generación de modelos de llamadas a herramientas.</td></tr>
</table>
---
## Instalación rápida
### Linux, macOS, WSL2, Termux
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
```
### Windows (nativo, PowerShell)
> **Nota:** En Windows nativo, Hermes funciona sin WSL — la CLI, el gateway, la TUI y las herramientas funcionan de forma nativa. Si prefieres usar WSL2, el comando de Linux/macOS de arriba también funciona allí. ¿Encontraste un error? Por favor [crea un issue](https://github.com/NousResearch/hermes-agent/issues).
Ejecuta esto en PowerShell:
```powershell
iex (irm https://hermes-agent.nousresearch.com/install.ps1)
```
El instalador se encarga de todo: uv, Python 3.11, Node.js, ripgrep, ffmpeg, **y un Git Bash portátil** (MinGit, descomprimido en `%LOCALAPPDATA%\hermes\git` — no requiere administrador, completamente aislado de cualquier instalación de Git del sistema). Hermes usa este Git Bash incluido para ejecutar comandos de shell.
Si ya tienes Git instalado, el instalador lo detecta y lo usa en su lugar. De lo contrario, una descarga de ~45MB de MinGit es todo lo que necesitas — no tocará ni interferirá con ningún Git del sistema.
> **Android / Termux:** La ruta manual probada está documentada en la [guía de Termux](https://hermes-agent.nousresearch.com/docs/getting-started/termux). En Termux, Hermes instala el extra `.[termux]` curado porque el extra completo `.[all]` actualmente incluye dependencias de voz incompatibles con Android.
>
> **Windows:** Windows nativo es totalmente compatible — el comando de PowerShell de arriba instala todo. Si prefieres usar WSL2, el comando de Linux también funciona allí. La instalación nativa de Windows se encuentra en `%LOCALAPPDATA%\hermes`; WSL2 instala en `~/.hermes` como en Linux.
Después de la instalación:
```bash
source ~/.bashrc # recargar shell (o: source ~/.zshrc)
hermes # ¡empieza a chatear!
```
---
## Primeros pasos
```bash
hermes # CLI interactiva — inicia una conversación
hermes model # Elige tu proveedor y modelo LLM
hermes tools # Configura qué herramientas están habilitadas
hermes config set # Establece valores de configuración individuales
hermes gateway # Inicia el gateway de mensajería (Telegram, Discord, etc.)
hermes setup # Ejecuta el asistente de configuración completo
hermes claw migrate # Migra desde OpenClaw (si vienes de OpenClaw)
hermes update # Actualiza a la última versión
hermes doctor # Diagnostica cualquier problema
```
📖 **[Documentación completa →](https://hermes-agent.nousresearch.com/docs/)**
---
## Evita la colección de claves API — Nous Portal
Hermes funciona con cualquier proveedor que quieras — eso no cambiará. Pero si prefieres no recopilar cinco claves API separadas para el modelo, búsqueda web, generación de imágenes, TTS y un navegador en la nube, **[Nous Portal](https://portal.nousresearch.com)** las cubre todas bajo una sola suscripción:
- **Más de 300 modelos** — elige cualquiera con `/model <nombre>`
- **Tool Gateway** — búsqueda web (Firecrawl), generación de imágenes (FAL), texto a voz (OpenAI), navegador en la nube (Browser Use), todo enrutado a través de tu suscripción. Sin cuentas adicionales.
Un comando desde una instalación nueva:
```bash
hermes setup --portal
```
Esto te autentica vía OAuth, establece Nous como tu proveedor y activa el Tool Gateway. Comprueba qué está conectado en cualquier momento con `hermes portal info`. Detalles completos en la [página de documentación del Tool Gateway](https://hermes-agent.nousresearch.com/docs/user-guide/features/tool-gateway).
Puedes seguir usando tus propias claves por herramienta cuando quieras — el gateway es por backend, no todo o nada.
---
## Referencia rápida: CLI vs Mensajería
Hermes tiene dos puntos de entrada: inicia la interfaz de terminal con `hermes`, o ejecuta el gateway y habla con él desde Telegram, Discord, Slack, WhatsApp, Signal o Email. Una vez en una conversación, muchos comandos de barra son compartidos entre ambas interfaces.
| Acción | CLI | Plataformas de mensajería |
| ----------------------------------- | --------------------------------------------- | --------------------------------------------------------------------------------- |
| Empezar a chatear | `hermes` | Ejecuta `hermes gateway setup` + `hermes gateway start`, luego envía un mensaje al bot |
| Nueva conversación | `/new` o `/reset` | `/new` o `/reset` |
| Cambiar modelo | `/model [proveedor:modelo]` | `/model [proveedor:modelo]` |
| Establecer personalidad | `/personality [nombre]` | `/personality [nombre]` |
| Reintentar o deshacer último turno | `/retry`, `/undo` | `/retry`, `/undo` |
| Comprimir contexto / ver uso | `/compress`, `/usage`, `/insights [--days N]` | `/compress`, `/usage`, `/insights [days]` |
| Explorar habilidades | `/skills` o `/<nombre-habilidad>` | `/<nombre-habilidad>` |
| Interrumpir trabajo actual | `Ctrl+C` o enviar un nuevo mensaje | `/stop` o enviar un nuevo mensaje |
| Estado específico de plataforma | `/platforms` | `/status`, `/sethome` |
Para las listas de comandos completas, consulta la [guía de CLI](https://hermes-agent.nousresearch.com/docs/user-guide/cli) y la [guía del Gateway de Mensajería](https://hermes-agent.nousresearch.com/docs/user-guide/messaging).
---
## Documentación
Toda la documentación está en **[hermes-agent.nousresearch.com/docs](https://hermes-agent.nousresearch.com/docs/)**:
| Sección | Contenido |
| --------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| [Inicio rápido](https://hermes-agent.nousresearch.com/docs/getting-started/quickstart) | Instalar → configurar → primera conversación en 2 minutos |
| [Uso de CLI](https://hermes-agent.nousresearch.com/docs/user-guide/cli) | Comandos, atajos de teclado, personalidades, sesiones |
| [Configuración](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | Archivo de configuración, proveedores, modelos, todas las opciones |
| [Gateway de Mensajería](https://hermes-agent.nousresearch.com/docs/user-guide/messaging) | Telegram, Discord, Slack, WhatsApp, Signal, Home Assistant |
| [Seguridad](https://hermes-agent.nousresearch.com/docs/user-guide/security) | Aprobación de comandos, emparejamiento por DM, aislamiento en contenedor |
| [Herramientas y Toolsets](https://hermes-agent.nousresearch.com/docs/user-guide/features/tools) | Más de 40 herramientas, sistema de toolsets, backends de terminal |
| [Sistema de Habilidades](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills) | Memoria procedimental, Skills Hub, creación de habilidades |
| [Memoria](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory) | Memoria persistente, perfiles de usuario, mejores prácticas |
| [Integración MCP](https://hermes-agent.nousresearch.com/docs/user-guide/features/mcp) | Conecta cualquier servidor MCP para capacidades extendidas |
| [Programación Cron](https://hermes-agent.nousresearch.com/docs/user-guide/features/cron) | Tareas programadas con entrega a plataforma |
| [Archivos de Contexto](https://hermes-agent.nousresearch.com/docs/user-guide/features/context-files) | Contexto de proyecto que da forma a cada conversación |
| [Arquitectura](https://hermes-agent.nousresearch.com/docs/developer-guide/architecture) | Estructura del proyecto, bucle del agente, clases principales |
| [Contribuir](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) | Configuración de desarrollo, proceso de PR, estilo de código |
| [Referencia de CLI](https://hermes-agent.nousresearch.com/docs/reference/cli-commands) | Todos los comandos y flags |
| [Variables de Entorno](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) | Referencia completa de variables de entorno |
---
## Migración desde OpenClaw
Si vienes de OpenClaw, Hermes puede importar automáticamente tu configuración, memorias, habilidades y claves API.
**Durante la configuración inicial:** El asistente de configuración (`hermes setup`) detecta automáticamente `~/.openclaw` y ofrece migrar antes de que comience la configuración.
**En cualquier momento después de instalar:**
```bash
hermes claw migrate # Migración interactiva (preset completo)
hermes claw migrate --dry-run # Vista previa de qué se migraría
hermes claw migrate --preset user-data # Migrar sin secretos
hermes claw migrate --overwrite # Sobreescribir conflictos existentes
```
Qué se importa:
- **SOUL.md** — archivo de personalidad
- **Memorias** — entradas de MEMORY.md y USER.md
- **Habilidades** — habilidades creadas por el usuario → `~/.hermes/skills/openclaw-imports/`
- **Lista de comandos permitidos** — patrones de aprobación
- **Configuración de mensajería** — configuración de plataformas, usuarios permitidos, directorio de trabajo
- **Claves API** — secretos en lista de permitidos (Telegram, OpenRouter, OpenAI, Anthropic, ElevenLabs)
- **Assets de TTS** — archivos de audio del espacio de trabajo
- **Instrucciones del espacio de trabajo** — AGENTS.md (con `--workspace-target`)
Consulta `hermes claw migrate --help` para todas las opciones, o usa la habilidad `openclaw-migration` para una migración guiada interactiva por el agente con vistas previas de dry-run.
---
## Contribuir
¡Las contribuciones son bienvenidas! Consulta la [Guía de Contribución](CONTRIBUTING.es.md) para la configuración del desarrollo, el estilo de código y el proceso de PR.
Inicio rápido para colaboradores — clona y comienza con `setup-hermes.sh`:
```bash
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh # instala uv, crea venv, instala .[all], enlaza ~/.local/bin/hermes
./hermes # detecta automáticamente el venv, no necesitas hacer `source` primero
```
Ruta manual (equivalente a lo anterior):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -e ".[all,dev]"
scripts/run_tests.sh
```
---
## Comunidad
- 💬 [Discord](https://discord.gg/NousResearch)
- 📚 [Skills Hub](https://agentskills.io)
- 🐛 [Issues](https://github.com/NousResearch/hermes-agent/issues)
- 🔌 [computer-use-linux](https://github.com/avifenesh/computer-use-linux) — Servidor MCP de control de escritorio Linux para Hermes y otros hosts MCP, con árboles de accesibilidad AT-SPI, entrada Wayland/X11, capturas de pantalla y targeting de ventanas del compositor.
- 🔌 [HermesClaw](https://github.com/AaronWong1999/hermesclaw) — Puente WeChat comunitario: Ejecuta Hermes Agent y OpenClaw en la misma cuenta de WeChat.
---
## Licencia
MIT — ver [LICENSE](LICENSE).
Creado por [Nous Research](https://nousresearch.com).
+13 -60
View File
@@ -3,22 +3,18 @@
</p>
# Hermes Agent ☤
<p align="center">
<a href="https://hermes-agent.nousresearch.com/">Hermes Agent</a> | <a href="https://hermes-agent.nousresearch.com/">Hermes Desktop</a>
</p>
<p align="center">
<a href="https://hermes-agent.nousresearch.com/docs/"><img src="https://img.shields.io/badge/Docs-hermes--agent.nousresearch.com-FFD700?style=for-the-badge" alt="Documentation"></a>
<a href="https://discord.gg/NousResearch"><img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
<a href="https://github.com/NousResearch/hermes-agent/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-green?style=for-the-badge" alt="License: MIT"></a>
<a href="https://nousresearch.com"><img src="https://img.shields.io/badge/Built%20by-Nous%20Research-blueviolet?style=for-the-badge" alt="Built by Nous Research"></a>
<a href="README.zh-CN.md"><img src="https://img.shields.io/badge/Lang-中文-red?style=for-the-badge" alt="中文"></a>
<a href="README.ur-pk.md"><img src="https://img.shields.io/badge/Lang-اردو-green?style=for-the-badge" alt="اردو"></a>
<a href="README.es.md"><img src="https://img.shields.io/badge/Lang-Español-orange?style=for-the-badge" alt="Español"></a>
</p>
**The self-improving AI agent built by [Nous Research](https://nousresearch.com).** It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.
Use any model you want — [Nous Portal](https://portal.nousresearch.com), OpenRouter, OpenAI, your own endpoint, and [many others](https://hermes-agent.nousresearch.com/docs/integrations/providers). Switch with `hermes model` — no code changes, no lock-in.
Use any model you want — [Nous Portal](https://portal.nousresearch.com), [OpenRouter](https://openrouter.ai) (200+ models), [NovitaAI](https://novita.ai) (AI-native cloud for Model API, Agent Sandbox, and GPU Cloud), [NVIDIA NIM](https://build.nvidia.com) (Nemotron), [Xiaomi MiMo](https://platform.xiaomimimo.com), [z.ai/GLM](https://z.ai), [Kimi/Moonshot](https://platform.moonshot.ai), [MiniMax](https://www.minimax.io), [Hugging Face](https://huggingface.co), OpenAI, or your own endpoint. Switch with `hermes model` — no code changes, no lock-in.
<table>
<tr><td><b>A real terminal interface</b></td><td>Full TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output.</td></tr>
@@ -37,7 +33,7 @@ Use any model you want — [Nous Portal](https://portal.nousresearch.com), OpenR
### Linux, macOS, WSL2, Termux
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
```
### Windows (native, PowerShell)
@@ -47,7 +43,7 @@ curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
Run this in PowerShell:
```powershell
iex (irm https://hermes-agent.nousresearch.com/install.ps1)
iex (irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1)
```
The installer handles everything: uv, Python 3.11, Node.js, ripgrep, ffmpeg, **and a portable Git Bash** (MinGit, unpacked to `%LOCALAPPDATA%\hermes\git` — no admin required, completely isolated from any system Git install). Hermes uses this bundled Git Bash to run shell commands.
@@ -56,7 +52,7 @@ If you already have Git installed, the installer detects it and uses that instea
> **Android / Termux:** The tested manual path is documented in the [Termux guide](https://hermes-agent.nousresearch.com/docs/getting-started/termux). On Termux, Hermes installs a curated `.[termux]` extra because the full `.[all]` extra currently pulls Android-incompatible voice dependencies.
>
> **Windows:** Native Windows is fully supported — the PowerShell one-liner above installs everything. If you'd rather use WSL2, the Linux command works there too. Native Windows install lives under `%LOCALAPPDATA%\hermes`; WSL2 installs under `~/.hermes` as on Linux.
> **Windows:** Native Windows is fully supported — the PowerShell one-liner above installs everything. If you'd rather use WSL2, the Linux command works there too. Native Windows install lives under `%LOCALAPPDATA%\hermes`; WSL2 installs under `~/.hermes` as on Linux. The only Hermes feature that currently needs WSL2 specifically is the browser-based dashboard chat pane (it uses a POSIX PTY — classic CLI and gateway both run natively).
After installation:
@@ -65,41 +61,6 @@ source ~/.bashrc # reload shell (or: source ~/.zshrc)
hermes # start chatting!
```
### Troubleshooting
#### Windows Defender or antivirus flags `uv.exe` as malware
If your antivirus (Bitdefender, Windows Defender, etc.) quarantines `uv.exe` from the Hermes `bin` folder (`%LOCALAPPDATA%\hermes\bin\uv.exe`), this is a **false positive**. The file is Astral's `uv` — the Rust Python package manager Hermes bundles to manage its Python environment. ML-based antivirus engines commonly flag unsigned Rust binaries that download and install packages.
**To verify your copy is authentic:**
```powershell
# Install GitHub CLI if needed
winget install --id GitHub.cli
# Login to GitHub
gh auth login
# Run verification
$uv = "$env:LOCALAPPDATA\hermes\bin\uv.exe"
$ver = (& $uv --version).Split(' ')[1]
[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12
$zip = "$env:TEMP\uv.zip"
Invoke-WebRequest "https://github.com/astral-sh/uv/releases/download/$ver/uv-x86_64-pc-windows-msvc.zip" -OutFile $zip -UseBasicParsing
gh attestation verify $zip --repo astral-sh/uv
Expand-Archive $zip "$env:TEMP\uv_x" -Force
(Get-FileHash "$env:TEMP\uv_x\uv.exe").Hash -eq (Get-FileHash $uv).Hash
```
If attestation says "Verification succeeded" and the last line prints `True`, you're good.
**To whitelist Hermes:**
- **Windows Defender:** Run PowerShell as Admin → `Add-MpPreference -ExclusionPath "$env:LOCALAPPDATA\hermes\bin"`
- **Bitdefender:** Add an exception in the Bitdefender console (Protection > Antivirus > Settings > Manage Exceptions)
- Whitelist the **folder**, not the file hash — Hermes updates `uv` and the hash changes every version
For more context, see the upstream Astral reports: [astral-sh/uv#13553](https://github.com/astral-sh/uv/issues/13553), [astral-sh/uv#15011](https://github.com/astral-sh/uv/issues/15011), [astral-sh/uv#10079](https://github.com/astral-sh/uv/issues/10079).
---
## Getting Started
@@ -217,29 +178,21 @@ See `hermes claw migrate --help` for all options, or use the `openclaw-migration
We welcome contributions! See the [Contributing Guide](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) for development setup, code style, and PR process.
Quick start for contributors — use the standard installer, then work from the
full git checkout it creates at `$HERMES_HOME/hermes-agent` (usually
`~/.hermes/hermes-agent`). This matches the layout used by `hermes update`, the
managed venv, lazy dependencies, gateway, and docs tooling.
Quick start for contributors — clone and go with `setup-hermes.sh`:
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
cd "${HERMES_HOME:-$HOME/.hermes}/hermes-agent"
uv pip install -e ".[all,dev]"
scripts/run_tests.sh
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh # installs uv, creates venv, installs .[all], symlinks ~/.local/bin/hermes
./hermes # auto-detects the venv, no need to `source` first
```
Manual clone fallback (for throwaway clones/CI where you intentionally do not
want the managed install layout):
Create the venv outside the cloned source tree — a venv inside the directory
the agent operates from can be wiped by a relative-path command the agent runs
against its own checkout, destroying the running runtime mid-session.
Manual path (equivalent to the above):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv ~/.hermes/venvs/hermes-dev --python 3.11
source ~/.hermes/venvs/hermes-dev/bin/activate
uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -e ".[all,dev]"
scripts/run_tests.sh
```
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@@ -1,261 +0,0 @@
<div dir="rtl">
<p align="center">
<img src="assets/banner.png" alt="Hermes Agent" width="100%">
</p>
# ہرمیس ایجنٹ ☤ (Hermes Agent)
<p align="center">
<a href="https://hermes-agent.nousresearch.com/docs/"><img src="https://img.shields.io/badge/Docs-hermes--agent.nousresearch.com-FFD700?style=for-the-badge" alt="Documentation"></a>
<a href="https://discord.gg/NousResearch"><img src="https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
<a href="https://github.com/NousResearch/hermes-agent/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-green?style=for-the-badge" alt="License: MIT"></a>
<a href="https://nousresearch.com"><img src="https://img.shields.io/badge/Built%20by-Nous%20Research-blueviolet?style=for-the-badge" alt="Built by Nous Research"></a>
<a href="README.md"><img src="https://img.shields.io/badge/Lang-English-lightgrey?style=for-the-badge" alt="English"></a>
<a href="README.zh-CN.md"><img src="https://img.shields.io/badge/Lang-中文-red?style=for-the-badge" alt="中文"></a>
</p>
**[نوس ریسرچ (Nous Research)](https://nousresearch.com) کا تیار کردہ خود کو بہتر بنانے والا اے آئی (AI) ایجنٹ۔** یہ واحد ایجنٹ ہے جس میں سیکھنے کا عمل (learning loop) پہلے سے موجود ہے — یہ اپنے تجربات سے نئی مہارتیں (skills) بناتا ہے، استعمال کے دوران ان کو بہتر کرتا ہے، معلومات کو محفوظ رکھنے کے لیے خود کو یاد دہانی کرواتا ہے، اپنی پرانی بات چیت کو تلاش کر سکتا ہے، اور مختلف سیشنز کے دوران آپ کے بارے میں ایک گہری سمجھ پیدا کرتا ہے۔ اسے $5 والے VPS پر چلائیں، GPU کلسٹر پر، یا سرور لیس (serverless) انفراسٹرکچر پر جس کی قیمت استعمال نہ ہونے پر تقریباً صفر ہے۔ یہ آپ کے لیپ ٹاپ تک محدود نہیں ہے — آپ ٹیلی گرام (Telegram) سے اس کے ساتھ بات چیت کر سکتے ہیں جبکہ یہ کلاؤڈ VM پر کام کر رہا ہو۔
آپ اپنی مرضی کا کوئی بھی ماڈل استعمال کر سکتے ہیں — [Nous Portal](https://portal.nousresearch.com)، [OpenRouter](https://openrouter.ai) (200 سے زائد ماڈلز)، [NovitaAI](https://novita.ai) (ماڈل API، ایجنٹ سینڈ باکس، اور GPU کلاؤڈ کے لیے اے آئی مقامی کلاؤڈ)، [NVIDIA NIM](https://build.nvidia.com) (Nemotron)، [Xiaomi MiMo](https://platform.xiaomimimo.com)، [z.ai/GLM](https://z.ai)، [Kimi/Moonshot](https://platform.moonshot.ai)، [MiniMax](https://www.minimax.io)، [Hugging Face](https://huggingface.co)، OpenAI، یا اپنا حسب ضرورت اینڈ پوائنٹ (endpoint) استعمال کریں۔ ماڈل تبدیل کرنے کے لیے صرف `hermes model` استعمال کریں — کسی کوڈ کو تبدیل کرنے کی ضرورت نہیں، کوئی پابندی نہیں۔
<table>
<tr><td><b>حقیقی ٹرمینل انٹرفیس</b></td><td>مکمل TUI جس میں ملٹی لائن ایڈیٹنگ، سلیش-کمانڈ آٹو کمپلیٹ، بات چیت کی ہسٹری، انٹرپٹ اور ری ڈائریکٹ، اور سٹریمنگ ٹول آؤٹ پٹ شامل ہے۔</td></tr>
<tr><td><b>یہ وہاں موجود ہے جہاں آپ ہیں</b></td><td>ٹیلی گرام، ڈسکارڈ (Discord)، سلیک (Slack)، واٹس ایپ (WhatsApp)، سگنل (Signal)، اور CLI — سب ایک ہی گیٹ وے پروسیس سے کام کرتے ہیں۔ وائس میمو (Voice memo) ٹرانسکرپشن، کراس پلیٹ فارم بات چیت کا تسلسل۔</td></tr>
<tr><td><b>سیکھنے کا ایک مکمل عمل</b></td><td>ایجنٹ کی اپنی ترتیب دی گئی میموری، جس میں وہ خود کو وقتاً فوقتاً یاد دہانی کرواتا ہے۔ پیچیدہ کاموں کے بعد خود کار طریقے سے مہارت (skill) کی تخلیق۔ استعمال کے دوران مہارتوں میں بہتری۔ LLM سمرائزیشن کے ساتھ FTS5 سیشن سرچ تاکہ پرانے سیشنز کی یاددہانی کی جا سکے۔ <a href="https://github.com/plastic-labs/honcho">Honcho</a> کے ذریعے صارف کی ماڈلنگ۔ <a href="https://agentskills.io">agentskills.io</a> اوپن سٹینڈرڈ کے ساتھ مکمل مطابقت۔</td></tr>
<tr><td><b>شیڈول کی گئی خودکار کارروائیاں</b></td><td>بلٹ ان (Built-in) کرون (cron) شیڈیولر جو کسی بھی پلیٹ فارم پر ڈیلیوری کے لیے استعمال ہو سکتا ہے۔ روزانہ کی رپورٹس، رات کے بیک اپس، ہفتہ وار آڈٹس — یہ سب کچھ قدرتی زبان (natural language) میں اور بغیر کسی نگرانی کے کام کرتا ہے۔</td></tr>
<tr><td><b>کام کی تقسیم اور متوازی عمل</b></td><td>متوازی (parallel) کاموں کے لیے الگ سے ذیلی ایجنٹس (subagents) بنائیں۔ پائتھون (Python) سکرپٹس لکھیں جو RPC کے ذریعے ٹولز کو استعمال کریں، تاکہ کئی مراحل پر مشتمل کاموں کو بغیر کسی سیاق و سباق (context) کے خرچ کے، ایک ہی باری میں انجام دیا جا سکے۔</td></tr>
<tr><td><b>کہیں بھی چلائیں، صرف اپنے لیپ ٹاپ پر نہیں</b></td><td>چھ (Six) ٹرمینل بیک اینڈز — لوکل، Docker، SSH، Singularity، Modal، اور Daytona۔ ڈیٹونا (Daytona) اور موڈل (Modal) سرور لیس (serverless) فعالیت پیش کرتے ہیں — جب آپ کا ایجنٹ فارغ ہوتا ہے تو اس کا ماحول سلیپ (hibernate) ہو جاتا ہے اور ضرورت پڑنے پر خود بخود جاگ جاتا ہے، جس کی وجہ سے سیشنز کے درمیان لاگت تقریباً صفر رہتی ہے۔ اسے $5 والے VPS یا GPU کلسٹر پر چلائیں۔</td></tr>
<tr><td><b>تحقیق کے لیے تیار</b></td><td>بیچ (Batch) ٹریجیکٹری (trajectory) جنریشن، اگلی نسل کے ٹول کالنگ ماڈلز کی تربیت کے لیے ٹریجیکٹری کمپریشن۔</td></tr>
</table>
---
## فوری انسٹالیشن (Quick Install)
### لینکس (Linux)، میک او ایس (macOS)، ڈبلیو ایس ایل ٹو (WSL2)، ٹرمکس (Termux)
<div dir="ltr">
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
```
</div>
### ونڈوز (نیٹو، پاور شیل)
> **توجہ فرمائیں:** مقامی ونڈوز (Native Windows) پر ہرمیس بغیر WSL کے چلتا ہے — CLI، گیٹ وے، TUI، اور ٹولز سب مقامی طور پر کام کرتے ہیں۔ اگر آپ WSL2 استعمال کرنا پسند کرتے ہیں، تو اوپر دی گئی لینکس/میک او ایس کی کمانڈ وہاں بھی کام کرے گی۔ کوئی مسئلہ نظر آیا؟ براہ کرم [مسائل (issues) درج کریں](https://github.com/NousResearch/hermes-agent/issues)۔
اسے پاور شیل (PowerShell) میں چلائیں:
<div dir="ltr">
```powershell
iex (irm https://hermes-agent.nousresearch.com/install.ps1)
```
</div>
انسٹالر سب کچھ خود سنبھالتا ہے: uv، Python 3.11، Node.js، ripgrep، ffmpeg، **اور ایک پورٹ ایبل (portable) گٹ بیش (Git Bash)** (یعنی MinGit، جو `%LOCALAPPDATA%\hermes\git` میں ان پیک ہوتا ہے — اس کے لیے ایڈمن کی اجازت درکار نہیں، اور یہ سسٹم کے کسی بھی گٹ انسٹال سے بالکل الگ ہے)۔ ہرمیس اس بنڈل شدہ گٹ بیش کو شیل کمانڈز چلانے کے لیے استعمال کرتا ہے۔
اگر آپ کے پاس پہلے سے گٹ (Git) انسٹال ہے، تو انسٹالر اسے شناخت کر لیتا ہے اور اسے ہی استعمال کرتا ہے۔ بصورت دیگر آپ کو صرف ~45MB کے MinGit ڈاؤنلوڈ کی ضرورت ہوگی — یہ آپ کے سسٹم کے گٹ پر کوئی اثر نہیں ڈالے گا۔
> **اینڈرائیڈ (Android) / ٹرمکس (Termux):** ٹیسٹ کیا گیا مینوئل طریقہ [Termux گائیڈ](https://hermes-agent.nousresearch.com/docs/getting-started/termux) میں موجود ہے۔ ٹرمکس پر ہرمیس ایک مخصوص `.[termux]` ایکسٹرا انسٹال کرتا ہے کیونکہ مکمل `.[all]` ایکسٹرا میں ایسی وائس ڈیپینڈینسیز شامل ہیں جو اینڈرائیڈ کے ساتھ مطابقت نہیں رکھتیں۔
>
> **ونڈوز (Windows):** مقامی ونڈوز کی مکمل سپورٹ موجود ہے — اوپر دی گئی پاور شیل کی کمانڈ سب کچھ انسٹال کر دیتی ہے۔ اگر آپ WSL2 استعمال کرنا چاہتے ہیں، تو لینکس کی کمانڈ وہاں کام کرتی ہے۔ مقامی ونڈوز میں انسٹالیشن `%LOCALAPPDATA%\hermes` میں ہوتی ہے؛ جبکہ WSL2 میں لینکس کی طرح `~/.hermes` میں ہوتی ہے۔ ہرمیس کا وہ واحد فیچر جسے فی الحال خاص طور پر WSL2 کی ضرورت ہے وہ براؤزر پر مبنی ڈیش بورڈ چیٹ پین ہے (یہ POSIX PTY استعمال کرتا ہے — کلاسک CLI اور گیٹ وے دونوں مقامی طور پر چلتے ہیں)۔
انسٹالیشن کے بعد:
<div dir="ltr">
```bash
source ~/.bashrc # شیل کو ری لوڈ کریں (یا: source ~/.zshrc)
hermes # بات چیت شروع کریں!
```
</div>
---
## آغاز کریں (Getting Started)
<div dir="ltr">
```bash
hermes # انٹرایکٹو CLI — بات چیت شروع کریں
hermes model # اپنا LLM پرووائیڈر اور ماڈل منتخب کریں
hermes tools # کنفیگر کریں کہ کون سے ٹولز ایکٹو ہیں
hermes config set # انفرادی کنفگ (config) ویلیوز سیٹ کریں
hermes gateway # میسجنگ گیٹ وے شروع کریں (ٹیلی گرام، ڈسکارڈ، وغیرہ)
hermes setup # مکمل سیٹ اپ وزرڈ چلائیں (یہ سب کچھ ایک ساتھ کنفیگر کر دے گا)
hermes claw migrate # OpenClaw سے مائیگریٹ کریں (اگر آپ OpenClaw سے آ رہے ہیں)
hermes update # لیٹسٹ ورژن پر اپ ڈیٹ کریں
hermes doctor # کسی بھی مسئلے کی تشخیص کریں
```
</div>
📖 **[مکمل دستاویزات →](https://hermes-agent.nousresearch.com/docs/)**
---
## API-کیز اکٹھی کرنے سے بچیں — Nous Portal
ہرمیس آپ کے پسندیدہ پرووائیڈر کے ساتھ کام کرتا ہے — یہ چیز تبدیل نہیں ہو رہی۔ لیکن اگر آپ ماڈل، ویب سرچ، امیج جنریشن، TTS، اور کلاؤڈ براؤزر کے لیے پانچ الگ الگ API کیز جمع نہیں کرنا چاہتے، تو **[Nous Portal](https://portal.nousresearch.com)** ان سب کو ایک ہی سبسکرپشن کے تحت کور کرتا ہے:
- **300+ ماڈلز** — ان میں سے کوئی بھی ماڈل `/model <name>` کے ذریعے منتخب کریں
- **ٹول گیٹ وے (Tool Gateway)** — ویب سرچ (Firecrawl)، امیج جنریشن (FAL)، ٹیکسٹ ٹو سپیچ (OpenAI)، کلاؤڈ براؤزر (Browser Use)، یہ سب آپ کی سبسکرپشن کے ذریعے چلتے ہیں۔ کسی اضافی اکاؤنٹ کی ضرورت نہیں۔
نئی انسٹالیشن کے بعد بس ایک کمانڈ کی ضرورت ہے:
<div dir="ltr">
```bash
hermes setup --portal
```
</div>
یہ آپ کو OAuth کے ذریعے لاگ ان کرواتا ہے، Nous کو آپ کا پرووائیڈر مقرر کرتا ہے، اور ٹول گیٹ وے کو آن کر دیتا ہے۔ `hermes portal info` کمانڈ استعمال کر کے آپ کسی بھی وقت چیک کر سکتے ہیں کہ کون کون سی سروسز منسلک ہیں۔ مکمل تفصیلات [Tool Gateway دستاویزات کے صفحے](https://hermes-agent.nousresearch.com/docs/user-guide/features/tool-gateway) پر موجود ہیں۔
آپ اب بھی کسی بھی ٹول کے لیے اپنی مرضی کی API کیز استعمال کر سکتے ہیں — گیٹ وے ہر سروس کے لیے الگ الگ کام کرتا ہے، ایسا نہیں کہ یا تو سب کچھ استعمال کریں یا کچھ بھی نہیں۔
---
## CLI بمقابلہ میسجنگ فوری حوالہ
ہرمیس کے دو بنیادی انٹر فیس ہیں: آپ ٹرمینل UI کو `hermes` کے ساتھ شروع کریں، یا گیٹ وے چلا کر اس کے ساتھ ٹیلی گرام، ڈسکارڈ، سلیک، واٹس ایپ، سگنل، یا ای میل کے ذریعے بات کریں۔ جب آپ کسی بات چیت میں ہوتے ہیں، تو بہت سی سلیش (slash) کمانڈز دونوں انٹرفیسز میں ایک جیسی ہوتی ہیں۔
<div dir="ltr">
| کارروائی (Action) | سی ایل آئی (CLI) | میسجنگ پلیٹ فارمز (Messaging platforms) |
| --------------------------------------- | --------------------------------------------- | -------------------------------------------------------------------------------- |
| بات چیت شروع کریں | `hermes` | `hermes gateway setup` اور `hermes gateway start` چلائیں، پھر بوٹ کو میسج بھیجیں |
| نئی بات چیت شروع کریں | `/new` یا `/reset` | `/new` یا `/reset` |
| ماڈل تبدیل کریں | `/model [provider:model]` | `/model [provider:model]` |
| پرسنلٹی (Personality) سیٹ کریں | `/personality [name]` | `/personality [name]` |
| پچھلی باری کو دوبارہ یا منسوخ (undo) کریں | `/retry`، `/undo` | `/retry`، `/undo` |
| کانٹیکسٹ (context) کمپریس کریں / استعمال چیک کریں | `/compress`، `/usage`، `/insights [--days N]` | `/compress`، `/usage`، `/insights [days]` |
| مہارتیں (Skills) براؤز کریں | `/skills` یا `/<skill-name>` | `/<skill-name>` |
| موجودہ کام کو روکیں | `Ctrl+C` دبائیں یا نیا میسج بھیجیں | `/stop` یا نیا میسج بھیجیں |
| پلیٹ فارم کے لحاظ سے سٹیٹس | `/platforms` | `/status`، `/sethome` |
</div>
مکمل کمانڈ لسٹ کے لیے، [CLI گائیڈ](https://hermes-agent.nousresearch.com/docs/user-guide/cli) اور [میسجنگ گیٹ وے گائیڈ](https://hermes-agent.nousresearch.com/docs/user-guide/messaging) دیکھیں۔
---
## دستاویزات (Documentation)
تمام دستاویزات **[hermes-agent.nousresearch.com/docs](https://hermes-agent.nousresearch.com/docs/)** پر موجود ہیں:
<div dir="ltr">
| سیکشن (Section) | تفصیل (What's Covered) |
| --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- |
| [فوری آغاز (Quickstart)](https://hermes-agent.nousresearch.com/docs/getting-started/quickstart) | انسٹالیشن → سیٹ اپ → 2 منٹ میں پہلی بات چیت شروع کریں |
| [CLI کا استعمال](https://hermes-agent.nousresearch.com/docs/user-guide/cli) | کمانڈز، کی بائنڈنگز (keybindings)، پرسنلٹیز (personalities)، سیشنز |
| [کنفیگریشن (Configuration)](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | کنفگ فائل، پرووائیڈرز، ماڈلز، اور تمام آپشنز |
| [میسجنگ گیٹ وے](https://hermes-agent.nousresearch.com/docs/user-guide/messaging) | ٹیلی گرام، ڈسکارڈ، سلیک، واٹس ایپ، سگنل، ہوم اسسٹنٹ |
| [سیکیورٹی (Security)](https://hermes-agent.nousresearch.com/docs/user-guide/security) | کمانڈ کی منظوری، DM پیئرنگ (pairing)، کنٹینر آئسولیشن |
| [ٹولز اور ٹول سیٹس](https://hermes-agent.nousresearch.com/docs/user-guide/features/tools) | 40 سے زائد ٹولز، ٹول سیٹ سسٹم، ٹرمینل بیک اینڈز |
| [مہارتوں کا سسٹم (Skills System)](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills)| پروسیجرل (Procedural) میموری، سکلز ہب، نئی مہارتیں بنانا |
| [میموری (Memory)](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory) | مستقل میموری، یوزر پروفائلز، بہترین طریقہ کار |
| [MCP انضمام (Integration)](https://hermes-agent.nousresearch.com/docs/user-guide/features/mcp) | صلاحیتوں کو بڑھانے کے لیے کسی بھی MCP سرور کو جوڑیں |
| [کرون (Cron) شیڈیولنگ](https://hermes-agent.nousresearch.com/docs/user-guide/features/cron) | پلیٹ فارم ڈیلیوری کے ساتھ شیڈول کیے گئے کام |
| [کانٹیکسٹ (Context) فائلز](https://hermes-agent.nousresearch.com/docs/user-guide/features/context-files)| پروجیکٹ کا سیاق و سباق (context) جو ہر بات چیت پر اثر انداز ہوتا ہے |
| [آرکیٹیکچر (Architecture)](https://hermes-agent.nousresearch.com/docs/developer-guide/architecture) | پروجیکٹ کا ڈھانچہ، ایجنٹ لوپ، اہم کلاسز |
| [تعاون (Contributing)](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) | ڈیویلپمنٹ سیٹ اپ، PR کا طریقہ کار، کوڈنگ کا انداز |
| [CLI حوالہ جات (Reference)](https://hermes-agent.nousresearch.com/docs/reference/cli-commands) | تمام کمانڈز اور فلیگز (flags) |
| [انوائرمنٹ ویری ایبلز](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) | مکمل انوائرمنٹ ویری ایبل حوالہ جات |
</div>
---
## OpenClaw سے منتقلی
اگر آپ OpenClaw سے منتقل ہو رہے ہیں، تو ہرمیس آپ کی سیٹنگز، یادیں (memories)، مہارتیں (skills)، اور API کیز کو خود بخود امپورٹ کر سکتا ہے۔
**پہلی بار سیٹ اپ کے دوران:** سیٹ اپ وزرڈ (`hermes setup`) خود بخود `~/.openclaw` کو پہچان لیتا ہے اور کنفیگریشن شروع ہونے سے پہلے مائیگریٹ (migrate) کرنے کا آپشن دیتا ہے۔
**انسٹالیشن کے بعد کسی بھی وقت:**
<div dir="ltr">
```bash
hermes claw migrate # انٹرایکٹو مائیگریشن (مکمل پری سیٹ)
hermes claw migrate --dry-run # جائزہ لیں کہ کیا کیا مائیگریٹ ہوگا
hermes claw migrate --preset user-data # حساس معلومات (secrets) کے بغیر مائیگریٹ کریں
hermes claw migrate --overwrite # موجودہ متصادم فائلوں کو اوور رائٹ کریں
```
</div>
جو چیزیں امپورٹ ہوتی ہیں:
- **SOUL.md** — پرسونا (persona) فائل
- **میموریز (Memories)** — MEMORY.md اور USER.md کی اندراجات
- **مہارتیں (Skills)** — صارف کی بنائی گئی مہارتیں → `~/.hermes/skills/openclaw-imports/`
- **کمانڈ الاؤ لسٹ (allowlist)** — منظوری کے پیٹرنز (approval patterns)
- **میسجنگ سیٹنگز** — پلیٹ فارم کنفیگریشنز، اجازت یافتہ صارفین، ورکنگ ڈائریکٹری
- **API کیز** — الاؤ لسٹ شدہ حساس معلومات (ٹیلی گرام، OpenRouter، OpenAI، Anthropic، ElevenLabs)
- **TTS اثاثے** — ورک اسپیس کی آڈیو فائلیں
- **ورک اسپیس کی ہدایات** — AGENTS.md (`--workspace-target` کے ساتھ)
تمام آپشنز دیکھنے کے لیے `hermes claw migrate --help` استعمال کریں، یا انٹرایکٹو ایجنٹ کی مدد سے مائیگریٹ کرنے کے لیے `openclaw-migration` سکل کا استعمال کریں (جس میں ڈرائی رن (dry-run) پریویوز شامل ہیں)۔
---
## تعاون کریں (Contributing)
ہم آپ کے تعاون کا خیرمقدم کرتے ہیں! ڈیویلپمنٹ سیٹ اپ، کوڈ کے انداز اور PR کے طریقہ کار کے لیے براہ کرم ہماری [Contributing گائیڈ](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) دیکھیں۔
معاونین (contributors) کے لیے فوری آغاز — کلون (clone) کریں اور `setup-hermes.sh` چلائیں:
<div dir="ltr">
```bash
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh # uv کو انسٹال کرتا ہے، venv بناتا ہے، .[all] کو انسٹال کرتا ہے، اور ~/.local/bin/hermes کا سیم لنک (symlink) بناتا ہے
./hermes # خود بخود venv کی شناخت کرتا ہے، پہلے `source` کرنے کی ضرورت نہیں
```
</div>
مینوئل طریقہ (اوپر والے طریقے کے مساوی):
<div dir="ltr">
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -e ".[all,dev]"
scripts/run_tests.sh
```
</div>
---
## کمیونٹی (Community)
- 💬 [ڈسکارڈ (Discord)](https://discord.gg/NousResearch)
- 📚 [سکلز ہب (Skills Hub)](https://agentskills.io)
- 🐛 [مسائل (Issues)](https://github.com/NousResearch/hermes-agent/issues)
- 🔌 [computer-use-linux](https://github.com/avifenesh/computer-use-linux) — ہرمیس اور دیگر MCP ہوسٹس کے لیے لینکس (Linux) ڈیسک ٹاپ کنٹرول MCP سرور، جس میں AT-SPI ایکسیسیبلٹی ٹریز، Wayland/X11 ان پٹ، سکرین شاٹس، اور کمپوزیٹر ونڈو ٹارگیٹنگ شامل ہے۔
- 🔌 [HermesClaw](https://github.com/AaronWong1999/hermesclaw) — کمیونٹی وی چیٹ (WeChat) برج: ہرمیس ایجنٹ اور OpenClaw کو ایک ہی وی چیٹ اکاؤنٹ پر چلائیں۔
---
## لائسنس (License)
MIT — تفصیلات کے لیے [LICENSE](LICENSE) دیکھیں۔
[نوس ریسرچ (Nous Research)](https://nousresearch.com) کی جانب سے تیار کردہ۔
</div>
+8 -15
View File
@@ -10,7 +10,6 @@
<a href="https://github.com/NousResearch/hermes-agent/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-green?style=for-the-badge" alt="License: MIT"></a>
<a href="https://nousresearch.com"><img src="https://img.shields.io/badge/Built%20by-Nous%20Research-blueviolet?style=for-the-badge" alt="Built by Nous Research"></a>
<a href="README.md"><img src="https://img.shields.io/badge/Lang-English-lightgrey?style=for-the-badge" alt="English"></a>
<a href="README.ur-pk.md"><img src="https://img.shields.io/badge/Lang-اردو-green?style=for-the-badge" alt="اردو"></a>
</p>
**由 [Nous Research](https://nousresearch.com) 构建的自进化 AI 代理。** 它是唯一内置学习闭环的智能代理——从经验中创建技能,在使用中改进技能,主动持久化知识,搜索过往对话,并在跨会话中逐步构建对你的深度理解。可以在 $5 的 VPS 上运行,也可以在 GPU 集群上运行,或者使用几乎零成本的 Serverless 基础设施。它不绑定你的笔记本——你可以在 Telegram 上与它对话,而它在云端 VM 上工作。
@@ -32,18 +31,14 @@
## 快速安装
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
```
支持 Linux、macOS、WSL2 和 Android (Termux)。安装程序会自动处理平台特定的配置。
> **Android / Termux:** 已测试的手动安装路径请参考 [Termux 指南](https://hermes-agent.nousresearch.com/docs/getting-started/termux)。在 Termux 上,Hermes 会安装精选的 `.[termux]` 扩展,因为完整的 `.[all]` 扩展会拉取 Android 不兼容的语音依赖。
>
> **Windows:** 在 PowerShell 中运行:
> ```powershell
> iex (irm https://hermes-agent.nousresearch.com/install.ps1)
> ```
> 安装完成后,可能需要重启终端,然后运行 `hermes` 开始对话。
> **Windows:** 原生 Windows 不受支持。请安装 [WSL2](https://learn.microsoft.com/zh-cn/windows/wsl/install) 并运行上述命令。
安装后:
@@ -168,18 +163,16 @@ hermes claw migrate --overwrite # 覆盖已有冲突
欢迎贡献!请参阅 [贡献指南](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) 了解开发设置、代码风格和 PR 流程。
贡献者快速开始——使用标准安装器,然后在它创建的完整 git checkout 中开发:
`$HERMES_HOME/hermes-agent`(通常是 `~/.hermes/hermes-agent`)。这会匹配
`hermes update`、托管 venv、lazy dependencies、gateway 和 docs tooling 使用的布局。
贡献者快速开始——克隆并使用 `setup-hermes.sh`:
```bash
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
cd "${HERMES_HOME:-$HOME/.hermes}/hermes-agent"
uv pip install -e ".[all,dev]"
scripts/run_tests.sh
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh # 安装 uv、创建 venv、安装 .[all]、创建符号链接 ~/.local/bin/hermes
./hermes # 自动检测 venv,无需先 source
```
手动克隆备用路径(用于一次性 clone / CI,或你明确不想使用 managed install layout 时):
手动安装(等效于上述命令):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
-322
View File
@@ -1,322 +0,0 @@
# Política de Seguridad de Hermes Agent
Este documento describe el modelo de confianza de Hermes Agent, identifica el
único límite de seguridad que el proyecto trata como estructural y define el
alcance para los informes de vulnerabilidades.
## 1. Reportar una Vulnerabilidad
Reporta de forma privada a través de [GitHub Security Advisories](https://github.com/NousResearch/hermes-agent/security/advisories/new)
o **security@nousresearch.com**. No abras issues públicos para
vulnerabilidades de seguridad. **Hermes Agent no opera un programa de
recompensas por errores.**
Un informe útil incluye:
- Una descripción concisa y evaluación de severidad.
- El componente afectado, identificado por ruta de archivo y rango de líneas
(ej. `path/to/file.py:120-145`).
- Detalles del entorno (`hermes version`, SHA del commit, SO, versión de Python).
- Una reproducción contra `main` o el último release.
- Una declaración de qué límite de confianza del §2 se cruza.
Por favor lee el §2 y el §3 antes de enviar. Los informes que demuestren
límites de una heurística en proceso que esta política no trate como un
límite serán cerrados como fuera de alcance bajo el §3 — pero consulta el §3.2:
siguen siendo bienvenidos como issues o pull requests regulares, simplemente no
a través del canal de seguridad privado.
---
## 2. Modelo de Confianza
Hermes Agent es un agente personal de un solo inquilino. Su postura es
por capas, y las capas no tienen el mismo peso. Los reportadores y
operadores deben razonar sobre ellas en los mismos términos.
### 2.1 Definiciones
- **Proceso del agente.** El intérprete Python que ejecuta Hermes Agent,
incluyendo cualquier módulo Python que haya cargado (habilidades, plugins,
manejadores de hooks).
- **Backend de terminal.** Un objetivo de ejecución conectado para la
herramienta `terminal()`. El predeterminado ejecuta comandos directamente en el host.
Otros backends ejecutan comandos dentro de un contenedor, sandbox en la nube o
host remoto.
- **Superficie de entrada.** Cualquier canal a través del cual el contenido entra en el
contexto del agente: entrada del operador, fetches web, email, mensajes del gateway,
lecturas de archivos, respuestas del servidor MCP, resultados de herramientas.
- **Envolvente de confianza.** El conjunto de recursos a los que un operador ha otorgado
implícitamente acceso a Hermes Agent al ejecutarlo — típicamente, todo lo que
la propia cuenta de usuario del operador puede alcanzar en el host.
- **Postura.** Una declaración explícita en la documentación o código de Hermes Agent
sobre cómo una capa consumidora (adaptador, UI, escritor de archivos,
shell) debe tratar la salida del agente — ej. "el dashboard renderiza
la salida del agente como HTML inerte."
### 2.2 El Límite: Aislamiento a Nivel de SO
**El único límite de seguridad contra un LLM adversario es el
sistema operativo.** Nada dentro del proceso del agente constituye
contención — ni la puerta de aprobación, ni la redacción de salida, ni ningún
escáner de patrones, ni ninguna lista de herramientas permitidas. Cualquier componente dentro
del proceso que filtre la salida del LLM es una heurística operando sobre una
cadena influenciada por el atacante, y esta política lo trata como tal.
Hermes Agent admite dos posturas de aislamiento a nivel de SO. Abordan
diferentes amenazas y un operador debe elegir deliberadamente.
#### Aislamiento del backend de terminal
Un backend de terminal no predeterminado ejecuta comandos de shell emitidos por el LLM
dentro de un contenedor, host remoto o sandbox en la nube. Las herramientas de archivos
(`read_file`, `write_file`, `patch`) también se ejecutan a través de este backend,
ya que están implementadas sobre el contrato del shell — no pueden
alcanzar rutas que el backend no exponga.
Lo que confina: todo lo que el agente hace emitiendo operaciones de shell o
de archivos. Lo que **no** confina: todo lo que el agente hace en su propio
proceso Python. Eso incluye la herramienta de ejecución de código (lanzada como
subproceso del host), subprocesos MCP (lanzados desde el entorno del agente),
carga de plugins, despacho de hooks y carga de habilidades (todos importados en el
intérprete del agente).
El aislamiento del backend de terminal es la postura correcta cuando la preocupación es
que el LLM emita comandos de shell destructivos o escrituras de herramientas de archivo no deseadas, y el
operador es de confianza.
#### Envoltura del proceso completo
La envoltura del proceso completo ejecuta todo el árbol de procesos del agente dentro de un
sandbox. Cada ruta de código — shell, ejecución de código, MCP, herramientas de archivos,
plugins, hooks, carga de habilidades — está sujeta a la misma política de sistema de archivos,
red, proceso e (donde sea aplicable) inferencia.
Hermes Agent admite esto de dos maneras:
- **La propia imagen Docker de Hermes Agent y la configuración de Compose.** Más
liviana; el agente se ejecuta en un contenedor estándar con montajes y
política de red configurados por el operador.
- **[NVIDIA OpenShell](https://github.com/NVIDIA/OpenShell)**.
OpenShell proporciona sandboxes por sesión con política declarativa
a través de capas de sistema de archivos, red (egreso L7), proceso/syscall e
enrutamiento de inferencia. Las políticas de red e inferencia son
recargables en caliente. Las credenciales se inyectan desde un almacén de Proveedor
y nunca tocan el sistema de archivos del sandbox.
Bajo una envoltura de proceso completo, las heurísticas en proceso de Hermes Agent
(§2.4) funcionan como prevención de accidentes en capas sobre un límite real.
Esta es la postura soportada cuando el agente ingiere contenido de superficies
que el operador no controla — la web abierta, email entrante, canales de
múltiples usuarios, servidores MCP no confiables — y para despliegues en
producción o compartidos.
Los operadores que ejecuten el backend local predeterminado con superficies de entrada
no confiables, o que ejecuten un sandbox de backend de terminal esperando que contenga
rutas de código que no pasan por el shell, están operando fuera de la postura de
seguridad soportada.
### 2.3 Alcance de Credenciales
Hermes Agent filtra el entorno que pasa a sus componentes en proceso de
menor confianza: subprocesos de shell, subprocesos MCP y el proceso hijo
de ejecución de código. Las credenciales como las claves API del proveedor y los
tokens del gateway se eliminan por defecto; las variables declaradas explícitamente
por el operador o por una habilidad cargada se pasan.
Esto reduce la exfiltración casual. No es contención. Cualquier
componente que se ejecute dentro del proceso del agente (habilidades, plugins, manejadores
de hooks) puede leer lo que el agente mismo puede leer, incluidas las
credenciales en memoria. La mitigación contra un componente en proceso comprometido
es la revisión del operador antes de instalar (§2.4, §2.5), no el
saneamiento del entorno.
### 2.4 Heurísticas en Proceso
Los siguientes componentes filtran o advierten sobre el comportamiento del LLM. Son
útiles. No son límites.
- La **puerta de aprobación** detecta patrones de shell destructivos comunes
y le pide al operador confirmación antes de la ejecución. El shell es Turing-
completo; una lista de denegación sobre cadenas de shell es estructuralmente
incompleta. La puerta detecta errores en modo cooperativo, no salidas
adversariales.
- **La redacción de salida** elimina patrones similares a secretos de la visualización.
Un productor de salida motivado la evitará.
- **Skills Guard** escanea el contenido de habilidades instalables en busca de patrones
de inyección. Es una ayuda de revisión; el límite para habilidades de terceros
es la revisión del operador antes de instalar. Revisar una habilidad significa
leer su código Python y scripts, no solo su descripción SKILL.md —
las habilidades ejecutan Python arbitrario en el momento de importación.
### 2.5 Modelo de Confianza de Plugins
Los plugins se cargan en el proceso del agente y se ejecutan con todos los privilegios
del agente: pueden leer las mismas credenciales, llamar a las mismas
herramientas, registrar los mismos hooks e importar los mismos módulos que
cualquier cosa incluida en el árbol. El límite para los plugins de terceros es
la revisión del operador antes de instalar — la misma regla que las habilidades (§2.4),
mencionado por separado porque los plugins son arquitectónicamente más pesados
y a menudo incluyen sus propios servicios en segundo plano, oyentes de red
y dependencias.
Un plugin malicioso o con errores no es una vulnerabilidad en Hermes Agent
en sí mismo. Los errores en la ruta de instalación o descubrimiento de plugins de Hermes Agent
que impidan al operador ver lo que está instalando están en alcance bajo el §3.1.
### 2.6 Superficies Externas
Una **superficie externa** es cualquier canal fuera del proceso del agente local
a través del cual un llamador puede despachar trabajo del agente, resolver
aprobaciones o recibir salida del agente. Cada superficie tiene su propio
modelo de autorización, pero las reglas a continuación se aplican uniformemente.
**Superficies en Hermes Agent:**
- **Adaptadores de plataforma del gateway.** Integraciones de mensajería en
`gateway/platforms/` (Telegram, Discord, Slack, email, SMS, etc.)
y adaptadores análogos incluidos como plugins.
- **Superficies HTTP expuestas en red.** El adaptador del servidor API, el
plugin del dashboard, los endpoints HTTP del plugin kanban, y cualquier
otro plugin que vincule un socket de escucha.
- **Adaptadores de Editor / IDE.** El adaptador ACP (`acp_adapter/`) e
integraciones equivalentes que aceptan solicitudes de un proceso cliente local.
- **El gateway TUI (`tui_gateway/`).** Backend JSON-RPC para la
UI de terminal Ink, alcanzado a través de IPC local.
**Reglas uniformes:**
1. **Se requiere autorización en cada superficie que cruce un límite de confianza.** Para
superficies de mensajería y HTTP en red, el límite es la red: la autorización
significa una lista de llamadores permitidos configurada por el operador. Para superficies
de editor e IPC local (ACP, gateway TUI), el límite es la cuenta de usuario del host:
la autorización significa depender del control de acceso a nivel de SO (permisos
de archivos, vinculaciones solo a loopback) y no exponer la superficie más allá
del usuario local sin una capa de autenticación de red explícita.
2. **Se requiere una lista de permitidos para cada adaptador de red habilitado.**
Los adaptadores deben rechazar despachar trabajo del agente, resolver
aprobaciones o transmitir salida hasta que se establezca una lista de permitidos. Las rutas
de código que fallan de forma abierta cuando no hay lista de permitidos configurada son errores de código en
alcance bajo el §3.1.
3. **Los identificadores de sesión son manejadores de enrutamiento, no límites de autorización.**
Conocer el ID de sesión de otro llamador no otorga acceso a sus aprobaciones o salida;
la autorización siempre se vuelve a verificar contra la lista de permitidos (o equivalente
a nivel de SO).
4. **Dentro del conjunto autorizado, todos los llamadores tienen la misma confianza.**
Hermes Agent no modela capacidades por llamador dentro de un único adaptador.
Los operadores que necesiten separación de capacidades deben ejecutar instancias
de agente separadas con listas de permitidos separadas.
5. **Vincular una superficie solo local a una interfaz no-loopback es una decisión de
operador de emergencia (§3.2).** El dashboard y otros servidores HTTP de plugins
son predeterminados a loopback; exponerlos a través de `--host 0.0.0.0` o equivalente
hace que el fortalecimiento de exposición pública (§4) sea responsabilidad del operador.
---
## 3. Alcance
### 3.1 En Alcance
- Escape de una postura de aislamiento a nivel de SO declarada (§2.2): una
ruta de código controlada por el atacante alcanzando estado que la postura
afirmó confinar.
- Acceso no autorizado a superficie externa: un llamador fuera del conjunto de
autorización configurado (lista de permitidos, o equivalente a nivel de SO
para superficies de IPC local) despachando trabajo, recibiendo salida o
resolviendo aprobaciones (§2.6).
- Exfiltración de credenciales: filtración de credenciales del operador o
material de autorización de sesión a un destino fuera del envolvente de
confianza, a través de un mecanismo que debería haberlo prevenido
(error de saneamiento de entorno, registro del adaptador, error de transporte
que vacía credenciales a un upstream, etc.).
- Violaciones de la documentación del modelo de confianza: código que se comporta
contrariamente a lo que esta política, la propia documentación de Hermes Agent o
las expectativas razonables del operador predecirían — incluyendo casos donde
Hermes Agent ha documentado una postura sobre cómo su salida debe ser
renderizada por una capa consumidora (dashboard, adaptador de gateway,
escritor de archivos, shell) y una ruta de código rompe esa postura.
### 3.2 Fuera de Alcance
"Fuera de alcance" aquí significa "no es una vulnerabilidad de seguridad bajo esta
política." No significa "no vale la pena reportarlo." Las mejoras a las
heurísticas en proceso, ideas de fortalecimiento y correcciones de UX son bienvenidas como
issues o pull requests regulares — la puerta de aprobación siempre puede detectar
más patrones, la redacción puede volverse más inteligente, el comportamiento del adaptador
puede apretarse siempre. Estos elementos simplemente no van a través del canal de
divulgación privada y no reciben avisos.
- **Bypasses de heurísticas en proceso (§2.4)** — bypasses de regex de la puerta de aprobación,
bypasses de redacción, bypasses de patrones de Skills Guard, e informes
análogos contra heurísticas futuras. Estos componentes no son límites;
vencerlos no es una vulnerabilidad bajo esta política.
- **Inyección de prompts per se.** Hacer que el LLM emita salida inusual
— a través de contenido inyectado, alucinación, artefactos de entrenamiento,
o cualquier otra causa — no es en sí mismo una vulnerabilidad. "Logré
inyección de prompts" sin un resultado encadenado del §3.1 no es un informe
procesable bajo esta política.
- **Consecuencias de una postura de aislamiento elegida.** Los informes de que
una ruta de código que opera dentro del alcance de su postura puede hacer lo que esa
postura permite no son vulnerabilidades. Ejemplos: herramientas de shell o archivos
que alcanzan estado del host bajo el backend local; subprocesos de ejecución de código
o MCP que alcanzan estado del host bajo aislamiento de backend de terminal que solo
sandboxea el shell; informes cuyas precondiciones requieren acceso de escritura preexistente
a archivos de configuración o credenciales propiedad del operador (esos ya están dentro
del envolvente de confianza).
- **Configuraciones documentadas de emergencia.** Compensaciones seleccionadas por el operador
que deshabilitan explícitamente protecciones: `--insecure` y flags equivalentes
en el dashboard u otros componentes, aprobaciones deshabilitadas,
backend local en producción, perfiles de desarrollo que evitan
la seguridad de hermes-home, y similares. Los informes contra esas
configuraciones no son vulnerabilidades — eso es el trabajo del flag.
- **Habilidades y plugins contribuidos por la comunidad.** Las habilidades de terceros
(incluyendo el repositorio de habilidades de la comunidad) y los plugins de terceros
están en la superficie de revisión del operador, no en la superficie de confianza de Hermes Agent
(§2.4, §2.5). Una habilidad o plugin que haga algo
malicioso es el modo de falla esperado de uno que no fue
revisado, no una vulnerabilidad en Hermes Agent. Los errores en la ruta de
instalación de habilidades o plugins de Hermes Agent que impidan al
operador ver lo que está instalando están en alcance bajo el §3.1.
- **Exposición pública sin controles externos.** Exponer el
gateway o la API a la internet pública sin autenticación,
VPN o firewall.
- **Restricciones de lectura/escritura a nivel de herramienta en una postura donde el shell está
permitido.** Si una ruta es alcanzable a través de la herramienta terminal, los informes
de que otras herramientas de archivos pueden alcanzarla no añaden nada.
---
## 4. Fortalecimiento del Despliegue
La decisión de fortalecimiento más importante es hacer coincidir el aislamiento
(§2.2) con la confianza del contenido que el agente ingerirá. Más allá de eso:
- Ejecuta el agente como usuario no-root. La imagen de contenedor proporcionada
hace esto por defecto.
- Mantén las credenciales en el archivo de credenciales del operador con permisos
estrictos, nunca en la configuración principal, nunca en control de versiones.
Bajo OpenShell, usa el almacén de Proveedores en lugar de un archivo de
credenciales en disco.
- No expongas el gateway o la API a la internet pública sin
VPN, Tailscale o protección de firewall. Bajo OpenShell, usa la
capa de política de red para restringir el egreso.
- Configura una lista de llamadores permitidos para cada adaptador de red expuesto
que habilites (§2.6).
- Revisa las habilidades y plugins de terceros antes de instalar (§2.4,
§2.5). Para las habilidades, esto significa leer el Python y los scripts,
no solo SKILL.md. Los informes de Skills Guard y el registro de auditoría
de instalación son la superficie de revisión.
- Hermes Agent incluye guardias de cadena de suministro para lanzamientos de servidores
MCP y para cambios de dependencias / paquetes incluidos en CI; consulta
`CONTRIBUTING.es.md` para más detalles.
---
## 5. Divulgación
- **Ventana de divulgación coordinada:** 90 días desde el informe, o hasta que se
publique una corrección, lo que ocurra primero.
- **Canal:** el hilo GHSA o correspondencia por email con
security@nousresearch.com.
- **Crédito:** los reportadores reciben crédito en las notas de versión a menos que
se solicite anonimato.
+4 -5
View File
@@ -121,11 +121,10 @@ outside the supported security posture.
### 2.3 Credential Scoping
Hermes Agent filters the environment it passes to its lower-trust
in-process components: shell subprocesses, MCP subprocesses,
cron job scripts, and the code-execution child. Credentials like
provider API keys and gateway tokens are stripped by default;
variables explicitly declared by the operator or by a loaded
skill are passed through.
in-process components: shell subprocesses, MCP subprocesses, and
the code-execution child. Credentials like provider API keys and
gateway tokens are stripped by default; variables explicitly
declared by the operator or by a loaded skill are passed through.
This reduces casual exfiltration. It is not containment. Any
component running inside the agent process (skills, plugins, hook
-5
View File
@@ -23,11 +23,6 @@ except ModuleNotFoundError:
# new code but ``uv pip install -e .`` didn't finish. Missing bootstrap
# means UTF-8 stdio setup is skipped on Windows; POSIX is unaffected.
pass
else:
# Stop a ``utils/``/``proxy/``/``ui/`` package in the launch directory from
# shadowing Hermes's own modules — ``hermes acp`` can be started from any
# cwd, including a project that has same-named packages on its path.
hermes_bootstrap.harden_import_path()
import argparse
import asyncio
-127
View File
@@ -1,127 +0,0 @@
"""Derive ACP session-provenance metadata from the existing compression chain.
This is an additive Hermes extension surfaced under ACP ``_meta.hermes`` so
existing ACP clients ignore it. It carries no new persisted state: everything
is derived on demand from the ``sessions`` table (``parent_session_id`` /
``end_reason``), which already models compression-continuation chains.
The ACP/editor ``session_id`` stays the stable public handle. When context
compression rotates the internal Hermes head, ``build_session_provenance`` lets
a client see the previous/current internal ids and the lineage root without
parsing status text, guessing from token drops, or reading ``state.db``.
"""
from __future__ import annotations
from typing import Any, Dict, Optional
# Bound defensive walks; compression chains this deep are pathological.
_MAX_WALK = 100
def build_session_provenance(
db: Any,
acp_session_id: str,
current_hermes_session_id: str,
*,
previous_hermes_session_id: Optional[str] = None,
) -> Optional[Dict[str, Any]]:
"""Build ``_meta.hermes.sessionProvenance`` for an ACP session.
Args:
db: A ``SessionDB`` (must expose ``get_session``).
acp_session_id: The stable ACP/editor-facing session handle.
current_hermes_session_id: The live internal Hermes DB session id
(``state.agent.session_id``).
previous_hermes_session_id: The internal id from before the most recent
turn, when known. Supplied by ``prompt()`` to flag a rotation.
Returns:
A dict suitable for ``{"hermes": {"sessionProvenance": <dict>}}`` under
ACP ``_meta``, or ``None`` if the session can't be read.
"""
try:
row = db.get_session(current_hermes_session_id)
except Exception:
return None
if not row:
return None
parent_id = row.get("parent_session_id")
end_reason = row.get("end_reason")
# Walk parents to the lineage root and count compression depth. Only
# compression-split parents (parent.end_reason == 'compression') count
# toward depth — delegate/branch children share the parent_session_id
# column but are not compaction boundaries.
root_id = current_hermes_session_id
compression_depth = 0
cursor_parent = parent_id
seen = {current_hermes_session_id}
for _ in range(_MAX_WALK):
if not cursor_parent or cursor_parent in seen:
break
seen.add(cursor_parent)
try:
prow = db.get_session(cursor_parent)
except Exception:
prow = None
if not prow:
break
root_id = cursor_parent
if prow.get("end_reason") == "compression":
compression_depth += 1
cursor_parent = prow.get("parent_session_id")
# A session is a compression continuation when its parent was ended with
# end_reason='compression'. Determine that from the immediate parent.
is_continuation = False
if parent_id:
try:
immediate_parent = db.get_session(parent_id)
except Exception:
immediate_parent = None
if immediate_parent and immediate_parent.get("end_reason") == "compression":
is_continuation = True
rotated = bool(
previous_hermes_session_id
and previous_hermes_session_id != current_hermes_session_id
)
provenance: Dict[str, Any] = {
"acpSessionId": acp_session_id,
"currentHermesSessionId": current_hermes_session_id,
"rootHermesSessionId": root_id,
"parentHermesSessionId": parent_id,
"sessionKind": "continuation" if is_continuation else "root",
"compressionDepth": compression_depth,
}
if previous_hermes_session_id:
provenance["previousHermesSessionId"] = previous_hermes_session_id
if rotated:
# The head moved during the last turn. The only mechanism that rotates
# the internal id mid-turn is compression-driven session splitting.
provenance["reason"] = "compression"
provenance["creatorKind"] = "compression"
return provenance
def session_provenance_meta(
db: Any,
acp_session_id: str,
current_hermes_session_id: str,
*,
previous_hermes_session_id: Optional[str] = None,
) -> Optional[Dict[str, Any]]:
"""Return a ready ``_meta`` payload: ``{"hermes": {"sessionProvenance": ...}}``."""
prov = build_session_provenance(
db,
acp_session_id,
current_hermes_session_id,
previous_hermes_session_id=previous_hermes_session_id,
)
if prov is None:
return None
return {"hermes": {"sessionProvenance": prov}}
+5 -112
View File
@@ -71,7 +71,6 @@ from acp_adapter.events import (
make_tool_progress_cb,
)
from acp_adapter.permissions import make_approval_callback
from acp_adapter.provenance import session_provenance_meta
from acp_adapter.session import SessionManager, SessionState, _expand_acp_enabled_toolsets
from acp_adapter.tools import build_tool_complete, build_tool_start
@@ -710,39 +709,8 @@ class HermesACPAgent(acp.Agent):
exc_info=True,
)
def _provenance_meta(
self,
acp_session_id: str,
current_hermes_session_id: str,
previous_hermes_session_id: Optional[str] = None,
) -> Optional[dict]:
"""Best-effort ``_meta.hermes.sessionProvenance`` for an ACP session."""
try:
return session_provenance_meta(
self.session_manager._get_db(),
acp_session_id,
current_hermes_session_id,
previous_hermes_session_id=previous_hermes_session_id,
)
except Exception:
logger.debug(
"Could not build ACP session provenance for %s", acp_session_id, exc_info=True
)
return None
async def _send_session_info_update(
self,
session_id: str,
*,
current_hermes_session_id: Optional[str] = None,
previous_hermes_session_id: Optional[str] = None,
) -> None:
"""Send ACP native session metadata after Hermes changes it.
When the internal Hermes head rotated (e.g. compression-driven session
split during a turn), pass ``previous_hermes_session_id`` so the
attached ``_meta.hermes.sessionProvenance`` flags the rotation reason.
"""
async def _send_session_info_update(self, session_id: str) -> None:
"""Send ACP native session metadata after Hermes changes it."""
if not self._conn:
return
try:
@@ -759,16 +727,10 @@ class HermesACPAgent(acp.Agent):
# the updated_at since we're emitting this notification precisely
# because the title was just refreshed.
updated_at = datetime.now(timezone.utc).isoformat()
meta = self._provenance_meta(
session_id,
current_hermes_session_id or session_id,
previous_hermes_session_id,
)
update = SessionInfoUpdate(
session_update="session_info_update",
title=title if isinstance(title, str) and title.strip() else None,
updated_at=updated_at,
field_meta=meta,
)
try:
await self._conn.session_update(
@@ -824,7 +786,6 @@ class HermesACPAgent(acp.Agent):
try:
from model_tools import get_tool_definitions
from agent.memory_manager import inject_memory_provider_tools
enabled_toolsets = _expand_acp_enabled_toolsets(
getattr(state.agent, "enabled_toolsets", None) or ["hermes-acp"],
@@ -840,7 +801,6 @@ class HermesACPAgent(acp.Agent):
state.agent.valid_tool_names = {
tool["function"]["name"] for tool in state.agent.tools or []
}
inject_memory_provider_tools(state.agent)
invalidate = getattr(state.agent, "_invalidate_system_prompt", None)
if callable(invalidate):
invalidate()
@@ -1121,9 +1081,6 @@ class HermesACPAgent(acp.Agent):
session_id=state.session_id,
models=self._build_model_state(state),
modes=self._session_modes(state),
field_meta=self._provenance_meta(
state.session_id, getattr(state.agent, "session_id", state.session_id)
),
)
async def load_session(
@@ -1168,9 +1125,6 @@ class HermesACPAgent(acp.Agent):
return LoadSessionResponse(
models=self._build_model_state(state),
modes=self._session_modes(state),
field_meta=self._provenance_meta(
session_id, getattr(state.agent, "session_id", session_id)
),
)
async def resume_session(
@@ -1203,9 +1157,6 @@ class HermesACPAgent(acp.Agent):
return ResumeSessionResponse(
models=self._build_model_state(state),
modes=self._session_modes(state),
field_meta=self._provenance_meta(
state.session_id, getattr(state.agent, "session_id", state.session_id)
),
)
async def cancel(self, session_id: str, **kwargs: Any) -> None:
@@ -1543,11 +1494,6 @@ class HermesACPAgent(acp.Agent):
logger.debug("Could not clear ACP session context", exc_info=True)
try:
# Snapshot the internal Hermes DB session id before the turn so we
# can detect a compression-driven session rotation afterwards. The
# ACP `session_id` stays the stable client handle; agent.session_id
# is the live internal head that compression may rotate.
pre_turn_hermes_id = getattr(state.agent, "session_id", None)
# Wrap the executor call in a fresh copy of the current context so
# concurrent ACP sessions on the shared ThreadPoolExecutor don't
# stomp on each other's ContextVar writes (HERMES_SESSION_KEY in
@@ -1566,41 +1512,8 @@ class HermesACPAgent(acp.Agent):
# Persist updated history so sessions survive process restarts.
self.session_manager.save_session(session_id)
# Detect a compression-driven internal session rotation. If the agent's
# DB head moved during the turn, emit a session_info_update carrying
# _meta.hermes.sessionProvenance so ACP clients can render the boundary
# and keep old/new ids in lineage. The ACP session_id is unchanged.
post_turn_hermes_id = getattr(state.agent, "session_id", None)
if (
conn
and post_turn_hermes_id
and pre_turn_hermes_id
and post_turn_hermes_id != pre_turn_hermes_id
):
try:
await self._send_session_info_update(
session_id,
current_hermes_session_id=post_turn_hermes_id,
previous_hermes_session_id=pre_turn_hermes_id,
)
except Exception:
logger.debug(
"Could not emit ACP provenance update after rotation for %s",
session_id,
exc_info=True,
)
final_response = result.get("final_response", "")
cancelled = bool(state.cancel_event and state.cancel_event.is_set())
interrupted = bool(result.get("interrupted")) or cancelled
# Hermes' local "waiting for model response" interrupt status is metadata,
# not assistant prose — clients get cancellation from stop_reason instead.
from agent.conversation_loop import INTERRUPT_WAITING_FOR_MODEL_PREFIX
suppress_interrupt_response = interrupted and final_response.startswith(
INTERRUPT_WAITING_FOR_MODEL_PREFIX
)
if final_response and not suppress_interrupt_response:
if final_response:
try:
from agent.title_generator import maybe_auto_title
@@ -1621,12 +1534,7 @@ class HermesACPAgent(acp.Agent):
)
except Exception:
logger.debug("Failed to auto-title ACP session %s", session_id, exc_info=True)
if (
final_response
and conn
and not suppress_interrupt_response
and (not streamed_message or result.get("response_transformed"))
):
if final_response and conn and (not streamed_message or result.get("response_transformed")):
# Deliver the final response when streaming did not already send it,
# or when a plugin hook transformed the response after streaming
# finished (e.g. transform_llm_output) — otherwise the appended /
@@ -1668,7 +1576,7 @@ class HermesACPAgent(acp.Agent):
await self._send_usage_update(state)
stop_reason = "cancelled" if cancelled else "end_turn"
stop_reason = "cancelled" if state.cancel_event and state.cancel_event.is_set() else "end_turn"
return PromptResponse(stop_reason=stop_reason, usage=usage)
# ---- Slash commands (headless) -------------------------------------------
@@ -1781,25 +1689,10 @@ class HermesACPAgent(acp.Agent):
def _cmd_tools(self, args: str, state: SessionState) -> str:
try:
from model_tools import get_tool_definitions
from types import SimpleNamespace
from agent.memory_manager import inject_memory_provider_tools
toolsets = _expand_acp_enabled_toolsets(
getattr(state.agent, "enabled_toolsets", None) or ["hermes-acp"]
)
tools = get_tool_definitions(enabled_toolsets=toolsets, quiet_mode=True)
tool_view = SimpleNamespace(
tools=list(tools or []),
valid_tool_names={
tool.get("function", {}).get("name")
for tool in tools or []
if isinstance(tool, dict)
},
enabled_toolsets=toolsets,
_memory_manager=getattr(state.agent, "_memory_manager", None),
)
inject_memory_provider_tools(tool_view)
tools = tool_view.tools
if not tools:
return "No tools available."
lines = [f"Available tools ({len(tools)}):"]
+6 -5
View File
@@ -457,7 +457,12 @@ class SessionManager:
else:
# Update model_config (contains cwd) if changed.
try:
db.update_session_meta(state.session_id, cwd_json, model_str)
with db._lock:
db._conn.execute(
"UPDATE sessions SET model_config = ?, model = COALESCE(?, model) WHERE id = ?",
(cwd_json, model_str, state.session_id),
)
db._conn.commit()
except Exception:
logger.debug("Failed to update ACP session metadata", exc_info=True)
@@ -617,10 +622,6 @@ class SessionManager:
_register_task_cwd(session_id, cwd)
agent = AIAgent(**kwargs)
# Codex app-server sessions are spawned lazily on the first turn. Stamp
# the ACP workspace onto the agent so the Codex runtime starts from the
# editor/session cwd instead of the Hermes daemon's process cwd.
agent.session_cwd = cwd
# ACP stdio transport requires stdout to remain protocol-only JSON-RPC.
# Route any incidental human-readable agent output to stderr instead.
agent._print_fn = _acp_stderr_print
+1 -1
View File
@@ -74,7 +74,7 @@ _POLISHED_TOOLS = {
"kanban_create", "kanban_show", "kanban_comment", "kanban_complete",
"kanban_block", "kanban_link", "kanban_heartbeat",
"yb_query_group_info", "yb_query_group_members", "yb_search_sticker",
"yb_send_dm", "yb_send_sticker",
"yb_send_dm", "yb_send_sticker", "mixture_of_agents",
}
+2 -2
View File
@@ -1,7 +1,7 @@
{
"id": "hermes-agent",
"name": "Hermes Agent",
"version": "0.17.0",
"version": "0.15.1",
"description": "Self-improving open-source AI agent by Nous Research with ACP editor integration, persistent memory, skills, and rich tool support.",
"repository": "https://github.com/NousResearch/hermes-agent",
"website": "https://hermes-agent.nousresearch.com/docs/user-guide/features/acp",
@@ -9,7 +9,7 @@
"license": "MIT",
"distribution": {
"uvx": {
"package": "hermes-agent[acp]==0.17.0",
"package": "hermes-agent[acp]==0.15.1",
"args": ["hermes-acp"]
}
}
+1 -313
View File
@@ -1,10 +1,8 @@
from __future__ import annotations
import logging
import math
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import TYPE_CHECKING, Any, Optional
from typing import Any, Optional
import httpx
@@ -12,11 +10,6 @@ from agent.anthropic_adapter import _is_oauth_token, resolve_anthropic_token
from hermes_cli.auth import _read_codex_tokens, resolve_codex_runtime_credentials
from hermes_cli.runtime_provider import resolve_runtime_provider
if TYPE_CHECKING:
from typing import TypeGuard
logger = logging.getLogger(__name__)
def _utc_now() -> datetime:
return datetime.now(timezone.utc)
@@ -120,311 +113,6 @@ def render_account_usage_lines(snapshot: Optional[AccountUsageSnapshot], *, mark
return lines
def _fmt_usd(d: float) -> str:
return f"${d:,.2f}"
def _is_finite_num(v: Any) -> TypeGuard[float]:
"""True iff v is a real numeric value (int or float, not bool, not NaN/Inf).
Typed as a ``TypeGuard[float]`` so the type checker narrows ``v`` to a real
number in the positive branch — callers can then do arithmetic / pass it to
``_fmt_usd`` without a None-operand warning.
"""
return isinstance(v, (int, float)) and not isinstance(v, bool) and math.isfinite(v)
def build_nous_credits_snapshot(account_info) -> Optional[AccountUsageSnapshot]:
"""Map a NousPortalAccountInfo into an AccountUsageSnapshot for /usage.
Shows dollar magnitudes (subscription / top-up / total) + renewal date + a
portal CTA. When the portal supplies a subscription denominator
(``monthly_credits``), also emits a subscription-usage window so the renderer
shows a real ``% used`` gauge; when it's absent (older portals) the view
gracefully degrades to magnitudes-only. Returns None when there's no usable
account info to show (fail-open: caller just shows nothing).
"""
try:
from hermes_cli.nous_account import nous_portal_topup_url
if account_info is None or not getattr(account_info, "logged_in", False):
return None
access = getattr(account_info, "paid_service_access_info", None)
sub = getattr(account_info, "subscription", None)
windows: list[AccountUsageWindow] = []
details: list[str] = []
# Subscription usage gauge — only when the portal supplies a positive
# monthly_credits denominator AND a finite remaining balance that does
# not exceed the cap. Money math is on float dollars (allowed: numeric
# account fields, NOT a server-provided *_usd string). used = cap -
# remaining; clamp [0,100] so a debt balance (remaining < 0) reads 100%.
# Excluded on purpose:
# - non-finite values (NaN/Infinity slip past isinstance and json.loads
# parses bare NaN/Infinity by default) → would render "$nan"/"$inf"
# and a falsely-confident gauge;
# - remaining > cap (rollover balance spanning the period) → monthly_credits
# is no longer a meaningful denominator, and "$X of $Y left" with X>Y
# reads as a contradiction. Both fall back to the magnitudes lines.
if sub is not None:
monthly_credits = getattr(sub, "monthly_credits", None)
sub_remaining = getattr(sub, "credits_remaining", None)
if (
_is_finite_num(monthly_credits)
and monthly_credits > 0
and _is_finite_num(sub_remaining)
and sub_remaining <= monthly_credits
):
used = monthly_credits - sub_remaining
used_pct = max(0.0, min(100.0, used / monthly_credits * 100.0))
windows.append(
AccountUsageWindow(
label="Subscription",
used_percent=used_pct,
detail=f"{_fmt_usd(sub_remaining)} of {_fmt_usd(monthly_credits)} left",
)
)
if access is not None:
sub_credits = getattr(access, "subscription_credits_remaining", None)
if _is_finite_num(sub_credits):
details.append(f"Subscription credits: {_fmt_usd(sub_credits)}")
purchased = getattr(access, "purchased_credits_remaining", None)
if _is_finite_num(purchased):
details.append(f"Top-up credits: {_fmt_usd(purchased)}")
total_usable = getattr(access, "total_usable_credits", None)
if _is_finite_num(total_usable):
details.append(f"Total usable: {_fmt_usd(total_usable)}")
if sub is not None:
rollover = getattr(sub, "rollover_credits", None)
if _is_finite_num(rollover) and rollover > 0:
details.append(f"Rollover: {_fmt_usd(rollover)}")
period_end = getattr(sub, "current_period_end", None)
if period_end:
details.append(f"Renews: {period_end}")
paid = getattr(account_info, "paid_service_access", None)
if paid is False:
details.append("Status: access depleted — top up to restore")
if not windows and not details:
return None
details.append(f"Top up: {nous_portal_topup_url(account_info)}")
details.append("(or run /credits)")
plan = getattr(sub, "plan", None) if sub is not None else None
return AccountUsageSnapshot(
provider="nous",
source="portal-account",
fetched_at=_utc_now(),
title="Nous credits",
plan=plan,
windows=tuple(windows),
details=tuple(details),
)
except (AttributeError, TypeError):
return None
def nous_credits_lines(*, markdown: bool = False, timeout: float = 10.0) -> list[str]:
"""Return rendered Nous-credits /usage lines, or [] when there's nothing to show.
Account-independent of any live agent: gated on "a Nous account is logged in"
(a cheap local auth-state check), then a wall-clock-bounded portal fetch. Shared
by the CLI ``_show_usage`` and the TUI ``session.usage`` RPC so both surfaces show
the same block regardless of session API-call count or resume state. Fail-open:
any auth/portal hiccup or timeout returns [] (the caller shows nothing).
Dev override: when HERMES_DEV_CREDITS_FIXTURE selects a fixture state, /usage
renders from that fixture instead of the real portal (so the block + gauge are
testable without a live account). Throwaway scaffolding.
"""
# Dev fixture short-circuit — render /usage from the injected state, no portal.
try:
from agent.credits_tracker import dev_fixture_credits_state
fixture = dev_fixture_credits_state()
except Exception:
fixture = None
if fixture is not None:
snapshot = _snapshot_from_credits_state(fixture)
return render_account_usage_lines(snapshot, markdown=markdown)
try:
from hermes_cli.auth import get_provider_auth_state
tok = (get_provider_auth_state("nous") or {}).get("access_token")
if not (isinstance(tok, str) and tok.strip()):
return []
except Exception:
return []
try:
import concurrent.futures
from hermes_cli.nous_account import get_nous_portal_account_info
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
account = pool.submit(
get_nous_portal_account_info, force_fresh=True
).result(timeout=timeout)
snapshot = build_nous_credits_snapshot(account)
return render_account_usage_lines(snapshot, markdown=markdown)
except Exception:
# Fail-open (caller shows nothing), but leave a breadcrumb so a dead
# /usage credits block is diagnosable in agent.log without a dev flag.
logger.debug("credits ▸ /usage portal fetch/render failed (fail-open)", exc_info=True)
return []
def _snapshot_from_credits_state(state) -> Optional[AccountUsageSnapshot]:
"""Map a header-shaped CreditsState (e.g. a dev fixture) to the /usage snapshot.
Renders the same magnitudes + monthly-grant % window the portal path produces,
so HERMES_DEV_CREDITS_FIXTURE can exercise /usage without a live account. The
*_usd strings are mock display values here (not server balance to compute on);
the % comes from CreditsState.used_fraction (micros math). Fail-open → None.
"""
try:
if state is None:
return None
windows: list[AccountUsageWindow] = []
details: list[str] = []
uf = getattr(state, "used_fraction", None)
if isinstance(uf, (int, float)) and math.isfinite(uf):
cap_usd = getattr(state, "subscription_limit_usd", None)
sub_usd = getattr(state, "subscription_usd", None)
detail = None
if sub_usd and cap_usd:
detail = f"${sub_usd} of ${cap_usd} left"
windows.append(
AccountUsageWindow(
label="Subscription",
used_percent=max(0.0, min(100.0, uf * 100.0)),
detail=detail,
)
)
sub_usd = getattr(state, "subscription_usd", None)
if sub_usd:
details.append(f"Subscription credits: ${sub_usd}")
purchased_usd = getattr(state, "purchased_usd", None)
if purchased_usd:
details.append(f"Top-up credits: ${purchased_usd}")
remaining_usd = getattr(state, "remaining_usd", None)
if remaining_usd:
details.append(f"Total usable: ${remaining_usd}")
if getattr(state, "paid_access", True) is False:
details.append("Status: access depleted — top up to restore")
if not windows and not details:
return None
details.append("(dev fixture — HERMES_DEV_CREDITS_FIXTURE)")
return AccountUsageSnapshot(
provider="nous",
source="dev-fixture",
fetched_at=_utc_now(),
title="Nous credits",
windows=tuple(windows),
details=tuple(details),
)
except (AttributeError, TypeError):
return None
@dataclass(frozen=True)
class CreditsView:
"""Surface-agnostic data for the ``/credits`` command.
One portal fetch, one parse — consumed identically by the CLI panel, the
gateway button, and any other money surface. Fail-open: when not logged in
or the portal is unreachable, ``logged_in`` is False / ``topup_url`` is None
and callers degrade gracefully.
"""
logged_in: bool
balance_lines: tuple[str, ...] = ()
identity_line: Optional[str] = None
topup_url: Optional[str] = None
depleted: bool = False
def build_credits_view(*, markdown: bool = False, timeout: float = 10.0) -> CreditsView:
"""Build the /credits view: balance block + identity line + top-up URL.
Reuses the same account fetch + snapshot + URL builder as the /usage credits
block, so the numbers always match. The balance block is the rendered
snapshot MINUS its trailing top-up/command-hint lines (the /credits surface
supplies its own affordance). Fail-open → ``CreditsView(logged_in=False)``.
"""
not_logged_in = CreditsView(logged_in=False)
try:
from hermes_cli.auth import get_provider_auth_state
tok = (get_provider_auth_state("nous") or {}).get("access_token")
if not (isinstance(tok, str) and tok.strip()):
return not_logged_in
except Exception:
return not_logged_in
try:
import concurrent.futures
from hermes_cli.nous_account import (
get_nous_portal_account_info,
nous_portal_topup_url,
)
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
account = pool.submit(get_nous_portal_account_info, force_fresh=True).result(
timeout=timeout
)
except Exception:
logger.debug("credits ▸ /credits portal fetch failed (fail-open)", exc_info=True)
return not_logged_in
if account is None or not getattr(account, "logged_in", False):
return not_logged_in
snapshot = build_nous_credits_snapshot(account)
# Balance lines = the snapshot block minus the two trailing affordance lines
# ("Top up: <url>" + "(or run /credits)") that build_nous_credits_snapshot
# appends for the /usage surface. /credits renders its own button/panel.
balance_lines: list[str] = []
if snapshot is not None:
rendered = render_account_usage_lines(snapshot, markdown=markdown)
balance_lines = [
line
for line in rendered
if not line.lstrip().startswith("Top up:")
and not line.lstrip().startswith("(or run")
]
# Identity line — shown before any open (roadmap §4.4).
email = getattr(account, "email", None)
org_name = getattr(account, "org_name", None)
who: list[str] = []
if email:
who.append(str(email))
if org_name:
who.append(f"org {org_name}")
identity_line = ("Topping up as " + " / ".join(who)) if who else None
return CreditsView(
logged_in=True,
balance_lines=tuple(balance_lines),
identity_line=identity_line,
topup_url=nous_portal_topup_url(account),
depleted=getattr(account, "paid_service_access", None) is False,
)
def _resolve_codex_usage_url(base_url: str) -> str:
normalized = (base_url or "").strip().rstrip("/")
if not normalized:
+53 -278
View File
@@ -27,7 +27,7 @@ import threading
import time
import uuid
from datetime import datetime
from typing import Any, Callable, Dict, List, Optional
from typing import Any, Dict, List, Optional
from urllib.parse import urlparse, parse_qs, urlunparse
from agent.context_compressor import ContextCompressor
@@ -50,7 +50,7 @@ from agent.tool_guardrails import (
from hermes_cli.config import cfg_get
from hermes_cli.timeouts import get_provider_request_timeout
from hermes_constants import get_hermes_home
from utils import base_url_host_matches, is_truthy_value
from utils import base_url_host_matches
# Use the same logger name as run_agent so tests patching ``run_agent.logger``
# capture our warnings. (run_agent.py also does
@@ -68,24 +68,6 @@ def _ra():
return run_agent
def _build_codex_gpt55_autoraise_notice(autoraise: Dict[str, float]) -> str:
"""Build the one-time notice shown when Codex gpt-5.5 raises compaction.
``autoraise`` is ``{"from": <old_ratio>, "to": <new_ratio>}``. The same
text is printed inline for CLI users and replayed via ``status_callback``
for gateway users, so it must be self-contained and include the exact
opt-back-out command.
"""
from_pct = int(round(autoraise["from"] * 100))
to_pct = int(round(autoraise["to"] * 100))
return (
f"ℹ Codex gpt-5.5 caps context at 272K, so auto-compaction was raised "
f"to {to_pct}% (from {from_pct}%) to use more of the window before "
f"summarizing.\n"
f" Opt back out: hermes config set compression.codex_gpt55_autoraise false"
)
def _normalized_custom_base_url(value: Any) -> str:
if not isinstance(value, str):
return ""
@@ -106,12 +88,7 @@ def _custom_provider_extra_body_for_agent(
base_url: str,
custom_providers: List[Dict[str, Any]],
) -> Optional[Dict[str, Any]]:
provider_norm = (provider or "").strip().lower()
if provider_norm == "custom":
provider_key_filter = ""
elif provider_norm.startswith("custom:"):
provider_key_filter = provider_norm.split(":", 1)[1].strip()
else:
if (provider or "").strip().lower() != "custom":
return None
target_url = _normalized_custom_base_url(base_url)
@@ -122,13 +99,6 @@ def _custom_provider_extra_body_for_agent(
for entry in custom_providers or []:
if not isinstance(entry, dict):
continue
if provider_key_filter:
entry_keys = {
str(entry.get("provider_key", "") or "").strip().lower(),
str(entry.get("name", "") or "").strip().lower(),
}
if provider_key_filter not in entry_keys:
continue
if _normalized_custom_base_url(entry.get("base_url")) != target_url:
continue
extra_body = entry.get("extra_body")
@@ -181,7 +151,6 @@ def init_agent(
save_trajectories: bool = False,
verbose_logging: bool = False,
quiet_mode: bool = False,
tool_progress_mode: str = "all",
ephemeral_system_prompt: str = None,
log_prefix_chars: int = 100,
log_prefix: str = "",
@@ -199,15 +168,11 @@ def init_agent(
thinking_callback: callable = None,
reasoning_callback: callable = None,
clarify_callback: callable = None,
read_terminal_callback: callable = None,
step_callback: callable = None,
stream_delta_callback: callable = None,
interim_assistant_callback: callable = None,
tool_gen_callback: callable = None,
status_callback: callable = None,
notice_callback: callable = None,
notice_clear_callback: callable = None,
event_callback: Optional[Callable[[str, dict], None]] = None,
max_tokens: int = None,
reasoning_config: Dict[str, Any] = None,
service_tier: str = None,
@@ -277,8 +242,7 @@ def init_agent(
output_config.format instead of a trailing-assistant prefill.
platform (str): The interface platform the user is on (e.g. "cli", "telegram", "discord", "whatsapp").
Used to inject platform-specific formatting hints into the system prompt.
skip_context_files (bool): If True, skip auto-injection of project context files
(SOUL.md, .hermes.md, AGENTS.md, CLAUDE.md, .cursorrules) from the cwd / HERMES_HOME
skip_context_files (bool): If True, skip auto-injection of SOUL.md, AGENTS.md, and .cursorrules
into the system prompt. Use this for batch processing and data generation to avoid
polluting trajectories with user-specific persona or project instructions.
load_soul_identity (bool): If True, still use ~/.hermes/SOUL.md as the primary
@@ -296,7 +260,6 @@ def init_agent(
agent.save_trajectories = save_trajectories
agent.verbose_logging = verbose_logging
agent.quiet_mode = quiet_mode
agent.tool_progress_mode = tool_progress_mode
agent.ephemeral_system_prompt = ephemeral_system_prompt
agent.platform = platform # "cli", "telegram", "discord", "whatsapp", etc.
agent._user_id = user_id # Platform user identifier (gateway sessions)
@@ -313,7 +276,6 @@ def init_agent(
# would mangle the escape sequences. None = use builtins.print.
agent._print_fn = None
agent.background_review_callback = None # Optional sync callback for gateway delivery
agent.memory_notifications = "on" # Memory update notifications: "off", "on", "verbose"
agent.skip_context_files = skip_context_files
agent.load_soul_identity = load_soul_identity
agent.pass_session_id = pass_session_id
@@ -433,14 +395,10 @@ def init_agent(
agent.thinking_callback = thinking_callback
agent.reasoning_callback = reasoning_callback
agent.clarify_callback = clarify_callback
agent.read_terminal_callback = read_terminal_callback
agent.step_callback = step_callback
agent.stream_delta_callback = stream_delta_callback
agent.interim_assistant_callback = interim_assistant_callback
agent.status_callback = status_callback
agent.notice_callback = notice_callback
agent.notice_clear_callback = notice_clear_callback
agent.event_callback = event_callback
agent.tool_gen_callback = tool_gen_callback
@@ -544,27 +502,11 @@ def init_agent(
agent._last_activity_desc: str = "initializing"
agent._current_tool: str | None = None
agent._api_call_count: int = 0
# Opt-out flag for the between-turns MCP tool refresh (build_turn_context).
# Set on internal forks (e.g. background_review) that must keep ``tools[]``
# byte-identical to a parent for provider cache parity.
agent._skip_mcp_refresh = False
# Registry generation the current tool snapshot was derived from. Lets a
# late/concurrent refresh reject a stale (older-generation) rebuild instead
# of clobbering a newer one. Set adjacent to the tool snapshot below.
agent._tool_snapshot_generation = 0
# Rate limit tracking — updated from x-ratelimit-* response headers
# after each API call. Accessed by /usage slash command.
agent._rate_limit_state: Optional["RateLimitState"] = None
# Credits tracking (dev-only, L0 usage-aware-credits) — updated from
# x-nous-credits-* response headers after each API call. Session-start
# remaining is latched the first time a header is ever seen so we can
# report cumulative micros spent. Surfaced behind HERMES_DEV_CREDITS.
agent._credits_state = None
agent._credits_session_start_micros = None
# Threshold-notice latch (L4): active sticky-notice keys + the warn90 crossing gate.
agent._credits_latch = {"active": set(), "seen_below_90": False, "usage_band": None}
# OpenRouter response cache hit counter — incremented when
# X-OpenRouter-Cache-Status: HIT is seen in streaming response headers.
agent._or_cache_hits: int = 0
@@ -619,7 +561,6 @@ def init_agent(
# (e.g. CLI voice mode adds a temporary prefix for the live call only).
agent._persist_user_message_idx = None
agent._persist_user_message_override = None
agent._persist_user_message_timestamp = None
# Cache anthropic image-to-text fallbacks per image payload/URL so a
# single tool loop does not repeatedly re-run auxiliary vision on the
@@ -719,55 +660,6 @@ def init_agent(
print("🔑 Using credentials: Microsoft Entra ID")
elif isinstance(effective_key, str) and len(effective_key) > 12:
print(f"🔑 Using token: {effective_key[:8]}...{effective_key[-4:]}")
elif agent.provider == "moa":
from agent.moa_loop import MoAClient
agent.api_mode = "chat_completions"
# Route reference-model outputs to the agent's tool_progress_callback so
# every surface that already consumes it (CLI spinner/scrollback, TUI,
# desktop, gateway) can show each reference's answer as a labelled block
# before the aggregator acts. The facade emits "moa.reference" and
# "moa.aggregating" events; we forward them through the same callback
# the tool lifecycle uses. Best-effort and cache-safe — these are
# display-only events, they never touch the message history.
def _moa_reference_relay(event: str, **kwargs: Any) -> None:
cb = getattr(agent, "tool_progress_callback", None)
if cb is None:
return
try:
if event == "moa.reference":
label = str(kwargs.get("label") or "")
text = str(kwargs.get("text") or "")
idx = kwargs.get("index")
count = kwargs.get("count")
cb(
"moa.reference",
label,
text,
None,
moa_index=idx,
moa_count=count,
)
elif event == "moa.aggregating":
cb(
"moa.aggregating",
str(kwargs.get("aggregator") or ""),
None,
None,
moa_ref_count=kwargs.get("ref_count"),
)
except Exception:
pass
agent.client = MoAClient(
agent.model or "default",
reference_callback=_moa_reference_relay,
)
agent._client_kwargs = {}
agent.api_key = api_key or "moa-virtual-provider"
agent.base_url = "moa://local"
if not agent.quiet_mode:
print(f"🤖 AI Agent initialized with MoA preset: {agent.model}")
elif agent.api_mode == "bedrock_converse":
# AWS Bedrock — uses boto3 directly, no OpenAI client needed.
# Region is extracted from the base_url or defaults to us-east-1.
@@ -869,8 +761,6 @@ def init_agent(
# _custom_headers; older/mocked clients may expose
# _default_headers instead.
_routed_headers = getattr(_routed_client, "_custom_headers", None)
if not _routed_headers:
_routed_headers = getattr(_routed_client, "default_headers", None)
if not _routed_headers:
_routed_headers = getattr(_routed_client, "_default_headers", None)
if _routed_headers:
@@ -924,8 +814,6 @@ def init_agent(
if _provider_timeout is not None:
client_kwargs["timeout"] = _provider_timeout
_fb_headers = getattr(_fb_client, "_custom_headers", None)
if not _fb_headers:
_fb_headers = getattr(_fb_client, "default_headers", None)
if not _fb_headers:
_fb_headers = getattr(_fb_client, "_default_headers", None)
if _fb_headers:
@@ -966,20 +854,9 @@ def init_agent(
headers["x-anthropic-beta"] = _FINE_GRAINED
client_kwargs["default_headers"] = headers
# User-configured request headers (model.default_headers in
# config.yaml) override provider/SDK defaults. Lets custom
# OpenAI-compatible endpoints behind a gateway/WAF that rejects the
# OpenAI SDK's identifying headers swap in a plain User-Agent. (#40033)
# client_kwargs is the same dict object as agent._client_kwargs, so
# this mutation is reflected in the client built just below.
agent._apply_user_default_headers()
agent.api_key = client_kwargs.get("api_key", "")
agent.base_url = client_kwargs.get("base_url", agent.base_url)
try:
from agent.ssl_guard import verify_ca_bundle_with_fallback
verify_ca_bundle_with_fallback()
agent.client = agent._create_openai_client(client_kwargs, reason="agent_init", shared=True)
if not agent.quiet_mode:
print(f"🤖 AI Agent initialized with model: {agent.model}")
@@ -1026,14 +903,7 @@ def init_agent(
print(f"🔄 Fallback chain ({len(agent._fallback_chain)} providers): " +
" → ".join(f"{f['model']} ({f['provider']})" for f in agent._fallback_chain))
# Get available tools with filtering. Capture the registry generation this
# snapshot is derived from FIRST, so a later concurrent refresh can tell
# whether it holds a newer or staler view (see refresh_agent_mcp_tools).
try:
from tools.registry import registry as _snapshot_registry
agent._tool_snapshot_generation = _snapshot_registry._generation
except Exception:
agent._tool_snapshot_generation = 0
# Get available tools with filtering
agent.tools = _ra().get_tool_definitions(
enabled_toolsets=enabled_toolsets,
disabled_toolsets=disabled_toolsets,
@@ -1161,12 +1031,6 @@ def init_agent(
agent._parent_session_id = parent_session_id
agent._last_flushed_db_idx = 0 # tracks DB-write cursor to prevent duplicate writes
agent._session_db_created = False # DB row deferred to run_conversation()
# Most agents own their session row and should finalize it on close().
# Some temporary helper agents (manual compression / session-hygiene /
# background-review forks) rotate or share the session forward to a
# continuation row that must remain open after the helper is torn down;
# those callers explicitly set this flag to False.
agent._end_session_on_close = True
agent._session_init_model_config = {
"max_iterations": agent.max_iterations,
"reasoning_config": reasoning_config,
@@ -1242,9 +1106,6 @@ def init_agent(
"hermes_home": str(get_hermes_home()),
"agent_context": "primary",
}
if _init_kwargs["platform"] == "cli":
_init_kwargs["warning_callback"] = agent._emit_warning
_init_kwargs["status_callback"] = agent._emit_status
# Thread session title for memory provider scoping
# (e.g. honcho uses this to derive chat-scoped session keys)
if agent._session_db:
@@ -1289,8 +1150,38 @@ def init_agent(
_ra().logger.warning("Memory provider plugin init failed: %s", _mpe)
agent._memory_manager = None
from agent.memory_manager import inject_memory_provider_tools as _inject_memory_provider_tools
_inject_memory_provider_tools(agent)
# Inject memory provider tool schemas into the tool surface.
# Skip tools whose names already exist (plugins may register the
# same tools via ctx.register_tool(), which lands in agent.tools
# through _ra().get_tool_definitions()). Duplicate function names cause
# 400 errors on providers that enforce unique names (e.g. Xiaomi
# MiMo via Nous Portal).
#
# Respect the platform's enabled_toolsets configuration (#5544):
# enabled_toolsets is None → no filter, inject (backward compat)
# "memory" in enabled_toolsets → user opted in, inject
# otherwise (incl. []) → user excluded memory, skip injection
#
# Without this gate, `platform_toolsets: telegram: []` still leaks memory
# provider tools (fact_store, etc.) into the tool surface — a 10x latency
# penalty on local models and a frequent trigger of tool-call loops.
if agent._memory_manager and agent.tools is not None and (
agent.enabled_toolsets is None or "memory" in agent.enabled_toolsets
):
_existing_tool_names = {
t.get("function", {}).get("name")
for t in agent.tools
if isinstance(t, dict)
}
for _schema in agent._memory_manager.get_all_tool_schemas():
_tname = _schema.get("name", "")
if _tname and _tname in _existing_tool_names:
continue # already registered via plugin path
_wrapped = {"type": "function", "function": _schema}
agent.tools.append(_wrapped)
if _tname:
agent.valid_tool_names.add(_tname)
_existing_tool_names.add(_tname)
# Skills config: nudge interval for skill creation reminders
agent._skill_nudge_interval = 10
@@ -1307,47 +1198,18 @@ def init_agent(
_agent_section = {}
agent._tool_use_enforcement = _agent_section.get("tool_use_enforcement", "auto")
# Intent-ack continuation config: "auto" (default — codex_responses only,
# the historical gate), true (all api_modes), false (never), or a list of
# model-name substrings. Resolved against the active api_mode/model in the
# conversation loop's intent-ack block.
agent._intent_ack_continuation = _agent_section.get("intent_ack_continuation", "auto")
# Universal task-completion guidance toggle. Default True. Surfaced
# as a separate flag from tool_use_enforcement because the guidance
# applies to ALL models, not just the model families enforcement
# targets.
agent._task_completion_guidance = bool(_agent_section.get("task_completion_guidance", True))
# Universal parallel-tool-call guidance toggle. Default True. Separate
# flag from task_completion_guidance because a user may want one but not
# the other. Steers the model to batch independent tool calls into a
# single turn; the runtime already executes such batches concurrently.
agent._parallel_tool_call_guidance = bool(_agent_section.get("parallel_tool_call_guidance", True))
# Local Python toolchain probe toggle. Default True. When False,
# the probe is skipped entirely (no subprocess calls, no system-prompt
# line). Useful for users on exotic setups where the probe heuristics
# are noisy.
agent._environment_probe = bool(_agent_section.get("environment_probe", True))
# Per-platform prompt-hint overrides (config.yaml → platform_hints).
# Lets an enterprise admin append to or replace Hermes' built-in
# platform hint for a single messaging platform (e.g. WhatsApp) without
# affecting other platforms. Shape:
# platform_hints:
# whatsapp:
# append: "When tabular output would help, invoke the ... skill."
# slack:
# replace: "Custom Slack hint that fully replaces the default."
# Stored verbatim; resolution happens in agent/system_prompt.py against
# the active platform. Invalid shapes are ignored defensively so a bad
# config entry can never break prompt assembly.
_platform_hints_cfg = _agent_cfg.get("platform_hints", {})
if not isinstance(_platform_hints_cfg, dict):
_platform_hints_cfg = {}
agent._platform_hint_overrides = _platform_hints_cfg
# App-level API retry count (wraps each model API call). Default 3,
# overridable via agent.api_max_retries in config.yaml. See #11616.
try:
@@ -1365,41 +1227,11 @@ def init_agent(
if not isinstance(_compression_cfg, dict):
_compression_cfg = {}
compression_threshold = float(_compression_cfg.get("threshold", 0.50))
# Per-model/route compaction-threshold override. Codex gpt-5.5 raises to
# 85% (the Codex backend caps the window at 272K, so the default 50% would
# compact at ~136K — half the usable context). Gated by an opt-out config
# flag so the user can fall back to the global threshold; when the override
# fires we stash a one-time notification (replayed on the first turn) that
# tells the user what changed and how to revert.
_codex_gpt55_autoraise = str(
_compression_cfg.get("codex_gpt55_autoraise", True)
).lower() in {"true", "1", "yes"}
agent._compression_threshold_autoraised = None
try:
from agent.auxiliary_client import (
_compression_threshold_for_model as _cthresh_fn,
_is_codex_gpt55 as _is_codex_gpt55_fn,
)
_model_cthresh = _cthresh_fn(
agent.model,
agent.provider,
allow_codex_gpt55_autoraise=_codex_gpt55_autoraise,
)
from agent.auxiliary_client import _compression_threshold_for_model as _cthresh_fn
_model_cthresh = _cthresh_fn(agent.model)
if _model_cthresh is not None:
_prev_threshold = compression_threshold
compression_threshold = _model_cthresh
# Notify only for the Codex gpt-5.5 autoraise (the Arcee Trinity
# override is a long-standing silent default). Skip the notice when
# the user's global threshold already meets/exceeds the raised
# value, since nothing actually changed for them.
if (
_is_codex_gpt55_fn(agent.model, agent.provider)
and _model_cthresh > _prev_threshold + 1e-9
):
agent._compression_threshold_autoraised = {
"from": _prev_threshold,
"to": _model_cthresh,
}
except Exception:
pass
compression_enabled = str(_compression_cfg.get("enabled", True)).lower() in {"true", "1", "yes"}
@@ -1417,14 +1249,6 @@ def init_agent(
compression_abort_on_summary_failure = str(
_compression_cfg.get("abort_on_summary_failure", False)
).lower() in {"true", "1", "yes"}
# In-place compaction: when True, compress_context() rewrites the message
# list + rebuilds the system prompt WITHOUT rotating the session id (no
# parent_session_id chain, no `name #N` renumber). See #38763 and
# agent/conversation_compression.py. Consumed by compress_context(), not the
# compressor, so it rides on the agent.
compression_in_place = is_truthy_value(
_compression_cfg.get("in_place"), default=False
)
# Read optional explicit context_length override for the auxiliary
# compression model. Custom endpoints often cannot report this via
@@ -1573,7 +1397,6 @@ def init_agent(
# 3. Check general plugin system (user-installed plugins)
# 4. Fall back to built-in ContextCompressor
_selected_engine = None
_copy_failed = False
_engine_name = "compressor" # default
try:
_ctx_cfg = _agent_cfg.get("context", {}) if isinstance(_agent_cfg, dict) else {}
@@ -1591,35 +1414,15 @@ def init_agent(
# Try general plugin system as fallback
if _selected_engine is None:
_candidate = None
try:
from hermes_cli.plugins import get_plugin_context_engine
_candidate = get_plugin_context_engine()
if _candidate and _candidate.name == _engine_name:
_selected_engine = _candidate
except Exception:
_candidate = None
if _candidate is not None and _candidate.name == _engine_name:
# Deep-copy the shared plugin singleton so a child agent's
# update_model() can't mutate the parent's compressor (#42449).
# Copy can fail for engines holding uncopyable state (locks, DB
# connections, clients); in that case fall back to the built-in
# compressor with an ACCURATE message rather than silently
# mislabelling it "not found".
import copy
try:
_selected_engine = copy.deepcopy(_candidate)
except Exception as _copy_err:
_copy_failed = True
_ra().logger.warning(
"Context engine '%s' could not be safely copied for this "
"agent (%s) — falling back to built-in compressor. Plugin "
"engines that hold uncopyable state (locks, DB connections) "
"should implement __deepcopy__ to copy only mutable budget "
"state.",
_engine_name, _copy_err,
)
_selected_engine = None
pass
if _selected_engine is None and not _copy_failed:
if _selected_engine is None:
_ra().logger.warning(
"Context engine '%s' not found — falling back to built-in compressor",
_engine_name,
@@ -1663,10 +1466,8 @@ def init_agent(
provider=agent.provider,
api_mode=agent.api_mode,
abort_on_summary_failure=compression_abort_on_summary_failure,
max_tokens=agent.max_tokens,
)
agent.compression_enabled = compression_enabled
agent.compression_in_place = compression_in_place
# Reject models whose context window is below the minimum required
# for reliable tool-calling workflows (64K tokens).
@@ -1676,10 +1477,8 @@ def init_agent(
f"Model {agent.model} has a context window of {_ctx:,} tokens, "
f"which is below the minimum {MINIMUM_CONTEXT_LENGTH:,} required "
f"by Hermes Agent. Choose a model with at least "
f"{MINIMUM_CONTEXT_LENGTH // 1000}K context. If your server "
f"reports a window smaller than the model's true window, set "
f"model.context_length in config.yaml to the real value "
f"(this must be at least {MINIMUM_CONTEXT_LENGTH // 1000}K)."
f"{MINIMUM_CONTEXT_LENGTH // 1000}K context, or set "
f"model.context_length in config.yaml to override."
)
# Inject context engine tool schemas (e.g. lcm_grep, lcm_describe, lcm_expand).
@@ -1711,27 +1510,16 @@ def init_agent(
for t in agent.tools
if isinstance(t, dict)
}
from agent.memory_manager import normalize_tool_schema as _normalize_tool_schema
for _raw_schema in agent.context_compressor.get_tool_schemas():
_schema = _normalize_tool_schema(_raw_schema)
if _schema is None:
# A schema with no resolvable name (e.g. an already-wrapped
# entry) would append a nameless tool that strict providers
# 400 on, disabling the whole toolset (#47707). Skip it.
_ra().logger.warning(
"Context engine returned a tool schema with no resolvable "
"name; skipping to avoid poisoning the request (%r)",
_raw_schema,
)
continue
_tname = _schema["name"]
if _tname in _existing_tool_names:
for _schema in agent.context_compressor.get_tool_schemas():
_tname = _schema.get("name", "")
if _tname and _tname in _existing_tool_names:
continue # already registered via plugin/cache path
_wrapped = {"type": "function", "function": _schema}
agent.tools.append(_wrapped)
agent.valid_tool_names.add(_tname)
agent._context_engine_tool_names.add(_tname)
_existing_tool_names.add(_tname)
if _tname:
agent.valid_tool_names.add(_tname)
agent._context_engine_tool_names.add(_tname)
_existing_tool_names.add(_tname)
# Notify context engine of session start
if hasattr(agent, "context_compressor") and agent.context_compressor:
@@ -1820,24 +1608,11 @@ def init_agent(
print(f"📊 Context limit: {agent.context_compressor.context_length:,} tokens (compress at {int(compression_threshold*100)}% = {agent.context_compressor.threshold_tokens:,})")
else:
print(f"📊 Context limit: {agent.context_compressor.context_length:,} tokens (auto-compression disabled)")
# One-time notice when the Codex gpt-5.5 autoraise kicked in, with the
# exact opt-back-out command. Printed inline at startup for CLI users;
# gateway users get the same text replayed via _compression_warning on
# turn 1 (set below, after the warning slot is initialized).
_autoraise = getattr(agent, "_compression_threshold_autoraised", None)
if _autoraise and compression_enabled:
print(_build_codex_gpt55_autoraise_notice(_autoraise))
# Check immediately so CLI users see the warning at startup.
# Gateway status_callback is not yet wired, so any warning is stored
# in _compression_warning and replayed in the first run_conversation().
agent._compression_warning = None
# Gateway parity for the Codex gpt-5.5 autoraise notice: the startup print
# above only reaches the CLI, so stash the same text here to be replayed
# through status_callback on the first turn (Telegram/Discord/Slack/etc.).
_autoraise = getattr(agent, "_compression_threshold_autoraised", None)
if _autoraise and compression_enabled:
agent._compression_warning = _build_codex_gpt55_autoraise_notice(_autoraise)
# Lazy feasibility check: deferred to the first turn that approaches the
# compression threshold. Running it eagerly here costs ~400ms cold (network
# probe of the auxiliary provider chain + /models lookup) on every agent
+139 -530
View File
@@ -32,7 +32,6 @@ from pathlib import Path
from typing import Any, Dict, List, Optional
from hermes_cli.timeouts import get_provider_request_timeout
from agent.prompt_builder import format_steer_marker
from agent.tool_dispatch_helpers import _trajectory_normalize_msg, make_tool_result_message
from agent.trajectory import convert_scratchpad_to_think
from agent.credential_pool import STATUS_EXHAUSTED
@@ -42,14 +41,6 @@ from utils import base_url_host_matches, base_url_hostname, env_var_enabled, ato
logger = logging.getLogger(__name__)
# Max consecutive successful credential-pool token refreshes of the SAME entry
# on a persistent auth failure before we give up and let the fallback chain
# activate. A single-entry OAuth pool can re-mint a fresh token indefinitely
# even when the upstream keeps rejecting it, so without this cap the retry loop
# spins forever and never reaches ``_try_activate_fallback``. See #26080.
_MAX_AUTH_REFRESH_ATTEMPTS = 2
def _ra():
"""Lazy ``run_agent`` reference for test-patch routing."""
import run_agent
@@ -57,7 +48,7 @@ def _ra():
AGENT_RUNTIME_POST_HOOK_TOOL_NAMES = frozenset(
{"todo", "session_search", "memory", "clarify", "read_terminal", "delegate_task"}
{"todo", "session_search", "memory", "clarify", "delegate_task"}
)
@@ -453,45 +444,6 @@ def repair_message_sequence(agent, messages: List[Dict]) -> int:
return repairs
def repair_message_sequence_with_cursor(agent, messages: List[Dict]) -> int:
"""Run :func:`repair_message_sequence` and keep the SessionDB flush
cursor consistent with the compacted list (#44837).
``repair_message_sequence`` merges/drops messages in place, shrinking
the list. ``_last_flushed_db_idx`` (the DB-write cursor) indexes into
that list, so after compaction it can point past the new end — the
turn-end flush would then skip the assistant/tool chain entirely — or
past unflushed messages shifted to lower indexes.
Repair preserves object identity for surviving messages, so counting
the survivors from the previously-flushed prefix gives the exact new
cursor even when messages are dropped/merged at indexes *before* the
cursor — a plain ``min()`` clamp would silently skip that many
unflushed rows. Falls back to the clamp when no prefix snapshot is
available.
Returns the number of repairs made (same as ``repair_message_sequence``).
"""
pre_repair_flushed_ids = None
flush_cursor = getattr(agent, "_last_flushed_db_idx", None)
if isinstance(flush_cursor, int) and flush_cursor > 0:
pre_repair_flushed_ids = {id(m) for m in messages[:flush_cursor]}
repairs = repair_message_sequence(agent, messages)
if repairs > 0 and hasattr(agent, "_last_flushed_db_idx"):
if pre_repair_flushed_ids is not None:
agent._last_flushed_db_idx = sum(
1 for m in messages if id(m) in pre_repair_flushed_ids
)
else:
agent._last_flushed_db_idx = min(
agent._last_flushed_db_idx, len(messages)
)
return repairs
def strip_think_blocks(agent, content: str) -> str:
"""Remove reasoning/thinking blocks from content, returning only visible text.
@@ -626,33 +578,12 @@ def recover_with_credential_pool(
current_provider = (getattr(agent, "provider", "") or "").strip().lower()
pool_provider = (getattr(pool, "provider", "") or "").strip().lower()
if current_provider and pool_provider and current_provider != pool_provider:
# Custom endpoints use two naming conventions for the SAME provider:
# the agent carries the generic ``custom`` label while the pool is
# keyed ``custom:<name>`` (see CUSTOM_POOL_PREFIX). A literal string
# compare treats them as a mismatch and skips recovery for every
# custom-provider user — 401s/429s then burn the full retry cycle
# with no rotation or refresh. Accept the pair as matching only when
# the agent's CURRENT base_url actually resolves to this pool key,
# so a fallback provider (or a different custom endpoint) still
# triggers the guard.
_custom_match = False
if current_provider == "custom" and pool_provider.startswith("custom:"):
try:
from agent.credential_pool import get_custom_provider_pool_key
_agent_base = (getattr(agent, "base_url", "") or "").strip()
_custom_match = bool(_agent_base) and (
(get_custom_provider_pool_key(_agent_base) or "").strip().lower()
== pool_provider
)
except Exception:
_custom_match = False
if not _custom_match:
_ra().logger.warning(
"Credential pool provider mismatch: pool=%s, agent=%s — "
"skipping pool mutation to avoid cross-provider contamination",
pool_provider, current_provider,
)
return False, has_retried_429
_ra().logger.warning(
"Credential pool provider mismatch: pool=%s, agent=%s — "
"skipping pool mutation to avoid cross-provider contamination",
pool_provider, current_provider,
)
return False, has_retried_429
effective_reason = classified_reason
if effective_reason is None:
@@ -747,28 +678,15 @@ def recover_with_credential_pool(
# long-running TUI sessions stuck on stale tokens until the user
# exited and reopened.
is_entitlement = agent._is_entitlement_failure(error_context, status_code)
_auth_haystack = " ".join(
str(error_context.get(k) or "").lower()
for k in ("message", "reason", "code", "error")
if isinstance(error_context, dict)
)
if (
not is_entitlement
and status_code == 403
and "oauth authentication is currently not allowed for this organization" in _auth_haystack
):
is_entitlement = True
if (
not is_entitlement
and status_code == 403
and (agent.provider or "") == "anthropic"
and getattr(agent, "api_mode", "") == "anthropic_messages"
):
is_entitlement = True
if not is_entitlement and status_code == 403 and (agent.provider or "") == "xai-oauth":
_disambiguator_haystack = " ".join(
str(error_context.get(k) or "").lower()
for k in ("message", "reason", "code", "error")
if isinstance(error_context, dict)
)
_is_xai_auth_failure = (
"[wke=unauthenticated:" in _auth_haystack
or "oauth2 access token could not be validated" in _auth_haystack
"[wke=unauthenticated:" in _disambiguator_haystack
or "oauth2 access token could not be validated" in _disambiguator_haystack
)
if not _is_xai_auth_failure:
is_entitlement = True
@@ -783,30 +701,6 @@ def recover_with_credential_pool(
return False, has_retried_429
refreshed = pool.try_refresh_current()
if refreshed is not None:
# ``try_refresh_current()`` re-mints a fresh OAuth token and reports
# success even when the upstream keeps rejecting it — a single-entry
# pool (common for OAuth/Max subscribers) has nothing to rotate to,
# so a bare "refreshed → retry" loop spins forever on the same dead
# token and the configured fallback never activates. Cap consecutive
# same-entry refreshes and fall through to fallback once exceeded.
# See #26080.
refreshed_id = getattr(refreshed, "id", None)
if refreshed_id is not None:
refresh_counts = getattr(agent, "_auth_pool_refresh_counts", None)
if refresh_counts is None:
refresh_counts = {}
agent._auth_pool_refresh_counts = refresh_counts
refresh_key = (agent.provider, refreshed_id)
refresh_counts[refresh_key] = refresh_counts.get(refresh_key, 0) + 1
if refresh_counts[refresh_key] > _MAX_AUTH_REFRESH_ATTEMPTS:
_ra().logger.warning(
"Credential auth failure persists after %s refreshes for "
"pool entry %s — treating as unrecoverable and allowing "
"fallback to activate.",
refresh_counts[refresh_key] - 1,
refreshed_id,
)
return False, has_retried_429
_ra().logger.info(f"Credential auth failure — refreshed pool entry {getattr(refreshed, 'id', '?')}")
agent._swap_credential(refreshed)
return True, has_retried_429
@@ -913,8 +807,6 @@ def try_recover_primary_transport(
def drop_thinking_only_and_merge_users(
messages: List[Dict[str, Any]],
*,
drop_codex_reasoning_items: bool = True,
) -> List[Dict[str, Any]]:
"""Drop thinking-only assistant turns; merge any adjacent user messages left behind.
@@ -936,13 +828,7 @@ def drop_thinking_only_and_merge_users(
return messages
# Pass 1: drop thinking-only assistant turns.
kept = [
m for m in messages
if not _ra().AIAgent._is_thinking_only_assistant(
m,
drop_codex_reasoning_items=drop_codex_reasoning_items,
)
]
kept = [m for m in messages if not _ra().AIAgent._is_thinking_only_assistant(m)]
dropped = len(messages) - len(kept)
if dropped == 0:
return messages
@@ -1078,43 +964,10 @@ def restore_primary_runtime(agent) -> bool:
api_mode=rt.get("compressor_api_mode", ""),
)
# ── Re-select from the credential pool if one is available ──
# The snapshot's api_key was captured at construction time. Across
# turns the pool may have rotated (token revocation, billing/rate-limit
# exhaustion, cooldown), leaving the snapshot key stale. Restoring it
# blindly re-fails on the first request and burns through the remaining
# pool entries before cross-provider fallback even gets a chance. Ask
# the pool for its current best entry and swap the live credential in.
# When the pool is absent, empty, or the entry has no usable key, we
# keep the snapshot key (the existing behavior). Fixes #25205.
pool = getattr(agent, "_credential_pool", None)
if pool is not None and pool.has_available():
entry = pool.select()
if entry is not None:
entry_key = (
getattr(entry, "runtime_api_key", None)
or getattr(entry, "access_token", "")
)
if entry_key:
# ``_swap_credential`` rebuilds the OpenAI/Anthropic client,
# reapplies base-url-scoped headers, and carries the
# accumulated base_url / OAuth-detection fixes (#33163).
agent._swap_credential(entry)
logger.info(
"Restore re-selected pool entry %s (%s)",
getattr(entry, "id", "?"),
getattr(entry, "label", "?"),
)
# ── Reset fallback chain for the new turn ──
agent._fallback_activated = False
agent._fallback_index = 0
# Undo the fallback's identity rewrite so the prompt is
# byte-identical to the stored copy again (prefix cache match).
from agent.chat_completion_helpers import rewrite_prompt_model_identity
rewrite_prompt_model_identity(agent, rt["model"], rt["provider"])
logger.info(
"Primary runtime restored for new turn: %s (%s)",
agent.model, agent.provider,
@@ -1281,28 +1134,13 @@ def dump_api_request_debug(
dump_payload["error"] = error_info
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
# Sanitize the session ID into a traversal-free path segment — it can
# originate from untrusted input (X-Hermes-Session-Id header), and an
# unsanitized "../"-shaped ID would write the dump outside logs_dir.
safe_sid = _ra()._safe_session_filename_component(agent.session_id)
dump_file = agent.logs_dir / f"request_dump_{safe_sid}_{timestamp}.json"
# Redact secrets before persisting/printing. This dump captures the
# full request body (system prompt, tool defs, context-embedded
# values), and this path fires unconditionally on API errors — so it
# otherwise lands any context-embedded secret in cleartext on disk.
# Run the serialized dump through the same scrubber used for logs/tool
# output, then hand the resulting payload back to the shared atomic
# JSON writer so request dumps keep the same write semantics as before.
from agent.redact import redact_sensitive_text
_serialized = json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str)
_redacted_payload = json.loads(redact_sensitive_text(_serialized, force=True))
atomic_json_write(dump_file, _redacted_payload, default=str)
dump_file = agent.logs_dir / f"request_dump_{agent.session_id}_{timestamp}.json"
atomic_json_write(dump_file, dump_payload, default=str)
agent._vprint(f"{agent.log_prefix}🧾 Request debug dump written to: {dump_file}")
if env_var_enabled("HERMES_DUMP_REQUEST_STDOUT"):
print(json.dumps(_redacted_payload, ensure_ascii=False, indent=2, default=str))
print(json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str))
return dump_file
except Exception as dump_error:
@@ -1442,6 +1280,22 @@ def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: boo
agent._client_log_context(),
)
return client
if agent.provider == "google-gemini-cli" or str(client_kwargs.get("base_url", "")).startswith("cloudcode-pa://"):
from agent.gemini_cloudcode_adapter import GeminiCloudCodeClient
# Strip OpenAI-specific kwargs the Gemini client doesn't accept
safe_kwargs = {
k: v for k, v in client_kwargs.items()
if k in {"api_key", "base_url", "default_headers", "project_id", "timeout"}
}
client = GeminiCloudCodeClient(**safe_kwargs)
_ra().logger.info(
"Gemini Cloud Code Assist client created (%s, shared=%s) %s",
reason,
shared,
agent._client_log_context(),
)
return client
if agent.provider == "gemini":
from agent.gemini_native_adapter import GeminiNativeClient, is_native_gemini_base_url
@@ -1484,15 +1338,6 @@ def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: boo
keepalive_http = agent._build_keepalive_http_client(client_kwargs.get("base_url", ""))
if keepalive_http is not None:
client_kwargs["http_client"] = keepalive_http
# Delegate all rate-limit / 5xx retry to hermes's outer conversation loop,
# which honors Retry-After and applies adaptive/jittered backoff. The OpenAI
# SDK default (max_retries=2) uses its own 1-2s backoff that ignores
# Retry-After and double-retries inside our loop — the same deadlock the
# Anthropic clients hit (#26293). This is the single chokepoint every primary
# OpenAI/aggregator client passes through (init, switch_model, recovery,
# restore, request-scoped); auxiliary_client builds its own clients and keeps
# SDK retries because it is NOT wrapped by the conversation loop.
client_kwargs.setdefault("max_retries", 0)
# Uses the module-level `OpenAI` name, resolved lazily on first
# access via __getattr__ below. Tests patch via `run_agent.OpenAI`.
client = _ra().OpenAI(**client_kwargs)
@@ -1572,10 +1417,6 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
# _client_kwargs is a dict — snapshot a shallow copy so mutating the
# live dict doesn't poison the rollback target.
_snapshot["_client_kwargs"] = dict(getattr(agent, "_client_kwargs", {}) or {})
# Snapshot the credential pool reference so a failed client rebuild can
# restore the original pool (issue #52727: pool reload is part of this
# switch and must be reversible on rollback).
_snapshot["_credential_pool"] = getattr(agent, "_credential_pool", _MISSING)
try:
# Clear the per-config context_length override so the new model's
@@ -1600,36 +1441,8 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
if api_key:
agent.api_key = api_key
# ── Reload credential pool for the new provider (issue #52727) ──
# Without this, ``recover_with_credential_pool`` sees a
# ``pool.provider != agent.provider`` mismatch and short-circuits,
# leaving the new provider with no rotation/recovery on 401/429 and
# burning the original pool's entries. Only reload when the provider
# actually changed (or the pool was missing) — re-selecting the same
# provider must not churn the pool reference. A reload failure is
# logged + swallowed: the switch itself must still complete.
old_norm = (old_provider or "").strip().lower()
new_norm = (new_provider or "").strip().lower()
if old_norm != new_norm or getattr(agent, "_credential_pool", None) is None:
try:
from agent.credential_pool import load_pool
agent._credential_pool = load_pool(new_provider)
except Exception as _pool_exc: # noqa: BLE001
logger.warning(
"switch_model: credential pool reload failed for %s (%s); "
"continuing without pool rotation this turn",
new_provider, _pool_exc,
)
# ── Build new client ──
if (new_provider or "").strip().lower() == "moa":
from agent.moa_loop import MoAClient
agent.api_key = api_key or "moa-virtual-provider"
agent.base_url = "moa://local"
agent._client_kwargs = {}
agent.client = MoAClient(agent.model or "default")
elif api_mode == "anthropic_messages":
if api_mode == "anthropic_messages":
from agent.anthropic_adapter import (
build_anthropic_client,
resolve_anthropic_token,
@@ -1802,62 +1615,17 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
old_model, old_provider, new_model, new_provider,
)
# ── Persist billing route to session DB ──
# The agent's _session_db / session_id may not be set in all contexts
# (tests, bare agents without a session DB, etc.). This ensures the
# dashboard Model cards show the actual provider after a mid-session
# /model switch instead of the stale session-creation provider.
# See #48248 for the full bug description.
_session_db = getattr(agent, "_session_db", None)
_session_id = getattr(agent, "session_id", None)
if _session_db is not None and _session_id:
try:
_session_db.update_session_billing_route(
_session_id,
provider=agent.provider,
base_url=agent.base_url,
billing_mode=getattr(agent, "api_mode", None),
)
except Exception:
logger.warning(
"Failed to persist billing route after model switch",
exc_info=True,
)
def invoke_tool(agent, function_name: str, function_args: dict, effective_task_id: str,
tool_call_id: Optional[str] = None, messages: list = None,
pre_tool_block_checked: bool = False,
skip_tool_request_middleware: bool = False,
tool_request_middleware_trace: Optional[List[Dict[str, Any]]] = None) -> str:
pre_tool_block_checked: bool = False) -> str:
"""Invoke a single tool and return the result string. No display logic.
Handles both agent-level tools (todo, memory, etc.) and registry-dispatched
tools. Used by the concurrent execution path; the sequential path retains
its own inline invocation for backward-compatible display handling.
"""
if not isinstance(function_args, dict):
function_args = {}
_tool_middleware_trace = list(tool_request_middleware_trace or [])
try:
from hermes_cli.middleware import apply_tool_request_middleware
if not skip_tool_request_middleware:
_tool_request_mw = apply_tool_request_middleware(
function_name,
function_args,
task_id=effective_task_id or "",
session_id=getattr(agent, "session_id", "") or "",
tool_call_id=tool_call_id or "",
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
)
function_args = _tool_request_mw.payload
_tool_middleware_trace = _tool_request_mw.trace
except Exception as _mw_err:
logger.debug("tool_request middleware error: %s", _mw_err)
# Check plugin hooks for a block directive before executing anything.
block_message: Optional[str] = None
if not pre_tool_block_checked:
@@ -1871,7 +1639,6 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
tool_call_id=tool_call_id or "",
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
middleware_trace=list(_tool_middleware_trace),
)
except Exception:
pass
@@ -1891,7 +1658,6 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
status="blocked",
error_type="plugin_block",
error_message=block_message,
middleware_trace=list(_tool_middleware_trace),
)
except Exception:
pass
@@ -1899,13 +1665,12 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
tool_start_time = time.monotonic()
def _finish_agent_tool(result: Any, observed_args: Optional[dict] = None) -> Any:
hook_args = observed_args if isinstance(observed_args, dict) else function_args
def _finish_agent_tool(result: Any) -> Any:
try:
from model_tools import _emit_post_tool_call_hook
_emit_post_tool_call_hook(
function_name=function_name,
function_args=hook_args,
function_args=function_args,
result=result,
task_id=effective_task_id or "",
session_id=getattr(agent, "session_id", "") or "",
@@ -1913,127 +1678,89 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
duration_ms=int((time.monotonic() - tool_start_time) * 1000),
middleware_trace=list(_tool_middleware_trace),
)
except Exception:
pass
return result
if function_name == "todo":
def _execute(next_args: dict) -> Any:
from tools.todo_tool import todo_tool as _todo_tool
return _finish_agent_tool(
_todo_tool(
todos=next_args.get("todos"),
merge=next_args.get("merge", False),
store=agent._todo_store,
),
next_args,
from tools.todo_tool import todo_tool as _todo_tool
return _finish_agent_tool(
_todo_tool(
todos=function_args.get("todos"),
merge=function_args.get("merge", False),
store=agent._todo_store,
)
)
elif function_name == "session_search":
def _execute(next_args: dict) -> Any:
session_db = agent._get_session_db_for_recall()
if not session_db:
from hermes_state import format_session_db_unavailable
return _finish_agent_tool(json.dumps({"success": False, "error": format_session_db_unavailable()}), next_args)
from tools.session_search_tool import session_search as _session_search
return _finish_agent_tool(
_session_search(
query=next_args.get("query", ""),
role_filter=next_args.get("role_filter"),
limit=next_args.get("limit", 3),
session_id=next_args.get("session_id"),
around_message_id=next_args.get("around_message_id"),
window=next_args.get("window", 5),
sort=next_args.get("sort"),
db=session_db,
current_session_id=agent.session_id,
),
next_args,
session_db = agent._get_session_db_for_recall()
if not session_db:
from hermes_state import format_session_db_unavailable
return _finish_agent_tool(json.dumps({"success": False, "error": format_session_db_unavailable()}))
from tools.session_search_tool import session_search as _session_search
return _finish_agent_tool(
_session_search(
query=function_args.get("query", ""),
role_filter=function_args.get("role_filter"),
limit=function_args.get("limit", 3),
session_id=function_args.get("session_id"),
around_message_id=function_args.get("around_message_id"),
window=function_args.get("window", 5),
sort=function_args.get("sort"),
db=session_db,
current_session_id=agent.session_id,
)
)
elif function_name == "memory":
def _execute(next_args: dict) -> Any:
target = next_args.get("target", "memory")
operations = next_args.get("operations")
from tools.memory_tool import memory_tool as _memory_tool
result = _memory_tool(
action=next_args.get("action"),
target=target,
content=next_args.get("content"),
old_text=next_args.get("old_text"),
operations=operations,
store=agent._memory_store,
)
# Mirror successful built-in memory writes to external providers.
# All gating/op-expansion lives behind the manager interface
# (MemoryManager.notify_memory_tool_write).
if agent._memory_manager:
agent._memory_manager.notify_memory_tool_write(
result,
next_args,
build_metadata=lambda: agent._build_memory_write_metadata(
target = function_args.get("target", "memory")
from tools.memory_tool import memory_tool as _memory_tool
result = _memory_tool(
action=function_args.get("action"),
target=target,
content=function_args.get("content"),
old_text=function_args.get("old_text"),
store=agent._memory_store,
)
# Bridge: notify external memory provider of built-in memory writes
if agent._memory_manager and function_args.get("action") in {"add", "replace"}:
try:
agent._memory_manager.on_memory_write(
function_args.get("action", ""),
target,
function_args.get("content", ""),
metadata=agent._build_memory_write_metadata(
task_id=effective_task_id,
tool_call_id=tool_call_id,
),
)
return _finish_agent_tool(result, next_args)
except Exception:
pass
return _finish_agent_tool(result)
elif agent._memory_manager and agent._memory_manager.has_tool(function_name):
def _execute(next_args: dict) -> Any:
return _finish_agent_tool(agent._memory_manager.handle_tool_call(function_name, next_args), next_args)
return _finish_agent_tool(agent._memory_manager.handle_tool_call(function_name, function_args))
elif function_name == "clarify":
def _execute(next_args: dict) -> Any:
from tools.clarify_tool import clarify_tool as _clarify_tool
return _finish_agent_tool(
_clarify_tool(
question=next_args.get("question", ""),
choices=next_args.get("choices"),
callback=agent.clarify_callback,
),
next_args,
)
elif function_name == "read_terminal":
def _execute(next_args: dict) -> Any:
from tools.read_terminal_tool import read_terminal_tool as _read_terminal_tool
return _finish_agent_tool(
_read_terminal_tool(
start_line=next_args.get("start_line"),
count=next_args.get("count"),
callback=getattr(agent, "read_terminal_callback", None),
),
next_args,
from tools.clarify_tool import clarify_tool as _clarify_tool
return _finish_agent_tool(
_clarify_tool(
question=function_args.get("question", ""),
choices=function_args.get("choices"),
callback=agent.clarify_callback,
)
)
elif function_name == "delegate_task":
def _execute(next_args: dict) -> Any:
return _finish_agent_tool(agent._dispatch_delegate_task(next_args), next_args)
return _finish_agent_tool(agent._dispatch_delegate_task(function_args))
else:
def _execute(next_args: dict) -> Any:
return _ra().handle_function_call(
function_name, next_args, effective_task_id,
tool_call_id=tool_call_id,
session_id=agent.session_id or "",
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
enabled_tools=list(agent.valid_tool_names) if agent.valid_tool_names else None,
skip_pre_tool_call_hook=True,
skip_tool_request_middleware=True,
enabled_toolsets=getattr(agent, "enabled_toolsets", None),
disabled_toolsets=getattr(agent, "disabled_toolsets", None),
tool_request_middleware_trace=list(_tool_middleware_trace),
)
from hermes_cli.middleware import run_tool_execution_middleware
return run_tool_execution_middleware(
function_name,
function_args,
lambda next_args: _execute(next_args if isinstance(next_args, dict) else function_args),
original_args=function_args,
task_id=effective_task_id or "",
session_id=getattr(agent, "session_id", "") or "",
tool_call_id=tool_call_id or "",
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
)
return _ra().handle_function_call(
function_name, function_args, effective_task_id,
tool_call_id=tool_call_id,
session_id=agent.session_id or "",
turn_id=getattr(agent, "_current_turn_id", "") or "",
api_request_id=getattr(agent, "_current_api_request_id", "") or "",
enabled_tools=list(agent.valid_tool_names) if agent.valid_tool_names else None,
skip_pre_tool_call_hook=True,
enabled_toolsets=getattr(agent, "enabled_toolsets", None),
disabled_toolsets=getattr(agent, "disabled_toolsets", None),
)
@@ -2064,27 +1791,6 @@ def repair_tool_call(agent, tool_name: str) -> str | None:
if not tool_name:
return None
# VolcEngine api/plan workaround (issue #33007): the endpoint's
# protocol-translation layer occasionally leaks raw XML attribute
# fragments into tool_use.name, e.g.
# `terminal" parameter="command" string="true`
# `execute_code" parameter="code" string="true`
# `session_search" parameter="session_id" string="true`
# We trim at the first unambiguous XML/quote character so the rest
# of the repair pipeline (lowercase / snake_case / fuzzy match)
# can resolve the cleaned name to a real tool.
#
# Crucially we DO NOT split on whitespace: legitimate inputs like
# "write file" must keep flowing through ``_norm`` -> ``write_file``
# (covered by test_space_to_underscore in
# tests/run_agent/test_repair_tool_call_name.py).
for _xml_sep in ('"', "'", "<", ">"):
_idx = tool_name.find(_xml_sep)
if _idx > 0:
tool_name = tool_name[:_idx]
if not tool_name:
return None
def _norm(s: str) -> str:
return s.lower().replace("-", "_").replace(" ", "_")
@@ -2209,21 +1915,8 @@ def looks_like_codex_intermediate_ack(
user_message: str,
assistant_content: str,
messages: List[Dict[str, Any]],
require_workspace: bool = True,
) -> bool:
"""Detect a planning/ack message that should continue instead of ending the turn.
``require_workspace`` (default True) keeps the original codex-coding scope:
the ack must reference a filesystem/repo workspace. The conversation loop
passes ``require_workspace=False`` when the user has explicitly opted into
intent-ack continuation for all api_modes (``agent.intent_ack_continuation``
is ``true`` or a model-list), so general autonomous workflows ("I'll run a
health check on the server", "I'll start the deployment") — which carry a
future-ack and an action verb but no filesystem reference — are caught too.
The future-ack + short-content + no-prior-tools + action-verb requirements
always apply, which is what keeps conversational "I'll help you brainstorm"
replies from tripping it.
"""
"""Detect a planning/ack message that should continue instead of ending the turn."""
if any(isinstance(msg, dict) and msg.get("role") == "tool" for msg in messages):
return False
@@ -2276,67 +1969,17 @@ def looks_like_codex_intermediate_ack(
"path",
)
assistant_mentions_action = any(marker in assistant_text for marker in action_markers)
if not assistant_mentions_action:
return False
# Opted-in (all-api_mode) path: a future-ack + action verb + no prior tool
# call is enough — the user asked us to keep going when the model only
# announces intent, regardless of whether a filesystem is involved.
if not require_workspace:
return True
user_text = (user_message or "").strip().lower()
user_targets_workspace = (
any(marker in user_text for marker in workspace_markers)
or "~/" in user_text
or "/" in user_text
)
assistant_mentions_action = any(marker in assistant_text for marker in action_markers)
assistant_targets_workspace = any(
marker in assistant_text for marker in workspace_markers
)
return user_targets_workspace or assistant_targets_workspace
def intent_ack_continuation_mode(agent) -> str:
"""Classify the resolved intent-ack continuation mode for this turn.
Returns one of:
* ``"off"`` — never continue.
* ``"codex_only"`` — historical scope: continue only on the
``codex_responses`` api_mode, and only for codebase/workspace acks
(``require_workspace=True``).
* ``"all"`` — user opted in for every api_mode; continue on any
future-ack + action verb (``require_workspace=False``).
Mirrors the four-mode shape of ``agent.tool_use_enforcement``: ``"auto"``
(default) → codex_only; ``True``/"true"/"always"/"yes"/"on" → all;
``False``/"false"/"never"/"no"/"off" → off; ``list`` → all when a substring
matches the active model name, else off.
"""
mode = getattr(agent, "_intent_ack_continuation", "auto")
if mode is True or (isinstance(mode, str) and mode.lower() in {"true", "always", "yes", "on"}):
return "all"
if mode is False or (isinstance(mode, str) and mode.lower() in {"false", "never", "no", "off"}):
return "off"
if isinstance(mode, list):
model_lower = (agent.model or "").lower()
return "all" if any(p.lower() in model_lower for p in mode if isinstance(p, str)) else "off"
# "auto" or any unrecognised value — historical codex-only behavior.
return "codex_only" if agent.api_mode == "codex_responses" else "off"
def intent_ack_continuation_enabled(agent) -> bool:
"""Whether intent-ack continuation should fire at all for this turn.
The ``codex_ack_continuations < 2`` per-turn cap and the
``looks_like_codex_intermediate_ack`` detector are applied by the caller;
this only decides the on/off gate. Callers that also need to know whether
the workspace requirement applies should use ``intent_ack_continuation_mode``
directly (``"codex_only"`` ⇒ require_workspace=True, ``"all"`` ⇒ False).
"""
return intent_ack_continuation_mode(agent) != "off"
return (user_targets_workspace or assistant_targets_workspace) and assistant_mentions_action
@@ -2346,36 +1989,25 @@ def copy_reasoning_content_for_api(agent, source_msg: dict, api_msg: dict) -> No
if source_msg.get("role") != "assistant":
return
needs_thinking_pad = agent._needs_thinking_reasoning_pad()
# 1. Explicit reasoning_content already set.
# 1. Explicit reasoning_content already set — preserve it verbatim
# (includes DeepSeek/Kimi's own space-placeholder written at creation
# time, and any valid reasoning content from the same provider).
#
# When the active provider enforces the thinking-mode echo-back
# (DeepSeek / Kimi / MiMo), preserve it verbatim — that includes their
# own space-placeholder written at creation time and any valid reasoning
# from the same provider. Sessions persisted BEFORE #17341 have
# empty-string placeholders pinned at creation time; DeepSeek V4 Pro
# rejects those with HTTP 400, so upgrade "" → " " on replay.
#
# When the active provider does NOT enforce echo-back, strip the field
# entirely. Strict OpenAI-compatible providers (Mistral, Cerebras, Groq,
# SambaNova, …) reject ANY reasoning_content key in input messages with
# HTTP 400/422 ("Extra inputs are not permitted"), even an empty string
# or a single-space pad. This is the cross-provider fallback case: a
# reasoning primary (DeepSeek/Kimi/MiMo) pads history with " ", then a
# fallback to a strict provider replays that pad and 422s. Stripping
# here covers the rebuild path; reapply_reasoning_echo_for_provider()
# covers the already-built api_messages path. Refs #45655.
# Exception: sessions persisted BEFORE #17341 have empty-string
# placeholders pinned at creation time. DeepSeek V4 Pro rejects
# those with HTTP 400. When the active provider enforces the
# thinking-mode echo, upgrade "" → " " on replay so stale history
# doesn't 400 the user on the next turn.
existing = source_msg.get("reasoning_content")
if isinstance(existing, str):
if not needs_thinking_pad:
api_msg.pop("reasoning_content", None)
elif existing == "":
if existing == "" and agent._needs_thinking_reasoning_pad():
api_msg["reasoning_content"] = " "
else:
api_msg["reasoning_content"] = existing
return
needs_thinking_pad = agent._needs_thinking_reasoning_pad()
# 2. Cross-provider poisoned history (#15748): on DeepSeek/Kimi,
# if the source turn has tool_calls AND a 'reasoning' field but no
# 'reasoning_content' key, the 'reasoning' text was written by a
@@ -2401,13 +2033,9 @@ def copy_reasoning_content_for_api(agent, source_msg: dict, api_msg: dict) -> No
# for providers that use the internal 'reasoning' key.
# This must happen before the unconditional empty-string fallback so
# genuine reasoning content is not overwritten (#15812 regression in
# PR #15478). Only promote for providers that enforce echo-back —
# strict providers reject the field (refs #45655).
# PR #15478).
if isinstance(normalized_reasoning, str) and normalized_reasoning:
if needs_thinking_pad:
api_msg["reasoning_content"] = normalized_reasoning
else:
api_msg.pop("reasoning_content", None)
api_msg["reasoning_content"] = normalized_reasoning
return
# 4. DeepSeek / Kimi thinking mode: all assistant messages need
@@ -2428,53 +2056,34 @@ def copy_reasoning_content_for_api(agent, source_msg: dict, api_msg: dict) -> No
def reapply_reasoning_echo_for_provider(agent, api_messages: list) -> int:
"""Re-pad (or strip) assistant turns' reasoning_content for the active provider.
"""Re-pad assistant turns with reasoning_content for the active provider.
``api_messages`` is built once, before the retry loop, while the *primary*
provider is active. A mid-conversation fallback can then switch providers,
so the reasoning fields baked into ``api_messages`` are shaped for the
*prior* provider and must be reconciled against the *current* one:
provider is active. If a mid-conversation fallback then switches to a
require-side provider (DeepSeek / Kimi / MiMo thinking mode), assistant
turns that were built when the prior provider did NOT need the echo-back go
out without ``reasoning_content`` and the new provider rejects them with
HTTP 400 ("The reasoning_content in the thinking mode must be passed back").
* Switching TO a require-side provider (DeepSeek / Kimi / MiMo thinking
mode): assistant turns built when the prior provider did NOT need the
echo-back go out without ``reasoning_content`` and the new provider
rejects them with HTTP 400 ("The reasoning_content in the thinking mode
must be passed back"). Re-apply the pad.
Calling this immediately before building the request kwargs re-applies the
pad against the *current* provider. It is idempotent and a no-op unless
``_needs_thinking_reasoning_pad()`` is True for the active provider, so it
is safe to call every iteration and covers every fallback path.
* Switching TO a strict provider that rejects the field (Mistral,
Cerebras, Groq, SambaNova, …): assistant turns built under a reasoning
primary carry a ``reasoning_content`` pad (often a single space ``" "``),
and the strict provider rejects it with HTTP 400/422 ("Extra inputs are
not permitted"). Strip the field. This is the exact cross-provider
fallback bug from #45655 — a DeepSeek primary pads history with ``" "``,
the request falls back to Mistral, and Mistral 422s on the stale pad.
Calling this immediately before building the request kwargs reconciles the
fields against the *current* provider. It is idempotent and safe to call
every iteration; it covers every fallback path.
Returns the number of assistant turns whose reasoning_content was added or
removed.
Returns the number of assistant turns that gained reasoning_content.
"""
needs_pad = agent._needs_thinking_reasoning_pad()
changed = 0
if not agent._needs_thinking_reasoning_pad():
return 0
padded = 0
for api_msg in api_messages:
if api_msg.get("role") != "assistant":
continue
if needs_pad:
if api_msg.get("reasoning_content"):
continue
copy_reasoning_content_for_api(agent, api_msg, api_msg)
if api_msg.get("reasoning_content"):
changed += 1
else:
# Strict provider — strip any stale reasoning_content pad left
# over from a reasoning primary so the fallback request doesn't
# 400/422 on it.
if "reasoning_content" in api_msg:
api_msg.pop("reasoning_content", None)
changed += 1
return changed
if api_msg.get("reasoning_content"):
continue
copy_reasoning_content_for_api(agent, api_msg, api_msg)
if api_msg.get("reasoning_content"):
padded += 1
return padded
def _iter_pool_sockets(client: Any):
@@ -2715,7 +2324,7 @@ def apply_pending_steer_to_tool_results(agent, messages: list, num_tool_msgs: in
existing = getattr(agent, "_pending_steer", None)
agent._pending_steer = (existing + "\n" + steer_text) if existing else steer_text
return
marker = format_steer_marker(steer_text)
marker = f"\n\nUser guidance: {steer_text}"
existing_content = messages[target_idx].get("content", "")
if not isinstance(existing_content, str):
# Anthropic multimodal content blocks — preserve them and append
+70 -499
View File
@@ -73,50 +73,20 @@ ADAPTIVE_EFFORT_MAP = {
"minimal": "low",
}
# ── Anthropic thinking-mode classification ────────────────────────────
# Claude 4.6 replaced budget-based extended thinking with *adaptive* thinking,
# and 4.7 additionally forbids the manual ``thinking`` block entirely and drops
# temperature/top_p/top_k. Newer Claude releases (4.8, and named models like
# claude-fable-5) follow the same modern contract — but they share no common
# version substring, so an allowlist of version numbers ("4.6", "4.7", …) goes
# stale the moment a model ships without a recognized number and silently
# routes it down the legacy manual-thinking path.
#
# Instead we DEFAULT unknown Claude models to the modern contract and keep an
# explicit *legacy* list of the older Claude families that still require manual
# thinking. This mirrors _get_anthropic_max_output's "default to newest" design
# (future models are unlikely to regress to the older contract), so each new
# Claude release works without a code change.
#
# Non-Claude Anthropic-Messages models (minimax, qwen3, GLM, …) are NOT Claude,
# so they fall through to the legacy path automatically — exactly what those
# manual-thinking endpoints need.
# Older Claude families that DON'T support adaptive thinking (manual thinking
# with budget_tokens only). Substring-matched against the model name.
_LEGACY_MANUAL_THINKING_CLAUDE_SUBSTRINGS = (
"claude-3", # 3, 3.5, 3.7
"claude-opus-4-0", "claude-opus-4.0", "claude-opus-4-1", "claude-opus-4.1",
"claude-sonnet-4-0", "claude-sonnet-4.0",
"claude-opus-4-2025", "claude-sonnet-4-2025", # date-stamped 4.0 IDs
"claude-opus-4-5", "claude-opus-4.5",
"claude-sonnet-4-5", "claude-sonnet-4.5",
"claude-haiku-4-5", "claude-haiku-4.5",
)
# Older Claude families that DON'T accept the "xhigh" effort level (4.6 only
# supports low/medium/high/max). xhigh arrived with Opus 4.7. Adaptive models
# not in this list (4.7, 4.8, fable, future) accept xhigh.
_NO_XHIGH_CLAUDE_SUBSTRINGS = (
"claude-opus-4-6", "claude-opus-4.6",
"claude-sonnet-4-6", "claude-sonnet-4.6",
)
def _is_claude_model(model: str | None) -> bool:
return "claude" in (model or "").lower()
# Models that accept the "xhigh" output_config.effort level. Opus 4.7 added
# xhigh as a distinct level between high and max; older adaptive-thinking
# models (4.6) reject it with a 400. Keep this substring list in sync with
# the Anthropic migration guide as new model families ship.
_XHIGH_EFFORT_SUBSTRINGS = ("4-7", "4.7", "4-8", "4.8")
# Models where extended thinking is deprecated/removed (4.6+ behavior: adaptive
# is the only supported mode; 4.7 additionally forbids manual thinking entirely
# and drops temperature/top_p/top_k).
_ADAPTIVE_THINKING_SUBSTRINGS = ("4-6", "4.6", "4-7", "4.7", "4-8", "4.8")
# Models where temperature/top_p/top_k return 400 if set to non-default values.
# This is the Opus 4.7 contract; future 4.x+ models are expected to follow it.
_NO_SAMPLING_PARAMS_SUBSTRINGS = ("4-7", "4.7", "4-8", "4.8")
_FAST_MODE_SUPPORTED_SUBSTRINGS = ("opus-4-6", "opus-4.6")
# ── Max output token limits per Anthropic model ───────────────────────
@@ -124,8 +94,6 @@ _FAST_MODE_SUPPORTED_SUBSTRINGS = ("opus-4-6", "opus-4.6")
# max_tokens as a mandatory field. Previously we hardcoded 16384, which
# starves thinking-enabled models (thinking tokens count toward the limit).
_ANTHROPIC_OUTPUT_LIMITS = {
# Mythos-class named models (claude-fable-5, …) — 1M context, reasoning
"claude-fable": 128_000,
# Claude 4.8
"claude-opus-4-8": 128_000,
# Claude 4.7
@@ -240,17 +208,8 @@ def _resolve_anthropic_messages_max_tokens(
def _supports_adaptive_thinking(model: str) -> bool:
"""Return True for Claude models that use adaptive thinking (4.6+).
Defaults *unknown* Claude models to adaptive (the modern contract) and
only returns False for the explicit legacy list of older Claude families
that require manual budget-based thinking. Non-Claude Anthropic-Messages
models (minimax, qwen3, …) return False so they keep the manual path.
"""
if not _is_claude_model(model):
return False
m = model.lower()
return not any(v in m for v in _LEGACY_MANUAL_THINKING_CLAUDE_SUBSTRINGS)
"""Return True for Claude 4.6+ models that support adaptive thinking."""
return any(v in model for v in _ADAPTIVE_THINKING_SUBSTRINGS)
def _supports_xhigh_effort(model: str) -> bool:
@@ -260,33 +219,18 @@ def _supports_xhigh_effort(model: str) -> bool:
Pre-4.7 adaptive models (Opus/Sonnet 4.6) only accept low/medium/high/max
and reject xhigh with an HTTP 400. Callers should downgrade xhigh→max
when this returns False.
Defaults unknown adaptive Claude models to accepting xhigh (4.7+ contract);
only the 4.6 family and legacy manual-thinking models are excluded.
"""
if not _supports_adaptive_thinking(model):
return False
m = model.lower()
return not any(v in m for v in _NO_XHIGH_CLAUDE_SUBSTRINGS)
return any(v in model for v in _XHIGH_EFFORT_SUBSTRINGS)
def _forbids_sampling_params(model: str) -> bool:
"""Return True for models that 400 on any non-default temperature/top_p/top_k.
Opus 4.7 introduced this restriction; later Claude releases follow it.
Defaults unknown Claude models to forbidding sampling params (the modern
contract). The 4.6 family still accepts them, and the legacy manual-thinking
families (4.5 and older) accept them too, so both are excluded. Non-Claude
models are unaffected. Callers should omit these fields entirely rather than
passing zero/default values (the API rejects anything non-null).
Opus 4.7 explicitly rejects sampling parameters; later Claude releases are
expected to follow suit. Callers should omit these fields entirely rather
than passing zero/default values (the API rejects anything non-null).
"""
if not _is_claude_model(model):
return False
m = model.lower()
# 4.6 family is adaptive but still accepts sampling params.
if any(v in m for v in _NO_XHIGH_CLAUDE_SUBSTRINGS):
return False
return not any(v in m for v in _LEGACY_MANUAL_THINKING_CLAUDE_SUBSTRINGS)
return any(v in model for v in _NO_SAMPLING_PARAMS_SUBSTRINGS)
def _supports_fast_mode(model: str) -> bool:
@@ -372,7 +316,7 @@ def _detect_claude_code_version() -> str:
_CLAUDE_CODE_SYSTEM_PREFIX = "You are Claude Code, Anthropic's official CLI for Claude."
_MCP_TOOL_PREFIX = "mcp__"
_MCP_TOOL_PREFIX = "mcp_"
def _get_claude_code_version() -> str:
@@ -673,9 +617,6 @@ def _build_anthropic_client_with_bearer_hook(
kwargs = {
"timeout": timeout_obj,
"http_client": http_client,
# Delegate retry to hermes's outer loop (honors Retry-After); the SDK
# default max_retries=2 ignores it and double-retries. (#26293)
"max_retries": 0,
# The SDK requires *something* for api_key/auth_token. Our
# event hook overrides Authorization per request so this value
# is never sent. The sentinel string makes accidental leaks
@@ -754,18 +695,9 @@ def build_anthropic_client(
from httpx import Timeout
normalized_base_url = _normalize_base_url_text(base_url)
if normalized_base_url:
import re as _re
normalized_base_url = _re.sub(r"/v1/?$", "", normalized_base_url.rstrip("/"))
_read_timeout = timeout if (isinstance(timeout, (int, float)) and timeout > 0) else 900.0
kwargs = {
"timeout": Timeout(timeout=float(_read_timeout), connect=10.0),
# Delegate all rate-limit / 5xx retry to hermes's outer conversation
# loop, which honors Retry-After. The SDK default (max_retries=2) uses
# its own 1-2s backoff that ignores Retry-After and double-retries
# inside our loop — burning request slots against a bucket that won't
# refill for minutes. (#26293)
"max_retries": 0,
}
if normalized_base_url:
# Azure Anthropic endpoints require an ``api-version`` query parameter.
@@ -861,9 +793,6 @@ def build_anthropic_bedrock_client(region: str):
return _anthropic_sdk.AnthropicBedrock(
aws_region=region,
timeout=Timeout(timeout=900.0, connect=10.0),
# Delegate retry to hermes's outer loop (honors Retry-After); the SDK
# default max_retries=2 ignores it and double-retries. (#26293)
max_retries=0,
default_headers={"anthropic-beta": ",".join([*_COMMON_BETAS, _CONTEXT_1M_BETA])},
)
@@ -892,7 +821,6 @@ def _read_claude_code_credentials_from_keychain() -> Optional[Dict[str, Any]]:
capture_output=True,
text=True,
timeout=5,
stdin=subprocess.DEVNULL,
)
except (OSError, subprocess.TimeoutExpired):
logger.debug("Keychain: security command not available or timed out")
@@ -926,72 +854,44 @@ def _read_claude_code_credentials_from_keychain() -> Optional[Dict[str, Any]]:
return None
def _read_claude_code_credentials_from_file() -> Optional[Dict[str, Any]]:
"""Read Claude Code OAuth credentials from ~/.claude/.credentials.json.
Returns dict with {accessToken, refreshToken?, expiresAt?, source} or None.
"""
cred_path = Path.home() / ".claude" / ".credentials.json"
if not cred_path.exists():
return None
try:
data = json.loads(cred_path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError, IOError) as e:
logger.debug("Failed to read ~/.claude/.credentials.json: %s", e)
return None
oauth_data = data.get("claudeAiOauth")
if not (oauth_data and isinstance(oauth_data, dict)):
return None
access_token = oauth_data.get("accessToken", "")
if not access_token:
return None
return {
"accessToken": access_token,
"refreshToken": oauth_data.get("refreshToken", ""),
"expiresAt": oauth_data.get("expiresAt", 0),
"source": "claude_code_credentials_file",
}
def read_claude_code_credentials() -> Optional[Dict[str, Any]]:
"""Read refreshable Claude Code OAuth credentials.
Reads from two possible sources and reconciles them:
Checks two sources in order:
1. macOS Keychain (Darwin only) — "Claude Code-credentials" entry
2. ~/.claude/.credentials.json file
Selection rules when both are present:
- If exactly one is non-expired, prefer that one. (Handles the case
where Claude Code refreshes one source but not the other — observed
in the wild on Claude Code 2.1.x.)
- Otherwise, prefer the source with the later ``expiresAt`` so that
any subsequent refresh uses the most recent ``refreshToken``.
This intentionally excludes ~/.claude.json primaryApiKey. Opencode's
subscription flow is OAuth/setup-token based with refreshable credentials,
and native direct Anthropic provider usage should follow that path rather
than auto-detecting Claude's first-party managed key.
Returns dict with {accessToken, refreshToken?, expiresAt?, source} or None.
Returns dict with {accessToken, refreshToken?, expiresAt?} or None.
"""
# Try macOS Keychain first (covers Claude Code >=2.1.114)
kc_creds = _read_claude_code_credentials_from_keychain()
file_creds = _read_claude_code_credentials_from_file()
if kc_creds:
return kc_creds
if kc_creds and file_creds:
kc_valid = is_claude_code_token_valid(kc_creds)
file_valid = is_claude_code_token_valid(file_creds)
if kc_valid and not file_valid:
return kc_creds
if file_valid and not kc_valid:
return file_creds
# Both valid or both expired: prefer the later expiresAt so the
# downstream refresh path uses the freshest refresh_token.
kc_exp = kc_creds.get("expiresAt", 0) or 0
file_exp = file_creds.get("expiresAt", 0) or 0
return kc_creds if kc_exp >= file_exp else file_creds
# Fall back to JSON file
cred_path = Path.home() / ".claude" / ".credentials.json"
if cred_path.exists():
try:
data = json.loads(cred_path.read_text(encoding="utf-8"))
oauth_data = data.get("claudeAiOauth")
if oauth_data and isinstance(oauth_data, dict):
access_token = oauth_data.get("accessToken", "")
if access_token:
return {
"accessToken": access_token,
"refreshToken": oauth_data.get("refreshToken", ""),
"expiresAt": oauth_data.get("expiresAt", 0),
"source": "claude_code_credentials_file",
}
except (json.JSONDecodeError, OSError, IOError) as e:
logger.debug("Failed to read ~/.claude/.credentials.json: %s", e)
return kc_creds or file_creds
return None
def is_claude_code_token_valid(creds: Dict[str, Any]) -> bool:
@@ -1074,40 +974,8 @@ def refresh_anthropic_oauth_pure(refresh_token: str, *, use_json: bool = False)
def _refresh_oauth_token(creds: Dict[str, Any]) -> Optional[str]:
"""Attempt to refresh an expired Claude Code OAuth token.
Claude Code's OAuth refresh tokens are single-use: a successful refresh
rotates the pair and invalidates the old refresh token. Claude Code itself
also refreshes on its own schedule (IDE/CLI activity), so by the time
Hermes notices an expired token, Claude Code may have already rotated it.
POSTing our now-stale refresh token in that window races Claude Code and
fails with ``invalid_grant``.
So before refreshing, re-read the live credential sources. If Claude Code
has already produced a valid token, adopt it and skip the POST entirely.
Only fall back to refreshing ourselves when no fresh credential is found.
"""
# Claude Code may have already refreshed — adopt its token rather than
# racing it with our (possibly already-rotated) refresh token. Only adopt
# when the live re-read produced a DIFFERENT token with a real future
# expiry: re-adopting the same credential we were just handed would be a
# no-op, and a 0/absent ``expiresAt`` means "managed key / unknown expiry"
# (see is_claude_code_token_valid) which must NOT be treated as a fresh
# refresh here.
current = read_claude_code_credentials()
if current:
current_token = current.get("accessToken", "")
current_exp = current.get("expiresAt", 0) or 0
if (
current_token
and current_token != creds.get("accessToken", "")
and current_exp > 0
and is_claude_code_token_valid(current)
):
logger.debug("Adopted Claude Code's already-refreshed OAuth token")
return current_token
refresh_token = (current or {}).get("refreshToken", "") or creds.get("refreshToken", "")
"""Attempt to refresh an expired Claude Code OAuth token."""
refresh_token = creds.get("refreshToken", "")
if not refresh_token:
logger.debug("No refresh token available — cannot refresh")
return None
@@ -1231,46 +1099,6 @@ def _prefer_refreshable_claude_code_token(env_token: str, creds: Optional[Dict[s
return None
def _resolve_anthropic_pool_token() -> Optional[str]:
"""Return the first available Anthropic OAuth token from credential_pool.
Read-only: enumerates with ``clear_expired=False, refresh=False`` so a bare
token *resolve* (which runs from diagnostic/read-only call sites such as
``account_usage`` and ``hermes models``) never mutates ``~/.hermes/auth.json``
or makes a network refresh call. Refresh-on-expiry is owned by the API call
path's pool recovery, not the resolver.
"""
try:
from agent.credential_pool import AUTH_TYPE_OAUTH, load_pool
except Exception:
return None
try:
pool = load_pool("anthropic")
# Enumerate read-only (clear_expired=False, refresh=False): never persist
# to auth.json or trigger a network refresh from a bare resolve. select()
# is deliberately NOT used — it runs clear_expired=True, refresh=True,
# which would violate this read-only contract.
entries = pool._available_entries(clear_expired=False, refresh=False)
except Exception:
logger.debug("Failed to read Anthropic credential_pool", exc_info=True)
return None
for entry in entries:
if getattr(entry, "auth_type", None) != AUTH_TYPE_OAUTH:
continue
# access_token is a declared field but a persisted entry can carry an
# explicit null (or a partially-written OAuth entry), so coerce before
# strip — a bare None.strip() here would escape the try/excepts above
# and crash the whole resolver, taking down the source #5 fallback too.
# Matches the aux-client analog (auxiliary_client.py: str(key or "")).
token = (getattr(entry, "access_token", None) or "").strip()
if token:
return token
return None
def resolve_anthropic_token() -> Optional[str]:
"""Resolve an Anthropic token from all available sources.
@@ -1279,8 +1107,7 @@ def resolve_anthropic_token() -> Optional[str]:
2. CLAUDE_CODE_OAUTH_TOKEN env var
3. Claude Code credentials (~/.claude.json or ~/.claude/.credentials.json)
— with automatic refresh if expired and a refresh token is available
4. Anthropic credential_pool OAuth entry (~/.hermes/auth.json)
5. ANTHROPIC_API_KEY env var (regular API key, or legacy fallback)
4. ANTHROPIC_API_KEY env var (regular API key, or legacy fallback)
Returns the token string or None.
"""
@@ -1307,12 +1134,7 @@ def resolve_anthropic_token() -> Optional[str]:
if resolved_claude_token:
return resolved_claude_token
# 4. Hermes credential_pool OAuth entry.
resolved_pool_token = _resolve_anthropic_pool_token()
if resolved_pool_token:
return resolved_pool_token
# 5. Regular API key, or a legacy OAuth token saved in ANTHROPIC_API_KEY.
# 4. Regular API key, or a legacy OAuth token saved in ANTHROPIC_API_KEY.
# This remains as a compatibility fallback for pre-migration Hermes configs.
api_key = os.getenv("ANTHROPIC_API_KEY", "").strip()
if api_key:
@@ -1341,10 +1163,7 @@ def run_oauth_setup_token() -> Optional[str]:
"Install it with: npm install -g @anthropic-ai/claude-code"
)
# Run interactively — stdin/stdout/stderr inherited so the user can
# complete the OAuth login prompt. Must keep inherited stdin; the TUI-EOF
# concern does not apply to an interactive login the user explicitly
# invokes. noqa: subprocess-stdin
# Run interactively — stdin/stdout/stderr inherited so user can interact
try:
subprocess.run([claude_path, "setup-token"])
except (KeyboardInterrupt, EOFError):
@@ -1369,15 +1188,7 @@ def run_oauth_setup_token() -> Optional[str]:
# Stores credentials in ~/.hermes/.anthropic_oauth.json (our own file).
_OAUTH_CLIENT_ID = "9d1c250a-e61b-44d9-88ed-5944d1962f5e"
# Anthropic migrated the OAuth token endpoint to platform.claude.com;
# console.anthropic.com now 404s. Callers should iterate _OAUTH_TOKEN_URLS
# (new host first, console fallback). _OAUTH_TOKEN_URL is kept as the primary
# for backward compatibility with existing imports and now points at the live host.
_OAUTH_TOKEN_URLS = [
"https://platform.claude.com/v1/oauth/token",
"https://console.anthropic.com/v1/oauth/token",
]
_OAUTH_TOKEN_URL = _OAUTH_TOKEN_URLS[0]
_OAUTH_TOKEN_URL = "https://console.anthropic.com/v1/oauth/token"
_OAUTH_REDIRECT_URI = "https://console.anthropic.com/oauth/code/callback"
_OAUTH_SCOPES = "org:create_api_key user:profile user:inference"
_HERMES_OAUTH_FILE = get_hermes_home() / ".anthropic_oauth.json"
@@ -1475,34 +1286,18 @@ def run_hermes_oauth_login_pure() -> Optional[Dict[str, Any]]:
"code_verifier": verifier,
}).encode()
# Anthropic migrated the OAuth token endpoint to platform.claude.com;
# console.anthropic.com now 404s. Try the new host first, then fall
# back to console for older deployments (mirrors the refresh path).
result = None
last_error = None
for endpoint in _OAUTH_TOKEN_URLS:
req = urllib.request.Request(
endpoint,
data=exchange_data,
headers={
"Content-Type": "application/json",
"User-Agent": f"claude-cli/{_get_claude_code_version()} (external, cli)",
},
method="POST",
)
try:
with urllib.request.urlopen(req, timeout=15) as resp:
result = json.loads(resp.read().decode())
break
except Exception as exc:
last_error = exc
logger.debug("Anthropic token exchange failed at %s: %s", endpoint, exc)
continue
req = urllib.request.Request(
_OAUTH_TOKEN_URL,
data=exchange_data,
headers={
"Content-Type": "application/json",
"User-Agent": f"claude-cli/{_get_claude_code_version()} (external, cli)",
},
method="POST",
)
if result is None:
raise last_error if last_error is not None else ValueError(
"Anthropic token exchange failed"
)
with urllib.request.urlopen(req, timeout=15) as resp:
result = json.loads(resp.read().decode())
except Exception as e:
print(f"Token exchange failed: {e}")
return None
@@ -1716,15 +1511,6 @@ def _convert_content_part_to_anthropic(part: Any) -> Optional[Dict[str, Any]]:
if ptype == "input_text":
block: Dict[str, Any] = {"type": "text", "text": part.get("text", "")}
elif ptype == "text":
# A stored Anthropic text block. Rebuild from whitelisted fields only —
# SDK response text blocks carry output-only siblings (parsed_output,
# citations=None) that the Messages INPUT schema rejects with HTTP 400
# "Extra inputs are not permitted". Do NOT dict(part) it verbatim.
block = {"type": "text", "text": part.get("text", "")}
cits = part.get("citations")
if isinstance(cits, list) and cits:
block["citations"] = cits
elif ptype in {"image_url", "input_image"}:
image_value = part.get("image_url", {})
url = image_value.get("url", "") if isinstance(image_value, dict) else str(image_value or "")
@@ -1839,58 +1625,6 @@ def _content_parts_to_anthropic_blocks(parts: Any) -> List[Dict[str, Any]]:
return out
def _sanitize_replay_block(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""Strip output-only fields from a stored Anthropic content block so it is
valid as REQUEST input on replay.
The SDK response objects carry output-only attributes that the Messages
*input* schema forbids ("Extra inputs are not permitted"): text blocks get
``parsed_output``/``citations`` (when null), tool_use blocks get ``caller``,
etc. ``normalize_response`` captured blocks verbatim via ``_to_plain_data``,
so these leak back as input on the next turn → HTTP 400.
Whitelist per type (NOT a blacklist) so future SDK output-only fields can't
reintroduce the bug. Returns a clean block, or None to drop it.
"""
if not isinstance(b, dict):
return None
btype = b.get("type")
if btype == "text":
out: Dict[str, Any] = {"type": "text", "text": b.get("text", "")}
# citations is input-valid ONLY when it's a non-empty list; the SDK
# emits citations=None on responses, which the input schema rejects.
cits = b.get("citations")
if isinstance(cits, list) and cits:
out["citations"] = cits
if isinstance(b.get("cache_control"), dict):
out["cache_control"] = b["cache_control"]
return out
if btype == "thinking":
out = {"type": "thinking", "thinking": b.get("thinking", "")}
if b.get("signature"):
out["signature"] = b["signature"]
return out
if btype == "redacted_thinking":
# Only valid with its data payload; drop if missing.
return {"type": "redacted_thinking", "data": b["data"]} if b.get("data") else None
if btype == "tool_use":
out = {
"type": "tool_use",
"id": _sanitize_tool_id(b.get("id", "")),
"name": b.get("name", ""),
"input": b.get("input", {}),
}
if isinstance(b.get("cache_control"), dict):
out["cache_control"] = b["cache_control"]
return out
if btype == "image":
src = b.get("source")
return {"type": "image", "source": src} if isinstance(src, dict) else None
# Unknown/unsupported block type on the input path — drop rather than risk
# another "Extra inputs are not permitted".
return None
def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
"""Convert an assistant message to Anthropic content blocks.
@@ -1898,55 +1632,6 @@ def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
reasoning_content injection for Kimi/DeepSeek endpoints.
"""
content = m.get("content", "")
# Anthropic interleaved-thinking fast path: when this turn carries a
# verbatim, order-preserving block list (set by normalize_response only
# for turns that interleave SIGNED thinking with tool_use), replay it.
# Each block is run through _sanitize_replay_block to strip output-only
# SDK fields (parsed_output, caller, citations=None, …) that the Messages
# INPUT schema forbids — replaying them verbatim caused HTTP 400 "Extra
# inputs are not permitted" (text.parsed_output). Block ORDER is preserved
# (the reason this channel exists); only forbidden sibling fields are
# dropped, leaving thinking signatures and tool_use id/name/input intact.
ordered_blocks = m.get("anthropic_content_blocks")
if isinstance(ordered_blocks, list) and ordered_blocks:
# Re-source each tool_use input from the stored tool_calls map rather
# than the captured block. The ordered-blocks list captures tool_use
# input from the RAW API response (normalize_response), which is NOT
# credential-redacted; tool_calls[].function.arguments IS redacted at
# storage time (build_assistant_message, #19798). Replaying the raw
# block input would resurrect a secret the model inlined into a tool
# call (e.g. terminal(command="curl -H 'Authorization: Bearer sk-...'")
# onto the wire, even though the same value is redacted everywhere else
# in history. Keying by sanitized tool id preserves interleave order
# (the reason this channel exists) while swapping in the redacted
# input. Adapted from #36071 (replay-time tool-input re-sourcing).
redacted_input_by_id: Dict[str, Any] = {}
for tc in m.get("tool_calls", []) or []:
if not isinstance(tc, dict):
continue
fn = tc.get("function", {}) or {}
raw_args = fn.get("arguments", "{}")
try:
parsed_args = json.loads(raw_args) if isinstance(raw_args, str) else raw_args
except (json.JSONDecodeError, ValueError):
parsed_args = {}
redacted_input_by_id[_sanitize_tool_id(tc.get("id", ""))] = parsed_args
replayed: List[Dict[str, Any]] = []
for b in ordered_blocks:
clean = _sanitize_replay_block(b)
if clean is None:
continue
if clean.get("type") == "tool_use":
# Override raw (un-redacted) input with the redacted copy when
# we have one for this id; fall back to the sanitized block
# input only if the tool_call is missing (shape mismatch).
redacted = redacted_input_by_id.get(clean.get("id", ""))
if redacted is not None:
clean["input"] = redacted
replayed.append(clean)
if replayed:
return {"role": "assistant", "content": replayed}
blocks = _extract_preserved_thinking_blocks(m)
if content:
if isinstance(content, list):
@@ -2491,46 +2176,25 @@ def build_anthropic_kwargs(
text = text.replace("Nous Research", "Anthropic")
block["text"] = text
# 3. Normalize tool names so NOTHING goes on the OAuth wire with a
# single-underscore ``mcp_`` prefix. Anthropic's subscription/OAuth
# billing classifier treats a single-underscore ``mcp_`` tool name as
# a third-party-app fingerprint and rejects the request with HTTP 400
# "Third-party apps now draw from extra usage, not plan limits"
# (verified empirically: a single ``mcp_foo`` tool flips a request
# from plan-billing to the extra-usage lane; ``mcp__foo`` is accepted).
#
# Two cases, both must land on the double-underscore ``mcp__`` form:
# a) bare Hermes-native tools (``read_file``) -> ``mcp__read_file``
# b) native MCP server tools registered under their full
# single-underscore ``mcp_<server>_<tool>`` name
# (``mcp_linear_get_issue``) -> ``mcp__linear_get_issue``
# Case (b) is the gap that the bare ``mcp_``->``mcp__`` constant swap
# left open: those tools were *skipped* and stayed single-underscore,
# so any session with an MCP server configured still tripped the
# classifier. normalize_response reverses both forms via registry
# lookup so the dispatcher still sees the original name. GH-25255.
def _to_oauth_wire_name(name: str) -> str:
if name.startswith("mcp__"):
return name # already correct, don't double-prefix
if name.startswith("mcp_"):
# single-underscore native MCP tool -> promote to double
return "mcp__" + name[len("mcp_"):]
return _MCP_TOOL_PREFIX + name # bare name -> mcp__<name>
# 3. Prefix tool names with mcp_ (Claude Code convention)
# Skip names that already begin with the marker — native MCP server
# tools (from mcp_servers: in config.yaml) are registered under their
# full mcp_<server>_<tool> name and would double-prefix otherwise,
# breaking round-trip registry lookup in normalize_response. GH-25255.
if anthropic_tools:
for tool in anthropic_tools:
if "name" in tool:
tool["name"] = _to_oauth_wire_name(tool["name"])
if "name" in tool and not tool["name"].startswith(_MCP_TOOL_PREFIX):
tool["name"] = _MCP_TOOL_PREFIX + tool["name"]
# 4. Apply the same normalization to tool names in message history
# (tool_use blocks) so replayed turns match the wire names above.
# 4. Prefix tool names in message history (tool_use and tool_result blocks)
for msg in anthropic_messages:
content = msg.get("content")
if isinstance(content, list):
for block in content:
if isinstance(block, dict):
if block.get("type") == "tool_use" and "name" in block:
block["name"] = _to_oauth_wire_name(block["name"])
if not block["name"].startswith(_MCP_TOOL_PREFIX):
block["name"] = _MCP_TOOL_PREFIX + block["name"]
elif block.get("type") == "tool_result" and "tool_use_id" in block:
pass # tool_result uses ID, not name
@@ -2637,96 +2301,3 @@ def build_anthropic_kwargs(
kwargs["extra_headers"] = {"anthropic-beta": ",".join(betas)}
return kwargs
# Keys that belong exclusively to the OpenAI Responses / Codex API shape.
# The Anthropic Messages SDK (``messages.create()`` / ``messages.stream()``)
# raises ``TypeError: ... got an unexpected keyword argument`` on any of them.
_RESPONSES_ONLY_KWARGS = frozenset(
{"instructions", "input", "store", "parallel_tool_calls"}
)
def sanitize_anthropic_kwargs(api_kwargs: Any, *, log_prefix: str = "") -> Any:
"""Drop Responses-API-only keys before an Anthropic Messages SDK call.
Defensive boundary guard for #31673: under rare api_mode-flip races
(e.g. a concurrent auxiliary call mutating a shared agent between the
kwargs build and the stream dispatch), a Responses-shaped payload
carrying ``instructions=`` can reach ``messages.stream()`` /
``messages.create()``. The Anthropic SDK rejects it with a
non-retryable ``TypeError`` that nukes the whole turn and propagates
the entire fallback chain.
Mutates ``api_kwargs`` in place and returns it. When a foreign key is
present we log a WARNING so the underlying race stays visible in the
wild instead of being silently papered over.
"""
if not isinstance(api_kwargs, dict):
return api_kwargs
leaked = _RESPONSES_ONLY_KWARGS.intersection(api_kwargs)
if leaked:
for _key in leaked:
api_kwargs.pop(_key, None)
logger.warning(
"%sStripped Responses-only kwarg(s) %s from an Anthropic Messages "
"call (api_mode flip race — see #31673). The call will proceed; "
"this breadcrumb means a kwargs build ran under a Responses "
"api_mode while dispatch ran under anthropic_messages.",
log_prefix,
sorted(leaked),
)
return api_kwargs
def _is_stream_unavailable_error(exc: Exception) -> bool:
"""Return True when an Anthropic stream call should fall back to create()."""
err_lower = str(exc).lower()
if "stream" in err_lower and "not supported" in err_lower:
return True
if "invokemodelwithresponsestream" in err_lower:
from agent.bedrock_adapter import is_streaming_access_denied_error
return is_streaming_access_denied_error(exc)
return False
def create_anthropic_message(
client: Any,
api_kwargs: dict,
*,
log_prefix: str = "",
prefer_stream: bool = True,
) -> Any:
"""Create an Anthropic message, aggregating via stream when available.
Some Anthropic-compatible gateways are SSE-only: they ignore non-streaming
requests and return ``text/event-stream`` even for ``messages.create()``.
The SDK can surface that as raw text, so callers that expect a Message then
crash on ``.content``. Prefer ``messages.stream().get_final_message()`` to
match the main turn path, falling back to ``create()`` only for providers
that explicitly do not support streaming, such as restricted Bedrock roles.
"""
sanitize_anthropic_kwargs(api_kwargs, log_prefix=log_prefix)
messages_api = getattr(client, "messages", None)
stream_fn = getattr(messages_api, "stream", None)
if prefer_stream and callable(stream_fn):
stream_kwargs = dict(api_kwargs)
stream_kwargs.pop("stream", None)
try:
with stream_fn(**stream_kwargs) as stream:
return stream.get_final_message()
except Exception as exc:
if not _is_stream_unavailable_error(exc):
raise
logger.debug(
"%sAnthropic Messages stream unavailable; falling back to "
"messages.create(): %s",
log_prefix,
exc,
)
create_kwargs = dict(api_kwargs)
create_kwargs.pop("stream", None)
return messages_api.create(**create_kwargs)
+152 -945
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+46 -323
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@@ -27,131 +27,6 @@ from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Background-review aux-model selector + routed digest.
#
# The review fork runs on the MAIN model by default ("auto"), replaying the
# full conversation — already warm in the prompt cache, so cheap cache reads.
# Optimal and unchanged. A user can route the review to a different, cheaper
# model via auxiliary.background_review.{provider,model}. A different model
# cannot reuse the parent's cache (different key), so the fork is cold
# regardless — replaying the full transcript would just cold-write it. So when
# (and only when) routed to a different model, we replay a compact DIGEST to
# minimise cold-written tokens. Same model -> full replay; different model ->
# digest. That's the whole policy.
# ---------------------------------------------------------------------------
def _resolve_review_runtime(agent: Any) -> Dict[str, Any]:
"""Resolve provider/model/credentials for the review fork.
Default (auto / unset / same as parent): inherit the parent's live runtime
(with codex_app_server -> codex_responses downgrade). ``routed`` is False —
the fork uses the main model and the warm cache, exactly as before. When
``auxiliary.background_review.{provider,model}`` names a concrete model
different from the parent's, resolve that runtime and set ``routed=True``.
"""
parent_runtime = agent._current_main_runtime()
parent_api_mode = parent_runtime.get("api_mode") or None
if parent_api_mode == "codex_app_server":
parent_api_mode = "codex_responses"
parent = {
"provider": agent.provider,
"model": agent.model,
"api_key": parent_runtime.get("api_key") or None,
"base_url": parent_runtime.get("base_url") or None,
"api_mode": parent_api_mode,
"routed": False,
}
try:
from hermes_cli.config import load_config
cfg = load_config()
except Exception:
return parent
aux = cfg.get("auxiliary", {}) if isinstance(cfg.get("auxiliary"), dict) else {}
task = aux.get("background_review", {}) if isinstance(aux.get("background_review"), dict) else {}
task_provider = (str(task.get("provider", "")).strip() or None)
task_model = (str(task.get("model", "")).strip() or None)
task_base_url = (str(task.get("base_url", "")).strip() or None)
task_api_key = (str(task.get("api_key", "")).strip() or None)
if not (task_provider and task_provider != "auto" and task_model):
return parent
if task_provider == (agent.provider or "") and task_model == (agent.model or ""):
return parent # same model/provider as parent -> not routed
try:
from hermes_cli.runtime_provider import resolve_runtime_provider
rp = resolve_runtime_provider(
requested=task_provider,
target_model=task_model,
explicit_api_key=task_api_key,
explicit_base_url=task_base_url,
)
return {
"provider": rp.get("provider") or task_provider,
"model": task_model,
"api_key": rp.get("api_key"),
"base_url": rp.get("base_url"),
"api_mode": rp.get("api_mode"),
"routed": True,
}
except Exception as e:
logger.debug("background-review aux routing failed (%s); using main model", e)
return parent
def _msg_text(m: Dict) -> str:
c = m.get("content")
if isinstance(c, str):
return c.strip()
if isinstance(c, list):
return " ".join(b.get("text", "") for b in c if isinstance(b, dict)).strip()
return ""
def _digest_history(messages_snapshot: List[Dict], tail: int = 24) -> List[Dict]:
"""Compact replay for the routed (different-model) path only.
Keeps the recent ``tail`` messages verbatim, collapses older turns into one
synthetic user-role digest, preserving role alternation. Used ONLY when
routed to a different model (cache cold regardless, so fewer cold-written
tokens is a pure win). Never on the main-model path (full replay stays warm).
"""
msgs = list(messages_snapshot or [])
if len(msgs) <= tail:
return msgs
keep = msgs[-tail:]
while keep and isinstance(keep[0], dict) and keep[0].get("role") == "tool":
tail += 1
if len(msgs) <= tail:
return msgs
keep = msgs[-tail:]
old = msgs[:-len(keep)]
lines: List[str] = []
for m in old:
if not isinstance(m, dict):
continue
role = m.get("role")
text = _msg_text(m).replace("\n", " ")
if role == "user" and text:
lines.append(f"USER: {text[:300]}")
elif role == "assistant":
tcs = m.get("tool_calls") or []
if tcs:
names = [(tc.get("function") or {}).get("name", "?") for tc in tcs if isinstance(tc, dict)]
lines.append(f"ASSISTANT[tools: {', '.join(names)}]")
if text:
lines.append(f"ASSISTANT: {text[:200]}")
digest = {
"role": "user",
"content": (
"[Earlier conversation digest — older turns summarised to bound the "
"review's cold-write cost on the routed aux model. Recent turns "
"follow verbatim below.]\n" + "\n".join(lines)
),
}
return [digest] + keep
# Review-prompt strings — used by ``spawn_background_review_thread`` to build
# the user-message that the forked review agent receives. AIAgent exposes
# them as class attributes (``_MEMORY_REVIEW_PROMPT`` etc.) for back-compat;
@@ -362,25 +237,18 @@ _COMBINED_REVIEW_PROMPT = (
def summarize_background_review_actions(
review_messages: List[Dict],
prior_snapshot: List[Dict],
notification_mode: str = "on",
) -> List[str]:
"""Build the human-facing action summary for a background review pass.
Walks the review agent's session messages and collects successful memory
and skill-management actions to surface to the user. Tool messages already
present in ``prior_snapshot`` are skipped so stale inherited results are
not re-surfaced as fresh background work (issue #14944).
Walks the review agent's session messages and collects "successful tool
action" descriptions to surface to the user (e.g. "Memory updated").
Tool messages already present in ``prior_snapshot`` are skipped so we
don't re-surface stale results from the prior conversation that the
review agent inherited via ``conversation_history`` (issue #14944).
``notification_mode`` controls display detail:
- ``off``: return no actions.
- ``on``: generic "Memory updated"/tool messages.
- ``verbose``: include compact content previews from tool-call arguments.
Matching is by ``tool_call_id`` when available, with a content-equality
fallback for tool messages that lack one.
"""
mode = str(notification_mode or "on").lower()
if mode == "off":
return []
verbose = mode == "verbose"
existing_tool_call_ids = set()
existing_tool_contents = set()
for prior in prior_snapshot or []:
@@ -394,43 +262,6 @@ def summarize_background_review_actions(
if isinstance(content, str):
existing_tool_contents.add(content)
# Map review-agent tool results back to the calls that produced them. The
# result JSON only says "Entry added"; the call arguments contain action,
# target, and content previews. Restricting to notify_tools also prevents
# helper tools from surfacing as memory work just because they succeeded.
notify_tools = {"memory", "skill_manage"}
all_tool_call_ids: set = set()
call_details: dict = {}
for msg in review_messages or []:
if not isinstance(msg, dict) or msg.get("role") != "assistant":
continue
for tc in msg.get("tool_calls", []) or []:
if not isinstance(tc, dict):
continue
fn = tc.get("function", {}) or {}
fn_name = fn.get("name", "")
tcid = tc.get("id")
if tcid:
all_tool_call_ids.add(tcid)
if fn_name not in notify_tools:
continue
try:
args = json.loads(fn.get("arguments", "{}"))
except (json.JSONDecodeError, TypeError):
args = {}
if tcid:
call_details[tcid] = {
"tool": fn_name,
"action": args.get("action", "?"),
"target": args.get("target", "memory"),
"content": args.get("content", ""),
"old_text": args.get("old_text", ""),
"operations": args.get("operations") or [],
"name": args.get("name", ""),
"old_string": args.get("old_string", ""),
"new_string": args.get("new_string", ""),
}
actions: List[str] = []
for msg in review_messages or []:
if not isinstance(msg, dict) or msg.get("role") != "tool":
@@ -442,8 +273,6 @@ def summarize_background_review_actions(
content_str = msg.get("content")
if isinstance(content_str, str) and content_str in existing_tool_contents:
continue
if tcid and all_tool_call_ids and tcid not in call_details:
continue
try:
data = json.loads(msg.get("content", "{}"))
except (json.JSONDecodeError, TypeError):
@@ -451,92 +280,19 @@ def summarize_background_review_actions(
if not isinstance(data, dict) or not data.get("success"):
continue
message = data.get("message", "")
detail = call_details.get(tcid, {})
target = data.get("target", "") or detail.get("target", "")
is_skill = detail.get("tool") == "skill_manage"
message_lower = message.lower()
if not verbose:
if "created" in message_lower:
actions.append(message)
continue
if "updated" in message_lower:
actions.append(message)
continue
if is_skill and "patched" in message_lower:
actions.append(message)
continue
if is_skill:
label = "Skill"
elif target:
target = data.get("target", "")
if "created" in message.lower():
actions.append(message)
elif "updated" in message.lower():
actions.append(message)
elif "added" in message.lower() or (target and "add" in message.lower()):
label = "Memory" if target == "memory" else "User profile" if target == "user" else target
actions.append(f"{label} updated")
elif "Entry added" in message:
label = "Memory" if target == "memory" else "User profile" if target == "user" else target
actions.append(f"{label} updated")
elif "removed" in message.lower() or "replaced" in message.lower():
label = "Memory" if target == "memory" else "User profile" if target == "user" else target
else:
continue
if verbose:
action = detail.get("action", "")
content = detail.get("content", "")
old_text = detail.get("old_text", "")
skill_name = detail.get("name", "")
operations = detail.get("operations") or []
max_preview = 120
if is_skill:
change = data.get("_change", {})
old_string = change.get("old", "") or detail.get("old_string", "")
new_string = change.get("new", "") or detail.get("new_string", "")
description = change.get("description", "")
if action == "patch" and (old_string or new_string):
old_preview = old_string[:80].replace("\n", " ") + (
"…" if len(old_string) > 80 else ""
)
new_preview = new_string[:80].replace("\n", " ") + (
"…" if len(new_string) > 80 else ""
)
actions.append(
f"📝 Skill '{skill_name}' patched: "
f"\"{old_preview}\" → \"{new_preview}\""
)
elif action == "create" and description:
actions.append(f"📝 Skill '{skill_name}' created: {description}")
elif action == "edit" and description:
actions.append(f"📝 Skill '{skill_name}' rewritten: {description}")
else:
actions.append(f"📝 {message}" if message else f"Skill {action}")
elif operations:
for op in operations:
op = op or {}
op_act = op.get("action", "")
op_content = (op.get("content") or "")
op_old = (op.get("old_text") or "")
if op_act == "add" and op_content:
preview = op_content[:max_preview] + ("…" if len(op_content) > max_preview else "")
actions.append(f"{label} ➕ {preview}")
elif op_act == "replace" and op_content:
preview = op_content[:max_preview] + ("…" if len(op_content) > max_preview else "")
actions.append(f"{label} ✏️ {preview}")
elif op_act == "remove" and op_old:
preview = op_old[:60] + ("…" if len(op_old) > 60 else "")
actions.append(f"{label} ➖ {preview}")
elif action == "add" and content:
preview = content[:max_preview] + ("…" if len(content) > max_preview else "")
actions.append(f"{label} ➕ {preview}")
elif action == "replace" and content:
preview = content[:max_preview] + ("…" if len(content) > max_preview else "")
actions.append(f"{label} ✏️ {preview}")
elif action == "remove" and old_text:
preview = old_text[:60] + ("…" if len(old_text) > 60 else "")
actions.append(f"{label} ➖ {preview}")
else:
actions.append(f"{label} updated")
elif (
"added" in message_lower
or "replaced" in message_lower
or "removed" in message_lower
or "applied" in message_lower
or (target and "add" in message.lower())
or "Entry added" in message
):
actions.append(f"{label} updated")
return actions
@@ -613,13 +369,18 @@ def _run_review_in_thread(
# creds, or credential-pool setups where the resolver can't
# reconstruct auth from scratch -- producing the spurious
# "No LLM provider configured" warning at end of turn.
# _resolve_review_runtime() returns the parent's live runtime by
# default (routed=False; main model, warm cache), or — when the user
# set auxiliary.background_review.{provider,model} to a different
# model — that model's runtime (routed=True). The codex_app_server
# -> codex_responses downgrade is applied inside the resolver.
_rt = _resolve_review_runtime(agent)
_routed = bool(_rt.get("routed"))
_parent_runtime = agent._current_main_runtime()
_parent_api_mode = _parent_runtime.get("api_mode") or None
# The review fork needs to call agent-loop tools (memory,
# skill_manage). Those tools require Hermes' own dispatch,
# which the codex_app_server runtime bypasses entirely
# (it runs the turn inside codex's subprocess). So when
# the parent is on codex_app_server, downgrade the review
# fork to codex_responses — same auth/credentials, but
# talks to the OpenAI Responses API directly so Hermes
# owns the loop and the agent-loop tools dispatch.
if _parent_api_mode == "codex_app_server":
_parent_api_mode = "codex_responses"
# skip_memory=True keeps the review fork from
# touching external memory plugins (honcho, mem0,
# supermemory, etc.). Without it, the fork's
@@ -639,14 +400,14 @@ def _run_review_in_thread(
# in the request body — Anthropic's cache key includes it.
# (The runtime whitelist below still restricts dispatch.)
review_agent = AIAgent(
model=_rt.get("model") or agent.model,
model=agent.model,
max_iterations=16,
quiet_mode=True,
platform=agent.platform,
provider=_rt.get("provider") or agent.provider,
api_mode=_rt.get("api_mode"),
base_url=_rt.get("base_url") or None,
api_key=_rt.get("api_key") or None,
provider=agent.provider,
api_mode=_parent_api_mode,
base_url=_parent_runtime.get("base_url") or None,
api_key=_parent_runtime.get("api_key") or None,
credential_pool=getattr(agent, "_credential_pool", None),
parent_session_id=agent.session_id,
enabled_toolsets=getattr(agent, "enabled_toolsets", None),
@@ -655,13 +416,6 @@ def _run_review_in_thread(
)
review_agent._memory_write_origin = "background_review"
review_agent._memory_write_context = "background_review"
# The review fork pins the parent's cached system prompt and keeps
# ``tools[]`` byte-identical to the parent so its outbound request
# hits the same provider cache prefix (see the toolset-parity note
# above). The between-turns MCP refresh in build_turn_context would
# add late-connecting MCP tools to this fork and break that parity,
# so opt the review fork out of it.
review_agent._skip_mcp_refresh = True
review_agent._memory_store = agent._memory_store
review_agent._memory_enabled = agent._memory_enabled
review_agent._user_profile_enabled = agent._user_profile_enabled
@@ -685,39 +439,16 @@ def _run_review_in_thread(
# issue #25322 and PR #17276 for the full analysis +
# measured impact (~26% end-to-end cost reduction on
# Sonnet 4.5).
# Share the parent's warm cached system prompt ONLY when the review
# runs on the SAME model (not routed). When routed to a different
# model the parent's cached prompt is for the wrong model/cache key
# and would miss anyway, so let the routed fork build its own.
if not _routed:
review_agent._cached_system_prompt = agent._cached_system_prompt
# Defensive: pin session_start + session_id to the
# parent's so any code path that re-renders parts of
# the system prompt (compression, plugin hooks) still
# produces byte-identical output. The cached-prompt
# assignment above already short-circuits the normal
# rebuild path, but these pins guarantee parity even
# if a future code path bypasses the cache.
review_agent.session_start = agent.session_start
review_agent._cached_system_prompt = agent._cached_system_prompt
# Defensive: pin session_start + session_id to the
# parent's so any code path that re-renders parts of
# the system prompt (compression, plugin hooks) still
# produces byte-identical output. The cached-prompt
# assignment above already short-circuits the normal
# rebuild path, but these pins guarantee parity even
# if a future code path bypasses the cache.
review_agent.session_start = agent.session_start
review_agent.session_id = agent.session_id
# The fork shares the parent's live session_id (pinned above for
# prefix-cache parity). It is single-lifecycle and calls close()
# right after this run_conversation(); without opting out, close()
# would finalize the parent's still-active session row mid
# conversation (the review fires every ~10 turns). Leave session
# finalization to the real owner (CLI close / gateway reset / cron).
review_agent._end_session_on_close = False
# Never let the review fork compress. It shares the parent's
# session_id, so if it won a compression race it would rotate the
# parent into a NEW child that the gateway never adopts (the fork
# is single-lifecycle and dies right after this run_conversation).
# The foreground turn would then start from the stale parent and
# compress it again, leaving the same parent with two sibling
# children (issue #38727). Review also needs full context to
# produce a good memory/skill summary — compressing would strip
# detail. Both compression triggers in conversation_loop.py gate on
# agent.compression_enabled, so this short-circuits both paths.
review_agent.compression_enabled = False
from model_tools import get_tool_definitions
from hermes_cli.plugins import (
@@ -740,13 +471,6 @@ def _run_review_in_thread(
),
)
try:
# Routed to a different model -> replay a digest (cache is cold
# on that model anyway, so minimise cold-written tokens). Same
# model -> replay the full snapshot (warm cache reads).
_review_history = (
_digest_history(messages_snapshot) if _routed
else messages_snapshot
)
review_agent.run_conversation(
user_message=(
prompt
@@ -754,7 +478,7 @@ def _run_review_in_thread(
"management tools. Other tools will be denied "
"at runtime — do not attempt them."
),
conversation_history=_review_history,
conversation_history=messages_snapshot,
)
finally:
clear_thread_tool_whitelist()
@@ -787,7 +511,6 @@ def _run_review_in_thread(
actions = summarize_background_review_actions(
review_messages,
messages_snapshot,
notification_mode=getattr(agent, "memory_notifications", "on"),
)
if actions:
+6 -71
View File
@@ -58,34 +58,17 @@ _bedrock_runtime_client_cache: Dict[str, Any] = {}
_bedrock_control_client_cache: Dict[str, Any] = {}
_MIN_BOTO3_VERSION = (1, 34, 59)
def _require_boto3():
"""Import boto3, raising a clear error if not installed or too old."""
"""Import boto3, raising a clear error if not installed."""
try:
import boto3
return boto3
except ImportError:
raise ImportError(
"The 'boto3' package is required for the AWS Bedrock provider. "
"Install it with: pip install boto3\n"
"Or install Hermes with Bedrock support: pip install -e '.[bedrock]'"
)
# converse() / converse_stream() were added in boto3 1.34.59.
# When Hermes is installed editable into system Python, the system boto3
# (e.g. Ubuntu 24.04 ships 1.34.46) may take precedence over the venv
# version pinned in pyproject.toml.
try:
version = tuple(int(x) for x in boto3.__version__.split(".")[:3])
except (AttributeError, ValueError):
return boto3 # can't parse — don't block on version check
if version < _MIN_BOTO3_VERSION:
raise RuntimeError(
f"boto3 {boto3.__version__} does not support converse_stream "
f"(minimum 1.34.59 required). Upgrade with: "
f"pip install --upgrade boto3"
)
return boto3
def _get_bedrock_runtime_client(region: str):
@@ -225,41 +208,6 @@ def is_stale_connection_error(exc: BaseException) -> bool:
return False
def is_streaming_access_denied_error(exc: BaseException) -> bool:
"""Return True when AWS denied the ``bedrock:InvokeModelWithResponseStream`` action.
IAM policies scoped to ``bedrock:InvokeModel`` only (a common least-privilege
setup) reject ``converse_stream()`` with an ``AccessDeniedException`` whose
message names the streaming action, e.g.::
User: arn:aws:iam::123456789012:user/x is not authorized to perform:
bedrock:InvokeModelWithResponseStream on resource: ...
This is permanent for the session — retrying the stream can never succeed —
so callers should flip to the non-streaming ``converse()`` path (which maps
to ``bedrock:InvokeModel``) instead of burning retries.
Detection is deliberately message-based: boto3 surfaces this as a
``ClientError`` with ``Error.Code == "AccessDeniedException"``, and the
AnthropicBedrock SDK wraps the same AWS response in its own exception
types, but both preserve the action name in the message.
"""
msg = str(exc).lower()
if "invokemodelwithresponsestream" not in msg:
return False
# ClientError with an explicit access-denied code is the canonical form.
try:
from botocore.exceptions import ClientError
except ImportError: # pragma: no cover — botocore always present with boto3
ClientError = None # type: ignore[assignment]
if ClientError is not None and isinstance(exc, ClientError):
code = (getattr(exc, "response", None) or {}).get("Error", {}).get("Code", "")
return code in ("AccessDeniedException", "UnauthorizedException")
# Wrapped forms (e.g. AnthropicBedrock SDK PermissionDeniedError) — match
# on the authorization-failure phrasing AWS uses.
return "not authorized" in msg or "accessdenied" in msg
# ---------------------------------------------------------------------------
# AWS credential detection
# ---------------------------------------------------------------------------
@@ -952,14 +900,11 @@ def build_converse_kwargs(
if system_prompt:
kwargs["system"] = system_prompt
from agent.anthropic_adapter import _forbids_sampling_params
if temperature is not None:
kwargs["inferenceConfig"]["temperature"] = temperature
if not _forbids_sampling_params(model):
if temperature is not None:
kwargs["inferenceConfig"]["temperature"] = temperature
if top_p is not None:
kwargs["inferenceConfig"]["topP"] = top_p
if top_p is not None:
kwargs["inferenceConfig"]["topP"] = top_p
if stop_sequences:
kwargs["inferenceConfig"]["stopSequences"] = stop_sequences
@@ -1058,16 +1003,6 @@ def call_converse_stream(
try:
response = client.converse_stream(**kwargs)
except Exception as exc:
if is_streaming_access_denied_error(exc):
# IAM allows bedrock:InvokeModel but not
# InvokeModelWithResponseStream — permanent for this session.
# Fall back to the non-streaming converse() path.
logger.info(
"bedrock: converse_stream denied by IAM on (region=%s, model=%s) — "
"falling back to non-streaming converse().",
region, model,
)
return normalize_converse_response(client.converse(**kwargs))
if is_stale_connection_error(exc):
logger.warning(
"bedrock: stale-connection error on converse_stream(region=%s, "
-295
View File
@@ -1,295 +0,0 @@
"""Surface-agnostic core for the Phase 2b terminal-billing screens.
One fetch/parse per concern, consumed identically by the CLI handler
(``cli.py::_show_billing``), the TUI JSON-RPC methods
(``tui_gateway/server.py``), and any other surface. Mirrors the proven
``agent/account_usage.py::build_credits_view`` pattern: parse the server payload
into a frozen dataclass; **fail open** — when not logged in or the portal is
unreachable, return a struct with ``logged_in=False`` and let the surface degrade
gracefully (never crash).
Money discipline: the server emits decimal STRINGS (``"142.5"``, not fixed 2dp).
We keep them as :class:`decimal.Decimal` end-to-end and only format for display.
"""
from __future__ import annotations
import logging
import uuid
from dataclasses import dataclass, field
from decimal import Decimal, InvalidOperation
from typing import Any, Optional
logger = logging.getLogger(__name__)
# =============================================================================
# Decimal money helpers
# =============================================================================
def parse_money(value: Any) -> Optional[Decimal]:
"""Parse a server money value (decimal string) into :class:`Decimal`.
Returns None for missing/invalid input. Never raises. Accepts str/int (and,
defensively, float — though the server always sends strings).
"""
if value is None:
return None
try:
# Decimal(str(...)) avoids binary-float artifacts if a float ever sneaks in.
return Decimal(str(value).strip())
except (InvalidOperation, ValueError, TypeError):
return None
def format_money(value: Optional[Decimal]) -> str:
"""Format a Decimal as ``$X`` / ``$X.YY`` for display.
Whole dollars show no decimals; any fractional amount shows exactly 2dp:
``Decimal("142.5")`` → ``"$142.50"``, ``Decimal("100")`` → ``"$100"``,
``Decimal("0.01")`` → ``"$0.01"``.
"""
if value is None:
return "—"
if value == value.to_integral_value():
# Whole dollars — no decimal point. format(..., "f") avoids 1E+3 for 1000.
return f"${format(value.to_integral_value(), 'f')}"
# Fractional — always show 2dp.
return f"${format(value.quantize(Decimal('0.01')), 'f')}"
# =============================================================================
# Parsed sub-structures
# =============================================================================
@dataclass(frozen=True)
class CardInfo:
brand: str
last4: str
@property
def masked(self) -> str:
return f"{self.brand} ····{self.last4}"
@dataclass(frozen=True)
class MonthlyCap:
limit_usd: Optional[Decimal] = None
spent_this_month_usd: Optional[Decimal] = None
is_default_ceiling: bool = False
@dataclass(frozen=True)
class AutoReload:
enabled: bool = False
threshold_usd: Optional[Decimal] = None
reload_to_usd: Optional[Decimal] = None
@dataclass(frozen=True)
class BillingState:
"""Parsed ``GET /api/billing/state`` — the overview screen's data.
Fail-open: ``logged_in=False`` (and empty fields) when not logged in or the
portal is unreachable.
"""
logged_in: bool
org_id: Optional[str] = None
org_slug: Optional[str] = None
org_name: Optional[str] = None
role: Optional[str] = None # "OWNER" | "ADMIN" | "MEMBER"
balance_usd: Optional[Decimal] = None
cli_billing_enabled: bool = False
charge_presets: tuple[Decimal, ...] = ()
min_usd: Optional[Decimal] = None
max_usd: Optional[Decimal] = None
card: Optional[CardInfo] = None
monthly_cap: Optional[MonthlyCap] = None
auto_reload: Optional[AutoReload] = None
portal_url: Optional[str] = None
# When the fetch failed (vs cleanly not-logged-in), the message for the surface.
error: Optional[str] = None
@property
def is_admin(self) -> bool:
"""True for OWNER/ADMIN — the roles that can manage billing."""
return (self.role or "").upper() in ("OWNER", "ADMIN")
@property
def can_charge(self) -> bool:
"""True when the UI should offer charge/auto-reload actions.
Admin role AND the per-org kill-switch on. (The server still enforces;
this is just for graying out actions the user can't take.)
"""
return self.is_admin and self.cli_billing_enabled
def _parse_card(raw: Any) -> Optional[CardInfo]:
if not isinstance(raw, dict):
return None
brand = raw.get("brand")
last4 = raw.get("last4")
if isinstance(brand, str) and isinstance(last4, str):
return CardInfo(brand=brand, last4=last4)
return None
def _parse_monthly_cap(raw: Any) -> Optional[MonthlyCap]:
if not isinstance(raw, dict):
return None
return MonthlyCap(
limit_usd=parse_money(raw.get("limitUsd")),
spent_this_month_usd=parse_money(raw.get("spentThisMonthUsd")),
is_default_ceiling=bool(raw.get("isDefaultCeiling")),
)
def _parse_auto_reload(raw: Any) -> Optional[AutoReload]:
if not isinstance(raw, dict):
return None
return AutoReload(
enabled=bool(raw.get("enabled")),
threshold_usd=parse_money(raw.get("thresholdUsd")),
reload_to_usd=parse_money(raw.get("reloadToUsd")),
)
def billing_state_from_payload(
payload: dict[str, Any], *, portal_url: Optional[str] = None
) -> BillingState:
"""Map a raw ``/api/billing/state`` JSON dict into :class:`BillingState`."""
raw_org = payload.get("org")
org: dict[str, Any] = raw_org if isinstance(raw_org, dict) else {}
raw_bounds = payload.get("bounds")
bounds: dict[str, Any] = raw_bounds if isinstance(raw_bounds, dict) else {}
presets: list[Decimal] = []
for item in payload.get("chargePresets") or ():
parsed = parse_money(item)
if parsed is not None:
presets.append(parsed)
return BillingState(
logged_in=True,
org_id=org.get("id"),
org_slug=org.get("slug"),
org_name=org.get("name"),
role=org.get("role"),
balance_usd=parse_money(payload.get("balanceUsd")),
cli_billing_enabled=bool(payload.get("cliBillingEnabled")),
charge_presets=tuple(presets),
min_usd=parse_money(bounds.get("minUsd")),
max_usd=parse_money(bounds.get("maxUsd")),
card=_parse_card(payload.get("card")),
monthly_cap=_parse_monthly_cap(payload.get("monthlyCap")),
auto_reload=_parse_auto_reload(payload.get("autoReload")),
portal_url=portal_url,
)
# =============================================================================
# Fail-open builders (the surface front doors)
# =============================================================================
def build_billing_state(*, timeout: float = 15.0) -> BillingState:
"""Fetch + parse ``/api/billing/state``. Fail-open.
Returns ``BillingState(logged_in=False)`` when not logged in. On a portal/HTTP
failure, returns ``logged_in=False`` with ``error`` set so the surface can show
a clear message rather than crashing.
"""
try:
from hermes_cli.nous_billing import (
BillingAuthError,
BillingError,
_absolutize_portal_url,
get_billing_state,
resolve_portal_base_url,
)
except Exception:
return BillingState(logged_in=False, error="billing client unavailable")
try:
payload = get_billing_state(timeout=timeout)
except BillingAuthError:
return BillingState(logged_in=False)
except BillingError as exc:
logger.debug("billing ▸ /state fetch failed (fail-open)", exc_info=True)
return BillingState(logged_in=False, error=str(exc))
except Exception:
logger.debug("billing ▸ /state unexpected error (fail-open)", exc_info=True)
return BillingState(logged_in=False, error="could not load billing state")
# Prefer a server-supplied portalUrl if present (resolved to absolute in case
# it's relative); else build the standard one.
raw_portal = payload.get("portalUrl") if isinstance(payload, dict) else None
portal_url = _absolutize_portal_url(raw_portal) if raw_portal else None
if not portal_url:
try:
portal_url = _fallback_portal_url(resolve_portal_base_url())
except Exception:
portal_url = None
return billing_state_from_payload(payload, portal_url=portal_url)
def _fallback_portal_url(base: str) -> str:
"""Standard billing deep-link when the server omits ``portalUrl``."""
return f"{base.rstrip('/')}/billing?topup=open"
# =============================================================================
# Idempotency
# =============================================================================
def new_idempotency_key() -> str:
"""Fresh UUID for a user-confirmed purchase (reuse on retry of the SAME buy).
The ``Idempotency-Key`` header is mandatory on ``POST /charge``; generate one
per confirmed purchase and reuse it across retries so a double-submit collapses
to a single charge. Never reuse a key across different amounts (the server
returns 409 idempotency_conflict).
"""
return str(uuid.uuid4())
# =============================================================================
# Amount validation (Screen 3 custom input)
# =============================================================================
@dataclass(frozen=True)
class AmountValidation:
ok: bool
amount: Optional[Decimal] = None
error: Optional[str] = None
def validate_charge_amount(
raw: str, *, min_usd: Optional[Decimal], max_usd: Optional[Decimal]
) -> AmountValidation:
"""Validate a custom charge amount against bounds + 2dp (multipleOf 0.01).
Mirrors the server's accept/reject so the UI can give instant feedback rather
than round-tripping a sure-to-fail charge. The server is still authoritative.
"""
cleaned = (raw or "").strip().lstrip("$").strip()
amount = parse_money(cleaned)
if amount is None:
return AmountValidation(ok=False, error="Enter a dollar amount, e.g. 100")
if amount <= 0:
return AmountValidation(ok=False, error="Amount must be greater than $0")
# multipleOf 0.01 — reject sub-cent precision.
if amount != amount.quantize(Decimal("0.01")):
return AmountValidation(ok=False, error="Amount can't be smaller than a cent")
if min_usd is not None and amount < min_usd:
return AmountValidation(ok=False, error=f"Minimum is {format_money(min_usd)}")
if max_usd is not None and amount > max_usd:
return AmountValidation(ok=False, error=f"Maximum is {format_money(max_usd)}")
return AmountValidation(ok=True, amount=amount)
+49 -454
View File
@@ -28,28 +28,15 @@ from typing import Any, Dict, Optional
from hermes_cli.timeouts import get_provider_request_timeout, get_provider_stale_timeout
from hermes_constants import PARTIAL_STREAM_STUB_ID, FINISH_REASON_LENGTH
from agent.error_classifier import FailoverReason
from agent.gemini_native_adapter import is_native_gemini_base_url
from agent.model_metadata import is_local_endpoint
from agent.message_sanitization import (
_sanitize_surrogates,
_repair_tool_call_arguments,
)
from tools.terminal_tool import is_persistent_env
from utils import base_url_host_matches, base_url_hostname, env_float, env_int
from utils import base_url_host_matches, base_url_hostname
logger = logging.getLogger(__name__)
_OPENROUTER_PROVIDER_SORT_VALUES = {"throughput", "latency", "price"}
# When the fallback chain is fully exhausted on a non-rate-limit failure
# (e.g. every provider returns a non-retryable client error like HTTP 400),
# arm a short cooldown so the NEXT turn's restore_primary_runtime stays gated
# and does not reset _fallback_index=0 to replay the entire chain again.
# Without this, a client/gateway that re-submits immediately would re-marshal
# the full (potentially 80k-token) context once per provider every turn and
# can drive a constrained host into memory/swap exhaustion. Rate-limit /
# billing reasons keep their own 60s cooldown (set above); this is the
# narrower non-rate-limit case. See issue #24996.
_FALLBACK_EXHAUSTED_COOLDOWN_S = 5.0
def _ra():
@@ -128,23 +115,6 @@ def _is_openai_codex_backend(agent) -> bool:
)
def _validated_openrouter_provider_sort(raw_sort: Any) -> Optional[str]:
"""Return a normalized OpenRouter provider.sort value or None."""
if not isinstance(raw_sort, str):
return None
sort_value = raw_sort.strip().lower()
if not sort_value:
return None
if sort_value in _OPENROUTER_PROVIDER_SORT_VALUES:
return sort_value
logger.warning(
"Ignoring invalid OpenRouter provider.sort value %r (allowed: %s)",
raw_sort,
", ".join(sorted(_OPENROUTER_PROVIDER_SORT_VALUES)),
)
return None
def _env_float(name: str, default: float) -> float:
try:
return float(os.getenv(name, str(default)))
@@ -169,15 +139,6 @@ def interruptible_api_call(agent, api_kwargs: dict):
result = {"response": None, "error": None}
request_client_holder = {"client": None, "owner_tid": None}
request_client_lock = threading.Lock()
# Request-local cancellation flag. Distinct from agent._interrupt_requested
# because that flag is cleared at run_conversation() turn boundaries, but
# this daemon worker thread can outlive the turn (the gateway caches
# AIAgent instances per session). Tracks whether THIS specific request was
# cancelled by the main thread's interrupt handler, so the transport error
# that is the expected consequence of our own force-close isn't misread as
# a network bug and surfaced to the caller. (PR #6600 — cascading interrupt
# hang.)
_request_cancelled = {"value": False}
def _set_request_client(client):
with request_client_lock:
@@ -259,11 +220,6 @@ def interruptible_api_call(agent, api_kwargs: dict):
invalidate_runtime_client(region)
raise
result["response"] = normalize_converse_response(raw_response)
elif agent.provider == "moa":
# MoA is a virtual chat-completions provider backed by the
# in-process MoAClient facade. Do not rebuild a request-local
# OpenAI client from the virtual runtime metadata.
result["response"] = agent.client.chat.completions.create(**api_kwargs)
else:
request_client = _set_request_client(
agent._create_request_openai_client(
@@ -273,17 +229,6 @@ def interruptible_api_call(agent, api_kwargs: dict):
)
result["response"] = request_client.chat.completions.create(**api_kwargs)
except Exception as e:
# If the request was cancelled by the main thread's interrupt
# handler, the transport error is the expected consequence of our
# own force-close, NOT a network bug. Swallow it instead of
# surfacing — the main thread raises InterruptedError. (#6600)
if _request_cancelled["value"]:
logger.debug(
"Non-streaming worker caught %s after request cancellation — "
"exiting without surfacing a network error.",
type(e).__name__,
)
return
result["error"] = e
finally:
_close_request_client_once("request_complete")
@@ -561,14 +506,6 @@ def interruptible_api_call(agent, api_kwargs: dict):
break
if agent._interrupt_requested:
# Mark THIS request cancelled before force-closing so the worker's
# exception handler recognizes the forced transport error as a
# cancel and exits cleanly instead of surfacing a network error or
# (in the streaming path) burning full retry cycles. (#6600)
_request_cancelled["value"] = True
logger.debug(
"Force-closing httpx client due to interrupt (not a network error)."
)
# Force-close the in-flight worker-local HTTP connection to stop
# token generation without poisoning the shared client used to
# seed future retries.
@@ -733,9 +670,8 @@ def build_api_kwargs(agent, api_messages: list) -> dict:
_prefs["ignore"] = agent.providers_ignored
if agent.providers_order:
_prefs["order"] = agent.providers_order
_provider_sort = _validated_openrouter_provider_sort(agent.provider_sort)
if _provider_sort:
_prefs["sort"] = _provider_sort
if agent.provider_sort:
_prefs["sort"] = agent.provider_sort
if agent.provider_require_parameters:
_prefs["require_parameters"] = True
if agent.provider_data_collection:
@@ -988,18 +924,6 @@ def build_assistant_message(agent, assistant_message, finish_reason: str) -> dic
if preserved:
msg["reasoning_details"] = preserved
# Anthropic interleaved-thinking replay: when a turn interleaves signed
# thinking blocks with tool_use, the parallel reasoning_details +
# tool_calls fields lose the cross-type ordering, and reconstruction
# front-loads thinking — reordering signed blocks and triggering HTTP 400
# ("thinking ... blocks in the latest assistant message cannot be
# modified"). Carry the verbatim ordered block list so the adapter can
# replay the latest assistant message unchanged. See
# agent/transports/anthropic.py and agent/anthropic_adapter.py.
ordered_blocks = getattr(assistant_message, "anthropic_content_blocks", None)
if ordered_blocks:
msg["anthropic_content_blocks"] = ordered_blocks
# Codex Responses API: preserve encrypted reasoning items for
# multi-turn continuity. These get replayed as input on the next turn.
codex_items = getattr(assistant_message, "codex_reasoning_items", None)
@@ -1051,23 +975,18 @@ def build_assistant_message(agent, assistant_message, finish_reason: str) -> dic
"arguments": tool_call.function.arguments
},
}
# Tool-call arguments are intentionally NOT redacted here. This
# dict enters the in-memory conversation history that is replayed
# to the model on every subsequent turn AND persisted to state.db,
# which is itself replayed verbatim on session resume
# (get_messages_as_conversation). Masking a credential to `***`
# here poisons that replay: the model reads back its own
# `PGPASSWORD='***' psql ...` call and copies the placeholder into
# the next tool call, breaking every credential-dependent command
# on the second turn (#43083). The masking also provided no real
# protection — the same secret still leaks verbatim through tool
# OUTPUT (file contents, command output, diffs, the compaction
# block), none of which this pass ever touched. Keeping secrets
# out of the replayable store is a separate tokenization/vault
# concern, not something arg-redaction can deliver without
# breaking replay. Storage-time redaction remains governed by the
# `security.redact_secrets` toggle. (#19798 introduced this;
# #43083 removed it.)
# Defence-in-depth: redact credentials from tool call arguments
# before they enter conversation history. Tool execution uses the
# raw API response object, not this dict, so redacting the
# persisted shape is safe and only affects storage. Catches the
# case where a model accidentally inlines a secret into a tool
# call (e.g. `terminal(command="curl -H 'Authorization: Bearer
# sk-...'")`). (#19798)
if isinstance(tc_dict["function"]["arguments"], str):
from agent.redact import redact_sensitive_text
tc_dict["function"]["arguments"] = redact_sensitive_text(
tc_dict["function"]["arguments"]
)
# Preserve extra_content (e.g. Gemini thought_signature) so it
# is sent back on subsequent API calls. Without this, Gemini 3
# thinking models reject the request with a 400 error.
@@ -1083,35 +1002,6 @@ def build_assistant_message(agent, assistant_message, finish_reason: str) -> dic
def rewrite_prompt_model_identity(agent, model: str, provider: str) -> None:
"""Point the cached system prompt's ``Model:``/``Provider:`` lines at
the active runtime after a provider switch.
The system prompt is session-stable and replayed verbatim for prefix-cache
warmth, but after a failover the new backend's cache is cold anyway —
while a stale identity line makes the agent misreport which model it is
when asked. Rewrite the lines in place WITHOUT persisting to the session
DB: the stored row keeps the primary's labels, so when the primary is
restored the prompt is byte-identical to the stored copy again and its
prefix cache still matches.
Only the LAST occurrence of each line is touched — the identity lines
live in the volatile tail of the prompt, and earlier matches could be
user content (memory snapshots, context files).
"""
sp = getattr(agent, "_cached_system_prompt", None)
if not isinstance(sp, str) or not sp:
return
for label, value in (("Model", model), ("Provider", provider)):
if not value:
continue
matches = list(re.finditer(rf"(?m)^{label}: .*$", sp))
if matches:
last = matches[-1]
sp = f"{sp[:last.start()]}{label}: {value}{sp[last.end():]}"
agent._cached_system_prompt = sp
def try_activate_fallback(agent, reason: "FailoverReason | None" = None) -> bool:
"""Switch to the next fallback model/provider in the chain.
@@ -1134,22 +1024,8 @@ def try_activate_fallback(agent, reason: "FailoverReason | None" = None) -> bool
if (not fallback_already_active) or (primary_provider and current_provider == primary_provider):
agent._rate_limited_until = time.monotonic() + 60
if agent._fallback_index >= len(agent._fallback_chain):
# Chain exhausted. If we actually walked a non-empty chain and the
# failure was NOT a rate-limit/billing event (those already armed
# their own 60s cooldown above), arm a short cooldown so the next
# turn's restore_primary_runtime stays gated instead of resetting
# _fallback_index=0 and re-marshaling the whole context across every
# provider again. Guards the cross-turn replay storm in #24996.
if (
len(agent._fallback_chain) > 0
and reason not in {FailoverReason.rate_limit, FailoverReason.billing}
):
_existing_cooldown = getattr(agent, "_rate_limited_until", 0) or 0
agent._rate_limited_until = max(
_existing_cooldown,
time.monotonic() + _FALLBACK_EXHAUSTED_COOLDOWN_S,
)
return False
fb = agent._fallback_chain[agent._fallback_index]
agent._fallback_index += 1
fb_provider = (fb.get("provider") or "").strip().lower()
@@ -1265,16 +1141,14 @@ def try_activate_fallback(agent, reason: "FailoverReason | None" = None) -> bool
agent._transport_cache.clear()
agent._fallback_activated = True
# Rebind the credential pool to the fallback provider when the provider
# changes. Keeping the primary pool attached would make downstream
# recovery (rate_limit / billing / auth) mutate the wrong credential
# set and can overwrite the fallback's base_url back to the primary
# endpoint. See #33163.
#
# Clear the credential pool when the fallback provider doesn't match
# the pool's provider. The pool was seeded for the primary provider;
# leaving it attached means downstream recovery (rate_limit / billing /
# auth) calls ``_swap_credential`` with a primary entry which overwrites
# the agent's ``base_url`` back to the primary's endpoint — every
# fallback request then 404s against the wrong host. See #33163.
# When the fallback shares the pool's provider (e.g. both openrouter
# entries with different routing) the pool is preserved. When the
# providers differ, load the fallback provider's own pool if one exists
# so provider-specific rotation continues to work after the switch.
# entries with different routing) the pool is preserved.
_existing_pool = getattr(agent, "_credential_pool", None)
if _existing_pool is not None:
_pool_provider = (getattr(_existing_pool, "provider", "") or "").strip().lower()
@@ -1285,22 +1159,6 @@ def try_activate_fallback(agent, reason: "FailoverReason | None" = None) -> bool
fb_provider, fb_model, _pool_provider,
)
agent._credential_pool = None
if getattr(agent, "_credential_pool", None) is None:
try:
from agent.credential_pool import load_pool
fallback_pool = load_pool(fb_provider)
if fallback_pool and fallback_pool.has_credentials():
agent._credential_pool = fallback_pool
logger.info(
"Fallback to %s/%s: attached fallback credential pool",
fb_provider, fb_model,
)
except Exception as exc:
logger.debug(
"Fallback to %s/%s: could not attach credential pool: %s",
fb_provider, fb_model, exc,
)
# Honor per-provider / per-model request_timeout_seconds for the
# fallback target (same knob the primary client uses). None = use
@@ -1389,10 +1247,6 @@ def try_activate_fallback(agent, reason: "FailoverReason | None" = None) -> bool
api_mode=agent.api_mode,
)
# Keep the prompt's self-identity in sync with the model actually
# answering, so "what model are you?" doesn't report the primary.
rewrite_prompt_model_identity(agent, fb_model, fb_provider)
agent._buffer_status(
f"🔄 Primary model failed — switching to fallback: "
f"{fb_model} via {fb_provider}"
@@ -1531,9 +1385,8 @@ def handle_max_iterations(agent, messages: list, api_call_count: int) -> str:
provider_preferences["ignore"] = agent.providers_ignored
if agent.providers_order:
provider_preferences["order"] = agent.providers_order
_provider_sort = _validated_openrouter_provider_sort(agent.provider_sort)
if _provider_sort:
provider_preferences["sort"] = _provider_sort
if agent.provider_sort:
provider_preferences["sort"] = agent.provider_sort
if provider_preferences and (
(agent.provider or "").strip().lower() == "openrouter"
or agent._is_openrouter_url()
@@ -1722,8 +1575,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
_get_bedrock_runtime_client,
invalidate_runtime_client,
is_stale_connection_error,
is_streaming_access_denied_error,
normalize_converse_response,
stream_converse_with_callbacks,
)
region = api_kwargs.pop("__bedrock_region__", "us-east-1")
@@ -1732,29 +1583,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
try:
raw_response = client.converse_stream(**api_kwargs)
except Exception as _bedrock_exc:
# IAM policies scoped to bedrock:InvokeModel only (no
# InvokeModelWithResponseStream) reject converse_stream()
# with AccessDeniedException. That denial is permanent for
# the session — fall back to the non-streaming converse()
# inline (it maps to bedrock:InvokeModel) and disable
# streaming for subsequent calls so we don't re-fail every
# turn.
if is_streaming_access_denied_error(_bedrock_exc):
agent._disable_streaming = True
agent._safe_print(
"\n⚠ AWS IAM denied bedrock:InvokeModelWithResponseStream — "
"falling back to non-streaming InvokeModel.\n"
" Grant that action to restore streaming output.\n"
)
logger.info(
"bedrock: converse_stream denied by IAM (%s) — "
"using non-streaming converse() for this session.",
type(_bedrock_exc).__name__,
)
result["response"] = normalize_converse_response(
client.converse(**api_kwargs)
)
return
# Evict the cached client on stale-connection failures
# so the outer retry loop builds a fresh client/pool.
if is_stale_connection_error(_bedrock_exc):
@@ -1797,14 +1625,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
result = {"response": None, "error": None, "partial_tool_names": []}
request_client_holder = {"client": None, "diag": None, "owner_tid": None}
request_client_lock = threading.Lock()
# Request-local cancellation flag — see interruptible_api_call for the full
# rationale. The streaming retry loop is where the 7-minute cascading-
# interrupt hang originated: a force-close raised RemoteProtocolError, the
# loop classified it as a transient network error, and burned full retry
# cycles (and emitted "reconnecting" noise) on a request the user already
# cancelled. The token lets the worker recognize its own forced close and
# exit immediately instead of retrying. (PR #6600.)
_request_cancelled = {"value": False}
def _set_request_client(client):
with request_client_lock:
@@ -1842,14 +1662,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
# poll loop uses this to detect stale connections that keep receiving
# SSE keep-alive pings but no actual data.
last_chunk_time = {"t": time.time()}
# Stale-stream patience, shared between the httpx socket read timeout
# (built in ``_call_chat_completions`` below) and the stale-stream detector
# (computed further down, before the worker thread starts). Initialized
# here so the read-timeout builder can floor itself at the stale value and
# never fire before the detector. ``None`` until the detector value is
# resolved, so the builder degrades to its plain default if it ever runs
# first.
_stream_stale_timeout = None
def _fire_first_delta():
if not first_delta_fired["done"] and on_first_delta:
@@ -1868,14 +1680,14 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
_base_timeout = (
_provider_timeout_cfg
if _provider_timeout_cfg is not None
else env_float("HERMES_API_TIMEOUT", 1800.0)
else float(os.getenv("HERMES_API_TIMEOUT", 1800.0))
)
# Read timeout: config wins here too. Otherwise use
# HERMES_STREAM_READ_TIMEOUT (default 120s) for cloud providers.
if _provider_timeout_cfg is not None:
_stream_read_timeout = _provider_timeout_cfg
else:
_stream_read_timeout = env_float("HERMES_STREAM_READ_TIMEOUT", 120.0)
_stream_read_timeout = float(os.getenv("HERMES_STREAM_READ_TIMEOUT", 120.0))
# Local providers (Ollama, llama.cpp, vLLM) can take minutes for
# prefill on large contexts before producing the first token.
# Auto-increase the httpx read timeout unless the user explicitly
@@ -1886,32 +1698,13 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
"Local provider detected (%s) — stream read timeout raised to %.0fs",
agent.base_url, _stream_read_timeout,
)
elif (
_stream_read_timeout == 120.0
and _stream_stale_timeout is not None
and _stream_stale_timeout != float("inf")
and _stream_stale_timeout > _stream_read_timeout
):
# Cloud reasoning models (e.g. Opus) routinely pause mid-stream
# for minutes during extended thinking. The stale-stream
# detector is deliberately scaled up to tolerate this (180–300s,
# see the stale-timeout block below), but the raw httpx socket
# read timeout defaulted to a flat 120s and fired *first* —
# tearing down a healthy reasoning stream before the stale
# detector (which owns retry + diagnostics) could act. Keep the
# socket read timeout in step with the detector so it no longer
# preempts it.
_stream_read_timeout = _stream_stale_timeout
logger.debug(
"Cloud reasoning stream — read timeout raised to %.0fs to "
"match stale-stream detector", _stream_read_timeout,
)
# Cap connect/pool at 60s even when provider timeout is higher.
# connect/pool cover TCP handshake, not model inference.
_conn_cap = min(_base_timeout, 60.0) if _provider_timeout_cfg is not None else 30.0
stream_kwargs = {
**api_kwargs,
"stream": True,
"stream_options": {"include_usage": True},
"timeout": _httpx.Timeout(
connect=_conn_cap,
read=_stream_read_timeout,
@@ -1919,14 +1712,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
pool=_conn_cap,
),
}
# OpenAI's `stream_options={"include_usage": True}` drives usage
# accounting on OpenAI-compatible endpoints (incl. the Gemini OpenAI
# compat shim and aggregators like OpenRouter). Google's *native*
# Gemini REST endpoint rejects the keyword outright
# (`Completions.create() got an unexpected keyword argument
# 'stream_options'`), so omit it only for that endpoint.
if not is_native_gemini_base_url(agent.base_url):
stream_kwargs["stream_options"] = {"include_usage": True}
request_client = _set_request_client(
agent._create_request_openai_client(
reason="chat_completion_stream_request",
@@ -1948,7 +1733,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
# The OpenAI SDK Stream object exposes the underlying httpx
# response via .response before any chunks are consumed.
agent._capture_rate_limits(getattr(stream, "response", None))
agent._capture_credits(getattr(stream, "response", None))
# Snapshot diagnostic headers (cf-ray, x-openrouter-provider, etc.)
# so they survive even when the stream dies before any chunk
# arrives. Best-effort; never raises.
@@ -2151,72 +1935,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
),
))
# Zero-chunk guard: stream yielded nothing usable — a provider/upstream
# error or malformed SSE, not a legitimate empty completion. Raise so the
# retry machinery handles it instead of fabricating a successful turn.
if (
finish_reason is None
and not content_parts
and not reasoning_parts
and not tool_calls_acc
):
raise RuntimeError(
"Provider returned an empty stream with no finish_reason "
"(possible upstream error or malformed SSE response)."
)
# A stream that delivered a tool call but only partial/unparseable
# JSON args splits into two very different cases:
#
# 1. Provider sent finish_reason="length" → a genuine output-cap
# truncation. Boosting max_tokens on retry is the right move.
#
# 2. Provider sent NO finish_reason (the SSE simply stopped after
# the opening "{" with no terminator and no [DONE]) → the
# upstream dropped/stalled the connection mid tool-call. This
# is NOT an output cap — the model never reported hitting one.
# Some dedicated endpoints (e.g. NVIDIA Nemotron Ultra on the
# Nous dedicated endpoint) stall for minutes during large
# tool-arg generation, then close the stream cleanly without a
# finish_reason. Stamping "length" here sends it down the
# max_tokens-boost truncation path, which retries 3× to no
# effect and finally reports the misleading "Response truncated
# due to output length limit" — the red herring this guards
# against. Route it through the partial-stream-stub path
# instead so the loop reports an honest mid-tool-call stream
# drop and fails fast rather than escalating output budget.
_tool_args_dropped_no_finish = has_truncated_tool_args and finish_reason is None
if _tool_args_dropped_no_finish:
_dropped_names = [
(tool_calls_acc[idx]["function"]["name"] or "?")
for idx in sorted(tool_calls_acc)
]
logger.warning(
"Stream ended with no finish_reason while a tool call's "
"arguments were still incomplete (tools=%s); treating as a "
"mid-tool-call stream drop, not an output-length truncation.",
_dropped_names,
)
full_reasoning = "".join(reasoning_parts) or None
mock_message = SimpleNamespace(
role=role,
content=full_content,
tool_calls=None,
reasoning_content=full_reasoning,
)
mock_choice = SimpleNamespace(
index=0,
message=mock_message,
finish_reason=FINISH_REASON_LENGTH,
)
return SimpleNamespace(
id=PARTIAL_STREAM_STUB_ID,
model=model_name,
choices=[mock_choice],
usage=usage_obj,
_dropped_tool_names=_dropped_names or None,
)
effective_finish_reason = finish_reason or "stop"
if has_truncated_tool_args:
effective_finish_reason = "length"
@@ -2255,14 +1973,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
# Per-attempt diagnostic dict for the retry block to consume.
_diag = agent._stream_diag_init()
request_client_holder["diag"] = _diag
# Defensive: strip Responses-only kwargs (instructions, input, ...)
# that can leak in under an api_mode-flip race. The Anthropic SDK
# raises a non-retryable TypeError on them, killing the turn. See
# #31673 / sanitize_anthropic_kwargs().
from agent.anthropic_adapter import sanitize_anthropic_kwargs
sanitize_anthropic_kwargs(
api_kwargs, log_prefix=getattr(agent, "log_prefix", "")
)
# Use the Anthropic SDK's streaming context manager
with agent._anthropic_client.messages.stream(**api_kwargs) as stream:
# The Anthropic SDK exposes the raw httpx response on
@@ -2327,21 +2037,13 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
_fire_first_delta()
agent._fire_reasoning_delta(thinking_text)
# Return the native Anthropic Message for downstream processing.
# If the stream was interrupted (the event loop broke out above on
# agent._interrupt_requested), do NOT call get_final_message() — on
# a partially-consumed stream the SDK may hang draining remaining
# events or return a Message with incomplete tool_use blocks (partial
# JSON in `input`). The outer poll loop raises InterruptedError, so
# this return value is discarded anyway.
if agent._interrupt_requested:
return None
# Return the native Anthropic Message for downstream processing
return stream.get_final_message()
def _call():
import httpx as _httpx
_max_stream_retries = env_int("HERMES_STREAM_RETRIES", 2)
_max_stream_retries = int(os.getenv("HERMES_STREAM_RETRIES", 2))
try:
for _stream_attempt in range(_max_stream_retries + 1):
@@ -2361,21 +2063,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
result["response"] = _call_chat_completions()
return # success
except Exception as e:
# If the main poll loop force-closed this request because
# of an interrupt, the resulting transport error is the
# expected consequence of our own close — NOT a transient
# network error. Exit immediately: no retry, no fallback,
# no "reconnecting" status. The outer poll loop raises
# InterruptedError. This is the fix for the cascading-
# interrupt hang where doomed retries burned full
# stream-stale-timeout cycles. (#6600)
if _request_cancelled["value"]:
logger.debug(
"Streaming worker caught %s after request "
"cancellation — exiting without retry.",
type(e).__name__,
)
return
_is_timeout = isinstance(
e, (_httpx.ReadTimeout, _httpx.ConnectTimeout, _httpx.PoolTimeout)
)
@@ -2480,19 +2167,12 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
diag=request_client_holder.get("diag"),
)
_close_request_client_once("stream_mid_tool_retry_cleanup")
if agent.api_mode == "anthropic_messages":
try:
agent._anthropic_client.close()
agent._rebuild_anthropic_client()
except Exception:
pass
else:
try:
agent._replace_primary_openai_client(
reason="stream_mid_tool_retry_pool_cleanup"
)
except Exception:
pass
try:
agent._replace_primary_openai_client(
reason="stream_mid_tool_retry_pool_cleanup"
)
except Exception:
pass
continue
# SSE error events from proxies (e.g. OpenRouter sends
@@ -2540,19 +2220,12 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
_close_request_client_once("stream_retry_cleanup")
# Also rebuild the primary client to purge
# any dead connections from the pool.
if agent.api_mode == "anthropic_messages":
try:
agent._anthropic_client.close()
agent._rebuild_anthropic_client()
except Exception:
pass
else:
try:
agent._replace_primary_openai_client(
reason="stream_retry_pool_cleanup"
)
except Exception:
pass
try:
agent._replace_primary_openai_client(
reason="stream_retry_pool_cleanup"
)
except Exception:
pass
continue
# Retries exhausted. Log the final failure with
# full diagnostic detail (chain, headers,
@@ -2585,34 +2258,9 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
"stream" in _err_lower
and "not supported" in _err_lower
)
# AWS Bedrock (AnthropicBedrock SDK path): IAM policies
# with bedrock:InvokeModel but not
# InvokeModelWithResponseStream reject messages.stream()
# with a permission error naming the streaming action.
# Permanent for the session — flip to non-streaming
# (messages.create() maps to bedrock:InvokeModel).
_is_bedrock_stream_denied = False
if (
not _is_stream_unsupported
and "invokemodelwithresponsestream" in _err_lower
):
# Cheap message pre-check before importing the
# adapter — bedrock_adapter triggers a lazy boto3
# install at import time, which must not run for
# unrelated providers' stream errors.
from agent.bedrock_adapter import (
is_streaming_access_denied_error,
)
_is_bedrock_stream_denied = (
is_streaming_access_denied_error(e)
)
if _is_stream_unsupported or _is_bedrock_stream_denied:
if _is_stream_unsupported:
agent._disable_streaming = True
agent._safe_print(
"\n⚠ AWS IAM denied bedrock:InvokeModelWithResponseStream. "
"Switching to non-streaming.\n"
" Grant that action to restore streaming output.\n"
if _is_bedrock_stream_denied else
"\n⚠ Streaming is not supported for this "
"model/provider. Switching to non-streaming.\n"
" To avoid this delay, set display.streaming: false "
@@ -2644,7 +2292,7 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
if _cfg_stale is not None:
_stream_stale_timeout_base = _cfg_stale
else:
_stream_stale_timeout_base = env_float("HERMES_STREAM_STALE_TIMEOUT", 180.0)
_stream_stale_timeout_base = float(os.getenv("HERMES_STREAM_STALE_TIMEOUT", 180.0))
# Local providers (Ollama, oMLX, llama-cpp) can take 300+ seconds
# for prefill on large contexts. Disable the stale detector unless
# the user explicitly set HERMES_STREAM_STALE_TIMEOUT.
@@ -2664,17 +2312,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
_stream_stale_timeout = max(_stream_stale_timeout_base, 240.0)
else:
_stream_stale_timeout = _stream_stale_timeout_base
# Reasoning-model floor: known reasoning models (Nemotron 3 Ultra,
# OpenAI o1/o3, Anthropic Opus 4.x thinking, DeepSeek R1, Qwen QwQ,
# xAI Grok reasoning, etc.) routinely exceed the default 180s chat-
# model threshold during their thinking phase. The cloud gateway
# upstream kills the socket first, surfacing as BrokenPipeError.
# Raises the floor only — never overrides explicit user config
# (handled by get_provider_stale_timeout above).
from agent.reasoning_timeouts import get_reasoning_stale_timeout_floor
_reasoning_floor = get_reasoning_stale_timeout_floor(api_kwargs.get("model"))
if _reasoning_floor is not None:
_stream_stale_timeout = max(_stream_stale_timeout, _reasoning_floor)
t = threading.Thread(target=_call, daemon=True)
t.start()
@@ -2723,17 +2360,10 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
pass
# Rebuild the primary client too — its connection pool
# may hold dead sockets from the same provider outage.
if agent.api_mode == "anthropic_messages":
try:
agent._anthropic_client.close()
agent._rebuild_anthropic_client()
except Exception:
pass
else:
try:
agent._replace_primary_openai_client(reason="stale_stream_pool_cleanup")
except Exception:
pass
try:
agent._replace_primary_openai_client(reason="stale_stream_pool_cleanup")
except Exception:
pass
# Reset the timer so we don't kill repeatedly while
# the inner thread processes the closure.
last_chunk_time["t"] = time.time()
@@ -2742,15 +2372,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
)
if agent._interrupt_requested:
# Mark THIS request cancelled before force-closing so the worker's
# exception handler recognizes the forced transport error as a
# cancel and exits without retrying or surfacing a network error.
# (#6600)
_request_cancelled["value"] = True
logger.debug(
"Force-closing streaming httpx client due to interrupt "
"(not a network error)."
)
try:
if agent.api_mode == "anthropic_messages":
agent._anthropic_client.close()
@@ -2809,30 +2430,7 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
role="assistant", content=_partial_text, tool_calls=None,
reasoning_content=None,
)
# Detect provider output-layer content filtering (e.g. MiniMax
# "output new_sensitive (1027)", Azure/OpenAI content_filter,
# Anthropic safety refusal). The raw error is about to be
# swallowed into a finish_reason=length stub, so classify it HERE
# while we still have it and stamp the stub. Retrying such a
# content-deterministic filter on the same primary just re-hits
# the filter — the conversation loop reads this tag and activates
# the fallback chain instead of burning continuation retries.
# error_classifier is the single source of truth for "what counts
# as a content filter" (#32421).
_content_filter_terminated = False
try:
from agent.error_classifier import classify_api_error, FailoverReason
_cls = classify_api_error(
result["error"],
provider=str(getattr(agent, "provider", "") or ""),
model=str(getattr(agent, "model", "") or ""),
)
_content_filter_terminated = (
_cls.reason == FailoverReason.content_policy_blocked
)
except Exception:
_content_filter_terminated = False
_stub = SimpleNamespace(
return SimpleNamespace(
id=PARTIAL_STREAM_STUB_ID,
model=getattr(agent, "model", "unknown"),
choices=[SimpleNamespace(
@@ -2841,9 +2439,6 @@ def interruptible_streaming_api_call(agent, api_kwargs: dict, *, on_first_delta=
usage=None,
_dropped_tool_names=_partial_names or None,
)
if _content_filter_terminated:
_stub._content_filter_terminated = True
return _stub
raise result["error"]
return result["response"]
+10 -86
View File
@@ -127,21 +127,14 @@ def _chat_content_to_responses_parts(content: Any, *, role: str = "user") -> Lis
return converted
def _summarize_user_message_for_log(content: Any, *, sep: str = " ") -> str:
"""Flatten message content to a plain-text summary.
def _summarize_user_message_for_log(content: Any) -> str:
"""Return a short text summary of a user message for logging/trajectory.
Multimodal messages arrive as a list of ``{type:"text"|"image_url", ...}``
parts from the API server. Several consumers want a plain string:
- Logging, spinner previews, and trajectory files (the default ``sep=" "``).
- External memory providers, which feed the text to regexes
(``sanitize_context``) and text APIs — a raw list crashes the sync with
``expected string or bytes-like object, got 'list'`` (use ``sep="\\n"``).
Text parts are joined with ``sep``; images become a ``[N image(s)]`` marker
so the turn isn't recorded as if the attachment never existed. Returns an
empty string for empty lists and ``str(content)`` for unexpected scalar
types.
parts from the API server. Logging, spinner previews, and trajectory
files all want a plain string — this helper extracts the first chunk of
text and notes any attached images. Returns an empty string for empty
lists and ``str(content)`` for unexpected scalar types.
"""
if content is None:
return ""
@@ -164,7 +157,7 @@ def _summarize_user_message_for_log(content: Any, *, sep: str = " ") -> str:
text_bits.append(text)
elif ptype in {"image_url", "input_image"}:
image_count += 1
summary = sep.join(text_bits).strip()
summary = " ".join(text_bits).strip()
if image_count:
note = f"[{image_count} image{'s' if image_count != 1 else ''}]"
summary = f"{note} {summary}" if summary else note
@@ -262,26 +255,6 @@ def _responses_tools(tools: Optional[List[Dict[str, Any]]] = None) -> Optional[L
return converted or None
# Provider-executed built-in tool *declaration* types accepted on the
# Responses ``tools`` array. These are declared by ``type`` alone (no
# client-side name/parameters schema) and run server-side — the provider
# owns the implementation and reports progress via the matching ``*_call``
# output items. Hermes injects xAI's native ``web_search`` for the xAI
# transport (see agent/transports/codex.py); the rest are listed so the
# preflight validator passes them through rather than rejecting them as
# "unsupported type". Mirrors the ``*_call`` item-type set used in
# _normalize_codex_response.
_RESPONSES_BUILTIN_TOOL_TYPES = {
"web_search",
"web_search_preview",
"file_search",
"code_interpreter",
"image_generation",
"computer_use_preview",
"local_shell",
}
# ---------------------------------------------------------------------------
# Message format conversion
# ---------------------------------------------------------------------------
@@ -822,22 +795,7 @@ def _preflight_codex_api_kwargs(
for idx, tool in enumerate(tools):
if not isinstance(tool, dict):
raise ValueError(f"Codex Responses tools[{idx}] must be an object.")
tool_type = tool.get("type")
# Provider-executed built-in tools (xAI native web_search, code
# interpreter, etc.) are declared by ``type`` alone and carry no
# ``name``/``parameters`` schema — the provider owns the
# implementation. Pass them through verbatim instead of forcing
# them through the function-tool validation below (which would
# otherwise reject them with "unsupported type"). See
# agent/transports/codex.py for where xAI's native web_search is
# injected.
if tool_type in _RESPONSES_BUILTIN_TOOL_TYPES:
normalized_tools.append(dict(tool))
continue
if tool_type != "function":
if tool.get("type") != "function":
raise ValueError(f"Codex Responses tools[{idx}] has unsupported type {tool.get('type')!r}.")
name = tool.get("name")
@@ -1116,38 +1074,10 @@ def _normalize_codex_response(
message_items_raw: List[Dict[str, Any]] = []
tool_calls: List[Any] = []
has_incomplete_items = response_status in {"queued", "in_progress", "incomplete"}
saw_streaming_or_item_incomplete = response_status in {"queued", "in_progress"}
saw_commentary_phase = False
saw_final_answer_phase = False
saw_reasoning_item = False
# Server-side built-in tool calls (xAI's native web_search, code
# interpreter, etc.) are executed by the provider and reported as
# discrete ``*_call`` output items. xAI's /v1/responses surface
# (e.g. grok-composer-2.5-fast on SuperGrok OAuth) routinely leaves
# these items at ``status="in_progress"`` even when the overall
# ``response.status == "completed"`` — the search ran to completion
# server-side, the per-item status simply isn't reconciled. These
# are NOT a signal that the model's turn is unfinished, so they must
# not flip ``has_incomplete_items``. Only the response-level status
# and genuine model output items (message/reasoning/function_call)
# govern the incomplete verdict. Without this guard, any turn where
# grok-composer invokes server-side search is misclassified as
# ``finish_reason="incomplete"`` and burns 3 fruitless continuation
# retries before failing with "Codex response remained incomplete
# after 3 continuation attempts". client-side function/custom tool
# calls keep their own in_progress handling below (they are skipped,
# not awaited).
_SERVER_SIDE_TOOL_CALL_TYPES = {
"web_search_call",
"file_search_call",
"code_interpreter_call",
"image_generation_call",
"computer_call",
"local_shell_call",
"mcp_call",
}
for item in output:
item_type = getattr(item, "type", None)
item_status = getattr(item, "status", None)
@@ -1156,12 +1086,8 @@ def _normalize_codex_response(
else:
item_status = None
if (
item_status in {"queued", "in_progress", "incomplete"}
and item_type not in _SERVER_SIDE_TOOL_CALL_TYPES
):
if item_status in {"queued", "in_progress", "incomplete"}:
has_incomplete_items = True
saw_streaming_or_item_incomplete = True
if item_type == "message":
item_phase = getattr(item, "phase", None)
@@ -1319,9 +1245,7 @@ def _normalize_codex_response(
finish_reason = "tool_calls"
elif leaked_tool_call_text:
finish_reason = "incomplete"
elif saw_streaming_or_item_incomplete:
finish_reason = "incomplete"
elif (has_incomplete_items or saw_commentary_phase) and not saw_final_answer_phase:
elif has_incomplete_items or (saw_commentary_phase and not saw_final_answer_phase):
finish_reason = "incomplete"
elif (reasoning_items_raw or reasoning_parts or saw_reasoning_item) and not final_text:
# Response contains only reasoning (encrypted thinking state and/or
+2 -229
View File
@@ -25,209 +25,6 @@ from typing import Any, Dict, List
logger = logging.getLogger(__name__)
def _codex_note_to_tool_progress(note: dict) -> tuple[str, str, dict] | None:
"""Map a Codex app-server ``item/started`` notification to a Hermes
tool-progress event ``(tool_name, preview, args)``.
The Codex app-server runtime processes ``item/started`` notifications for
command execution, file changes, and MCP/dynamic tool calls, but never
surfaced them as Hermes tool-progress events — so gateways (Telegram, etc.)
showed no verbose "running X" breadcrumbs on this route while every other
provider did (#38835). Returns None for items that aren't tool-shaped.
"""
if not isinstance(note, dict) or note.get("method") != "item/started":
return None
params = note.get("params") or {}
item = params.get("item") or {}
if not isinstance(item, dict):
return None
item_type = item.get("type") or ""
if item_type == "commandExecution":
command = item.get("command") or ""
return "exec_command", command, {"command": command, "cwd": item.get("cwd") or ""}
if item_type == "fileChange":
changes = item.get("changes") or []
preview = "file changes"
if isinstance(changes, list) and changes:
paths = [
str(change.get("path"))
for change in changes
if isinstance(change, dict) and change.get("path")
]
if paths:
preview = ", ".join(paths[:3])
if len(paths) > 3:
preview += f", +{len(paths) - 3} more"
return "apply_patch", preview, {"changes": changes}
if item_type == "mcpToolCall":
server = item.get("server") or "mcp"
tool = item.get("tool") or "unknown"
args = item.get("arguments") or {}
if not isinstance(args, dict):
args = {"arguments": args}
return f"mcp.{server}.{tool}", tool, args
if item_type == "dynamicToolCall":
tool = item.get("tool") or "unknown"
args = item.get("arguments") or {}
if not isinstance(args, dict):
args = {"arguments": args}
return tool, tool, args
return None
def _coerce_usage_int(value: Any) -> int:
if isinstance(value, bool):
return 0
if isinstance(value, int):
return max(value, 0)
if isinstance(value, float):
return max(int(value), 0)
if isinstance(value, str):
try:
return max(int(value), 0)
except ValueError:
return 0
return 0
def _record_codex_app_server_usage(agent, turn) -> dict[str, Any]:
"""Translate Codex app-server token usage into Hermes accounting.
Codex app-server reports usage via thread/tokenUsage/updated as:
inputTokens, cachedInputTokens, outputTokens, reasoningOutputTokens,
totalTokens.
Hermes' canonical prompt bucket includes uncached input + cached input.
The Codex app-server protocol does not currently expose cache-write tokens,
so that bucket remains zero on this runtime.
Even when Codex omits usage for a turn, Hermes should still count that turn
as one API call for session/status accounting.
"""
agent.session_api_calls += 1
usage = getattr(turn, "token_usage_last", None)
if not isinstance(usage, dict) or not usage:
if agent._session_db and agent.session_id:
try:
if not agent._session_db_created:
agent._ensure_db_session()
agent._session_db.update_token_counts(
agent.session_id,
model=agent.model,
api_call_count=1,
)
except Exception as exc:
logger.debug(
"Codex app-server api-call persistence failed (session=%s): %s",
agent.session_id, exc,
)
return {}
from agent.usage_pricing import CanonicalUsage, estimate_usage_cost
input_tokens = _coerce_usage_int(usage.get("inputTokens"))
cache_read_tokens = _coerce_usage_int(usage.get("cachedInputTokens"))
output_tokens = _coerce_usage_int(usage.get("outputTokens"))
reasoning_tokens = _coerce_usage_int(usage.get("reasoningOutputTokens"))
reported_total = _coerce_usage_int(usage.get("totalTokens"))
canonical_usage = CanonicalUsage(
input_tokens=input_tokens,
output_tokens=output_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=0,
reasoning_tokens=reasoning_tokens,
raw_usage=usage,
)
prompt_tokens = canonical_usage.prompt_tokens
completion_tokens = canonical_usage.output_tokens
total_tokens = reported_total or canonical_usage.total_tokens
usage_dict = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"input_tokens": canonical_usage.input_tokens,
"output_tokens": canonical_usage.output_tokens,
"cache_read_tokens": canonical_usage.cache_read_tokens,
"cache_write_tokens": canonical_usage.cache_write_tokens,
"reasoning_tokens": canonical_usage.reasoning_tokens,
}
compressor = getattr(agent, "context_compressor", None)
if compressor is not None:
try:
compressor.update_from_response(usage_dict)
context_window = getattr(turn, "model_context_window", None)
if isinstance(context_window, int) and context_window > 0:
compressor.context_length = context_window
except Exception:
logger.debug("codex app-server usage update failed", exc_info=True)
agent.session_prompt_tokens += prompt_tokens
agent.session_completion_tokens += completion_tokens
agent.session_total_tokens += total_tokens
agent.session_input_tokens += canonical_usage.input_tokens
agent.session_output_tokens += canonical_usage.output_tokens
agent.session_cache_read_tokens += canonical_usage.cache_read_tokens
agent.session_cache_write_tokens += canonical_usage.cache_write_tokens
agent.session_reasoning_tokens += canonical_usage.reasoning_tokens
cost_result = estimate_usage_cost(
agent.model,
canonical_usage,
provider=agent.provider,
base_url=agent.base_url,
api_key=getattr(agent, "api_key", ""),
)
if cost_result.amount_usd is not None:
agent.session_estimated_cost_usd += float(cost_result.amount_usd)
agent.session_cost_status = cost_result.status
agent.session_cost_source = cost_result.source
if agent._session_db and agent.session_id:
try:
if not agent._session_db_created:
agent._ensure_db_session()
agent._session_db.update_token_counts(
agent.session_id,
input_tokens=canonical_usage.input_tokens,
output_tokens=canonical_usage.output_tokens,
cache_read_tokens=canonical_usage.cache_read_tokens,
cache_write_tokens=canonical_usage.cache_write_tokens,
reasoning_tokens=canonical_usage.reasoning_tokens,
estimated_cost_usd=float(cost_result.amount_usd)
if cost_result.amount_usd is not None else None,
cost_status=cost_result.status,
cost_source=cost_result.source,
billing_provider=agent.provider,
billing_base_url=agent.base_url,
billing_mode="subscription_included"
if cost_result.status == "included" else None,
model=agent.model,
api_call_count=1,
)
except Exception as exc:
logger.debug(
"Codex app-server token persistence failed (session=%s, tokens=%d): %s",
agent.session_id, total_tokens, exc,
)
return {
**usage_dict,
"last_prompt_tokens": prompt_tokens,
"estimated_cost_usd": float(cost_result.amount_usd)
if cost_result.amount_usd is not None else None,
"cost_status": cost_result.status,
"cost_source": cost_result.source,
}
def run_codex_app_server_turn(
agent,
*,
@@ -250,9 +47,7 @@ def run_codex_app_server_turn(
# Spawned on first turn, reused across turns, closed at AIAgent
# shutdown (see _cleanup hook).
if not hasattr(agent, "_codex_session") or agent._codex_session is None:
from agent.runtime_cwd import resolve_agent_cwd
cwd = getattr(agent, "session_cwd", None) or str(resolve_agent_cwd())
cwd = getattr(agent, "session_cwd", None) or os.getcwd()
# Approval callback: defer to Hermes' standard prompt flow if a
# CLI thread has installed one. Gateway / cron contexts get the
# codex-side fail-closed default.
@@ -261,27 +56,9 @@ def run_codex_app_server_turn(
approval_callback = _get_approval_callback()
except Exception:
approval_callback = None
def _on_codex_event(note: dict) -> None:
# Bridge Codex app-server item/started notifications to Hermes
# tool-progress so gateways show verbose "running X" breadcrumbs
# on this route too (#38835).
progress_callback = getattr(agent, "tool_progress_callback", None)
if progress_callback is None:
return
mapped = _codex_note_to_tool_progress(note)
if mapped is None:
return
tool_name, preview, args = mapped
try:
progress_callback("tool.started", tool_name, preview, args)
except Exception:
logger.debug("codex tool-progress callback raised", exc_info=True)
agent._codex_session = CodexAppServerSession(
cwd=cwd,
approval_callback=approval_callback,
on_event=_on_codex_event,
)
# NOTE: the user message is ALREADY appended to messages by the
@@ -343,8 +120,6 @@ def run_codex_app_server_turn(
agent._iters_since_skill = (
getattr(agent, "_iters_since_skill", 0) + turn.tool_iterations
)
usage_result = _record_codex_app_server_usage(agent, turn)
api_calls = 1
# Now check the skill nudge AFTER iters were incremented — same
# pattern the chat_completions path uses (line ~15432).
@@ -365,7 +140,6 @@ def run_codex_app_server_turn(
original_user_message=original_user_message,
final_response=turn.final_text,
interrupted=False,
messages=messages,
)
except Exception:
logger.debug("external memory sync raised", exc_info=True)
@@ -390,13 +164,12 @@ def run_codex_app_server_turn(
return {
"final_response": turn.final_text,
"messages": messages,
"api_calls": api_calls,
"api_calls": 1, # one app-server "turn" maps to one logical API call
"completed": not turn.interrupted and turn.error is None,
"partial": turn.interrupted or turn.error is not None,
"error": turn.error,
"codex_thread_id": turn.thread_id,
"codex_turn_id": turn.turn_id,
**usage_result,
}
-883
View File
@@ -1,883 +0,0 @@
"""Coding-context awareness — base Hermes, every interactive surface.
When the user runs Hermes inside a code workspace (CLI, TUI, desktop app, or an
editor over ACP), Hermes shifts into a **coding posture**. This module is the
single place that decides whether we're in that posture and what it implies,
so the rest of the codebase never re-derives "are we coding?" on its own.
Architecture — one seam, many consumers
----------------------------------------
The posture is modelled as a frozen :class:`RuntimeMode` selected from a small
:class:`ContextProfile` registry (today: ``coding`` and ``general``). A profile
is *data* — it declares the toolset to collapse to, the operating brief to
inject, and hints for other domains (model routing, memory, subagents). Every
domain reads the same resolved object instead of probing git/config itself:
* **System prompt** — ``RuntimeMode.system_blocks()`` → the operating brief +
a live git/workspace snapshot (``agent/system_prompt.py``).
* **Toolset** — ``RuntimeMode.toolset_selection()`` → the ``coding`` toolset
plus the user's enabled MCP servers (``cli.py`` / ``tui_gateway``). Only
under the opt-in ``focus`` mode: the default posture is prompt-only and
never touches the user's configured toolsets (toolsets like messaging /
smart-home / music are off-by-default anyway, and someone who explicitly
enabled image-gen or Spotify shouldn't lose it for being in a git repo).
* **Delegation** — subagents inherit the parent's toolset and run through the
same prompt builder, so the coding posture propagates to children for free.
* **Model / memory / compression** — declared on the profile
(``model_hint``, ``memory_policy``) as the extension seam; consumers read
``mode.profile`` rather than re-deciding.
Cache safety
------------
The mode is resolved **once** and is immutable. The workspace snapshot is built
once at prompt-build time and baked into the *stable* system-prompt tier — never
re-probed per turn (that would shatter the prompt cache). Branch and dirty state
drift mid-session, so the brief tells the model to re-check with ``git`` before
acting on the snapshot. A ``/coding`` flip therefore only takes effect next
session (deferred), the same contract as ``/skills install`` vs ``--now``.
Activation (config ``agent.coding_context``):
* ``auto`` (default) — posture (brief + snapshot) on an interactive coding
surface sitting in a code workspace (git repo or recognised project root).
Prompt-only; toolsets and the skill index untouched.
* ``focus`` — like ``auto``, but additionally collapses the toolset to the
``coding`` set + enabled MCP servers and demotes non-coding skill
categories to names-only in the prompt's skill index (no skill is ever
hidden). Explicit opt-in for a lean schema.
* ``on`` — force the posture anywhere (incl. non-workspaces). Prompt-only.
* ``off`` — disable entirely.
"""
from __future__ import annotations
import json
import logging
import os
import re
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional
from hermes_cli._subprocess_compat import IS_WINDOWS, windows_hide_flags
logger = logging.getLogger("hermes.coding_context")
CODING_TOOLSET = "coding"
# Surfaces where a coding posture makes sense under ``auto``. Messaging
# platforms (telegram, discord, slack, …) are intentionally absent — a chat bot
# in a group is not pair-programming.
INTERACTIVE_CODING_PLATFORMS = {"cli", "tui", "acp", "desktop", ""}
# Project-root signals that mark a directory as a code workspace even when it
# isn't (yet) a git repo. Cheap filename checks — no parsing.
_PROJECT_MARKERS = (
"pyproject.toml", "setup.py", "setup.cfg", "requirements.txt",
"package.json", "tsconfig.json", "deno.json",
"Cargo.toml", "go.mod", "pom.xml", "build.gradle", "build.gradle.kts",
"Gemfile", "composer.json", "mix.exs", "pubspec.yaml",
"CMakeLists.txt", "Makefile", "Dockerfile",
"AGENTS.md", "CLAUDE.md", ".cursorrules",
)
# Agent-instruction files surfaced separately from manifests in the snapshot.
_CONTEXT_FILES = ("AGENTS.md", "CLAUDE.md", ".cursorrules")
# Source-file extensions that make a git repo a *code* workspace even with no
# manifest. Without this, `git init` on a notes/writing/research folder (a huge
# non-coding use case) would flip the whole session into the coding posture just
# for having a `.git`. A manifest still wins on its own (see `_PROJECT_MARKERS`).
_CODE_EXTENSIONS = frozenset({
".py", ".pyi", ".ipynb", ".js", ".jsx", ".ts", ".tsx", ".mjs", ".cjs",
".go", ".rs", ".java", ".kt", ".kts", ".scala", ".rb", ".php", ".c", ".h",
".cc", ".cpp", ".hpp", ".cs", ".swift", ".m", ".mm", ".dart", ".ex", ".exs",
".lua", ".sh", ".bash", ".zsh", ".sql", ".vue", ".svelte", ".r", ".jl",
".hs", ".clj", ".erl", ".pl",
})
# Dirs never worth scanning for the code check (deps/build/vcs/venv noise).
_CODE_SCAN_SKIP_DIRS = frozenset({
".git", "node_modules", "venv", ".venv", "__pycache__", "dist", "build",
"target", ".next", ".turbo", "vendor",
})
# Bounded sweep: a code workspace reveals itself in the first handful of entries.
_CODE_SCAN_MAX_ENTRIES = 500
def _has_code_files(root: Path) -> bool:
"""Cheap, bounded check for source files in a repo's top two levels.
Lets a git repo of loose scripts (no manifest) still read as a code
workspace while a bare notes/writing repo does not. Scans the root and its
immediate subdirectories only, capped at ``_CODE_SCAN_MAX_ENTRIES`` stats —
a handful of readdirs at session start, not a full walk.
"""
seen = 0
stack = [(root, True)]
while stack:
directory, is_root = stack.pop()
try:
with os.scandir(directory) as entries:
for entry in entries:
seen += 1
if seen > _CODE_SCAN_MAX_ENTRIES:
return False
name = entry.name
try:
if entry.is_file():
if os.path.splitext(name)[1].lower() in _CODE_EXTENSIONS:
return True
elif is_root and entry.is_dir() and name not in _CODE_SCAN_SKIP_DIRS and not name.startswith("."):
stack.append((Path(entry.path), False))
except OSError:
continue
except OSError:
continue
return False
# Lockfile → package manager, checked in priority order.
_PY_LOCKFILES = (("uv.lock", "uv"), ("poetry.lock", "poetry"), ("Pipfile.lock", "pipenv"))
_JS_LOCKFILES = (
("pnpm-lock.yaml", "pnpm"), ("bun.lockb", "bun"), ("bun.lock", "bun"),
("yarn.lock", "yarn"), ("package-lock.json", "npm"),
)
# package.json scripts / Makefile targets worth surfacing as verify commands.
_VERIFY_TARGETS = ("test", "tests", "lint", "typecheck", "check", "build", "fmt", "format")
_MAX_VERIFY_COMMANDS = 8
_MAX_FACT_FILE_BYTES = 256 * 1024
_GIT_TIMEOUT = 2.5
# Per-model edit-format steering. Matching the edit tool format to how a model
# was trained reduces mistakes and wasted reasoning (OpenAI/Codex handle
# patch-style diffs best; Anthropic models — and most open-weight coding
# models, whose RL scaffolds use str_replace-style editors — do best with
# string-replacement). Our `patch` tool exposes both: mode="patch" (V4A
# multi-file) and mode="replace" (find-and-swap). We nudge each family toward
# its native format. Unknown families get nothing (the brief's neutral wording
# stands). Substrings match the model id; aligned with TOOL_USE_ENFORCEMENT_MODELS.
#
# GPT/Codex get V4A for ALL edits, single-file included: in codex-rs,
# apply_patch (V4A — apply_patch.lark) is the ONLY file editor, no
# str_replace-style tool exists, and the shipped model prompts say to use
# apply_patch even "for single file edits" — so a replace-mode nudge would
# steer those models toward a format their first-party harness never taught
# them.
_EDIT_FORMAT_GUIDANCE: dict[str, tuple[tuple[str, ...], str]] = {
"patch": (
("gpt", "codex"),
"- Edit format: author new files with `write_file`; for edits to "
"existing code use `patch` with `mode='patch'` (V4A diff) — including "
"single-file edits. It's the edit format you handle most reliably.",
),
"replace": (
("claude", "sonnet", "opus", "haiku",
"gemini", "gemma", "deepseek", "qwen", "kimi", "glm", "grok",
"hermes", "llama", "mistral", "devstral", "minimax"),
"- Edit format: author new files with `write_file`; for edits to "
"existing code prefer `patch` in `mode='replace'` — match a unique "
"snippet and swap it. Reach for `mode='patch'` (V4A) only when an edit "
"genuinely spans several files at once.",
),
}
def _model_family(model: Optional[str]) -> Optional[str]:
"""Classify a model id into an edit-format family key, or ``None``.
Used to steer the coding posture toward the edit tool format a model was
trained on. Family-agnostic by design: an unrecognised model gets ``None``
and the operating brief's neutral edit wording applies.
"""
if not model:
return None
lowered = model.lower()
for family, (needles, _line) in _EDIT_FORMAT_GUIDANCE.items():
if any(n in lowered for n in needles):
return family
return None
def _edit_format_line(model: Optional[str]) -> str:
"""The edit-format guidance line for this model's family (``""`` if none)."""
family = _model_family(model)
if family is None:
return ""
return _EDIT_FORMAT_GUIDANCE[family][1]
# Operating brief for the coding posture. Tool names referenced here (read_file,
# search_files, patch, write_file, terminal, todo) are in the coding toolset and
# in _HERMES_CORE_TOOLS, so they're present on every surface this fires on.
CODING_AGENT_GUIDANCE = (
"You are a coding agent pairing with the user inside their codebase. "
"Operate like a careful senior engineer.\n"
"\n"
"Gather context first:\n"
"- Read the relevant files with `read_file` and locate code with "
"`search_files` before changing anything. Trace a symbol to its definition "
"and usages rather than guessing its shape.\n"
"- Batch independent lookups: when several reads/searches don't depend on "
"each other, issue them together in one turn instead of one at a time.\n"
"- Never invent files, symbols, APIs, or imports. If you haven't seen it in "
"the repo, go look. Don't assume a library is available — check the project "
"manifest (pyproject.toml / package.json / Cargo.toml / go.mod) and how "
"neighbouring files import it.\n"
"\n"
"Make changes through the tools, not the chat:\n"
"- Edit with `patch`/`write_file`. Do NOT print code blocks to the user as "
"a substitute for editing — apply the change, then summarise it. Only show "
"code when the user explicitly asks to see it.\n"
"- Match the project's existing style and conventions; AGENTS.md / "
"CLAUDE.md / .cursorrules already in context win over your defaults. Touch "
"only what the task needs — no drive-by refactors, renames, or reformatting "
"— and add any imports/dependencies your code requires.\n"
"- If an edit fails to apply, re-read the file to get the current exact "
"contents before retrying — don't repeat a stale patch. If the same region "
"fails twice, rewrite the enclosing function or file with `write_file` "
"instead of attempting a third patch.\n"
"\n"
"Verify, and know when to stop:\n"
"- Use `terminal` for git, builds, tests, and inspection. Run the relevant "
"tests/linter/build and confirm they pass before claiming the work is done.\n"
"- Terminal state persists across calls: current directory and exported "
"environment variables carry forward. Activate a virtualenv or export setup "
"vars once, then reuse that state instead of re-sourcing it before every "
"test command.\n"
"- Fix root causes, not symptoms: when you find a bug, check sibling call "
"paths for the same flaw and fix the class, not just the reported site.\n"
"- When fixing linter/type errors on a file, stop after about three "
"attempts on the same file and ask the user rather than looping.\n"
"- Track multi-step work with `todo`. Reference code as `path:line` instead "
"of pasting whole files.\n"
"\n"
"Respect the user's repo: don't commit, push, or rewrite history unless "
"asked, and never read, print, or commit secrets — leave `.env` and "
"credential files alone unless the user explicitly asks. The Workspace "
"block below is a snapshot from session start — re-run `git status`/"
"`git branch` before relying on it. Be concise: lead with the change or "
"answer, not a preamble."
)
# ── Context profiles (declarative posture definitions) ──────────────────────
@dataclass(frozen=True)
class ContextProfile:
"""A named operating posture. Pure data — consumers read these fields.
``toolset`` — collapse to this toolset (+ enabled MCP) when no explicit
selection is pinned; ``None`` keeps the platform default.
``guidance`` — operating brief injected into the stable system prompt;
``""`` injects nothing.
``model_hint`` — routing preference key for smart model routing
(extension seam; not yet consumed by the router).
``memory_policy``— memory namespace/weighting hint (extension seam).
``compact_skill_categories`` — skill categories DEMOTED to names-only in
the system-prompt skill index under the opt-in ``focus``
mode. Never hidden: every skill name stays visible
(so memory-anchored recall keeps working) — only the
descriptions are dropped to cut index noise. Deny-list
semantics so unknown/custom categories keep full
entries.
"""
name: str
toolset: Optional[str] = None
guidance: str = ""
model_hint: Optional[str] = None
memory_policy: str = "default"
compact_skill_categories: tuple[str, ...] = ()
# Skill categories that are clearly not part of a coding workflow. Demoted to
# names-only in the prompt's skill index under the opt-in ``focus`` mode only
# (deny-list — anything not listed here, incl. custom user categories, keeps
# full entries). Coding-adjacent categories (devops, github, mcp,
# data-science, diagramming, research, security, …) are intentionally absent.
_NON_CODING_SKILL_CATEGORIES = (
"apple", "communication", "cooking", "creative", "email", "finance",
"gaming", "gifs", "health", "media", "music", "note-taking",
"productivity", "shopping", "smart-home", "social-media", "travel",
"yuanbao",
)
GENERAL_PROFILE = ContextProfile(name="general")
CODING_PROFILE = ContextProfile(
name="coding",
toolset=CODING_TOOLSET,
guidance=CODING_AGENT_GUIDANCE,
model_hint="coding",
memory_policy="project",
compact_skill_categories=_NON_CODING_SKILL_CATEGORIES,
)
_PROFILES: dict[str, ContextProfile] = {
GENERAL_PROFILE.name: GENERAL_PROFILE,
CODING_PROFILE.name: CODING_PROFILE,
}
def get_profile(name: str) -> ContextProfile:
"""Return a registered profile, falling back to ``general``."""
return _PROFILES.get(name, GENERAL_PROFILE)
# ── Helpers ─────────────────────────────────────────────────────────────────
def _coding_mode(config: Optional[dict[str, Any]]) -> str:
"""Return the normalized ``agent.coding_context`` mode (auto/focus/on/off)."""
if config is None:
try:
from hermes_cli.config import load_config
config = load_config()
except Exception:
config = {}
raw = ((config or {}).get("agent", {}) or {}).get("coding_context", "auto")
mode = str(raw).strip().lower()
if mode in {"focus", "strict", "lean"}:
return "focus"
if mode in {"on", "true", "yes", "1", "always"}:
return "on"
if mode in {"off", "false", "no", "0", "never"}:
return "off"
return "auto"
def _coding_instructions(config: Optional[dict[str, Any]]) -> str:
"""Standing operator instructions for the coding posture (config).
``agent.coding_instructions`` — a string or list of strings appended to the
coding brief as an extra stable system block, so a user can pin project-wide
coding-workflow rules (e.g. "for UI work don't run tsc/lint until I approve;
clean the diff before committing") without editing the shipped brief.
Cache-safe: resolved once per session into the stable system-prompt tier,
like the rest of the posture.
"""
if config is None:
try:
from hermes_cli.config import load_config
config = load_config()
except Exception:
config = {}
raw = ((config or {}).get("agent", {}) or {}).get("coding_instructions", "")
if isinstance(raw, (list, tuple)):
return "\n".join(str(item).strip() for item in raw if str(item).strip())
return str(raw or "").strip()
def _resolve_cwd(cwd: Optional[str | Path]) -> Path:
if cwd:
return Path(cwd).expanduser()
try:
from agent.runtime_cwd import resolve_agent_cwd
return resolve_agent_cwd()
except Exception:
return Path(os.getcwd())
def _git_root(cwd: Path) -> Optional[Path]:
current = cwd.resolve()
for parent in [current, *current.parents]:
if (parent / ".git").exists():
return parent
return None
def _home() -> Optional[Path]:
try:
return Path.home().resolve()
except (OSError, RuntimeError):
return None
def _marker_root(cwd: Path) -> Optional[Path]:
"""Nearest ancestor that looks like a project root, or ``None``.
Walks up at most a few levels so a manifest in the workspace root counts
even when the user is in a subdirectory. ``$HOME`` itself is skipped — a
Makefile or AGENTS.md sitting in the home directory is global user config,
not a project-root signal.
"""
current = cwd.resolve()
home = _home()
for depth, parent in enumerate([current, *current.parents]):
if depth > 6:
break
if parent == home:
continue
for marker in _PROJECT_MARKERS:
if (parent / marker).exists():
return parent
return None
def _detect_profile_name(mode: str, platform: str, cwd_str: str) -> str:
"""Resolve which profile applies.
``auto``/``focus``: coding when the surface is interactive AND the cwd is a
code workspace (a git repo or a recognised project root). ``on``: always
coding. ``off``: always general.
A git repo rooted at ``$HOME`` (the dotfiles pattern) is NOT a workspace
signal — without the guard, every session anywhere under a dotfiles-managed
home directory would silently flip to the coding posture.
Detection is intentionally not memoized: it's a handful of ``stat`` calls,
and callers resolve the mode once per session anyway. Caching here would
risk a stale posture if a long-lived process (gateway/TUI) serves sessions
from different working directories.
"""
if mode == "off":
return GENERAL_PROFILE.name
if mode == "on":
return CODING_PROFILE.name
if platform and platform.strip().lower() not in INTERACTIVE_CODING_PLATFORMS:
return GENERAL_PROFILE.name
cwd = Path(cwd_str)
# A recognized project root (manifest / AGENTS.md / .cursorrules) is a code
# workspace on its own — cheap stat checks, no scan.
if _marker_root(cwd) is not None:
return CODING_PROFILE.name
git_root = _git_root(cwd)
if git_root is not None and git_root == _home():
git_root = None # dotfiles repo at $HOME — not a code workspace
# A bare git repo only counts when it actually holds code, so `git init` on a
# notes/writing/research folder stays in the general posture.
if git_root is not None and _has_code_files(git_root):
return CODING_PROFILE.name
return GENERAL_PROFILE.name
# ── RuntimeMode (the seam) ──────────────────────────────────────────────────
@dataclass(frozen=True)
class RuntimeMode:
"""The resolved operating posture for a session. Immutable by construction.
Built once via :func:`resolve_runtime_mode` and consumed by every domain
that cares about the coding/general distinction. Never mutate or re-resolve
mid-session — that would break the prompt cache.
"""
profile: ContextProfile
surface: str
cwd: Path
# The normalized ``agent.coding_context`` mode this posture was resolved
# under (auto/focus/on/off). Toolset collapse is gated on ``focus``.
config_mode: str = "auto"
# The model id this session runs (e.g. "anthropic/claude-opus-4.8"). Used
# only to steer edit-format guidance toward the model's family — see
# ``_edit_format_line``. Fixed for the session, so cache-safe.
model: Optional[str] = None
# Standing operator instructions (``agent.coding_instructions``), appended
# as an extra stable system block. Empty unless the user configures it.
instructions: str = ""
@property
def kind(self) -> str:
return self.profile.name
@property
def is_coding(self) -> bool:
return self.profile.name == CODING_PROFILE.name
def toolset_selection(self, config: Optional[dict[str, Any]] = None) -> Optional[list[str]]:
"""Toolset list for this posture, or ``None`` to keep the platform default.
Non-``None`` only under the opt-in ``focus`` mode. The default posture
is prompt-only: most strippable toolsets are off-by-default anyway, and
a user who explicitly enabled one (image-gen for frontend/game assets,
messaging for build notifications, …) keeps it while coding.
Callers apply this only when the user hasn't pinned an explicit
selection (``--toolsets``, ``HERMES_TUI_TOOLSETS``, …); they never
override a pin. Returns the profile's toolset plus enabled MCP servers.
"""
if self.config_mode != "focus":
return None
if self.profile.toolset is None:
return None
return [self.profile.toolset, *_enabled_mcp_servers(config)]
def system_blocks(self) -> list[str]:
"""Stable system-prompt blocks for this posture (brief + workspace).
The operating brief carries a model-family edit-format nudge appended
to it (one cached string, not a separate block) so the model is steered
toward the `patch` mode it handles best — see ``_edit_format_line``.
"""
if not self.is_coding:
return []
blocks: list[str] = []
if self.profile.guidance:
brief = self.profile.guidance
edit_line = _edit_format_line(self.model)
if edit_line:
brief = f"{brief}\n{edit_line}"
blocks.append(brief)
workspace = build_coding_workspace_block(self.cwd)
if workspace:
blocks.append(workspace)
# Operator instructions ride their own block so the brief (block 0) stays
# byte-stable and cache-keyed independently of user config.
if self.instructions:
blocks.append(f"Operator instructions (from config):\n{self.instructions}")
return blocks
def compact_skill_categories(self) -> frozenset[str]:
"""Skill categories to demote to names-only in the prompt's skill index.
Gated on the opt-in ``focus`` mode, like the toolset collapse: the
default posture leaves the skill index untouched. Users who didn't ask
for a lean prompt keep full entries for every category — index changes
under ``auto`` proved too surprising in practice, even names-only ones
(a demoted description is information the model no longer weighs when
deciding what to load).
Demoted — never hidden — even under ``focus``. An earlier revision
fully pruned these categories from the index, which caused silent
capability loss in a real workflow: agent-created skills are the
model's accumulated project memory (server-ops runbooks, learned
pitfalls, …), and models do not reliably reach for ``skills_list`` to
rediscover what the index stopped showing them. Names-only keeps every
skill loadable on recall while still cutting the description noise.
"""
if not self.is_coding or self.config_mode != "focus":
return frozenset()
return frozenset(self.profile.compact_skill_categories)
def resolve_runtime_mode(
*,
platform: Optional[str] = None,
cwd: Optional[str | Path] = None,
config: Optional[dict[str, Any]] = None,
model: Optional[str] = None,
) -> RuntimeMode:
"""Resolve the operating posture once. Cheap — a handful of ``stat`` calls.
This is the single entry point every domain should call. The returned
object is immutable and safe to cache for the session. Detection itself is
intentionally *not* memoized (see ``_detect_profile_name``) so a long-lived
process can't pin a stale posture; callers resolve once per session and
hold the result. ``model`` is recorded only to steer edit-format guidance;
it never affects detection.
"""
resolved_cwd = _resolve_cwd(cwd)
mode = _coding_mode(config)
name = _detect_profile_name(
mode, (platform or "").strip().lower(), str(resolved_cwd)
)
return RuntimeMode(
profile=get_profile(name),
surface=platform or "",
cwd=resolved_cwd,
config_mode=mode,
model=model,
instructions=_coding_instructions(config),
)
# ── Back-compat surface (thin wrappers over RuntimeMode) ────────────────────
def is_coding_context(
*,
platform: Optional[str] = None,
cwd: Optional[str | Path] = None,
config: Optional[dict[str, Any]] = None,
) -> bool:
"""Whether Hermes should operate in its coding posture right now."""
return resolve_runtime_mode(platform=platform, cwd=cwd, config=config).is_coding
def coding_selection(
*,
platform: Optional[str] = None,
cwd: Optional[str | Path] = None,
config: Optional[dict[str, Any]] = None,
) -> Optional[list[str]]:
"""Toolset selection for the coding posture.
``None`` unless the user opted into ``focus`` mode AND the posture is
active — the default coding posture never overrides configured toolsets.
"""
return resolve_runtime_mode(
platform=platform, cwd=cwd, config=config
).toolset_selection(config)
def coding_system_blocks(
*,
platform: Optional[str] = None,
cwd: Optional[str | Path] = None,
config: Optional[dict[str, Any]] = None,
model: Optional[str] = None,
) -> list[str]:
"""Stable system-prompt blocks for the current posture (empty when general).
``model`` steers the brief's edit-format nudge toward the model's family.
"""
return resolve_runtime_mode(
platform=platform, cwd=cwd, config=config, model=model
).system_blocks()
def coding_compact_skill_categories(
*,
platform: Optional[str] = None,
cwd: Optional[str | Path] = None,
config: Optional[dict[str, Any]] = None,
) -> frozenset[str]:
"""Skill categories the active posture demotes to names-only in the index.
Empty outside the coding posture and outside the opt-in ``focus`` mode —
the default posture never touches the skill index. Under ``focus``,
demoted — never hidden: every skill name stays in the index and remains
loadable via ``skill_view`` / ``skills_list``; only descriptions are
dropped.
"""
return resolve_runtime_mode(
platform=platform, cwd=cwd, config=config
).compact_skill_categories()
def _enabled_mcp_servers(config: Optional[dict[str, Any]]) -> list[str]:
"""Names of MCP servers the user has enabled — kept in the coding posture.
MCP servers (figma, browser, tophat, …) are explicitly configured and part
of the coding workflow, not noise to strip.
"""
try:
from hermes_cli.config import read_raw_config
from hermes_cli.tools_config import _parse_enabled_flag
servers = read_raw_config().get("mcp_servers") or {}
return [
str(name)
for name, cfg in servers.items()
if isinstance(cfg, dict)
and _parse_enabled_flag(cfg.get("enabled", True), default=True)
]
except Exception:
return []
# ── git/workspace probe ─────────────────────────────────────────────────────
def _git(cwd: Path, *args: str) -> str:
_popen_kwargs = {"creationflags": windows_hide_flags()} if IS_WINDOWS else {}
try:
out = subprocess.run(
["git", "-C", str(cwd), *args],
capture_output=True,
text=True,
timeout=_GIT_TIMEOUT,
**_popen_kwargs,
)
except (OSError, subprocess.SubprocessError):
return ""
return out.stdout.strip() if out.returncode == 0 else ""
def _parse_status(porcelain: str) -> tuple[dict[str, str], dict[str, int]]:
"""Parse ``git status --porcelain=2 --branch`` into branch + counts."""
branch: dict[str, str] = {}
counts = {"staged": 0, "modified": 0, "untracked": 0, "conflicts": 0}
for line in porcelain.splitlines():
if line.startswith("# branch.head"):
branch["head"] = line.split(maxsplit=2)[-1]
elif line.startswith("# branch.upstream"):
branch["upstream"] = line.split(maxsplit=2)[-1]
elif line.startswith("# branch.ab"):
parts = line.split()
branch["ahead"], branch["behind"] = parts[2].lstrip("+"), parts[3].lstrip("-")
elif line.startswith(("1 ", "2 ")):
xy = line.split(maxsplit=2)[1]
if xy[0] != ".":
counts["staged"] += 1
if xy[1] != ".":
counts["modified"] += 1
elif line.startswith("u "):
counts["conflicts"] += 1
elif line.startswith("? "):
counts["untracked"] += 1
return branch, counts
def _read_small(path: Path) -> str:
"""Read a small text file, or ``""`` — never raises, never reads huge files."""
try:
if not path.is_file() or path.stat().st_size > _MAX_FACT_FILE_BYTES:
return ""
return path.read_text(encoding="utf-8", errors="replace")
except OSError:
return ""
@dataclass(frozen=True)
class ProjectFacts:
"""Structured project facts — the model's verify loop, detected once.
The same data that feeds the workspace snapshot, exposed structurally so
non-prompt consumers (e.g. the desktop verify UI) read it instead of
re-detecting and drifting from the prompt.
"""
manifests: list[str]
package_managers: list[str]
verify_commands: list[str]
context_files: list[str]
def detect_project_facts(root: Path) -> ProjectFacts:
"""Detect manifests, package manager(s), verify commands, and context files.
Cheap: stat calls plus reads of a couple of small files. The single source
of truth for both the prompt snapshot (:func:`_project_facts`) and the
gateway's ``project.facts`` — so the UI never re-sniffs verify commands.
"""
manifests = [m for m in _PROJECT_MARKERS if m not in _CONTEXT_FILES and (root / m).is_file()]
package_managers = list(
dict.fromkeys(pm for lock, pm in (*_PY_LOCKFILES, *_JS_LOCKFILES) if (root / lock).is_file())
)
verify: list[str] = []
if (root / "scripts" / "run_tests.sh").is_file():
verify.append("scripts/run_tests.sh")
if (root / "package.json").is_file():
try:
scripts = json.loads(_read_small(root / "package.json") or "{}").get("scripts") or {}
except (json.JSONDecodeError, AttributeError):
scripts = {}
js_pm = next((pm for lock, pm in _JS_LOCKFILES if (root / lock).is_file()), "npm")
verify.extend(f"{js_pm} run {name}" for name in _VERIFY_TARGETS if name in scripts)
if (root / "pytest.ini").is_file() or "[tool.pytest" in _read_small(root / "pyproject.toml"):
verify.append("pytest")
makefile = _read_small(root / "Makefile")
if makefile:
verify.extend(
f"make {name}" for name in _VERIFY_TARGETS
if re.search(rf"^{re.escape(name)}\s*:", makefile, re.MULTILINE)
)
return ProjectFacts(
manifests=manifests,
package_managers=package_managers,
verify_commands=list(dict.fromkeys(verify))[:_MAX_VERIFY_COMMANDS],
context_files=[c for c in _CONTEXT_FILES if (root / c).is_file()],
)
def _project_facts(root: Path) -> list[str]:
"""Render :func:`detect_project_facts` as workspace-snapshot lines.
Hands the model its *verify loop* up front — which manifest, which package
manager, and the exact test/lint/build commands — instead of making it
rediscover them every session. Built once at prompt-build time; the string
output must stay byte-stable to preserve the prompt cache.
"""
f = detect_project_facts(root)
facts: list[str] = []
if f.manifests:
line = f"- Project: {', '.join(f.manifests[:6])}"
if f.package_managers:
line += f" ({'/'.join(f.package_managers)})"
facts.append(line)
if f.verify_commands:
facts.append(f"- Verify: {'; '.join(f.verify_commands)}")
if f.context_files:
facts.append(f"- Context files: {', '.join(f.context_files)}")
return facts
def project_facts_for(cwd: Optional[str | Path] = None) -> Optional[dict[str, Any]]:
"""Structured project facts for ``cwd`` — ``None`` outside a workspace.
Same detection the system-prompt snapshot uses (git root, else marker root),
exposed for non-prompt consumers (the desktop verify UI) so they never
re-derive "are we coding?" or duplicate the verify-command sniffing.
"""
resolved = _resolve_cwd(cwd)
root = _git_root(resolved) or _marker_root(resolved)
if root is None:
return None
f = detect_project_facts(root)
return {
"root": str(root),
"manifests": f.manifests,
"packageManagers": f.package_managers,
"verifyCommands": f.verify_commands,
"contextFiles": f.context_files,
}
def build_coding_workspace_block(cwd: Optional[str | Path] = None) -> str:
"""Workspace snapshot for the system prompt (empty outside a workspace).
Git state (branch/status/commits) when the cwd is in a repo, plus detected
project facts (manifest, package manager, verify commands, context files)
— so marker-only (non-git) projects still get a snapshot.
"""
resolved = _resolve_cwd(cwd)
git_root = _git_root(resolved)
root = git_root or _marker_root(resolved)
if root is None:
return ""
lines = ["Workspace (snapshot at session start — re-check with `git` before acting on it):"]
lines.append(f"- Root: {root}")
if git_root is not None:
branch, counts = _parse_status(_git(root, "status", "--porcelain=2", "--branch"))
head = branch.get("head", "")
if head and head != "(detached)":
line = f"- Branch: {head}"
if branch.get("upstream"):
line += f" \u2192 {branch['upstream']}"
ahead, behind = branch.get("ahead", "0"), branch.get("behind", "0")
if ahead != "0" or behind != "0":
line += f" (ahead {ahead}, behind {behind})"
lines.append(line)
elif head == "(detached)":
lines.append("- Branch: (detached HEAD)")
# Linked worktree: the per-worktree git dir differs from the shared common dir.
# We surface the fact that it's a worktree (so the model knows branches/stashes
# are shared state) but deliberately do NOT expose the primary tree path —
# giving the model a second absolute path causes it to sometimes run commands
# in the wrong directory.
git_dir, common_dir = _git(root, "rev-parse", "--git-dir"), _git(root, "rev-parse", "--git-common-dir")
if git_dir and common_dir and Path(git_dir).resolve() != Path(common_dir).resolve():
lines.append("- Worktree: linked (git state shared with primary tree)")
dirty = [f"{n} {label}" for label, n in (
("staged", counts["staged"]), ("modified", counts["modified"]),
("untracked", counts["untracked"]), ("conflicts", counts["conflicts"]),
) if n]
lines.append(f"- Status: {', '.join(dirty) if dirty else 'clean'}")
recent = _git(root, "log", "-3", "--pretty=%h %s")
if recent:
lines.append("- Recent commits:")
lines.extend(f" {c}" for c in recent.splitlines())
lines.extend(_project_facts(root))
return "\n".join(lines)
-156
View File
@@ -1,156 +0,0 @@
"""Live session context-window breakdown for UI surfaces.
Estimates how the next provider request is composed: system prompt tiers,
tool schemas, and conversation history. Uses the same rough char/4 heuristic
as ``agent.model_metadata.estimate_request_tokens_rough`` so numbers align
with compression thresholds — not exact tokenizer counts.
"""
from __future__ import annotations
import json
import re
from typing import Any, Dict, List, Optional, Sequence, Tuple
_SKILLS_BLOCK_RE = re.compile(r"<available_skills>.*?</available_skills>", re.DOTALL)
_SUBAGENT_TOOL_NAMES = frozenset({"delegate_task"})
_CATEGORY_COLORS = {
"system_prompt": "var(--context-usage-system)",
"tool_definitions": "var(--context-usage-tools)",
"rules": "var(--context-usage-rules)",
"skills": "var(--context-usage-skills)",
"mcp": "var(--context-usage-mcp)",
"subagent_definitions": "var(--context-usage-subagents)",
"memory": "var(--context-usage-memory)",
"conversation": "var(--context-usage-conversation)",
}
def _chars_to_tokens(text: str) -> int:
if not text:
return 0
return (len(text) + 3) // 4
def _json_tokens(value: Any) -> int:
if not value:
return 0
return _chars_to_tokens(json.dumps(value, ensure_ascii=False))
def _tool_name(tool: dict) -> str:
fn = tool.get("function") if isinstance(tool, dict) else None
if isinstance(fn, dict):
return str(fn.get("name") or "")
return str(tool.get("name") or "")
def _split_tools(tools: Sequence[dict]) -> Tuple[List[dict], List[dict], List[dict]]:
builtin: List[dict] = []
mcp: List[dict] = []
subagent: List[dict] = []
for tool in tools:
name = _tool_name(tool)
if name.startswith("mcp_"):
mcp.append(tool)
elif name in _SUBAGENT_TOOL_NAMES:
subagent.append(tool)
else:
builtin.append(tool)
return builtin, mcp, subagent
def _memory_blocks(agent: Any) -> Tuple[str, str]:
memory_block = ""
user_block = ""
store = getattr(agent, "_memory_store", None)
if store is None:
return memory_block, user_block
try:
if getattr(agent, "_memory_enabled", True):
memory_block = store.format_for_system_prompt("memory") or ""
if getattr(agent, "_user_profile_enabled", True):
user_block = store.format_for_system_prompt("user") or ""
except Exception:
pass
return memory_block, user_block
def _strip_blocks(text: str, *blocks: str) -> str:
out = text
for block in blocks:
if block:
out = out.replace(block, "")
return out.strip()
def compute_session_context_breakdown(
agent: Any,
messages: Optional[List[dict]] = None,
) -> Dict[str, Any]:
"""Return a Cursor-style context usage breakdown for one live agent."""
from agent.model_metadata import estimate_messages_tokens_rough
from agent.system_prompt import build_system_prompt_parts
parts = build_system_prompt_parts(agent)
stable = parts.get("stable", "") or ""
context = parts.get("context", "") or ""
volatile = parts.get("volatile", "") or ""
skills_match = _SKILLS_BLOCK_RE.search(stable)
skills_index = skills_match.group(0) if skills_match else ""
memory_block, user_block = _memory_blocks(agent)
memory_text = "\n\n".join(part for part in (memory_block, user_block) if part).strip()
system_core = _strip_blocks(stable, skills_index)
system_tail = _strip_blocks(volatile, memory_block, user_block)
system_prompt_text = "\n\n".join(part for part in (system_core, system_tail) if part).strip()
tools = list(getattr(agent, "tools", None) or [])
builtin_tools, mcp_tools, subagent_tools = _split_tools(tools)
conversation_tokens = estimate_messages_tokens_rough(messages or [])
categories = [
("system_prompt", "System prompt", _chars_to_tokens(system_prompt_text)),
("tool_definitions", "Tool definitions", _json_tokens(builtin_tools)),
("rules", "Rules", _chars_to_tokens(context)),
("skills", "Skills", _chars_to_tokens(skills_index)),
("mcp", "MCP", _json_tokens(mcp_tools)),
("subagent_definitions", "Subagent definitions", _json_tokens(subagent_tools)),
("memory", "Memory", _chars_to_tokens(memory_text)),
("conversation", "Conversation", conversation_tokens),
]
estimated_total = sum(tokens for _, _, tokens in categories)
comp = getattr(agent, "context_compressor", None)
context_max = int(getattr(comp, "context_length", 0) or 0) if comp else 0
measured_used = int(getattr(comp, "last_prompt_tokens", 0) or 0) if comp else 0
context_used = measured_used if measured_used > 0 else estimated_total
context_percent = (
max(0, min(100, round(context_used / context_max * 100)))
if context_max
else 0
)
return {
"categories": [
{
"color": _CATEGORY_COLORS.get(category_id, "var(--ui-text-tertiary)"),
"id": category_id,
"label": label,
"tokens": tokens,
}
for category_id, label, tokens in categories
if tokens > 0
],
"context_max": context_max,
"context_percent": context_percent,
"context_used": context_used,
"estimated_total": estimated_total,
"model": getattr(agent, "model", "") or "",
}
File diff suppressed because it is too large Load Diff
+3 -41
View File
@@ -12,7 +12,6 @@ from pathlib import Path
from typing import Awaitable, Callable
from agent.model_metadata import estimate_tokens_rough
from hermes_cli._subprocess_compat import IS_WINDOWS, windows_hide_flags
_QUOTED_REFERENCE_VALUE = r'(?:`[^`\n]+`|"[^"\n]+"|\'[^\'\n]+\')'
REFERENCE_PATTERN = re.compile(
@@ -247,14 +246,7 @@ def _expand_file_reference(
if not path.is_file():
return f"{ref.raw}: path is not a file", None
if _is_binary_file(path):
# A binary file can't be inlined as text, but it IS on disk (the agent's
# tools run where this resolves — the local cwd, or the staged copy in a
# remote session workspace). Returning a bare "not supported" warning
# with no content was a dead end: the model saw a failure and gave up
# (told the user the file type wasn't supported). Instead, hand it an
# actionable block — the path, type, size, and a nudge to use its tools —
# so it can read/convert/view the file itself.
return None, _binary_reference_block(ref, path)
return f"{ref.raw}: binary files are not supported", None
text = path.read_text(encoding="utf-8")
if ref.line_start is not None:
@@ -291,7 +283,6 @@ def _expand_git_reference(
args: list[str],
label: str,
) -> tuple[str | None, str | None]:
_popen_kwargs = {"creationflags": windows_hide_flags()} if IS_WINDOWS else {}
try:
result = subprocess.run(
["git", *args],
@@ -299,8 +290,6 @@ def _expand_git_reference(
capture_output=True,
text=True,
timeout=30,
stdin=subprocess.DEVNULL,
**_popen_kwargs,
)
except subprocess.TimeoutExpired:
return f"{ref.raw}: git command timed out (30s)", None
@@ -328,9 +317,9 @@ async def _fetch_url_content(
async def _default_url_fetcher(url: str) -> str:
from tools.web_tools import web_extract_tool
raw = await web_extract_tool([url], format="markdown")
raw = await web_extract_tool([url], format="markdown", use_llm_processing=True)
payload = json.loads(raw)
docs = payload.get("results", [])
docs = payload.get("data", {}).get("documents", [])
if not docs:
return ""
doc = docs[0]
@@ -486,7 +475,6 @@ def _iter_visible_entries(path: Path, cwd: Path, limit: int) -> list[Path]:
def _rg_files(path: Path, cwd: Path, limit: int) -> list[Path] | None:
_popen_kwargs = {"creationflags": windows_hide_flags()} if IS_WINDOWS else {}
try:
result = subprocess.run(
["rg", "--files", str(path.relative_to(cwd))],
@@ -494,8 +482,6 @@ def _rg_files(path: Path, cwd: Path, limit: int) -> list[Path] | None:
capture_output=True,
text=True,
timeout=10,
stdin=subprocess.DEVNULL,
**_popen_kwargs,
)
except (FileNotFoundError, OSError, subprocess.TimeoutExpired):
return None
@@ -505,30 +491,6 @@ def _rg_files(path: Path, cwd: Path, limit: int) -> list[Path] | None:
return files[:limit]
def _human_bytes(n: int) -> str:
size = float(n)
for unit in ("B", "KB", "MB", "GB"):
if size < 1024 or unit == "GB":
return f"{int(size)} {unit}" if unit == "B" else f"{size:.1f} {unit}"
size /= 1024
return f"{size:.1f} GB"
def _binary_reference_block(ref: ContextReference, path: Path) -> str:
mime, _ = mimetypes.guess_type(path.name)
mime = mime or "application/octet-stream"
try:
size = _human_bytes(path.stat().st_size)
except OSError:
size = "unknown size"
return (
f"📎 {ref.raw} ({mime}, {size}) — binary file, not inlined as text. "
f"It is available on disk at `{path}`. Use your tools to work with it "
f"(read or convert it, extract its text, or view/render it as needed); "
f"do not tell the user the file type is unsupported."
)
def _file_metadata(path: Path) -> str:
if _is_binary_file(path):
return f"{path.stat().st_size} bytes"
+96 -401
View File
@@ -40,16 +40,6 @@ from agent.model_metadata import estimate_request_tokens_rough
logger = logging.getLogger(__name__)
# Stable marker the gateway matches on to re-tag the auto-compaction lifecycle
# status as ``kind="compacting"`` (tui_gateway/server.py::_status_update), so
# drivers like the desktop app can show an explicit "Summarizing…" indicator
# instead of the transcript appearing to silently reset. Keep the marker phrase
# intact if you reword COMPACTION_STATUS.
COMPACTION_STATUS_MARKER = "Compacting context"
COMPACTION_STATUS = (
f"🗜️ {COMPACTION_STATUS_MARKER} — summarizing earlier conversation so I can continue..."
)
def _compression_lock_holder(agent: Any) -> str:
"""Build a unique holder id for the lock: pid:tid:agent-instance:uuid.
@@ -90,7 +80,6 @@ def check_compression_model_feasibility(agent: Any) -> None:
try:
from agent.auxiliary_client import (
_resolve_task_provider_model,
_try_configured_fallback_for_unavailable_client,
get_text_auxiliary_client,
)
from agent.model_metadata import (
@@ -98,6 +87,10 @@ def check_compression_model_feasibility(agent: Any) -> None:
get_model_context_length,
)
client, aux_model = get_text_auxiliary_client(
"compression",
main_runtime=agent._current_main_runtime(),
)
# Best-effort aux provider label for the warning message. The
# configured provider may be "auto", in which case we fall back
# to the client's base_url hostname so the user can still tell
@@ -106,19 +99,6 @@ def check_compression_model_feasibility(agent: Any) -> None:
_aux_cfg_provider, _, _, _, _ = _resolve_task_provider_model("compression")
except Exception:
_aux_cfg_provider = ""
client, aux_model = get_text_auxiliary_client(
"compression",
main_runtime=agent._current_main_runtime(),
)
if client is None or not aux_model:
fb_client, fb_model, fb_label = _try_configured_fallback_for_unavailable_client(
"compression",
_aux_cfg_provider,
)
if fb_client is not None and fb_model:
client, aux_model = fb_client, fb_model
if "(" in fb_label and fb_label.endswith(")"):
_aux_cfg_provider = fb_label.rsplit("(", 1)[1][:-1]
if client is None or not aux_model:
if _aux_cfg_provider and _aux_cfg_provider != "auto":
msg = (
@@ -288,29 +268,6 @@ def replay_compression_warning(agent: Any) -> None:
pass
def conversation_history_after_compression(agent: Any, messages: list) -> Optional[list]:
"""Return the correct flush baseline after a compression boundary.
Legacy compression rotates to a fresh child session. That child has not
seen the compacted transcript through the normal same-turn flush path yet,
so callers must clear ``conversation_history`` to ``None`` and let the next
persistence call write the whole compacted list.
In-place compaction is different: ``archive_and_compact()`` has already
soft-archived the previous active rows and inserted ``messages`` as the new
active live transcript under the same session id. If the same agent turn
continues with ``conversation_history=None``, the identity-based flush path
treats those already-persisted compacted dicts as new and appends them a
second time, doubling the active context and retriggering compression.
A shallow copy is intentional: it captures the current compacted dict
identities as history while allowing later same-turn appends to remain new.
"""
if bool(getattr(agent, "_last_compaction_in_place", False)):
return list(messages)
return None
def compress_context(
agent: Any,
messages: list,
@@ -361,23 +318,15 @@ def compress_context(
agent._compression_feasibility_checked = True
_pre_msg_count = len(messages)
# In-place compaction (config: compression.in_place, see #38763). When True,
# this compaction rewrites the message list + rebuilds the system prompt but
# keeps the SAME session_id — no end_session, no parent_session_id child, no
# `name #N` renumber, no contextvar/env/logging re-sync, no memory/context-
# engine session-switch. The conversation keeps one durable id for life,
# eliminating the session-rotation bug cluster. Default False during rollout.
in_place = bool(getattr(agent, "compression_in_place", False))
# Set True once the in-place DB write actually completes (the DB block can
# raise and skip it). Surfaced to the gateway via agent._last_compaction_in_place.
compacted_in_place = False
logger.info(
"context compression started: session=%s messages=%d tokens=~%s model=%s focus=%r",
agent.session_id or "none", _pre_msg_count,
f"{approx_tokens:,}" if approx_tokens else "unknown", agent.model,
focus_topic,
)
agent._emit_status(COMPACTION_STATUS)
agent._emit_status(
"🗜️ Compacting context — summarizing earlier conversation so I can continue..."
)
# ── Compression lock ────────────────────────────────────────────────
# Atomic, state.db-backed lock per session_id. Without this, two
@@ -551,243 +500,83 @@ def compress_context(
if agent._session_db:
try:
# Trigger memory extraction on the current session before the
# transcript is rewritten (runs in BOTH modes — the logical
# conversation's pre-compaction turns are about to be summarized
# away regardless of whether the id rotates).
# Propagate title to the new session with auto-numbering
old_title = agent._session_db.get_session_title(agent.session_id)
# Trigger memory extraction on the old session before it rotates.
agent.commit_memory_session(messages)
agent._session_db.end_session(agent.session_id, "compression")
old_session_id = agent.session_id
agent.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
try:
from gateway.session_context import set_current_session_id
if in_place:
# ── In-place compaction: keep the same session_id ──────────
# No end_session, no new row, no parent_session_id, no title
# renumber, no contextvar/env/logging re-sync. The session's
# id, title, cwd, /goal, and gateway routing all stay put.
#
# Durable, NON-DESTRUCTIVE replace: soft-archive the
# pre-compaction turns (active=0, kept on disk + FTS-searchable +
# recoverable) and insert `compressed` as the new live (active=1)
# set, atomically. `compressed` already carries the surviving
# tail (current-turn messages the compressor kept via
# protect_last_n), so we DON'T pre-flush here — a flush would
# INSERT current-turn rows that archive_and_compact would then
# archive alongside the rest (harmless but wasted writes). The
# live-context load filters active=1, so a resume reloads ONLY
# the compacted set; the original turns remain under the SAME id
# for search/recovery (Teknium review — keep one durable id
# WITHOUT destroying history, unlike a hard replace_messages).
# See #38763.
agent._session_db.archive_and_compact(agent.session_id, compressed)
# Reset the flush identity set so the next turn's appends are
# diffed against the COMPACTED transcript: the compacted dicts
# are passed as conversation_history next turn and skipped by
# identity, so only genuinely new turn messages get appended
# (no dup of the summary, no resurrection of dropped turns).
agent._flushed_db_message_ids = set()
# Rotation-independent signal: the conversation was compacted in
# place (id unchanged). The gateway reads this (NOT an id-change
# diff) to re-baseline transcript handling.
compacted_in_place = True
else:
# ── Rotation (legacy): end this session, fork a continuation ─
# Flush any un-persisted current-turn messages to the OLD
# session before ending it, so they survive in the preserved
# parent transcript (#47202). (In-place skips this — see above.)
set_current_session_id(agent.session_id)
except Exception:
os.environ["HERMES_SESSION_ID"] = agent.session_id
agent._session_db_created = False
agent._session_db.create_session(
session_id=agent.session_id,
source=agent.platform or os.environ.get("HERMES_SESSION_SOURCE", "cli"),
model=agent.model,
model_config=agent._session_init_model_config,
parent_session_id=old_session_id,
)
agent._session_db_created = True
# Auto-number the title for the continuation session
if old_title:
try:
agent._flush_messages_to_session_db(messages)
except Exception:
pass # best-effort — don't block compression on a flush error
# Propagate title to the new session with auto-numbering
old_title = agent._session_db.get_session_title(agent.session_id)
agent._session_db.end_session(agent.session_id, "compression")
old_session_id = agent.session_id
agent.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
# Ordering contract: the agent thread updates the contextvar here;
# the gateway propagates to SessionEntry after run_in_executor returns.
try:
from gateway.session_context import set_current_session_id
set_current_session_id(agent.session_id)
except Exception:
os.environ["HERMES_SESSION_ID"] = agent.session_id
# The gateway/tools session context (ContextVar + env) and the
# logging session context are SEPARATE mechanisms. The call above
# moves the former; the ``[session_id]`` tag on log lines comes
# from ``hermes_logging._session_context`` (set once per turn in
# conversation_loop.py). Without this, post-rotation log lines in
# the same turn keep the STALE old id while the message/DB/gateway
# state carry the new one — breaking log correlation exactly at the
# compaction boundary (see #34089). Guarded separately so a logging
# failure can never regress the routing update above.
try:
from hermes_logging import set_session_context
set_session_context(agent.session_id)
except Exception:
pass
agent._session_db_created = False
try:
agent._session_db.create_session(
session_id=agent.session_id,
source=agent.platform or os.environ.get("HERMES_SESSION_SOURCE", "cli"),
model=agent.model,
model_config=agent._session_init_model_config,
parent_session_id=old_session_id,
)
except Exception as _cs_err:
# The child row could not be created (e.g. FK constraint,
# contended write). Previously the outer handler simply
# warned and let the agent continue on the NEW id — which
# has no row in state.db, producing an orphan: the parent
# is ended, the child is never indexed, and every
# subsequent message is attributed to a session that
# doesn't exist (#33906/#33907). Roll the live id back to
# the parent so the conversation stays attached to a real,
# indexed session instead of a phantom.
logger.warning(
"Compression child session create failed (%s) — "
"rolling back to parent session %s to avoid an orphan.",
_cs_err, old_session_id,
)
agent.session_id = old_session_id
try:
from gateway.session_context import set_current_session_id
set_current_session_id(agent.session_id)
except Exception:
os.environ["HERMES_SESSION_ID"] = agent.session_id
try:
from hermes_logging import set_session_context
set_session_context(agent.session_id)
except Exception:
pass
# Re-open the parent: it was ended above, but we're
# continuing on it, so it must not stay closed.
try:
agent._session_db.reopen_session(old_session_id)
except Exception:
pass
old_session_id = None # no rotation happened
# The parent row already exists in state.db, so mark the
# session as created — _ensure_db_session would otherwise
# retry a (harmless INSERT OR IGNORE) create next turn.
agent._session_db_created = True
raise
agent._session_db_created = True
# Carry a persistent /goal onto the continuation session.
# Compression mints a fresh child id; load_goal does a flat
# per-session lookup with no parent walk, so without this an
# active goal silently dies at the boundary (#33618).
try:
from hermes_cli.goals import migrate_goal_to_session
migrate_goal_to_session(old_session_id, agent.session_id, reason="compression")
except Exception as _goal_err:
logger.debug("Could not migrate goal on compression: %s", _goal_err)
# Auto-number the title for the continuation session
if old_title:
try:
new_title = agent._session_db.get_next_title_in_lineage(old_title)
agent._session_db.set_session_title(agent.session_id, new_title)
except (ValueError, Exception) as e:
logger.debug("Could not propagate title on compression: %s", e)
# Shared post-write steps (both modes target agent.session_id, which
# in-place keeps and rotation has already reassigned to the new id):
# refresh the stored system prompt and reset the flush cursor so the
# next turn re-bases its append diff.
new_title = agent._session_db.get_next_title_in_lineage(old_title)
agent._session_db.set_session_title(agent.session_id, new_title)
except (ValueError, Exception) as e:
logger.debug("Could not propagate title on compression: %s", e)
agent._session_db.update_system_prompt(agent.session_id, new_system_prompt)
# Reset flush cursor — new session starts with no messages written
agent._last_flushed_db_idx = 0
except Exception as e:
# If the rotation rolled back to the parent (orphan-avoidance
# above), agent.session_id is the still-indexed parent and
# old_session_id was cleared — so this is recovery, not an
# un-indexed orphan. Otherwise an earlier step failed before the
# child was created and the warning's original meaning holds.
if locals().get("old_session_id") is None and not in_place:
logger.warning(
"Compression rotation aborted and rolled back to the "
"parent session (%s): %s", agent.session_id or "?", e,
)
else:
logger.warning("Session DB compression split failed — new session will NOT be indexed: %s", e)
logger.warning("Session DB compression split failed — new session will NOT be indexed: %s", e)
# Compaction-boundary bookkeeping, computed once. `old_session_id` is only
# bound in the rotation branch; in-place leaves it unset. `_boundary_parent`
# is the id the boundary notifications attribute the prior state to: the old
# id on rotation, the (unchanged) current id in-place.
_old_sid = locals().get("old_session_id")
_is_boundary = bool(_old_sid) or in_place
_boundary_parent = _old_sid or agent.session_id or ""
# Notify the context engine that a compaction boundary occurred. Plugin
# engines (e.g. hermes-lcm) use boundary_reason="compression" to preserve
# DAG lineage / checkpoint per-session state across the boundary instead of
# re-initializing fresh. See hermes-lcm#68. Built-in ContextCompressor
# ignores kwargs. Fires in BOTH modes: rotation passes old→new ids; in-place
# passes the SAME id (the boundary is real even though the id didn't move).
# Notify the context engine that the session_id rotated because of
# compression (not a fresh /new). Plugin engines (e.g. hermes-lcm) use
# boundary_reason="compression" to preserve DAG lineage across the
# rollover instead of re-initializing fresh per-session state.
# See hermes-lcm#68. Built-in ContextCompressor ignores kwargs.
try:
if _is_boundary and hasattr(agent.context_compressor, "on_session_start"):
_old_sid = locals().get("old_session_id")
if _old_sid and hasattr(agent.context_compressor, "on_session_start"):
agent.context_compressor.on_session_start(
agent.session_id or "",
boundary_reason="compression",
old_session_id=_boundary_parent,
platform=getattr(agent, "platform", None) or "cli",
old_session_id=_old_sid,
conversation_id=getattr(agent, "_gateway_session_key", None),
)
except Exception as _ce_err:
logger.debug("context engine on_session_start (compression): %s", _ce_err)
# Notify memory providers of the compaction boundary so provider-cached
# per-session state (Hindsight's _document_id, accumulated turn buffers,
# counters) refreshes. reset=False because the logical conversation
# continues. See #6672. Fires in BOTH modes: in-place uses the same id as
# parent (the conversation didn't fork, but the buffer must still be told
# the transcript was compacted so it doesn't double-count dropped turns).
# Notify memory providers of the compression-driven session_id rotation
# so provider-cached per-session state (Hindsight's _document_id,
# accumulated turn buffers, counters) refreshes. reset=False because
# the logical conversation continues; only the id and DB row rolled
# over. See #6672.
try:
if _is_boundary and agent._memory_manager:
_old_sid = locals().get("old_session_id")
if _old_sid and agent._memory_manager:
agent._memory_manager.on_session_switch(
agent.session_id or "",
parent_session_id=_boundary_parent,
parent_session_id=_old_sid,
reset=False,
reason="compression",
)
except Exception as _me_err:
logger.debug("memory manager on_session_switch (compression): %s", _me_err)
# Warn on repeated compressions (quality degrades with each pass).
# Route through _emit_status (like the other compression warnings above)
# so the warning reaches the TUI / Telegram / Discord via status_callback,
# not just CLI stdout. _emit_status still _vprints for the CLI, and
# storing it on _compression_warning lets replay_compression_warning
# re-deliver it once a late-bound gateway status_callback is wired (#36908).
# Warn on repeated compressions (quality degrades with each pass)
_cc = agent.context_compressor.compression_count
if _cc >= 2:
_cc_msg = (
agent._vprint(
f"{agent.log_prefix}⚠️ Session compressed {_cc} times — "
f"accuracy may degrade. Consider /new to start fresh."
f"accuracy may degrade. Consider /new to start fresh.",
force=True,
)
agent._compression_warning = _cc_msg
agent._emit_status(_cc_msg)
# Emit session:compress event so hooks (e.g. MemPalace sync) can ingest
# the completed old session before its details are lost. In in-place mode
# there is no old id (same session); ``in_place=True`` tells hooks the
# transcript was compacted on the same id rather than rotated.
if getattr(agent, "event_callback", None):
try:
agent.event_callback("session:compress", {
"platform": agent.platform or "",
"session_id": agent.session_id,
"old_session_id": _old_sid or "",
"in_place": in_place,
"compression_count": agent.context_compressor.compression_count,
})
except Exception as e:
logger.debug("event_callback error on session:compress: %s", e)
# Surface the compaction mode to the caller (run_conversation / gateway)
# via a rotation-independent flag. The gateway uses this — NOT an
# id-change diff — to re-baseline transcript handling (history_offset=0 +
# rewrite on the same id) when compaction happened in place. See #38763.
agent._last_compaction_in_place = compacted_in_place
# Keep the post-compression rough estimate for diagnostics, but do not
# treat it as provider-reported prompt usage. Schema-heavy rough estimates
@@ -825,11 +614,7 @@ def compress_context(
return compressed, new_system_prompt
def try_shrink_image_parts_in_messages(
api_messages: list,
*,
max_dimension: int = 8000,
) -> bool:
def try_shrink_image_parts_in_messages(api_messages: list) -> bool:
"""Re-encode all native image parts at a smaller size to recover from
image-too-large errors (Anthropic 5 MB, unknown other providers).
@@ -838,11 +623,9 @@ def try_shrink_image_parts_in_messages(
Pillow couldn't help (caller should surface the original error).
Strategy: look for ``image_url`` / ``input_image`` parts carrying a
``data:image/...;base64,...`` payload, plus Anthropic-native
``{"type": "image", "source": {"type": "base64", ...}}`` blocks.
For each one whose encoded size exceeds 4 MB (a safe target that slides
under Anthropic's 5 MB ceiling with header overhead) or whose longest side
exceeds ``max_dimension``, write the base64 to a tempfile, call
``data:image/...;base64,...`` payload. For each one whose encoded
size exceeds 4 MB (a safe target that slides under Anthropic's 5 MB
ceiling with header overhead), write the base64 to a tempfile, call
``vision_tools._resize_image_for_vision`` to produce a smaller data
URL, and substitute it in place.
@@ -864,9 +647,10 @@ def try_shrink_image_parts_in_messages(
# after a confirmed provider rejection, so the alternative is failure.
target_bytes = 4 * 1024 * 1024
# Anthropic enforces an 8000px per-side dimension cap independently of
# the 5 MB byte cap. In many-image requests, the provider can report a
# lower cap (observed: 2000px). The caller passes that parsed ceiling
# when the rejection includes it.
# the 5 MB byte cap. A tall screenshot can be well under 5 MB yet far
# over 8000px (e.g. 1200×12000 at 0.06 MB). We check pixel dimensions
# even when the byte budget is fine.
max_dimension = 8000
changed_count = 0
# Track parts that are over the target but could NOT be shrunk under it.
# If any survive, retrying is pointless — the same oversized payload will
@@ -875,58 +659,33 @@ def try_shrink_image_parts_in_messages(
# actually brought under the target.
unshrinkable_oversized = 0
def _decode_pixels(data_url: str) -> Optional[tuple]:
"""Return ``(width, height)`` of a base64 data URL, or None on failure.
Soft-depends on Pillow; returns None (caller falls back to a
bytes-only check) if Pillow is missing or the payload is corrupt.
"""
try:
import base64 as _b64_dim
import io as _io_dim
header_d, _, data_d = data_url.partition(",")
if not data_d or not data_url.startswith("data:"):
return None
from PIL import Image as _PILImage
with _PILImage.open(_io_dim.BytesIO(_b64_dim.b64decode(data_d))) as _img:
return _img.size
except Exception:
def _shrink_data_url(url: str) -> Optional[str]:
"""Return a smaller data URL, or None if shrink can't help."""
if not isinstance(url, str) or not url.startswith("data:"):
return None
def _shrink_data_url(url: str) -> tuple:
"""Return ``(resized_url, unshrinkable)`` for a data URL.
``resized_url`` is a smaller/dimension-correct data URL, or None when
no rewrite was applied. ``unshrinkable`` is True only when the image
exceeded a constraint (byte-size or dimensions) and the resize failed
to satisfy *that same* constraint — so the caller knows retrying is
pointless even if a different image in the request shrank.
"""
if not isinstance(url, str) or not url.startswith("data:"):
return None, False
# Determine which constraint is binding. The accept/reject gate below
# MUST be checked against the same axis that triggered the shrink: a
# downscaled screenshot PNG routinely re-encodes to *more* bytes than
# the original (PNG compression is non-monotonic in image size — a
# smaller raster with LANCZOS resampling noise compresses worse than a
# larger smooth one). Rejecting a pixel-correct downscale purely
# because its bytes grew permanently wedges sessions on the Anthropic
# many-image 2000px path (#48013).
# Check both byte size AND pixel dimensions.
needs_shrink = len(url) > target_bytes # over byte budget
triggered_by = "bytes" if needs_shrink else None
if not needs_shrink:
# Bytes are fine — check pixel dimensions against the provider's
# reported per-side cap. A screenshot can be tiny in bytes yet
# too large in pixels.
dims = _decode_pixels(url)
if dims is None:
# Pillow missing or corrupt data — fall back to byte-only.
return None, False
if max(dims) <= max_dimension:
return None, False # both bytes and pixels are within limits
needs_shrink = True
triggered_by = "dimension"
# Even if bytes are fine, check pixel dimensions against
# Anthropic's 8000px cap. A tall image can be tiny in bytes
# yet huge in pixels.
try:
import base64 as _b64_dim
header_d, _, data_d = url.partition(",")
if not data_d:
return None
raw_d = _b64_dim.b64decode(data_d)
from PIL import Image as _PILImage
import io as _io_dim
with _PILImage.open(_io_dim.BytesIO(raw_d)) as _img:
if max(_img.size) <= max_dimension:
return None # both bytes and pixels are fine
needs_shrink = True # pixels exceed limit, force shrink
except Exception:
# If we can't check dimensions (Pillow unavailable, corrupt
# image, etc.), fall back to byte-only check.
return None
try:
header, _, data = url.partition(",")
@@ -958,67 +717,13 @@ def try_shrink_image_parts_in_messages(
Path(tmp.name).unlink(missing_ok=True)
except Exception:
pass
if not resized:
# Resize returned nothing — Pillow couldn't help.
return None, True
if triggered_by == "bytes":
# Byte budget is the binding constraint — bytes must shrink.
if len(resized) >= len(url):
return None, True # re-encode made it bigger
# The per-side dimension cap is ALSO an active provider
# constraint on this request (the caller passes the parsed cap
# to both this helper and the resizer). _resize_image_for_vision
# returns a best-effort, possibly-over-cap blob when it
# exhausts its halving budget — it freezes the long side once
# the short side hits its 64px floor, so a very-high-aspect
# image can stay over the cap even after bytes shrank. If the
# output is still over the cap, retrying would re-400 on
# dimensions; treat it as unshrinkable. (Skip when dims can't
# be decoded — preserves historical byte-only behaviour.)
new_dims = _decode_pixels(resized)
if new_dims is not None and max(new_dims) > max_dimension:
return None, True
return resized, False
# triggered_by == "dimension": the per-side cap is binding. The
# re-encode may have grown in bytes; accept it as long as it is now
# within the dimension cap. Verify the new dimensions when we can.
new_dims = _decode_pixels(resized)
if new_dims is not None:
if max(new_dims) <= max_dimension:
return resized, False
# Still over the per-side cap — the resize didn't satisfy it.
return None, True
# Couldn't verify the re-encode's dimensions (corrupt output or
# Pillow gone mid-call). Fall back to the historical "bytes must
# shrink" gate so we never accept an unverifiable, byte-larger blob.
if len(resized) >= len(url):
return None, True
return resized, False
if not resized or len(resized) >= len(url):
# Shrink didn't help (or made it bigger — corrupt input?).
return None
return resized
except Exception as exc:
logger.warning("image-shrink recovery: re-encode failed — %s", exc)
return None, triggered_by is not None
def _source_to_data_url(source: Any) -> Optional[str]:
if not isinstance(source, dict) or source.get("type") != "base64":
return None
data = source.get("data")
if not isinstance(data, str) or not data:
return None
media_type = str(source.get("media_type") or "image/jpeg").strip()
if not media_type.startswith("image/"):
media_type = "image/jpeg"
return f"data:{media_type};base64,{data}"
def _write_data_url_to_source(source: dict, data_url: str) -> None:
header, _, data = data_url.partition(",")
media_type = "image/jpeg"
if header.startswith("data:"):
candidate = header[len("data:"):].split(";", 1)[0].strip()
if candidate.startswith("image/"):
media_type = candidate
source["type"] = "base64"
source["media_type"] = media_type
source["data"] = data
for msg in api_messages:
if not isinstance(msg, dict):
@@ -1030,16 +735,6 @@ def try_shrink_image_parts_in_messages(
if not isinstance(part, dict):
continue
ptype = part.get("type")
if ptype == "image":
source = part.get("source")
url = _source_to_data_url(source)
resized, unshrinkable = _shrink_data_url(url or "")
if resized and isinstance(source, dict):
_write_data_url_to_source(source, resized)
changed_count += 1
elif unshrinkable:
unshrinkable_oversized += 1
continue
if ptype not in {"image_url", "input_image"}:
continue
image_value = part.get("image_url")
@@ -1047,18 +742,20 @@ def try_shrink_image_parts_in_messages(
# OpenAI Responses: {"image_url": "data:..."}
if isinstance(image_value, dict):
url = image_value.get("url", "")
resized, unshrinkable = _shrink_data_url(url)
resized = _shrink_data_url(url)
if resized:
image_value["url"] = resized
changed_count += 1
elif unshrinkable:
elif isinstance(url, str) and url.startswith("data:") \
and len(url) > target_bytes:
unshrinkable_oversized += 1
elif isinstance(image_value, str):
resized, unshrinkable = _shrink_data_url(image_value)
resized = _shrink_data_url(image_value)
if resized:
part["image_url"] = resized
changed_count += 1
elif unshrinkable:
elif image_value.startswith("data:") \
and len(image_value) > target_bytes:
unshrinkable_oversized += 1
if changed_count:
@@ -1081,8 +778,6 @@ def try_shrink_image_parts_in_messages(
__all__ = [
"COMPACTION_STATUS",
"COMPACTION_STATUS_MARKER",
"check_compression_model_feasibility",
"replay_compression_warning",
"compress_context",
+903 -1015
View File
File diff suppressed because it is too large Load Diff
+20 -86
View File
@@ -21,14 +21,8 @@ from pathlib import Path
from types import SimpleNamespace
from typing import Any
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from agent.file_safety import get_read_block_error, is_write_denied
from agent.redact import redact_sensitive_text
from tools.environments.local import hermes_subprocess_env
ACP_MARKER_BASE_URL = "acp://copilot"
_DEFAULT_TIMEOUT_SECONDS = 900.0
@@ -76,6 +70,16 @@ def _resolve_args() -> list[str]:
def _resolve_home_dir() -> str:
"""Return a stable HOME for child ACP processes."""
try:
from hermes_constants import get_subprocess_home
profile_home = get_subprocess_home()
if profile_home:
return profile_home
except Exception:
pass
home = os.environ.get("HOME", "").strip()
if home:
return home
@@ -100,14 +104,8 @@ def _resolve_home_dir() -> str:
def _build_subprocess_env() -> dict[str, str]:
# Copilot ACP is a model-driving CLI executor: it legitimately needs LLM
# provider credentials. Route through the central helper so Tier-1 secrets
# (gateway bot tokens, GitHub auth, infra) are still stripped (#29157).
env = hermes_subprocess_env(inherit_credentials=True)
home = _resolve_home_dir()
env["HOME"] = home
from hermes_constants import apply_subprocess_home_env
apply_subprocess_home_env(env)
env = os.environ.copy()
env["HOME"] = _resolve_home_dir()
return env
@@ -233,73 +231,11 @@ def _render_message_content(content: Any) -> str:
return str(content).strip()
def _build_openai_tool_call(
*,
call_id: str,
name: str,
arguments: str,
) -> ChatCompletionMessageToolCall:
"""Build an OpenAI-compatible tool-call object for downstream handling."""
return ChatCompletionMessageToolCall(
id=call_id,
call_id=call_id,
response_item_id=None,
type="function",
function=Function(name=name, arguments=arguments),
)
def _completion_to_stream_chunks(completion: SimpleNamespace) -> list[SimpleNamespace]:
"""Convert a one-shot ACP response into OpenAI-style stream chunks."""
choice = completion.choices[0]
message = choice.message
tool_call_deltas = None
if message.tool_calls:
tool_call_deltas = []
for index, tool_call in enumerate(message.tool_calls):
tool_call_deltas.append(
SimpleNamespace(
index=index,
id=getattr(tool_call, "id", None),
type=getattr(tool_call, "type", "function"),
function=SimpleNamespace(
name=getattr(tool_call.function, "name", None),
arguments=getattr(tool_call.function, "arguments", None),
),
)
)
delta = SimpleNamespace(
role="assistant",
content=message.content or None,
tool_calls=tool_call_deltas,
reasoning_content=message.reasoning_content,
reasoning=message.reasoning,
)
data_chunk = SimpleNamespace(
choices=[
SimpleNamespace(
index=0,
delta=delta,
finish_reason=choice.finish_reason,
)
],
model=completion.model,
usage=None,
)
usage_chunk = SimpleNamespace(
choices=[],
model=completion.model,
usage=completion.usage,
)
return [data_chunk, usage_chunk]
def _extract_tool_calls_from_text(text: str) -> tuple[list[ChatCompletionMessageToolCall], str]:
def _extract_tool_calls_from_text(text: str) -> tuple[list[SimpleNamespace], str]:
if not isinstance(text, str) or not text.strip():
return [], ""
extracted: list[ChatCompletionMessageToolCall] = []
extracted: list[SimpleNamespace] = []
consumed_spans: list[tuple[int, int]] = []
def _try_add_tool_call(raw_json: str) -> None:
@@ -323,10 +259,12 @@ def _extract_tool_calls_from_text(text: str) -> tuple[list[ChatCompletionMessage
call_id = f"acp_call_{len(extracted)+1}"
extracted.append(
_build_openai_tool_call(
SimpleNamespace(
id=call_id,
call_id=call_id,
name=fn_name.strip(),
arguments=fn_args,
response_item_id=None,
type="function",
function=SimpleNamespace(name=fn_name.strip(), arguments=fn_args),
)
)
@@ -445,7 +383,6 @@ class CopilotACPClient:
timeout: float | None = None,
tools: list[dict[str, Any]] | None = None,
tool_choice: Any = None,
stream: bool = False,
**_: Any,
) -> Any:
prompt_text = _format_messages_as_prompt(
@@ -492,14 +429,11 @@ class CopilotACPClient:
)
finish_reason = "tool_calls" if tool_calls else "stop"
choice = SimpleNamespace(message=assistant_message, finish_reason=finish_reason)
completion = SimpleNamespace(
return SimpleNamespace(
choices=[choice],
usage=usage,
model=model or "copilot-acp",
)
if stream:
return _completion_to_stream_chunks(completion)
return completion
def _run_prompt(self, prompt_text: str, *, timeout_seconds: float) -> tuple[str, str]:
try:
+18 -151
View File
@@ -11,12 +11,10 @@ import uuid
import re
from dataclasses import dataclass, fields, replace
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple
from hermes_constants import OPENROUTER_BASE_URL
from hermes_cli.config import load_env
from agent.secret_scope import get_secret as _get_secret
from agent.credential_persistence import (
is_borrowed_credential_source,
sanitize_borrowed_credential_payload,
@@ -93,7 +91,6 @@ AUTH_TYPE_OAUTH = "oauth"
AUTH_TYPE_API_KEY = "api_key"
SOURCE_MANUAL = "manual"
SOURCE_MANUAL_DEVICE_CODE = f"{SOURCE_MANUAL}:device_code"
STRATEGY_FILL_FIRST = "fill_first"
STRATEGY_ROUND_ROBIN = "round_robin"
@@ -377,7 +374,7 @@ def _iter_custom_providers(config: Optional[dict] = None):
yield _normalize_custom_pool_name(name), entry
def get_custom_provider_pool_key(base_url: Optional[str], provider_name: Optional[str] = None) -> Optional[str]:
def get_custom_provider_pool_key(base_url: str, provider_name: Optional[str] = None) -> Optional[str]:
"""Look up the custom_providers list in config.yaml and return 'custom:<name>' for a matching base_url.
When provider_name is given, prefer matching by name first (solving the case where
@@ -448,63 +445,6 @@ def get_pool_strategy(provider: str) -> str:
DEFAULT_MAX_CONCURRENT_PER_CREDENTIAL = 1
def _write_through_provider_state_to_global_root(
provider_id: str, state: Dict[str, Any]
) -> None:
"""Persist a rotated OAuth ``state`` into the global-root auth.json.
Best-effort write-through for the multi-profile rotation hazard
(#48415 / #43589): nous, openai-codex, and xai-oauth rotate the
refresh_token on refresh, so when a profile pool refresh rotates a grant
it resolved from the root fallback, the rotated chain must land back in
root. Otherwise root keeps a now-revoked refresh token and every other
profile reading the stale root grant dies with ``refresh_token_reused`` /
``invalid_grant`` once its access token expires.
Only updates ``providers.<provider_id>`` in the root store; never touches
the profile store (the caller already saved that). Swallows all errors — a
failed write-through degrades to the pre-existing behavior (root stale), it
must never break the profile's own successful save. Mirrors
``hermes_cli.auth._write_through_xai_oauth_to_global_root`` (which covers
the non-pool xAI refresh path) for the credential-pool refresh path.
"""
try:
global_path = auth_mod._global_auth_file_path()
except Exception:
return
if global_path is None:
# Classic mode (profile == root); the profile save already hit root.
return
# Seat belt: under pytest, refuse to write the real user's
# ~/.hermes/auth.json even when HERMES_HOME points at a profile path
# (mirrors the read-side guard in _load_global_auth_store). Uses the
# unmodified HOME env, not Path.home() which fixtures may monkeypatch.
if os.environ.get("PYTEST_CURRENT_TEST"):
real_home_env = os.environ.get("HOME", "")
if real_home_env:
real_root = Path(real_home_env) / ".hermes" / "auth.json"
try:
if global_path.resolve(strict=False) == real_root.resolve(strict=False):
return
except Exception:
return
try:
if global_path.exists():
global_store = _load_auth_store(global_path)
else:
global_store = {}
if not isinstance(global_store, dict):
return
_store_provider_state(global_store, provider_id, dict(state), set_active=False)
auth_mod._save_auth_store(global_store, global_path)
except Exception as exc: # pragma: no cover - best effort
logger.debug(
"%s pool refresh: write-through to global root failed: %s",
provider_id,
exc,
)
class CredentialPool:
def __init__(self, provider: str, entries: List[PooledCredential]):
self.provider = provider
@@ -537,11 +477,10 @@ class CredentialPool:
self._entries[idx] = new
return
def _persist(self, *, removed_ids: Optional[List[str]] = None) -> None:
def _persist(self) -> None:
write_credential_pool(
self.provider,
[entry.to_dict() for entry in self._entries],
removed_ids=removed_ids,
)
def _is_terminal_auth_failure(
@@ -859,28 +798,6 @@ class CredentialPool:
try:
with _auth_store_lock():
auth_store = _load_auth_store()
# Decide BEFORE writing whether this profile is reading the
# grant from the global root (no own providers.<id> block) vs.
# genuinely shadowing it. A pool refresh rotates single-use
# OAuth refresh tokens, so a profile that resolved the grant
# from root MUST write the rotated chain back to root too —
# otherwise root keeps a revoked refresh token and every other
# profile reading the stale root grant dies with
# refresh_token_reused / invalid_grant once its access token
# expires. This mirrors the xAI write-through in
# hermes_cli.auth._save_xai_oauth_tokens (#43589); the pool
# refresh path is the Codex/xAI analog reported in #48415.
_wt_provider_id = {
"nous": "nous",
"openai-codex": "openai-codex",
"xai-oauth": "xai-oauth",
}.get(self.provider)
write_through_to_root = bool(_wt_provider_id) and not (
isinstance(auth_store.get("providers"), dict)
and isinstance(
auth_store["providers"].get(_wt_provider_id), dict
)
)
if self.provider == "nous":
state = _load_provider_state(auth_store, "nous")
if state is None:
@@ -936,10 +853,6 @@ class CredentialPool:
return
_save_auth_store(auth_store)
if write_through_to_root and _wt_provider_id:
_write_through_provider_state_to_global_root(
_wt_provider_id, state
)
except Exception as exc:
logger.debug("Failed to sync %s pool entry back to auth store: %s", self.provider, exc)
@@ -1125,17 +1038,13 @@ class CredentialPool:
logger.debug(
"Failed to clear terminal xAI OAuth state: %s", clear_exc
)
removed_ids = [
item.id for item in self._entries
if item.source == "loopback_pkce"
]
self._entries = [
item for item in self._entries
if item.source != "loopback_pkce"
]
if self._current_id == entry.id:
self._current_id = None
self._persist(removed_ids=removed_ids)
self._persist()
return None
# For openai-codex: same race as xAI/nous — another Hermes process
# may have consumed the refresh token between our proactive sync
@@ -1195,17 +1104,13 @@ class CredentialPool:
logger.debug(
"Failed to clear terminal Codex OAuth state: %s", clear_exc
)
removed_ids = [
item.id for item in self._entries
if item.source == "device_code"
]
self._entries = [
item for item in self._entries
if item.source != "device_code"
]
if self._current_id == entry.id:
self._current_id = None
self._persist(removed_ids=removed_ids)
self._persist()
return None
# For nous: another process may have consumed the refresh token
# between our proactive sync and the HTTP call. Re-sync from
@@ -1262,17 +1167,13 @@ class CredentialPool:
auth_mod.NOUS_DEVICE_CODE_SOURCE,
f"manual:{auth_mod.NOUS_DEVICE_CODE_SOURCE}",
}
removed_ids = [
item.id for item in self._entries
if item.source in singleton_sources
]
self._entries = [
item for item in self._entries
if item.source not in singleton_sources
]
if self._current_id == entry.id:
self._current_id = None
self._persist(removed_ids=removed_ids)
self._persist()
return None
self._mark_exhausted(entry, None)
return None
@@ -1434,7 +1335,7 @@ class CredentialPool:
pruned_ids = set(entries_to_prune)
self._entries = [e for e in self._entries if e.id not in pruned_ids]
if cleared_any:
self._persist(removed_ids=entries_to_prune)
self._persist()
return available
def _select_unlocked(self) -> Optional[PooledCredential]:
@@ -1608,11 +1509,7 @@ class CredentialPool:
replace(entry, priority=new_priority)
for new_priority, entry in enumerate(self._entries)
]
write_credential_pool(
self.provider,
[entry.to_dict() for entry in self._entries],
removed_ids=[removed.id],
)
self._persist()
if self._current_id == removed.id:
self._current_id = None
return removed
@@ -1768,7 +1665,7 @@ def _seed_from_singletons(provider: str, entries: List[PooledCredential]) -> Tup
_env_file = load_env()
def _env_val(key: str) -> str:
return (_env_file.get(key) or _get_secret(key, "") or "").strip()
return (_env_file.get(key) or os.environ.get(key) or "").strip()
anthropic_api_key = _env_val("ANTHROPIC_API_KEY")
anthropic_oauth_env = (
@@ -2054,7 +1951,7 @@ def _seed_from_env(provider: str, entries: List[PooledCredential]) -> Tuple[bool
# changes to the .env file.
def _get_env_prefer_dotenv(key: str) -> str:
env_file = load_env()
val = env_file.get(key) or _get_secret(key, "") or ""
val = env_file.get(key) or os.environ.get(key) or ""
return val.strip()
# Honour user suppression — `hermes auth remove <provider> <N>` for an
@@ -2163,34 +2060,19 @@ def _seed_from_env(provider: str, entries: List[PooledCredential]) -> Tuple[bool
return changed, active_sources
def _prune_stale_seeded_entries(
entries: List[PooledCredential],
active_sources: Set[str],
*,
prune_env_sources: bool = True,
) -> bool:
def _is_prunable(entry: PooledCredential) -> bool:
# ``env:*`` entries are persisted references that get re-hydrated from
# the environment on every load. A process that merely lacks the env
# var this call must NOT delete the on-disk entry for every other
# process — that destructive read is the bug behind #9331. Only prune
# an env source when ``prune_env_sources`` is explicitly requested
# (e.g. an `hermes auth` command that confirmed the source is gone).
if entry.source.startswith("env:"):
return prune_env_sources
# File-backed singletons (device-code OAuth, claude_code) and Hermes
# PKCE should disappear from the pool when their backing file is gone.
return (
is_borrowed_credential_source(entry.source, entry.provider)
or entry.source == "hermes_pkce"
)
def _prune_stale_seeded_entries(entries: List[PooledCredential], active_sources: Set[str]) -> bool:
retained = [
entry
for entry in entries
if _is_manual_source(entry.source)
or entry.source in active_sources
or not _is_prunable(entry)
or not (
is_borrowed_credential_source(entry.source, entry.provider)
# Hermes PKCE is Hermes-owned/persistable while present, but it is
# still a file-backed singleton and should disappear from the pool
# when the backing OAuth file is gone.
or entry.source == "hermes_pkce"
)
]
if len(retained) == len(entries):
return False
@@ -2274,11 +2156,6 @@ def _seed_custom_pool(pool_key: str, entries: List[PooledCredential]) -> Tuple[b
def load_pool(provider: str) -> CredentialPool:
provider = (provider or "").strip().lower()
raw_entries = read_credential_pool(provider)
disk_ids = {
entry.get("id")
for entry in raw_entries
if isinstance(entry, dict) and entry.get("id")
}
raw_needs_sanitization = any(
isinstance(payload, dict)
and sanitize_borrowed_credential_payload(payload, provider) != payload
@@ -2295,22 +2172,12 @@ def load_pool(provider: str) -> CredentialPool:
singleton_changed, singleton_sources = _seed_from_singletons(provider, entries)
env_changed, env_sources = _seed_from_env(provider, entries)
changed = raw_needs_sanitization or singleton_changed or env_changed
# ``load_pool()`` is a non-destructive read for env-seeded entries: a
# process missing a provider env var must not delete the persisted
# pool entry for every other process (#9331). File-backed singletons
# still prune when their backing file is gone.
changed |= _prune_stale_seeded_entries(
entries,
singleton_sources | env_sources,
prune_env_sources=False,
)
changed |= _prune_stale_seeded_entries(entries, singleton_sources | env_sources)
changed |= _normalize_pool_priorities(provider, entries)
if changed:
new_ids = {entry.id for entry in entries}
write_credential_pool(
provider,
[entry.to_dict() for entry in sorted(entries, key=lambda item: item.priority)],
removed_ids=disk_ids - new_ids,
)
return CredentialPool(provider, entries)
-794
View File
@@ -1,794 +0,0 @@
"""Credits tracking for Nous inference API responses.
Parses x-nous-credits-* (and optional x-nous-tool-pool-*) headers from
inference responses into a validated CreditsState dataclass. Provides
depletion detection (paid_access), subscription-cap used_fraction, and
warn-once schema-version gating. This is the hardened parser used by all
live consumers (run_agent, tui_gateway) — not a dev-only shim.
Header schema (x-nous-credits-* family):
x-nous-credits-version contract/schema version
x-nous-credits-remaining-micros total remaining balance (micros)
x-nous-credits-remaining-usd same, formatted USD string
x-nous-credits-subscription-micros subscription balance (SIGNED; may be negative/debt)
x-nous-credits-subscription-usd same, formatted USD string
x-nous-credits-subscription-limit-micros subscription cap (PAIRED/optional)
x-nous-credits-subscription-limit-usd same, formatted USD string (PAIRED/optional)
x-nous-credits-rollover-micros rolled-over balance (micros)
x-nous-credits-purchased-micros purchased balance (micros)
x-nous-credits-purchased-usd same, formatted USD string
x-nous-credits-denominator-kind "subscription_cap" | "none"
x-nous-credits-paid-access "true" | "false" (STRING!)
x-nous-credits-disabled-reason reason string (header omitted when null)
x-nous-credits-as-of-ms server-side timestamp (ms epoch)
Tool-pool headers use a SEPARATE prefix:
x-nous-tool-pool-micros tool-pool balance (micros)
x-nous-tool-pool-gated-off "true" | "false" (STRING!)
Money is handled as micros ints only; *_usd values are preserved verbatim as
the raw strings the server sent (never re-parsed to float).
"""
from __future__ import annotations
import logging
import os
import re
import time
from dataclasses import dataclass
from typing import Any, Mapping, Optional
from utils import is_truthy_value
logger = logging.getLogger(__name__)
# Warn-once latch: emit the version-unsupported warning at most once per process.
_version_warning_emitted: bool = False
# Valid denominator kinds (exhaustive set from the API contract).
_VALID_DENOMINATOR_KINDS = frozenset({"subscription_cap", "none"})
# USD format: optional leading minus, one-or-more digits, dot, exactly 2 digits.
_USD_RE = re.compile(r"^-?\d+\.\d{2}$")
# ── Internal helpers ─────────────────────────────────────────────────────────
_SENTINEL = object() # singleton sentinel for "parse failed"
def _safe_int(value: Any) -> Any:
"""Parse a header value to an exact int (money-safe).
The contract guarantees every ``*_micros`` field is an integer string —
we parse with ``int()`` directly, NOT ``int(float(...))``, to avoid float-
precision loss above 2**53 that would silently corrupt large money values.
Returns the parsed int, or ``_SENTINEL`` if the value is not a valid integer
string (including float-shaped strings like "1.5"). The sentinel lets callers
detect the failure and return None from the overall parse (fail-hard-on-bad-
input, not silently coerce).
"""
if value is None:
return _SENTINEL
try:
return int(str(value))
except (TypeError, ValueError):
return _SENTINEL
def _validate_usd(value: Optional[str]) -> bool:
"""Return True iff value is a non-None string matching ^-?\\d+\\.\\d{2}$."""
if value is None:
return False
return bool(_USD_RE.match(value))
# ── CreditsState dataclass ───────────────────────────────────────────────────
@dataclass
class CreditsState:
"""Full credits state parsed from x-nous-credits-* response headers."""
version: int = 0
remaining_micros: int = 0
remaining_usd: str = ""
subscription_micros: int = 0 # SIGNED — may be negative (debt). ONLY field allowed negative.
subscription_usd: str = ""
subscription_limit_micros: Optional[int] = None # PAIRED + OPTIONAL (only when subscription_cap)
subscription_limit_usd: Optional[str] = None
rollover_micros: int = 0
purchased_micros: int = 0
purchased_usd: str = ""
tool_pool_micros: int = 0
tool_pool_gated_off: bool = False
denominator_kind: str = "none" # "subscription_cap" | "none"
paid_access: bool = True # depletion keys off THIS == False, NEVER remaining==0
disabled_reason: Optional[str] = None # header omitted entirely when null
as_of_ms: int = 0
captured_at: float = 0.0 # time.time() when this was captured
from_header: bool = False # True only when populated by parse_credits_headers()
@property
def has_data(self) -> bool:
return self.captured_at > 0
@property
def age_seconds(self) -> float:
if not self.has_data:
return float("inf")
return time.time() - self.captured_at
@property
def depleted(self) -> bool:
"""True when the account has lost paid access.
Keyed off ``paid_access == False`` ONLY — never ``remaining_micros == 0``,
which would give a false positive whenever the balance is zero but access
is still live (e.g. subscription renewal pending).
"""
return not self.paid_access
@property
def used_fraction(self) -> Optional[float]:
"""Fraction of the subscription cap consumed, in [0.0, 1.0].
Computable only when ``subscription_limit_micros`` is a truthy (non-zero,
non-None) int. Guarded on the LIMIT FIELD, not ``denominator_kind`` —
the limit field is the real denominator; ``denominator_kind`` is metadata.
Returns None when there is no computable denominator (no limit, or limit==0).
"""
if not isinstance(self.subscription_limit_micros, int):
return None
if self.subscription_limit_micros <= 0:
return None
used = self.subscription_limit_micros - self.subscription_micros
return max(0.0, min(1.0, used / self.subscription_limit_micros))
# ── Credits policy constants ─────────────────────────────────────────────────
# Switching credits notices from sticky→TTL later would also require wiring a
# paired *_TTL_MS companion for each notice kind — the field exists on AgentNotice
# but is not yet plumbed through the policy loop.
CREDITS_NOTICE_KIND = "sticky" # v1: credits notices are sticky
CREDITS_RESTORED_TTL_MS = 8000 # the only TTL notice in v1 (depletion-recovery confirmation)
# Usage-gauge bands (ascending). Each is (threshold_fraction, level, label_pct).
# The notice shows the HIGHEST band the current used_fraction has reached — a single
# escalating status-bar line (50 → 75 → 90), not three stacked notices. Crossing the
# next band up replaces the line; recovering below a band steps it back down. Edit
# this list to retune the bands; the policy derives everything from it.
CREDITS_USAGE_BANDS: tuple[tuple[float, str, int], ...] = (
(0.50, "info", 50),
(0.75, "warn", 75),
(0.90, "warn", 90),
)
CREDITS_USAGE_KEY = "credits.usage" # single key for the escalating usage notice
# ── AgentNotice (out-of-band notice payload; driver-agnostic) ────────────────
@dataclass
class AgentNotice:
"""A structured, driver-agnostic out-of-band notice.
The agent fires these via ``AIAgent.notice_callback`` (and clears them via
``notice_clear_callback``); each driver renders it its own way — the TUI as a
status-bar override, the CLI as a console line, etc. v1 credits notices are all
``kind="sticky"``; ``kind``/``ttl_ms`` are kept fully expressive so a future
config/slash-command can switch them to TTL without touching the policy (a
single default seam — see L4).
"""
text: str
level: str = "info" # info | warn | error | success
kind: str = "sticky" # sticky | ttl
ttl_ms: Optional[int] = None # honored only when kind == "ttl"
key: Optional[str] = None # dedupe / fired-once-latch / clear key
id: Optional[str] = None
# ── is_free_tier_model (local-data-only free-model check) ────────────────────
def is_free_tier_model(model: str, base_url: str = "") -> bool:
"""Return True when *model* is a Nous free-tier model, using ONLY local data.
Two signals, both zero-network:
1. The ``:free`` suffix — the canonical Nous free SKU marker (e.g.
``nvidia/nemotron-3-ultra:free``). Free by construction on the API side
(spend is forced to 0 for ``:free`` ids).
2. A peek into the in-process pricing cache in ``hermes_cli.models``
(populated when the model picker fetched ``/v1/models`` pricing for
*base_url*). PEEK ONLY — a cache miss never triggers a fetch. This is
CLI/TUI-session best-effort: gateway sessions never run the picker's
pricing fetch, so suppression there rests entirely on the ``:free``
suffix (which all Nous free SKUs carry).
Fail-open to False (the depleted notice still shows) on any error: wrongly
showing the warning is recoverable noise; wrongly hiding it on a paid model
would mask a real billing block.
"""
if not model:
return False
if model.endswith(":free"):
return True
if not base_url:
return False
try:
from hermes_cli.models import _is_model_free, _pricing_cache
# Mirror get_pricing_for_provider's key normalization: the agent's
# Nous base_url is /v1-suffixed (https://inference-api.nousresearch.com/v1)
# but the picker keys _pricing_cache on the pre-/v1 root.
key = base_url.rstrip("/")
if key.endswith("/v1"):
key = key[:-3].rstrip("/")
pricing = _pricing_cache.get(key)
if not pricing:
return False
return _is_model_free(model, pricing)
except Exception:
return False
# ── evaluate_credits_notices (pure reconciliation function) ──────────────────
def evaluate_credits_notices(
state: CreditsState,
latch: dict,
*,
model_is_free: bool = False,
) -> tuple[list[AgentNotice], list[str]]:
"""Reconcile credits notices against the latch. Mutates ``latch`` IN PLACE.
latch = {"active": set[str], "seen_below_90": bool, "usage_band": Optional[int]}.
``model_is_free``: True when the session's active model is a Nous free-tier
model (see :func:`is_free_tier_model`). Suppresses the ``credits.depleted``
notice — a depleted account on a free model can keep inferencing, so the
error banner is noise (and confuses free-tier users who never had credits).
Suppression does NOT emit the "restored" success notice; that fires only on
a genuine ``paid_access`` flip back to True.
Returns ``(to_show: list[AgentNotice], to_clear: list[str])``.
Caller emits to_clear FIRST, then to_show.
Pure function — no I/O, no agent/run_agent imports.
"""
to_show: list[AgentNotice] = []
to_clear: list[str] = []
uf = state.used_fraction
# Crossing latch: once we've observed uf below the LOWEST band, escalating
# usage notices may fire. This prevents a brand-new session that opens
# mid-range from firing spuriously on the first observation (the cold-start
# seed primes this explicitly when it WANTS an open-high warning).
_lowest_band = CREDITS_USAGE_BANDS[0][0]
if uf is not None and uf < _lowest_band:
latch["seen_below_90"] = True # gate opened: usage-band notices may now fire
active = latch["active"]
# ── Conditions ───────────────────────────────────────────────────────────
# Highest band whose threshold the current usage has reached (None below all).
current_band: Optional[tuple[float, str, int]] = None
if uf is not None:
for band in CREDITS_USAGE_BANDS: # ascending → last match wins = highest
if uf >= band[0]:
current_band = band
# Top-up suppression: when the account holds purchased (top-up) credits,
# the subscription-cap gauge is the wrong denominator — warning "90% used"
# at a user sitting on $50 of top-up is noise (and it previously stuck
# PERMANENTLY alongside grant_spent at >=100%). Suppress the usage band
# entirely; the cap-reached case is covered by the grant_spent info notice
# below, which already names the remaining top-up balance. A top-up landing
# mid-session flips current_band → None and the clear path below removes
# any showing band line.
if state.purchased_micros > 0:
current_band = None
grant_cond = (
state.denominator_kind == "subscription_cap"
and uf is not None
and uf >= 1.0
and state.purchased_micros > 0
)
depleted_cond = not state.paid_access
# ── usage gauge (escalating single notice: 50 → 75 → 90) ──────────────────
# Show only the highest crossed band; replace the line when the band changes
# (climb or step-down on recovery); clear entirely when usage drops below the
# lowest band or the denominator disappears (uf is None).
shown_band = latch.get("usage_band") # the pct label currently displayed, or None
target_band = current_band[2] if (current_band and latch["seen_below_90"]) else None
if target_band != shown_band:
if CREDITS_USAGE_KEY in active:
to_clear.append(CREDITS_USAGE_KEY)
active.discard(CREDITS_USAGE_KEY)
if target_band is not None:
# Belt-and-suspenders: a producer could set subscription_limit_micros
# without subscription_limit_usd. Render "$? cap" rather than "$None cap".
_cap_usd = state.subscription_limit_usd or "?"
_level = current_band[1] # type: ignore[index] (current_band set when target_band set)
to_show.append(
AgentNotice(
text=f"{'⚠' if _level == 'warn' else '•'} Credits {target_band}% used · ${_cap_usd} cap",
level=_level,
kind=CREDITS_NOTICE_KIND,
key=CREDITS_USAGE_KEY,
id=CREDITS_USAGE_KEY,
)
)
active.add(CREDITS_USAGE_KEY)
latch["usage_band"] = target_band
# ── grant_spent ──────────────────────────────────────────────────────────
if grant_cond and "credits.grant_spent" not in active:
to_show.append(
AgentNotice(
text=f"• Grant spent · ${state.purchased_usd} top-up left",
level="info",
kind=CREDITS_NOTICE_KIND,
key="credits.grant_spent",
id="credits.grant_spent",
)
)
active.add("credits.grant_spent")
elif "credits.grant_spent" in active and not grant_cond:
to_clear.append("credits.grant_spent")
active.discard("credits.grant_spent")
# ── depleted ─────────────────────────────────────────────────────────────
# Suppressed while the active model is free: inference still works there,
# so the error banner would just alarm users (free-tier users especially,
# who never had paid credits to "lose").
show_depleted = depleted_cond and not model_is_free
if show_depleted and "credits.depleted" not in active:
to_show.append(
AgentNotice(
text="✕ Credit access paused · run /credits to top up",
level="error",
kind=CREDITS_NOTICE_KIND,
key="credits.depleted",
id="credits.depleted",
)
)
active.add("credits.depleted")
elif "credits.depleted" in active and not show_depleted:
to_clear.append("credits.depleted")
active.discard("credits.depleted")
if not depleted_cond:
# Genuine recovery (paid_access flipped back True): also emit the
# success notice. A clear caused by switching to a free model while
# still depleted must NOT claim access was restored.
to_show.append(
AgentNotice(
text="✓ Credit access restored",
level="success",
kind="ttl",
ttl_ms=CREDITS_RESTORED_TTL_MS,
key="credits.restored",
id="credits.restored",
)
)
return (to_show, to_clear)
# ── parse_credits_headers ────────────────────────────────────────────────────
def parse_credits_headers(
headers: Mapping[str, str],
provider: str = "",
) -> Optional[CreditsState]:
"""Parse x-nous-credits-* (and x-nous-tool-pool-*) headers into a CreditsState.
Returns None (miss) on ANY of:
- No ``x-nous-credits-version`` header present.
- Version != 1 (> 1 also emits a one-time logger.warning).
- Any ``*_micros`` field is non-integer, or negative for a non-subscription field.
- Any ``*_usd`` field doesn't match ``^-?\\d+\\.\\d{2}$``.
- ``denominator_kind`` is not in {"subscription_cap", "none"}.
- ``paid_access`` / ``tool_pool_gated_off`` is not exactly "true"/"false".
- ``as_of_ms`` is not a valid integer.
- Any unexpected exception.
Fail-open on the subscription_limit pair: a half-pair (only -micros or only
-usd present) is treated as both-absent; the overall parse STILL SUCCEEDS
but with subscription_limit_micros/usd both None.
"""
global _version_warning_emitted
try:
# Cheap probe before the full lowercase copy: bail when the version
# sentinel header is absent (the common case for non-Nous providers, on
# every API call) — skips allocating a dict over the whole response's
# headers on the hot path, while preserving case-insensitivity. Behaviour
# is identical: a missing version header was already a None return below.
if not any(k.lower() == "x-nous-credits-version" for k in headers):
return None
# Normalize to lowercase so lookups work regardless of how the server
# capitalises headers (HTTP header names are case-insensitive per RFC 7230).
lowered = {k.lower(): v for k, v in headers.items()}
# ── Version check ────────────────────────────────────────────────────
# Must be present and exactly 1; > 1 warns once then returns None.
version_raw = lowered.get("x-nous-credits-version")
if version_raw is None:
return None
version_val = _safe_int(version_raw)
if version_val is _SENTINEL:
return None
if version_val != 1:
if version_val > 1 and not _version_warning_emitted:
_version_warning_emitted = True
logger.warning(
"credits header version %d unsupported, ignoring — update Hermes",
version_val,
)
return None
# ── Helper: parse a required non-negative int field (fail → None) ───
def _req_nonneg(key: str) -> Any:
raw = lowered.get(key)
val = _safe_int(raw)
if val is _SENTINEL:
return _SENTINEL
if val < 0:
return _SENTINEL
return val
# ── Helper: parse a required int field that may be negative (subscription only) ─
def _req_int(key: str) -> Any:
raw = lowered.get(key)
val = _safe_int(raw)
if val is _SENTINEL:
return _SENTINEL
return val
# ── Parse micros fields ──────────────────────────────────────────────
remaining_micros = _req_nonneg("x-nous-credits-remaining-micros")
if remaining_micros is _SENTINEL:
return None
subscription_micros = _req_int("x-nous-credits-subscription-micros")
if subscription_micros is _SENTINEL:
return None
rollover_micros = _req_nonneg("x-nous-credits-rollover-micros")
if rollover_micros is _SENTINEL:
return None
purchased_micros = _req_nonneg("x-nous-credits-purchased-micros")
if purchased_micros is _SENTINEL:
return None
# tool_pool_micros is OPTIONAL: absent → 0 (default); present-but-invalid → None (miss).
_tp_raw = lowered.get("x-nous-tool-pool-micros")
if _tp_raw is None:
tool_pool_micros = 0
else:
_tp_val = _safe_int(_tp_raw)
if _tp_val is _SENTINEL or _tp_val < 0:
return None
tool_pool_micros = _tp_val
as_of_ms = _req_nonneg("x-nous-credits-as-of-ms")
if as_of_ms is _SENTINEL:
return None
# ── Validate USD strings ─────────────────────────────────────────────
remaining_usd = lowered.get("x-nous-credits-remaining-usd", "")
if not _validate_usd(remaining_usd):
return None
subscription_usd = lowered.get("x-nous-credits-subscription-usd", "")
if not _validate_usd(subscription_usd):
return None
purchased_usd = lowered.get("x-nous-credits-purchased-usd", "")
if not _validate_usd(purchased_usd):
return None
# ── subscription_limit_* PAIRED + OPTIONAL ───────────────────────────
# Both present → validate both; half-pair → treat BOTH as absent (parse
# still succeeds, just with no limit pair).
sub_limit_micros_raw = lowered.get("x-nous-credits-subscription-limit-micros")
sub_limit_usd_raw = lowered.get("x-nous-credits-subscription-limit-usd")
subscription_limit_micros: Optional[int] = None
subscription_limit_usd: Optional[str] = None
if sub_limit_micros_raw is not None and sub_limit_usd_raw is not None:
# Both present — validate both; any invalid → return None (bad data)
lm = _safe_int(sub_limit_micros_raw)
if lm is _SENTINEL:
return None
if lm < 0:
return None
if not _validate_usd(sub_limit_usd_raw):
return None
subscription_limit_micros = lm
subscription_limit_usd = sub_limit_usd_raw
# else: half-pair or both absent → leave both None, parse continues
# ── denominator_kind ─────────────────────────────────────────────────
denominator_kind = lowered.get("x-nous-credits-denominator-kind", "none")
if denominator_kind not in _VALID_DENOMINATOR_KINDS:
return None
# ── paid_access / tool_pool_gated_off ────────────────────────────────
# Both must be exactly "true" or "false" (case-insensitive). An absent
# paid_access header → fail-open (assume access); absent tool_pool_gated_off
# → default False. Present but invalid → return None.
if "x-nous-credits-paid-access" in lowered:
pa_raw = lowered["x-nous-credits-paid-access"].strip().lower()
if pa_raw not in ("true", "false"):
return None
paid_access = pa_raw == "true"
else:
paid_access = True # fail-open
if "x-nous-tool-pool-gated-off" in lowered:
tpgo_raw = lowered["x-nous-tool-pool-gated-off"].strip().lower()
if tpgo_raw not in ("true", "false"):
return None
tool_pool_gated_off = tpgo_raw == "true"
else:
tool_pool_gated_off = False
# ── disabled_reason: header omitted when null ────────────────────────
disabled_reason = lowered.get("x-nous-credits-disabled-reason") # None if absent
return CreditsState(
version=version_val,
remaining_micros=remaining_micros,
remaining_usd=remaining_usd,
subscription_micros=subscription_micros,
subscription_usd=subscription_usd,
subscription_limit_micros=subscription_limit_micros,
subscription_limit_usd=subscription_limit_usd,
rollover_micros=rollover_micros,
purchased_micros=purchased_micros,
purchased_usd=purchased_usd,
tool_pool_micros=tool_pool_micros,
tool_pool_gated_off=tool_pool_gated_off,
denominator_kind=denominator_kind,
paid_access=paid_access,
disabled_reason=disabled_reason,
as_of_ms=as_of_ms,
captured_at=time.time(),
from_header=True,
)
except Exception:
# Fail-open → miss, but leave a breadcrumb so a parser/import regression
# (feature silently dead) is distinguishable from a legitimate no-headers
# response in agent.log, without needing a dev flag.
logger.debug("credits ▸ parse_credits_headers raised (fail-open miss)", exc_info=True)
return None
# ── Dev test fixtures (HERMES_DEV_CREDITS_FIXTURE) ───────────────────────────
# Throwaway dev scaffolding: trigger any notice state on demand for testing,
# without real spend or Redis seeding. Set HERMES_DEV_CREDITS_FIXTURE to either a
# state NAME (fixed for the session) or a FILE PATH whose contents are a state
# name (re-read every turn → flip states live: `echo depleted > /tmp/cf`, take a
# turn; `echo healthy > /tmp/cf`, take a turn → recovery).
#
# A fixture drives THREE surfaces uniformly, so the whole credits UX is testable
# offline: (1) the per-turn capture/notice path (_capture_credits), (2) the
# cold-start seed at session open (conversation_loop → depletion/warn90 hydrate
# immediately), and (3) the /usage view (nous_credits_lines renders the fixture).
# `clear` / `none` / unset → real behaviour. Delete with the rest of the
# HERMES_DEV_CREDITS scaffolding.
_DEV_FIXTURES: dict[str, dict] = {
"healthy": dict( # used_fraction ~0.1, paid → no notice (recovery target)
remaining_micros=30_340_000, remaining_usd="30.34",
subscription_micros=18_000_000, subscription_usd="18.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
purchased_micros=12_340_000, purchased_usd="12.34",
denominator_kind="subscription_cap", paid_access=True,
),
"sub_50pct": dict( # used_fraction == 0.5 → credits.usage band 50 (info)
remaining_micros=10_000_000, remaining_usd="10.00",
subscription_micros=10_000_000, subscription_usd="10.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
denominator_kind="subscription_cap", paid_access=True,
),
"sub_75pct": dict( # used_fraction == 0.75 → credits.usage band 75 (warn)
remaining_micros=5_000_000, remaining_usd="5.00",
subscription_micros=5_000_000, subscription_usd="5.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
denominator_kind="subscription_cap", paid_access=True,
),
"sub_90pct": dict( # used_fraction == 0.9 → credits.usage band 90 (warn)
remaining_micros=2_000_000, remaining_usd="2.00",
subscription_micros=2_000_000, subscription_usd="2.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
denominator_kind="subscription_cap", paid_access=True,
),
"grant_exhausted": dict( # used_fraction == 1.0 + purchased>0 → credits.grant_spent
remaining_micros=12_340_000, remaining_usd="12.34",
subscription_micros=0, subscription_usd="0.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
purchased_micros=12_340_000, purchased_usd="12.34",
denominator_kind="subscription_cap", paid_access=True,
),
"depleted": dict( # paid_access False → credits.depleted (sticky)
remaining_micros=0, remaining_usd="0.00",
subscription_micros=0, subscription_usd="0.00",
purchased_micros=0, purchased_usd="0.00",
paid_access=False, disabled_reason="out_of_credits",
),
"debt": dict( # subscription in debt (negative, the only signed field) → depleted
remaining_micros=0, remaining_usd="0.00",
subscription_micros=-5_000_000, subscription_usd="-5.00",
subscription_limit_micros=20_000_000, subscription_limit_usd="20.00",
purchased_micros=0, purchased_usd="0.00",
denominator_kind="subscription_cap", paid_access=False,
disabled_reason="out_of_credits",
),
}
def dev_fixture_credits_state() -> Optional[CreditsState]:
"""Return a fixture CreditsState for HERMES_DEV_CREDITS_FIXTURE, or None.
The env value is a state name, OR a path to a file whose contents are a state
name (re-read each call → flip states live without a restart). Unknown name /
"clear" / "none" / unset → None (normal behaviour). Throwaway test scaffolding.
Hard prod-leak guard: a fixture applies ONLY when the dev flag HERMES_DEV_CREDITS
is also on, so a stray HERMES_DEV_CREDITS_FIXTURE (leaked into a shell profile, a
container env, a launch plist, …) can never surface fabricated balances/notices
on a real account.
"""
if not is_truthy_value(os.environ.get("HERMES_DEV_CREDITS")):
return None
raw = os.environ.get("HERMES_DEV_CREDITS_FIXTURE", "").strip()
if not raw:
return None
name = raw
if os.path.sep in raw or "/" in raw: # looks like a path → read the name from the file
try:
with open(raw, "r", encoding="utf-8") as fh:
name = fh.read().strip()
except OSError:
return None
spec = _DEV_FIXTURES.get(name.lower())
if not spec:
return None
# Stamp the fields the REAL parser always guarantees, so a fixture state is
# field-identical to a parse_credits_headers() result from equivalent headers
# (verified by the differential test): version is always 1, and purchased_usd
# is always a valid usd string (the parser rejects a missing/empty one, so a
# real zero-top-up account still carries "0.00"). Specs may override these.
merged = {"version": 1, "purchased_usd": "0.00", **spec}
return CreditsState(**merged, from_header=True, captured_at=time.time())
def _credits_state_from_account(info) -> Optional[CreditsState]:
"""Map a NousPortalAccountInfo into a header-shaped CreditsState for the seed.
Float account dollars → micros (plus a DISPLAY *_usd string — allowed, since
we're formatting account floats, NOT parsing a server-provided *_usd). Returns
None if the account can't yield a usable state (fail-open)."""
try:
_acc = getattr(info, "paid_service_access_info", None)
_sub = getattr(info, "subscription", None)
def _to_micros(dollars):
return int(round(dollars * 1_000_000)) if isinstance(dollars, (int, float)) else 0
def _to_usd(dollars):
# DISPLAY formatting of an account float (not a server *_usd string);
# "" when absent so render/notice copy falls back gracefully.
return f"{dollars:.2f}" if isinstance(dollars, (int, float)) else ""
_monthly = getattr(_sub, "monthly_credits", None)
_has_cap = isinstance(_monthly, (int, float)) and _monthly > 0
_paid = getattr(info, "paid_service_access", None)
return CreditsState(
remaining_micros=_to_micros(getattr(_acc, "total_usable_credits", None)),
remaining_usd=_to_usd(getattr(_acc, "total_usable_credits", None)),
subscription_micros=_to_micros(getattr(_acc, "subscription_credits_remaining", None)),
subscription_usd=_to_usd(getattr(_acc, "subscription_credits_remaining", None)),
subscription_limit_micros=_to_micros(_monthly) if _has_cap else None,
subscription_limit_usd=_to_usd(_monthly) if _has_cap else None,
purchased_micros=_to_micros(getattr(_acc, "purchased_credits_remaining", None)),
purchased_usd=_to_usd(getattr(_acc, "purchased_credits_remaining", None)),
rollover_micros=_to_micros(getattr(_sub, "rollover_credits", None)),
denominator_kind="subscription_cap" if _has_cap else "none",
paid_access=_paid if isinstance(_paid, bool) else True,
from_header=False,
captured_at=time.time(),
)
except Exception:
logger.debug("credits ▸ seed account→state mapping failed", exc_info=True)
return None
def _hydrate_seed_state(agent, state) -> None:
"""Install a seed CreditsState on the agent and fire the notice policy once.
Sets _credits_state, latches session-start remaining, and primes the crossing
gate (the cold-start snapshot IS the first observation, so a session that opens
already in a band warns immediately — the live header path keeps true crossing
semantics), then emits. Safe to call from a worker thread: emit already runs
off-thread in the TUI build path."""
agent._credits_state = state
if getattr(agent, "_credits_session_start_micros", None) is None:
agent._credits_session_start_micros = state.remaining_micros
_latch = getattr(agent, "_credits_latch", None)
if isinstance(_latch, dict) and state.used_fraction is not None:
_latch["seen_below_90"] = True
emit = getattr(agent, "_emit_credits_notices", None)
if callable(emit):
emit()
def seed_credits_at_session_start(agent) -> bool:
"""Hydrate agent._credits_state from /api/oauth/account (or a dev fixture) and
fire the notice policy, so depletion / usage-band warnings show at session OPEN.
Shared by (a) the TUI/desktop agent build (fires at "ready", before any message)
and (b) the first-turn conversation setup (fallback for plain CLI / when the
build path didn't seed). Idempotent: a second call is a no-op once a seed or a
real header has already populated _credits_state.
Returns True if it seeded this call, False otherwise (not nous / already seeded /
fail-open error). Never raises — credits must never block session startup.
"""
try:
if getattr(agent, "provider", "") != "nous":
return False
# Idempotent: don't re-seed if state already exists (seed or live header).
if getattr(agent, "_credits_state", None) is not None:
return False
fixture = None
try:
fixture = dev_fixture_credits_state()
except Exception:
fixture = None
if fixture is not None:
# Synchronous: a fixture is instant (no network), and tests rely on the
# state + notice landing before this returns.
_hydrate_seed_state(agent, fixture)
return True
# Real portal fetch is FIRE-AND-FORGET: a slow/unreachable portal must never
# delay session "ready". A daemon thread hydrates + emits when it resolves,
# re-checking idempotency first (a live inference header may land before it).
import threading
def _bg_seed() -> None:
try:
from hermes_cli.nous_account import get_nous_portal_account_info
info = get_nous_portal_account_info(force_fresh=True)
if getattr(agent, "_credits_state", None) is not None:
return # a live inference header beat us — don't clobber it
state = _credits_state_from_account(info)
if state is not None:
_hydrate_seed_state(agent, state)
except Exception:
logger.debug("credits ▸ session-start seed (background) failed", exc_info=True)
threading.Thread(target=_bg_seed, name="credits-seed", daemon=True).start()
return True
except Exception:
# Fail-open: any auth/portal hiccup leaves _credits_state as-is, never blocks.
# Innermost log across all four call sites (TUI build / CLI build / first
# turn / desktop), so a dead session-open seed is diagnosable in agent.log.
logger.debug("credits ▸ session-start seed failed (fail-open)", exc_info=True)
return False
+23 -156
View File
@@ -25,6 +25,7 @@ import json
import logging
import os
import re
import tempfile
import threading
from datetime import datetime, timedelta, timezone
from pathlib import Path
@@ -32,7 +33,6 @@ from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set
from hermes_constants import get_hermes_home
from tools import skill_usage
from utils import atomic_json_write
logger = logging.getLogger(__name__)
@@ -57,11 +57,6 @@ DEFAULT_INTERVAL_HOURS = 24 * 7 # 7 days
DEFAULT_MIN_IDLE_HOURS = 2
DEFAULT_STALE_AFTER_DAYS = 30
DEFAULT_ARCHIVE_AFTER_DAYS = 90
# Consolidation (the LLM umbrella-building fork) is OFF by default. The
# deterministic inactivity prune (apply_automatic_transitions) still runs
# whenever the curator is enabled; only the opinionated, aux-model-cost
# consolidation pass is opt-in.
DEFAULT_CONSOLIDATE = False
# ---------------------------------------------------------------------------
@@ -102,7 +97,20 @@ def load_state() -> Dict[str, Any]:
def save_state(data: Dict[str, Any]) -> None:
path = _state_file()
try:
atomic_json_write(path, data, indent=2, sort_keys=True)
path.parent.mkdir(parents=True, exist_ok=True)
fd, tmp = tempfile.mkstemp(dir=str(path.parent), prefix=".curator_state_", suffix=".tmp")
try:
with os.fdopen(fd, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2, sort_keys=True, ensure_ascii=False)
f.flush()
os.fsync(f.fileno())
os.replace(tmp, path)
except BaseException:
try:
os.unlink(tmp)
except OSError:
pass
raise
except Exception as e:
logger.debug("Failed to save curator state: %s", e, exc_info=True)
@@ -187,22 +195,6 @@ def get_prune_builtins() -> bool:
return bool(cfg.get("prune_builtins", True))
def get_consolidate() -> bool:
"""Whether the curator runs its LLM consolidation (umbrella-building) pass.
OFF by default. When off, a curator run does ONLY the deterministic
inactivity prune (mark stale / archive long-unused skills) and skips the
forked aux-model review entirely — no consolidation, no umbrella-building,
no aux-model cost. Set ``curator.consolidate: true`` to opt back into the
LLM pass that merges overlapping skills into class-level umbrellas.
The explicit ``hermes curator run --consolidate`` flag overrides this for
a single invocation regardless of the config value.
"""
cfg = _load_config()
return bool(cfg.get("consolidate", DEFAULT_CONSOLIDATE))
# ---------------------------------------------------------------------------
# Idle / interval check
# ---------------------------------------------------------------------------
@@ -273,21 +265,6 @@ def should_run_now(now: Optional[datetime] = None) -> bool:
# Automatic state transitions (pure function, no LLM)
# ---------------------------------------------------------------------------
def _cron_referenced_skills() -> Set[str]:
"""Skill names referenced by any cron job (incl. paused/disabled).
Best-effort: a cron-module import error or corrupt jobs store must never
break the curator, so any failure yields an empty set (no protection,
but no crash).
"""
try:
from cron.jobs import referenced_skill_names as _refs
return _refs()
except Exception as e:
logger.debug("Curator could not read cron skill references: %s", e, exc_info=True)
return set()
def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int]:
"""Walk every curator-managed skill and move active/stale/archived based on
the latest real activity timestamp. Pinned skills are never touched.
@@ -307,8 +284,6 @@ def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int
stale_cutoff = now - timedelta(days=get_stale_after_days())
archive_cutoff = now - timedelta(days=get_archive_after_days())
cron_referenced = _cron_referenced_skills()
counts = {"marked_stale": 0, "archived": 0, "reactivated": 0, "checked": 0, "seeded": 0}
for row in _u.agent_created_report():
@@ -317,15 +292,6 @@ def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int
if row.get("pinned"):
continue
# A skill referenced by any cron job (incl. paused/disabled) is in
# use by definition — resuming or the next fire must find it. The
# scheduler only bumps usage when a job actually fires, so jobs that
# fire less often than archive_after_days, paused jobs, and far-future
# one-shots would otherwise have their skills aged out from under
# them. Treat referenced skills like pinned: never auto-transition.
if name in cron_referenced:
continue
# First sight of a curation-eligible skill with no persisted record
# (e.g. a newly-eligible built-in): anchor its clock to now and defer.
if not row.get("_persisted", True):
@@ -342,18 +308,6 @@ def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int
current = row.get("state", _u.STATE_ACTIVE)
# Never-used skills (use_count == 0) get a grace floor: don't archive
# one until it is at least stale_after_days old. A use=0 skill is
# absence of evidence, not evidence of staleness — a skill created
# recently may simply not have had its trigger come up yet.
never_used = int(row.get("use_count", 0) or 0) == 0
if never_used and anchor > stale_cutoff:
# Younger than the stale window — leave it alone entirely.
if current == _u.STATE_STALE:
_u.set_state(name, _u.STATE_ACTIVE)
counts["reactivated"] += 1
continue
if anchor <= archive_cutoff and current != _u.STATE_ARCHIVED:
ok, _msg = _u.archive_skill(name)
if ok:
@@ -415,32 +369,16 @@ CURATOR_REVIEW_PROMPT = (
"bodies + `references/`, `templates/`, and `scripts/` subfiles for "
"session-specific detail — not one-session-one-skill micro-entries.\n\n"
"Hard rules — do not violate:\n"
"1. DO NOT touch bundled, hub-installed, or external-dir skills "
"(`skills.external_dirs`). The candidate list below is already filtered "
"to local curator-managed skills only; external skills are externally "
"owned and read-only to this background curator.\n"
"1. DO NOT touch bundled or hub-installed skills. The candidate list "
"below is already filtered to agent-created skills only.\n"
"2. DO NOT delete any skill. Archiving (moving the skill's directory "
"into ~/.hermes/skills/.archive/) is the maximum destructive action. "
"Archives are recoverable; deletion is not.\n"
"3. DO NOT touch skills shown as pinned=yes. Skip them entirely.\n"
"3b. DO NOT archive, delete, consolidate, move, or otherwise modify any "
"skill named in the protected built-ins list (currently: plan). These "
"back load-bearing UX (slash-command entry points referenced in docs and "
"tips) and are filtered out of the candidate list below — never resurrect "
"one as an archive or absorb target.\n"
"3c. DO NOT archive or prune any skill marked `cron=yes` in the candidate "
"list. A cron job depends on it and will fail to load it on its next "
"run. You MAY still consolidate it into an umbrella — but only because "
"the curator rewrites cron job skill references to follow consolidations; "
"never simply prune it.\n"
"4. DO NOT use usage counters as a reason to skip consolidation. The "
"counters are new and often mostly zero. Judge overlap on CONTENT, "
"not on use_count. 'use=0' is not evidence a skill is valuable; it's "
"absence of evidence either way. Corollary: 'use=0' is ALSO not a "
"reason to PRUNE a skill. Never archive a never-used skill (use=0) "
"unless it is at least 30 days old (check last_activity / created date) "
"AND its content is genuinely obsolete or fully absorbed elsewhere — a "
"recently-created skill simply may not have had its trigger come up yet.\n"
"absence of evidence either way.\n"
"5. DO NOT reject consolidation on the grounds that 'each skill has "
"a distinct trigger'. Pairwise distinctness is the wrong bar. The "
"right bar is: 'would a human maintainer write this as N separate "
@@ -518,9 +456,8 @@ CURATOR_REVIEW_PROMPT = (
"skill, or `absorbed_into=\"\"` when you're truly pruning with no "
"forwarding target. This drives cron-job skill-reference migration — "
"guessing from your YAML summary after the fact is fragile.\n"
" - terminal — move LOCAL candidate content into "
"a support subfile when package integrity requires it; never mv, cp, rm, "
"patch, or rewrite bundled, hub-installed, or external-dir skills\n\n"
" - terminal — mv a sibling into the archive "
"OR move its content into a support subfile\n\n"
"'keep' is a legitimate decision ONLY when the skill is already a "
"class-level umbrella and none of the proposed merges would improve "
"discoverability. 'This is narrow but distinct from its siblings' "
@@ -1460,14 +1397,12 @@ def _render_candidate_list() -> str:
rows = skill_usage.agent_created_report()
if not rows:
return "No agent-created skills to review."
cron_referenced = _cron_referenced_skills()
lines = [f"Agent-created skills ({len(rows)}):\n"]
for r in rows:
lines.append(
f"- {r['name']} "
f"state={r['state']} "
f"pinned={'yes' if r.get('pinned') else 'no'} "
f"cron={'yes' if r['name'] in cron_referenced else 'no'} "
f"activity={r.get('activity_count', 0)} "
f"use={r.get('use_count', 0)} "
f"view={r.get('view_count', 0)} "
@@ -1481,38 +1416,25 @@ def run_curator_review(
on_summary: Optional[Callable[[str], None]] = None,
synchronous: bool = False,
dry_run: bool = False,
consolidate: Optional[bool] = None,
) -> Dict[str, Any]:
"""Execute a single curator review pass.
Steps:
1. Apply automatic state transitions (pure, no LLM).
2. If consolidation is enabled AND there are agent-created skills, spawn
a forked AIAgent that runs the LLM review prompt against the current
candidate list.
2. If there are agent-created skills, spawn a forked AIAgent that runs
the LLM review prompt against the current candidate list.
3. Update .curator_state with last_run_at and a one-line summary.
4. Invoke *on_summary* with a user-visible description.
If *synchronous* is True, the LLM review runs in the calling thread; the
default is to spawn a daemon thread so the caller returns immediately.
*consolidate* gates the LLM umbrella-building pass. ``None`` (the default)
reads ``curator.consolidate`` from config (OFF by default). Passing
``True``/``False`` overrides the config for this invocation — used by the
``hermes curator run --consolidate`` flag. When consolidation is off, only
the deterministic inactivity prune runs and the forked aux-model review is
skipped entirely (no aux-model cost).
If *dry_run* is True, the automatic stale/archive transitions are SKIPPED
and the LLM review pass is instructed to produce a report only — no
skill_manage mutations, no terminal archive moves. The REPORT.md still
gets written and ``state.last_report_path`` still records it so users
can read what the curator WOULD have done. A dry-run also honors
*consolidate*: when consolidation is off, the preview only reports the
deterministic prune candidates.
can read what the curator WOULD have done.
"""
if consolidate is None:
consolidate = get_consolidate()
start = datetime.now(timezone.utc)
if dry_run:
# Count candidates without mutating state.
@@ -1575,53 +1497,6 @@ def run_curator_review(
before_report = []
before_names = {r.get("name") for r in before_report if isinstance(r, dict)}
# Consolidation gate. When off (the default), the curator does ONLY the
# deterministic inactivity prune above — no forked aux-model review, no
# umbrella-building, no aux-model cost. Record the run, write a report
# reflecting the prune-only outcome, and return without spawning a fork.
if not consolidate:
final_summary = (
f"{prefix}{auto_summary}; llm: skipped (consolidation off)"
)
llm_meta = {
"final": "",
"summary": "skipped (consolidation off)",
"model": "",
"provider": "",
"tool_calls": [],
"error": None,
}
elapsed = (datetime.now(timezone.utc) - start).total_seconds()
state2 = load_state()
state2["last_run_duration_seconds"] = elapsed
state2["last_run_summary"] = final_summary
try:
after_report = skill_usage.agent_created_report()
except Exception:
after_report = []
try:
report_path = _write_run_report(
started_at=start,
elapsed_seconds=elapsed,
auto_counts=counts,
auto_summary=auto_summary,
before_report=before_report,
before_names=before_names,
after_report=after_report,
llm_meta=llm_meta,
)
if report_path is not None:
state2["last_report_path"] = str(report_path)
except Exception as e:
logger.debug("Curator report write failed: %s", e, exc_info=True)
save_state(state2)
if on_summary:
try:
on_summary(f"curator: {final_summary}")
except Exception:
pass
return
llm_meta: Dict[str, Any] = {}
try:
candidate_list = _render_candidate_list()
@@ -1895,14 +1770,6 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
# Disable recursive nudges — the curator must never spawn its own review.
review_agent._memory_nudge_interval = 0
review_agent._skill_nudge_interval = 0
# Tag this fork as autonomous background curation so skill_manage's
# background-review write guard fires. Without this the fork inherits
# the default "assistant_tool" origin, is_background_review() is False,
# and the external/bundled/hub-installed skill_manage guards never
# trigger during the curation pass they exist to protect against.
# turn_context.py binds this onto the write-origin ContextVar at turn
# start (see agent/turn_context.py).
review_agent._memory_write_origin = "background_review"
# Redirect the forked agent's stdout/stderr to /dev/null while it
# runs so its tool-call chatter doesn't pollute the foreground
+10 -26
View File
@@ -46,7 +46,7 @@ import shutil
import tarfile
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple
from typing import Any, Dict, List, Optional, Tuple
from hermes_constants import get_hermes_home
from agent.skill_utils import is_excluded_skill_path
@@ -208,17 +208,13 @@ def _write_manifest(dest: Path, reason: str, archive_path: Path,
)
def snapshot_skills(reason: str = "manual", *, protect_ids: Optional[Set[str]] = None) -> Optional[Path]:
def snapshot_skills(reason: str = "manual") -> Optional[Path]:
"""Create a tar.gz snapshot of ``~/.hermes/skills/`` and prune old ones.
Returns the snapshot directory path, or ``None`` if the snapshot was
skipped (backup disabled, skills dir missing, or an IO error occurred —
in which case we log at debug and return None so the curator never
aborts a pass because of a backup failure).
``protect_ids`` is forwarded to the prune step so callers can guarantee
specific snapshot ids survive even when they fall outside the keep
window (rollback passes the id it is about to restore from).
"""
if not is_enabled():
logger.debug("Curator backup disabled by config; skipping snapshot")
@@ -280,19 +276,15 @@ def snapshot_skills(reason: str = "manual", *, protect_ids: Optional[Set[str]] =
pass
return None
_prune_old(keep=get_keep(), protect=protect_ids)
_prune_old(keep=get_keep())
logger.info("Curator snapshot created: %s (%s)", snap_id, reason)
return dest
def _prune_old(keep: int, protect: Optional[Set[str]] = None) -> List[str]:
def _prune_old(keep: int) -> List[str]:
"""Delete regular snapshots beyond the newest *keep*. Returns deleted
ids. Snapshot ids in *protect* are never deleted even when they fall
outside the keep window — rollback() uses this so the mandatory
pre-rollback safety snapshot can never evict the very snapshot being
restored. Staging dirs (``.rollback-staging-*``) are implementation
detail and pruned independently on every call."""
protect = protect or set()
ids. Staging dirs (``.rollback-staging-*``) are implementation detail
and pruned independently on every call."""
backups = _backups_dir()
if not backups.exists():
return []
@@ -313,8 +305,6 @@ def _prune_old(keep: int, protect: Optional[Set[str]] = None) -> List[str]:
entries.sort(key=lambda t: t[0], reverse=True)
deleted: List[str] = []
for _, path in entries[keep:]:
if path.name in protect:
continue
try:
shutil.rmtree(path)
deleted.append(path.name)
@@ -464,16 +454,16 @@ def _restore_cron_skill_links(snapshot_dir: Path) -> Dict[str, Any]:
report["attempted"] = True # we tried but there was nothing to do
return report
# Load and rewrite the live jobs under the scheduler's cross-process lock.
# Load and rewrite the live jobs under the scheduler's lock.
try:
from cron.jobs import load_jobs, save_jobs, _jobs_lock
from cron.jobs import load_jobs, save_jobs, _jobs_file_lock
except ImportError as e:
report["error"] = f"cron module unavailable: {e}"
return report
report["attempted"] = True
try:
with _jobs_lock():
with _jobs_file_lock:
live_jobs = load_jobs()
changed = False
@@ -574,13 +564,7 @@ def rollback(backup_id: Optional[str] = None) -> Tuple[bool, str, Optional[Path]
# out before touching anything — otherwise a failed extract could leave
# the user with no skills.
try:
# Protect the target from this snapshot's prune step: at the steady
# keep limit, pruning the oldest snapshot would otherwise delete the
# very snapshot we are about to extract from.
snapshot_skills(
reason=f"pre-rollback to {target.name}",
protect_ids={target.name},
)
snapshot_skills(reason=f"pre-rollback to {target.name}")
except Exception as e:
return (False, f"pre-rollback safety snapshot failed: {e}", None)
+6 -399
View File
@@ -6,17 +6,14 @@ Used by AIAgent._execute_tool_calls for CLI feedback.
import logging
import os
import re
import sys
import threading
import time
from dataclasses import dataclass, field
from difflib import unified_diff
from pathlib import Path
from typing import Any
from utils import safe_json_loads
from agent.redact import redact_sensitive_text
from agent.tool_result_classification import file_mutation_result_landed
# ANSI escape codes for coloring tool failure indicators
@@ -171,244 +168,6 @@ def _oneline(text: str) -> str:
return " ".join(text.split())
def _truncate_preview(text: str, max_len: int | None) -> str:
if max_len and max_len > 0 and len(text) > max_len:
if max_len <= 3:
return "." * max_len
return text[:max_len - 3] + "..."
return text
_SHELL_SILENT_HEADS = {"cd", "pushd", "popd", "export", "set", "unset", "source", ".", "true", "false", ":"}
_SHELL_PIPE_TAIL_HEADS = {"head", "tail", "wc", "sort", "uniq"}
def _shell_basename(head: str) -> str:
return head.rsplit("/", 1)[-1] if head else ""
def _split_shell_words(segment: str) -> list[str]:
words: list[str] = []
buf: list[str] = []
quote: str | None = None
for i, ch in enumerate(segment):
if quote:
buf.append(ch)
if ch == quote and (i == 0 or segment[i - 1] != "\\"):
quote = None
continue
if ch in {"'", '"'}:
quote = ch
buf.append(ch)
continue
if ch.isspace():
if buf:
words.append("".join(buf))
buf = []
continue
buf.append(ch)
if buf:
words.append("".join(buf))
return words
def _strip_shell_pipe_tail(segment: str) -> str:
words = _split_shell_words(segment)
out: list[str] = []
for i, word in enumerate(words):
if word == "|" and _shell_basename(words[i + 1] if i + 1 < len(words) else "") in _SHELL_PIPE_TAIL_HEADS:
break
out.append(word)
return " ".join(out).strip()
def _split_shell_compound(command: str) -> list[str]:
segments: list[str] = []
buf: list[str] = []
quote: str | None = None
i = 0
while i < len(command):
ch = command[i]
if quote:
buf.append(ch)
if ch == quote and (i == 0 or command[i - 1] != "\\"):
quote = None
i += 1
continue
if ch in {"'", '"'}:
quote = ch
buf.append(ch)
i += 1
continue
op_len = 2 if command.startswith("&&", i) or command.startswith("||", i) else 1 if ch in {";", "\n"} else 0
if op_len:
segment = _strip_shell_pipe_tail("".join(buf).strip())
if segment:
segments.append(segment)
buf = []
i += op_len
continue
buf.append(ch)
i += 1
segment = _strip_shell_pipe_tail("".join(buf).strip())
if segment:
segments.append(segment)
return segments
def _shell_head_word(segment: str) -> str:
words = _split_shell_words(segment)
index = 0
while index < len(words) and re.match(r"^[A-Za-z_]\w*=", words[index]):
index += 1
return _shell_basename(words[index] if index < len(words) else "")
def _clean_shell_segment(segment: str) -> str:
words = _split_shell_words(segment)
out: list[str] = []
i = 0
while i < len(words):
word = words[i]
if re.match(r"^\d*(?:>>?|<)$", word):
i += 2
continue
if re.match(r"^\d*(?:>&|<&)\d+$", word) or re.match(r"^\d*>&\d+$", word):
i += 1
continue
out.append(word)
i += 1
return " ".join(out).strip()
def _is_shell_boundary_echo(segment: str) -> bool:
words = _split_shell_words(segment)
if _shell_basename(words[0] if words else "") != "echo":
return False
rest = " ".join(words[1:])
return bool(re.search(r"-{2,}|_exit=|(?:^|\s|=)\$[?{]|PIPESTATUS", rest))
def summarize_shell_command(command: str) -> str:
"""Compact shell wrapper/plumbing for display while preserving raw command elsewhere."""
original = _oneline(command)
if not original:
return ""
segments = _split_shell_compound(original)
if len(segments) <= 1:
return _clean_shell_segment(segments[0] if segments else original) or original
core: list[str] = []
for segment in segments:
cleaned = _clean_shell_segment(segment)
head = _shell_head_word(cleaned)
if cleaned and head not in _SHELL_SILENT_HEADS and not _is_shell_boundary_echo(cleaned):
core.append(cleaned)
if not core:
return original
if len(core) == 1:
return core[0]
count = len(core) - 1
return f"{core[0]} + {count} {'command' if count == 1 else 'commands'}"
def _read_file_line_label(args: dict) -> str:
offset = args.get("offset")
limit = args.get("limit")
if not isinstance(offset, int) or offset <= 0:
return ""
if not isinstance(limit, int) or limit <= 1:
return f"L{offset}"
return f"L{offset}-{offset + limit - 1}"
def redact_browser_typed_text_for_display(value: Any, typed_text: Any) -> Any:
"""Apply secret redaction to browser_type text in display-facing payloads.
Backends sometimes echo the attempted input in error strings or fallback
metadata. When the raw typed value contains a recognizable secret (API
key, token, JWT, etc.) the redacted form differs from the raw value, so we
replace every occurrence of the raw value with its redacted form before a
browser_type result reaches logs, callbacks, the model, or chat history.
Normal typed text (search queries, addresses, form fields) matches no
secret pattern, so it passes through unchanged and stays readable.
Redaction is forced here regardless of the global ``security.redact_secrets``
preference: a typed credential leaking into chat history is a security
boundary, not mere log hygiene.
"""
if typed_text is None:
return value
needle = str(typed_text)
if needle == "":
return value
redacted = redact_sensitive_text(needle, force=True)
if redacted == needle:
# Nothing secret-looking in the typed text; leave payload untouched.
return value
if isinstance(value, str):
return value.replace(needle, redacted)
if isinstance(value, dict):
return {
key: redact_browser_typed_text_for_display(item, typed_text)
for key, item in value.items()
}
if isinstance(value, list):
return [redact_browser_typed_text_for_display(item, typed_text) for item in value]
if isinstance(value, tuple):
return tuple(redact_browser_typed_text_for_display(item, typed_text) for item in value)
return value
def redact_tool_args_for_display(tool_name: str, args: dict | None) -> dict | None:
"""Return a copy of tool args safe for logs/progress UI.
For ``browser_type`` the ``text`` argument is run through the same
secret-pattern redactor used for logs. Recognizable credentials (API
keys, tokens) are masked before the value reaches tool progress
notifications; normal typed text is left intact for debuggability.
"""
if not isinstance(args, dict):
return args
if tool_name == "browser_type" and isinstance(args.get("text"), str):
safe_args = dict(args)
safe_args["text"] = redact_sensitive_text(args["text"], force=True)
return safe_args
return args
def _delegate_task_goal_parts(tasks: Any, *, per_goal_len: int) -> tuple[int, list[str]]:
if not isinstance(tasks, list):
return 0, []
goals: list[str] = []
for task in tasks:
if not isinstance(task, dict):
continue
raw_goal = task.get("goal")
goal = "?" if raw_goal is None else _oneline(str(raw_goal))
goals.append(_truncate_preview(goal or "?", per_goal_len))
return len(goals), goals
def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -> str | None:
"""Build a short preview of a tool call's primary argument for display.
@@ -419,36 +178,19 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
max_len = _tool_preview_max_len
if not args:
return None
args = redact_tool_args_for_display(tool_name, args) or args
primary_args = {
"terminal": "command", "web_search": "query", "web_extract": "urls",
"read_file": "path", "write_file": "path", "patch": "path",
"search_files": "pattern", "browser_navigate": "url",
"browser_click": "ref", "browser_type": "text",
"image_generate": "prompt", "text_to_speech": "text",
"vision_analyze": "question",
"vision_analyze": "question", "mixture_of_agents": "user_prompt",
"skill_view": "name", "skills_list": "category",
"cronjob": "action",
"execute_code": "code", "delegate_task": "goal",
"clarify": "question", "skill_manage": "name",
}
# delegate_task: show goal (single) or individual task goals (batch)
if tool_name == "delegate_task":
tasks = args.get("tasks")
if tasks and isinstance(tasks, list):
task_count, goals = _delegate_task_goal_parts(tasks, per_goal_len=40)
preview = (
f"{task_count} tasks: " + " | ".join(goals)
if goals else f"{len(tasks)} parallel tasks"
)
return _truncate_preview(preview, max_len)
goal = args.get("goal", "")
if goal is None:
return None
preview = _oneline(str(goal))
return _truncate_preview(preview, max_len) if preview else None
if tool_name == "process":
action = args.get("action", "")
sid = args.get("session_id", "")
@@ -473,23 +215,6 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
else:
return f"planning {len(todos_arg)} task(s)"
if tool_name in {"terminal", "execute_code"}:
key = "code" if tool_name == "execute_code" else "command"
command = args.get(key)
if command is None:
return None
preview = summarize_shell_command(str(command))
return _truncate_preview(preview, max_len) if preview else None
if tool_name == "read_file":
path = args.get("path") or args.get("file") or args.get("filepath")
if path is None:
return None
label = Path(str(path).replace("\\", "/")).name or str(path)
line_label = _read_file_line_label(args)
preview = f"{label} {line_label}".strip()
return _truncate_preview(preview, max_len) if preview else None
if tool_name == "session_search":
query = _oneline(args.get("query", ""))
return f"recall: \"{query[:25]}{'...' if len(query) > 25 else ''}\""
@@ -537,122 +262,6 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
return preview
# =========================================================================
# Friendly tool labels (human-phrased verbs for built-in tools)
#
# Turns "web_search <query>" into "Searching the web for <query>" — the
# ChatGPT-style "Searching…/Reading…" surface. Curated and built-in only:
# we know each core tool's semantics, so the verb is fixed, not computed.
# Custom/plugin/MCP tools have no entry and fall back to the raw preview.
# =========================================================================
# Each entry maps a built-in tool name to its present-participle verb phrase.
# A trailing space-then-preview is appended by build_tool_label() when the
# tool's argument preview is available (e.g. "Reading docs/api.md").
_TOOL_VERBS: dict[str, str] = {
"web_search": "Searching the web",
"web_extract": "Reading",
"browser_navigate": "Browsing",
"browser_click": "Clicking",
"browser_type": "Typing",
"read_file": "Reading",
"write_file": "Writing",
"patch": "Editing",
"search_files": "Searching files",
"terminal": "Running",
"execute_code": "Running code",
"image_generate": "Generating image",
"video_generate": "Generating video",
"text_to_speech": "Generating speech",
"vision_analyze": "Looking at the image",
"session_search": "Searching past sessions",
"skill_view": "Reading skill",
"skills_list": "Listing skills",
"skill_manage": "Updating skill",
"delegate_task": "Delegating",
"cronjob": "Scheduling",
"clarify": "Asking",
"memory": "Updating memory",
"todo": "Updating tasks",
}
# Verbs that read better without the raw argument preview appended.
_TOOL_VERBS_NO_PREVIEW: frozenset[str] = frozenset({
"skills_list",
"session_search",
})
# Verbs that take a "for" connector before the preview (search-style phrasing):
# "Searching the web for <query>" reads better than "Searching the web <query>".
_TOOL_VERBS_FOR_CONNECTOR: frozenset[str] = frozenset({
"web_search",
"search_files",
})
_friendly_tool_labels: bool = True
def set_friendly_tool_labels(enabled: bool) -> None:
"""Toggle friendly human-phrased tool labels (display.friendly_tool_labels)."""
global _friendly_tool_labels
_friendly_tool_labels = bool(enabled)
def get_friendly_tool_labels() -> bool:
"""Return whether friendly tool labels are enabled."""
return _friendly_tool_labels
def get_tool_verb(tool_name: str) -> str | None:
"""Return the friendly verb for a built-in tool, or None.
Returns None when friendly labels are disabled or the tool has no curated
verb (custom/plugin/MCP tools). Callers that already hold a computed
argument preview can compose ``f"{verb} {preview}"`` themselves; use
:func:`tool_verb_connector` to pick the right joiner.
"""
if not _friendly_tool_labels:
return None
return _TOOL_VERBS.get(tool_name)
def tool_verb_connector(tool_name: str) -> str:
"""Return the connector between a verb and its preview (" for " or " ")."""
return " for " if tool_name in _TOOL_VERBS_FOR_CONNECTOR else " "
def verb_drops_preview(tool_name: str) -> bool:
"""Whether the verb should render alone, without the argument preview."""
return tool_name in _TOOL_VERBS_NO_PREVIEW
def build_tool_label(tool_name: str, args: dict, max_len: int | None = None) -> str | None:
"""Build a human-phrased status label for a tool call.
For built-in tools with a known verb (``web_search`` -> "Searching the
web for ..."), returns the verb optionally followed by the argument
preview. For everything else (custom/plugin/MCP tools, or when friendly
labels are disabled) returns the raw preview, so callers can use this as a
drop-in replacement for :func:`build_tool_preview`.
"""
if not _friendly_tool_labels:
return build_tool_preview(tool_name, args, max_len=max_len)
verb = _TOOL_VERBS.get(tool_name)
if not verb:
return build_tool_preview(tool_name, args, max_len=max_len)
if tool_name in _TOOL_VERBS_NO_PREVIEW:
return verb
preview = build_tool_preview(tool_name, args, max_len=max_len)
if not preview:
return verb
if tool_name in _TOOL_VERBS_FOR_CONNECTOR:
return f"{verb} for {preview}"
return f"{verb} {preview}"
# =========================================================================
# Inline diff previews for write actions
# =========================================================================
@@ -1259,7 +868,6 @@ def get_cute_tool_message(
When *result* is provided the line is checked for failure indicators.
Failed tool calls get a red prefix and an informational suffix.
"""
args = redact_tool_args_for_display(tool_name, args) or args
dur = f"{duration:.1f}s"
is_failure, failure_suffix = _detect_tool_failure(tool_name, result)
skin_prefix = get_skin_tool_prefix()
@@ -1297,7 +905,7 @@ def get_cute_tool_message(
return _wrap(f"┊ 📄 fetch {_trunc(domain, 35)}{extra} {dur}")
return _wrap(f"┊ 📄 fetch pages {dur}")
if tool_name == "terminal":
return _wrap(f"┊ 💻 $ {_trunc(build_tool_preview(tool_name, args) or args.get('command', ''), 42)} {dur}")
return _wrap(f"┊ 💻 $ {_trunc(args.get('command', ''), 42)} {dur}")
if tool_name == "process":
action = args.get("action", "?")
sid = args.get("session_id", "")[:12]
@@ -1305,7 +913,7 @@ def get_cute_tool_message(
"wait": f"wait {sid}", "kill": f"kill {sid}", "write": f"write {sid}", "submit": f"submit {sid}"}
return _wrap(f"┊ ⚙️ proc {labels.get(action, f'{action} {sid}')} {dur}")
if tool_name == "read_file":
return _wrap(f"┊ 📖 read {_trunc(build_tool_preview(tool_name, args) or args.get('path', ''), 42)} {dur}")
return _wrap(f"┊ 📖 read {_path(args.get('path', ''))} {dur}")
if tool_name == "write_file":
return _wrap(f"┊ ✍️ write {_path(args.get('path', ''))} {dur}")
if tool_name == "patch":
@@ -1391,6 +999,8 @@ def get_cute_tool_message(
return _wrap(f"┊ 🔊 speak {_trunc(args.get('text', ''), 30)} {dur}")
if tool_name == "vision_analyze":
return _wrap(f"┊ 👁️ vision {_trunc(args.get('question', ''), 30)} {dur}")
if tool_name == "mixture_of_agents":
return _wrap(f"┊ 🧠 reason {_trunc(args.get('user_prompt', ''), 30)} {dur}")
if tool_name == "send_message":
return _wrap(f"┊ 📨 send {args.get('target', '?')}: \"{_trunc(args.get('message', ''), 25)}\" {dur}")
if tool_name == "cronjob":
@@ -1409,10 +1019,7 @@ def get_cute_tool_message(
if tool_name == "delegate_task":
tasks = args.get("tasks")
if tasks and isinstance(tasks, list):
task_count, goals = _delegate_task_goal_parts(tasks, per_goal_len=30)
detail = " | ".join(goals) if goals else "parallel"
count_label = task_count or len(tasks)
return _wrap(f"┊ 🔀 delegate {count_label}x: {_trunc(detail, 35)} {dur}")
return _wrap(f"┊ 🔀 delegate {len(tasks)} parallel tasks {dur}")
return _wrap(f"┊ 🔀 delegate {_trunc(args.get('goal', ''), 35)} {dur}")
preview = build_tool_preview(tool_name, args) or ""
+17 -145
View File
@@ -133,31 +133,6 @@ _RATE_LIMIT_PATTERNS = [
"servicequotaexceededexception",
]
# Patterns that indicate provider-side overload, NOT a per-credential rate
# limit or billing problem. The credential is valid — the server is just
# busy — so the correct recovery is "back off and retry the same key", never
# "rotate the credential" (rotating exhausts the pool while the endpoint is
# still busy; a single-key user has nothing to rotate to). Some providers
# (notably Z.AI / Zhipu) reuse HTTP 429 for server-wide overload, so the 429
# status path matches the body against this list before falling through to
# the rate_limit default. Phrases are kept narrow and overload-flavoured so a
# normal rate-limit message ("you have been rate-limited") doesn't hit this
# bucket. (#14038, #15297)
_OVERLOADED_PATTERNS = [
"overloaded",
"temporarily overloaded",
"service is temporarily overloaded",
"service may be temporarily overloaded",
"server is overloaded",
"server overloaded",
"service overloaded",
"service is overloaded",
"upstream overloaded",
"currently overloaded",
"at capacity",
"over capacity",
]
# Usage-limit patterns that need disambiguation (could be billing OR rate_limit)
_USAGE_LIMIT_PATTERNS = [
"usage limit",
@@ -355,14 +330,6 @@ _CONTENT_POLICY_BLOCKED_PATTERNS = [
# echo back; the underscore form is provider-specific enough.
"content_filter",
"responsibleaipolicyviolation",
# MiniMax output-layer safety filter. The error string is surfaced
# verbatim by MiniMax SDK / OpenAI-compatible endpoints, usually in the
# form "output new_sensitive (1027)" when the model's *output* (often a
# large tool-call argument block) trips the upstream safety filter and
# the SSE stream is truncated mid-flight. ``new_sensitive`` is the
# filter name and is narrow enough that billing / format / auth error
# strings will not collide. See #32421.
"new_sensitive",
]
# Auth patterns (non-status-code signals)
@@ -582,32 +549,14 @@ def classify_api_error(
should_fallback=True,
)
# Anthropic thinking block recovery (400). Two distinct failure modes,
# same recovery (strip all reasoning_details and retry without thinking
# blocks — see the thinking_signature handler in conversation_loop.py):
# 1. Signature mismatch: a thinking block is signed against the full
# turn content; any upstream mutation (context compression, session
# truncation, message merging) invalidates the signature.
# Pattern: "signature" + "thinking".
# 2. Frozen-block mutation: Anthropic rejects any change to the
# thinking/redacted_thinking blocks in the *latest* assistant
# message — "`thinking` or `redacted_thinking` blocks in the latest
# assistant message cannot be modified. These blocks must remain as
# they were in the original response." This carries no "signature"
# token, so the original pattern missed it and the turn hard-aborted
# as a non-retryable client error instead of self-healing.
# Pattern: "thinking" + ("cannot be modified" | "must remain as they were").
# Anthropic thinking block signature invalid (400).
# Don't gate on provider — OpenRouter proxies Anthropic errors, so the
# provider may be "openrouter" even though the error is Anthropic-specific.
# The combined patterns are unique enough.
# The message pattern ("signature" + "thinking") is unique enough.
if (
status_code == 400
and "signature" in error_msg
and "thinking" in error_msg
and (
"signature" in error_msg
or "cannot be modified" in error_msg
or "must remain as they were" in error_msg
)
):
return _result(
FailoverReason.thinking_signature,
@@ -750,26 +699,6 @@ def classify_api_error(
is_disconnect = any(p in error_msg for p in _SERVER_DISCONNECT_PATTERNS)
if is_disconnect and not status_code:
# Reasoning-model override: a transport disconnect on a reasoning
# model is much more likely the upstream proxy idle-killing a
# long thinking stream than a true context overflow — even on
# large sessions. The default disconnect+large-session routing
# below would otherwise send the user into the compression
# branch (should_compress=True) and silently delete
# conversation history on a phantom context-length error.
# Reasoning models have multi-minute thinking phases that
# routinely exceed the cloud gateway's idle window (NVIDIA
# NIM ~120s — first-party repro at NVIDIA/NemoClaw#4846;
# OpenAI worker / Anthropic stream-idle similar). The
# per-reasoning-model stale-timeout floor in
# agent/reasoning_timeouts.py raises the stale-detector
# threshold to tolerate long thinking, so a true
# transport-layer failure here is recoverable via the retry
# path — not via context compression. Reclassify as timeout.
# (Part 1 of Fixes #52310.)
from agent.reasoning_timeouts import get_reasoning_stale_timeout_floor
if get_reasoning_stale_timeout_floor(model) is not None:
return _result(FailoverReason.timeout, retryable=True)
# Absolute token/message-count thresholds are only a proxy for smaller
# context windows. Large-context sessions can have hundreds of
# messages while still being far below their actual token budget.
@@ -896,19 +825,7 @@ def _classify_by_status(
)
if status_code == 429:
# Already checked long_context_tier above. Some providers (notably
# Z.AI / Zhipu) reuse HTTP 429 for server-wide overload — same status
# code as a true per-credential rate limit, but the credential is
# valid and the correct recovery is "back off and retry the same key",
# NOT "rotate the credential" (which exhausts the pool while the
# endpoint is still busy, and does nothing for a single-key user).
# Disambiguate on the error body so an overload 429 takes the
# transient-overload path instead of burning the pool. (#14038)
if any(p in error_msg for p in _OVERLOADED_PATTERNS):
return result_fn(
FailoverReason.overloaded,
retryable=True,
)
# Already checked long_context_tier above; this is a normal rate limit
return result_fn(
FailoverReason.rate_limit,
retryable=True,
@@ -1049,34 +966,6 @@ def _classify_400(
should_fallback=False,
)
# Request-validation errors (unsupported / unknown parameter) MUST be
# checked BEFORE context_overflow. A GPT-5 model rejecting max_tokens
# returns:
# "Unsupported parameter: 'max_tokens' is not supported with this model.
# Use 'max_completion_tokens' instead."
# That string contains the literal substring "max_tokens", which is one of
# the _CONTEXT_OVERFLOW_PATTERNS — so without this guard the 400 is
# misclassified as context_overflow, routed into the compression loop,
# re-sent with the same bad parameter, and ends in "Cannot compress
# further". These errors are deterministic (every retry gets the identical
# rejection), so classify as a non-retryable format_error and fall back.
#
# NOTE: we deliberately do NOT key off the generic ``invalid_request_error``
# code here — OpenAI stamps that same code on genuine context-overflow 400s,
# so matching it would mis-route real overflows away from compression. The
# unambiguous signals are the explicit "unsupported/unknown parameter"
# message text and the specific parameter-level error codes.
if (
any(p in error_msg for p in _REQUEST_VALIDATION_PATTERNS
if p != "invalid_request_error")
or error_code_lower in {"unknown_parameter", "unsupported_parameter"}
):
return result_fn(
FailoverReason.format_error,
retryable=False,
should_fallback=True,
)
# Context overflow from 400
if any(p in error_msg for p in _CONTEXT_OVERFLOW_PATTERNS):
return result_fn(
@@ -1259,17 +1148,6 @@ def _classify_by_message(
should_fallback=True,
)
# Overloaded / server-busy patterns — must come BEFORE the rate_limit and
# billing checks so that a message-only "overloaded" (no 503/529 status,
# e.g. some Anthropic-compatible proxies) classifies as a transient
# overload (backoff + retry) instead of falling through to `unknown` or
# incorrectly triggering credential rotation.
if any(p in error_msg for p in _OVERLOADED_PATTERNS):
return result_fn(
FailoverReason.overloaded,
retryable=True,
)
# Billing patterns
if any(p in error_msg for p in _BILLING_PATTERNS):
return result_fn(
@@ -1359,25 +1237,19 @@ def _extract_status_code(error: Exception) -> Optional[int]:
def _extract_error_body(error: Exception) -> dict:
"""Extract the structured error body from an SDK exception or its cause chain."""
current = error
for _ in range(5): # Match _extract_status_code() traversal depth.
body = getattr(current, "body", None)
if isinstance(body, dict):
return body
# Some errors have .response.json()
response = getattr(current, "response", None)
if response is not None:
try:
json_body = response.json()
if isinstance(json_body, dict):
return json_body
except Exception:
pass
cause = getattr(current, "__cause__", None) or getattr(current, "__context__", None)
if cause is None or cause is current:
break
current = cause
"""Extract the structured error body from an SDK exception."""
body = getattr(error, "body", None)
if isinstance(body, dict):
return body
# Some errors have .response.json()
response = getattr(error, "response", None)
if response is not None:
try:
json_body = response.json()
if isinstance(json_body, dict):
return json_body
except Exception:
pass
return {}
-3
View File
@@ -1,3 +0,0 @@
class SSLConfigurationError(Exception):
"""Raised when SSL/TLS certificate bundle configuration fails."""
pass
+29 -25
View File
@@ -46,6 +46,11 @@ def build_write_denied_paths(home: str) -> set[str]:
# Top-level Anthropic PKCE credential store remains sensitive even
# when a profile is active; default/non-profile sessions still read it.
str(hermes_root / ".anthropic_oauth.json"),
os.path.join(home, ".bashrc"),
os.path.join(home, ".zshrc"),
os.path.join(home, ".profile"),
os.path.join(home, ".bash_profile"),
os.path.join(home, ".zprofile"),
os.path.join(home, ".netrc"),
os.path.join(home, ".pgpass"),
os.path.join(home, ".npmrc"),
@@ -77,22 +82,15 @@ def build_write_denied_prefixes(home: str) -> list[str]:
]
def get_safe_write_roots() -> set[str]:
"""Return resolved HERMES_WRITE_SAFE_ROOT paths. Supports multiple directories
separated by ``os.pathsep`` (``:`` on Unix, ``;`` on Windows).
E.g., ``/opt/data:/var/www/html`` on Unix, ``C:\\data;D:\\www`` on Windows."""
env = os.getenv("HERMES_WRITE_SAFE_ROOT", "")
if not env:
return set()
roots: set[str] = set()
for path in env.split(os.pathsep):
if path:
try:
resolved = os.path.realpath(os.path.expanduser(path))
roots.add(resolved)
except (OSError, ValueError):
continue
return roots
def get_safe_write_root() -> Optional[str]:
"""Return the resolved HERMES_WRITE_SAFE_ROOT path, or None if unset."""
root = os.getenv("HERMES_WRITE_SAFE_ROOT", "")
if not root:
return None
try:
return os.path.realpath(os.path.expanduser(root))
except Exception:
return None
def is_write_denied(path: str) -> bool:
@@ -106,6 +104,12 @@ def is_write_denied(path: str) -> bool:
if resolved.startswith(prefix):
return True
# Hermes control-plane files: block both the ACTIVE profile's view
# (hermes_home) AND the global root view. Without the root pass, a
# profile-mode session leaves <root>/auth.json + <root>/config.yaml
# writable — letting a prompt-injected write_file overwrite the global
# files that every profile inherits from (same shape as #15981).
control_file_names = ("auth.json", "config.yaml", "webhook_subscriptions.json")
mcp_tokens_dir_name = "mcp-tokens"
hermes_dirs = []
@@ -118,6 +122,12 @@ def is_write_denied(path: str) -> bool:
continue
for base_real in hermes_dirs:
for name in control_file_names:
try:
if resolved == os.path.realpath(os.path.join(base_real, name)):
return True
except Exception:
continue
try:
mcp_real = os.path.realpath(os.path.join(base_real, mcp_tokens_dir_name))
if resolved == mcp_real or resolved.startswith(mcp_real + os.sep):
@@ -131,15 +141,9 @@ def is_write_denied(path: str) -> bool:
except Exception:
pass
safe_roots = get_safe_write_roots()
if safe_roots:
allowed = False
for safe_root in safe_roots:
if resolved == safe_root or resolved.startswith(safe_root + os.sep):
allowed = True
break
if not allowed:
return True
safe_root = get_safe_write_root()
if safe_root and not (resolved == safe_root or resolved.startswith(safe_root + os.sep)):
return True
return False
+909
View File
@@ -0,0 +1,909 @@
"""OpenAI-compatible facade that talks to Google's Cloud Code Assist backend.
This adapter lets Hermes use the ``google-gemini-cli`` provider as if it were
a standard OpenAI-shaped chat completion endpoint, while the underlying HTTP
traffic goes to ``cloudcode-pa.googleapis.com/v1internal:{generateContent,
streamGenerateContent}`` with a Bearer access token obtained via OAuth PKCE.
Architecture
------------
- ``GeminiCloudCodeClient`` exposes ``.chat.completions.create(**kwargs)``
mirroring the subset of the OpenAI SDK that ``run_agent.py`` uses.
- Incoming OpenAI ``messages[]`` / ``tools[]`` / ``tool_choice`` are translated
to Gemini's native ``contents[]`` / ``tools[].functionDeclarations`` /
``toolConfig`` / ``systemInstruction`` shape.
- The request body is wrapped ``{project, model, user_prompt_id, request}``
per Code Assist API expectations.
- Responses (``candidates[].content.parts[]``) are converted back to
OpenAI ``choices[0].message`` shape with ``content`` + ``tool_calls``.
- Streaming uses SSE (``?alt=sse``) and yields OpenAI-shaped delta chunks.
Attribution
-----------
Translation semantics follow jenslys/opencode-gemini-auth (MIT) and the public
Gemini API docs. Request envelope shape
(``{project, model, user_prompt_id, request}``) is documented nowhere; it is
reverse-engineered from the opencode-gemini-auth and clawdbot implementations.
"""
from __future__ import annotations
import json
import logging
import time
import uuid
from types import SimpleNamespace
from typing import Any, Dict, Iterator, List, Optional
import httpx
from agent import google_oauth
from agent.gemini_schema import sanitize_gemini_tool_parameters
from agent.google_code_assist import (
CODE_ASSIST_ENDPOINT,
CodeAssistError,
ProjectContext,
resolve_project_context,
)
logger = logging.getLogger(__name__)
# =============================================================================
# Request translation: OpenAI → Gemini
# =============================================================================
_ROLE_MAP_OPENAI_TO_GEMINI = {
"user": "user",
"assistant": "model",
"system": "user", # handled separately via systemInstruction
"tool": "user", # functionResponse is wrapped in a user-role turn
"function": "user",
}
def _coerce_content_to_text(content: Any) -> str:
"""OpenAI content may be str or a list of parts; reduce to plain text."""
if content is None:
return ""
if isinstance(content, str):
return content
if isinstance(content, list):
pieces: List[str] = []
for p in content:
if isinstance(p, str):
pieces.append(p)
elif isinstance(p, dict):
if p.get("type") == "text" and isinstance(p.get("text"), str):
pieces.append(p["text"])
# Multimodal (image_url, etc.) — stub for now; log and skip
elif p.get("type") in {"image_url", "input_audio"}:
logger.debug("Dropping multimodal part (not yet supported): %s", p.get("type"))
return "\n".join(pieces)
return str(content)
def _translate_tool_call_to_gemini(tool_call: Dict[str, Any]) -> Dict[str, Any]:
"""OpenAI tool_call -> Gemini functionCall part."""
fn = tool_call.get("function") or {}
args_raw = fn.get("arguments", "")
try:
args = json.loads(args_raw) if isinstance(args_raw, str) and args_raw else {}
except json.JSONDecodeError:
args = {"_raw": args_raw}
if not isinstance(args, dict):
args = {"_value": args}
return {
"functionCall": {
"name": fn.get("name") or "",
"args": args,
},
# Sentinel signature — matches opencode-gemini-auth's approach.
# Without this, Code Assist rejects function calls that originated
# outside its own chain.
"thoughtSignature": "skip_thought_signature_validator",
}
def _translate_tool_result_to_gemini(message: Dict[str, Any]) -> Dict[str, Any]:
"""OpenAI tool-role message -> Gemini functionResponse part.
The function name isn't in the OpenAI tool message directly; it must be
passed via the assistant message that issued the call. For simplicity we
look up ``name`` on the message (OpenAI SDK copies it there) or on the
``tool_call_id`` cross-reference.
"""
name = str(message.get("name") or message.get("tool_call_id") or "tool")
content = _coerce_content_to_text(message.get("content"))
# Gemini expects the response as a dict under `response`. We wrap plain
# text in {"output": "..."}.
try:
parsed = json.loads(content) if content.strip().startswith(("{", "[")) else None
except json.JSONDecodeError:
parsed = None
response = parsed if isinstance(parsed, dict) else {"output": content}
return {
"functionResponse": {
"name": name,
"response": response,
},
}
def _build_gemini_contents(
messages: List[Dict[str, Any]],
) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
"""Convert OpenAI messages[] to Gemini contents[] + systemInstruction."""
system_text_parts: List[str] = []
contents: List[Dict[str, Any]] = []
for msg in messages:
if not isinstance(msg, dict):
continue
role = str(msg.get("role") or "user")
if role == "system":
system_text_parts.append(_coerce_content_to_text(msg.get("content")))
continue
# Tool result message — emit a user-role turn with functionResponse
if role == "tool" or role == "function":
contents.append({
"role": "user",
"parts": [_translate_tool_result_to_gemini(msg)],
})
continue
gemini_role = _ROLE_MAP_OPENAI_TO_GEMINI.get(role, "user")
parts: List[Dict[str, Any]] = []
text = _coerce_content_to_text(msg.get("content"))
if text:
parts.append({"text": text})
# Assistant messages can carry tool_calls
tool_calls = msg.get("tool_calls") or []
if isinstance(tool_calls, list):
for tc in tool_calls:
if isinstance(tc, dict):
parts.append(_translate_tool_call_to_gemini(tc))
if not parts:
# Gemini rejects empty parts; skip the turn entirely
continue
contents.append({"role": gemini_role, "parts": parts})
system_instruction: Optional[Dict[str, Any]] = None
joined_system = "\n".join(p for p in system_text_parts if p).strip()
if joined_system:
system_instruction = {
"role": "system",
"parts": [{"text": joined_system}],
}
return contents, system_instruction
def _translate_tools_to_gemini(tools: Any) -> List[Dict[str, Any]]:
"""OpenAI tools[] -> Gemini tools[].functionDeclarations[]."""
if not isinstance(tools, list) or not tools:
return []
declarations: List[Dict[str, Any]] = []
for t in tools:
if not isinstance(t, dict):
continue
fn = t.get("function") or {}
if not isinstance(fn, dict):
continue
name = fn.get("name")
if not name:
continue
decl = {"name": str(name)}
if fn.get("description"):
decl["description"] = str(fn["description"])
params = fn.get("parameters")
if isinstance(params, dict):
decl["parameters"] = sanitize_gemini_tool_parameters(params)
declarations.append(decl)
if not declarations:
return []
return [{"functionDeclarations": declarations}]
def _translate_tool_choice_to_gemini(tool_choice: Any) -> Optional[Dict[str, Any]]:
"""OpenAI tool_choice -> Gemini toolConfig.functionCallingConfig."""
if tool_choice is None:
return None
if isinstance(tool_choice, str):
if tool_choice == "auto":
return {"functionCallingConfig": {"mode": "AUTO"}}
if tool_choice == "required":
return {"functionCallingConfig": {"mode": "ANY"}}
if tool_choice == "none":
return {"functionCallingConfig": {"mode": "NONE"}}
if isinstance(tool_choice, dict):
fn = tool_choice.get("function") or {}
name = fn.get("name")
if name:
return {
"functionCallingConfig": {
"mode": "ANY",
"allowedFunctionNames": [str(name)],
},
}
return None
def _normalize_thinking_config(config: Any) -> Optional[Dict[str, Any]]:
"""Accept thinkingBudget / thinkingLevel / includeThoughts (+ snake_case)."""
if not isinstance(config, dict) or not config:
return None
budget = config.get("thinkingBudget", config.get("thinking_budget"))
level = config.get("thinkingLevel", config.get("thinking_level"))
include = config.get("includeThoughts", config.get("include_thoughts"))
normalized: Dict[str, Any] = {}
if isinstance(budget, (int, float)):
normalized["thinkingBudget"] = int(budget)
if isinstance(level, str) and level.strip():
normalized["thinkingLevel"] = level.strip().lower()
if isinstance(include, bool):
normalized["includeThoughts"] = include
return normalized or None
def build_gemini_request(
*,
messages: List[Dict[str, Any]],
tools: Any = None,
tool_choice: Any = None,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
top_p: Optional[float] = None,
stop: Any = None,
thinking_config: Any = None,
) -> Dict[str, Any]:
"""Build the inner Gemini request body (goes inside ``request`` wrapper)."""
contents, system_instruction = _build_gemini_contents(messages)
body: Dict[str, Any] = {"contents": contents}
if system_instruction is not None:
body["systemInstruction"] = system_instruction
gemini_tools = _translate_tools_to_gemini(tools)
if gemini_tools:
body["tools"] = gemini_tools
tool_cfg = _translate_tool_choice_to_gemini(tool_choice)
if tool_cfg is not None:
body["toolConfig"] = tool_cfg
generation_config: Dict[str, Any] = {}
if isinstance(temperature, (int, float)):
generation_config["temperature"] = float(temperature)
if isinstance(max_tokens, int) and max_tokens > 0:
generation_config["maxOutputTokens"] = max_tokens
if isinstance(top_p, (int, float)):
generation_config["topP"] = float(top_p)
if isinstance(stop, str) and stop:
generation_config["stopSequences"] = [stop]
elif isinstance(stop, list) and stop:
generation_config["stopSequences"] = [str(s) for s in stop if s]
normalized_thinking = _normalize_thinking_config(thinking_config)
if normalized_thinking:
generation_config["thinkingConfig"] = normalized_thinking
if generation_config:
body["generationConfig"] = generation_config
return body
def wrap_code_assist_request(
*,
project_id: str,
model: str,
inner_request: Dict[str, Any],
user_prompt_id: Optional[str] = None,
) -> Dict[str, Any]:
"""Wrap the inner Gemini request in the Code Assist envelope."""
return {
"project": project_id,
"model": model,
"user_prompt_id": user_prompt_id or str(uuid.uuid4()),
"request": inner_request,
}
# =============================================================================
# Response translation: Gemini → OpenAI
# =============================================================================
def _translate_gemini_response(
resp: Dict[str, Any],
model: str,
) -> SimpleNamespace:
"""Non-streaming Gemini response -> OpenAI-shaped SimpleNamespace.
Code Assist wraps the actual Gemini response inside ``response``, so we
unwrap it first if present.
"""
inner = resp.get("response") if isinstance(resp.get("response"), dict) else resp
candidates = inner.get("candidates") or []
if not isinstance(candidates, list) or not candidates:
return _empty_response(model)
cand = candidates[0]
content_obj = cand.get("content") if isinstance(cand, dict) else {}
parts = content_obj.get("parts") if isinstance(content_obj, dict) else []
text_pieces: List[str] = []
reasoning_pieces: List[str] = []
tool_calls: List[SimpleNamespace] = []
for i, part in enumerate(parts or []):
if not isinstance(part, dict):
continue
# Thought parts are model's internal reasoning — surface as reasoning,
# don't mix into content.
if part.get("thought") is True:
if isinstance(part.get("text"), str):
reasoning_pieces.append(part["text"])
continue
if isinstance(part.get("text"), str):
text_pieces.append(part["text"])
continue
fc = part.get("functionCall")
if isinstance(fc, dict) and fc.get("name"):
try:
args_str = json.dumps(fc.get("args") or {}, ensure_ascii=False)
except (TypeError, ValueError):
args_str = "{}"
tool_calls.append(SimpleNamespace(
id=f"call_{uuid.uuid4().hex[:12]}",
type="function",
index=i,
function=SimpleNamespace(name=str(fc["name"]), arguments=args_str),
))
finish_reason = "tool_calls" if tool_calls else _map_gemini_finish_reason(
str(cand.get("finishReason") or "")
)
usage_meta = inner.get("usageMetadata") or {}
usage = SimpleNamespace(
prompt_tokens=int(usage_meta.get("promptTokenCount") or 0),
completion_tokens=int(usage_meta.get("candidatesTokenCount") or 0),
total_tokens=int(usage_meta.get("totalTokenCount") or 0),
prompt_tokens_details=SimpleNamespace(
cached_tokens=int(usage_meta.get("cachedContentTokenCount") or 0),
),
)
message = SimpleNamespace(
role="assistant",
content="".join(text_pieces) if text_pieces else None,
tool_calls=tool_calls or None,
reasoning="".join(reasoning_pieces) or None,
reasoning_content="".join(reasoning_pieces) or None,
reasoning_details=None,
)
choice = SimpleNamespace(
index=0,
message=message,
finish_reason=finish_reason,
)
return SimpleNamespace(
id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
object="chat.completion",
created=int(time.time()),
model=model,
choices=[choice],
usage=usage,
)
def _empty_response(model: str) -> SimpleNamespace:
message = SimpleNamespace(
role="assistant", content="", tool_calls=None,
reasoning=None, reasoning_content=None, reasoning_details=None,
)
choice = SimpleNamespace(index=0, message=message, finish_reason="stop")
usage = SimpleNamespace(
prompt_tokens=0, completion_tokens=0, total_tokens=0,
prompt_tokens_details=SimpleNamespace(cached_tokens=0),
)
return SimpleNamespace(
id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
object="chat.completion",
created=int(time.time()),
model=model,
choices=[choice],
usage=usage,
)
def _map_gemini_finish_reason(reason: str) -> str:
mapping = {
"STOP": "stop",
"MAX_TOKENS": "length",
"SAFETY": "content_filter",
"RECITATION": "content_filter",
"OTHER": "stop",
}
return mapping.get(reason.upper(), "stop")
# =============================================================================
# Streaming SSE iterator
# =============================================================================
class _GeminiStreamChunk(SimpleNamespace):
"""Mimics an OpenAI ChatCompletionChunk with .choices[0].delta."""
pass
def _make_stream_chunk(
*,
model: str,
content: str = "",
tool_call_delta: Optional[Dict[str, Any]] = None,
finish_reason: Optional[str] = None,
reasoning: str = "",
) -> _GeminiStreamChunk:
delta_kwargs: Dict[str, Any] = {
"role": "assistant",
"content": None,
"tool_calls": None,
"reasoning": None,
"reasoning_content": None,
}
if content:
delta_kwargs["content"] = content
if tool_call_delta is not None:
delta_kwargs["tool_calls"] = [SimpleNamespace(
index=tool_call_delta.get("index", 0),
id=tool_call_delta.get("id") or f"call_{uuid.uuid4().hex[:12]}",
type="function",
function=SimpleNamespace(
name=tool_call_delta.get("name") or "",
arguments=tool_call_delta.get("arguments") or "",
),
)]
if reasoning:
delta_kwargs["reasoning"] = reasoning
delta_kwargs["reasoning_content"] = reasoning
delta = SimpleNamespace(**delta_kwargs)
choice = SimpleNamespace(index=0, delta=delta, finish_reason=finish_reason)
return _GeminiStreamChunk(
id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
object="chat.completion.chunk",
created=int(time.time()),
model=model,
choices=[choice],
usage=None,
)
def _iter_sse_events(response: httpx.Response) -> Iterator[Dict[str, Any]]:
"""Parse Server-Sent Events from an httpx streaming response."""
buffer = ""
for chunk in response.iter_text():
if not chunk:
continue
buffer += chunk
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.rstrip("\r")
if not line:
continue
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]":
return
try:
yield json.loads(data)
except json.JSONDecodeError:
logger.debug("Non-JSON SSE line: %s", data[:200])
def _translate_stream_event(
event: Dict[str, Any],
model: str,
tool_call_counter: List[int],
) -> List[_GeminiStreamChunk]:
"""Unwrap Code Assist envelope and emit OpenAI-shaped chunk(s).
``tool_call_counter`` is a single-element list used as a mutable counter
across events in the same stream. Each ``functionCall`` part gets a
fresh, unique OpenAI ``index`` — keying by function name would collide
whenever the model issues parallel calls to the same tool (e.g. reading
three files in one turn).
"""
inner = event.get("response") if isinstance(event.get("response"), dict) else event
candidates = inner.get("candidates") or []
if not candidates:
return []
cand = candidates[0]
if not isinstance(cand, dict):
return []
chunks: List[_GeminiStreamChunk] = []
content = cand.get("content") or {}
parts = content.get("parts") if isinstance(content, dict) else []
for part in parts or []:
if not isinstance(part, dict):
continue
if part.get("thought") is True and isinstance(part.get("text"), str):
chunks.append(_make_stream_chunk(
model=model, reasoning=part["text"],
))
continue
if isinstance(part.get("text"), str) and part["text"]:
chunks.append(_make_stream_chunk(model=model, content=part["text"]))
fc = part.get("functionCall")
if isinstance(fc, dict) and fc.get("name"):
name = str(fc["name"])
idx = tool_call_counter[0]
tool_call_counter[0] += 1
try:
args_str = json.dumps(fc.get("args") or {}, ensure_ascii=False)
except (TypeError, ValueError):
args_str = "{}"
chunks.append(_make_stream_chunk(
model=model,
tool_call_delta={
"index": idx,
"name": name,
"arguments": args_str,
},
))
finish_reason_raw = str(cand.get("finishReason") or "")
if finish_reason_raw:
mapped = _map_gemini_finish_reason(finish_reason_raw)
if tool_call_counter[0] > 0:
mapped = "tool_calls"
chunks.append(_make_stream_chunk(model=model, finish_reason=mapped))
return chunks
# =============================================================================
# GeminiCloudCodeClient — OpenAI-compatible facade
# =============================================================================
MARKER_BASE_URL = "cloudcode-pa://google"
class _GeminiChatCompletions:
def __init__(self, client: "GeminiCloudCodeClient"):
self._client = client
def create(self, **kwargs: Any) -> Any:
return self._client._create_chat_completion(**kwargs)
class _GeminiChatNamespace:
def __init__(self, client: "GeminiCloudCodeClient"):
self.completions = _GeminiChatCompletions(client)
class GeminiCloudCodeClient:
"""Minimal OpenAI-SDK-compatible facade over Code Assist v1internal."""
def __init__(
self,
*,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
default_headers: Optional[Dict[str, str]] = None,
project_id: str = "",
**_: Any,
):
# `api_key` here is a dummy — real auth is the OAuth access token
# fetched on every call via agent.google_oauth.get_valid_access_token().
# We accept the kwarg for openai.OpenAI interface parity.
self.api_key = api_key or "google-oauth"
self.base_url = base_url or MARKER_BASE_URL
self._default_headers = dict(default_headers or {})
self._configured_project_id = project_id
self._project_context: Optional[ProjectContext] = None
self._project_context_lock = False # simple single-thread guard
self.chat = _GeminiChatNamespace(self)
self.is_closed = False
self._http = httpx.Client(timeout=httpx.Timeout(connect=15.0, read=600.0, write=30.0, pool=30.0))
def close(self) -> None:
self.is_closed = True
try:
self._http.close()
except Exception:
pass
# Implement the OpenAI SDK's context-manager-ish closure check
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def _ensure_project_context(self, access_token: str, model: str) -> ProjectContext:
"""Lazily resolve and cache the project context for this client."""
if self._project_context is not None:
return self._project_context
env_project = google_oauth.resolve_project_id_from_env()
creds = google_oauth.load_credentials()
stored_project = creds.project_id if creds else ""
# Prefer what's already baked into the creds
if stored_project:
self._project_context = ProjectContext(
project_id=stored_project,
managed_project_id=creds.managed_project_id if creds else "",
tier_id="",
source="stored",
)
return self._project_context
ctx = resolve_project_context(
access_token,
configured_project_id=self._configured_project_id,
env_project_id=env_project,
user_agent_model=model,
)
# Persist discovered project back to the creds file so the next
# session doesn't re-run the discovery.
if ctx.project_id or ctx.managed_project_id:
google_oauth.update_project_ids(
project_id=ctx.project_id,
managed_project_id=ctx.managed_project_id,
)
self._project_context = ctx
return ctx
def _create_chat_completion(
self,
*,
model: str = "gemini-2.5-flash",
messages: Optional[List[Dict[str, Any]]] = None,
stream: bool = False,
tools: Any = None,
tool_choice: Any = None,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
top_p: Optional[float] = None,
stop: Any = None,
extra_body: Optional[Dict[str, Any]] = None,
timeout: Any = None,
**_: Any,
) -> Any:
access_token = google_oauth.get_valid_access_token()
ctx = self._ensure_project_context(access_token, model)
thinking_config = None
if isinstance(extra_body, dict):
thinking_config = extra_body.get("thinking_config") or extra_body.get("thinkingConfig")
inner = build_gemini_request(
messages=messages or [],
tools=tools,
tool_choice=tool_choice,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stop=stop,
thinking_config=thinking_config,
)
wrapped = wrap_code_assist_request(
project_id=ctx.project_id,
model=model,
inner_request=inner,
)
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"Bearer {access_token}",
"User-Agent": "hermes-agent (gemini-cli-compat)",
"X-Goog-Api-Client": "gl-python/hermes",
"x-activity-request-id": str(uuid.uuid4()),
}
headers.update(self._default_headers)
if stream:
return self._stream_completion(model=model, wrapped=wrapped, headers=headers)
url = f"{CODE_ASSIST_ENDPOINT}/v1internal:generateContent"
response = self._http.post(url, json=wrapped, headers=headers)
if response.status_code != 200:
raise _gemini_http_error(response)
try:
payload = response.json()
except ValueError as exc:
raise CodeAssistError(
f"Invalid JSON from Code Assist: {exc}",
code="code_assist_invalid_json",
) from exc
return _translate_gemini_response(payload, model=model)
def _stream_completion(
self,
*,
model: str,
wrapped: Dict[str, Any],
headers: Dict[str, str],
) -> Iterator[_GeminiStreamChunk]:
"""Generator that yields OpenAI-shaped streaming chunks."""
url = f"{CODE_ASSIST_ENDPOINT}/v1internal:streamGenerateContent?alt=sse"
stream_headers = dict(headers)
stream_headers["Accept"] = "text/event-stream"
def _generator() -> Iterator[_GeminiStreamChunk]:
try:
with self._http.stream("POST", url, json=wrapped, headers=stream_headers) as response:
if response.status_code != 200:
# Materialize error body for better diagnostics
response.read()
raise _gemini_http_error(response)
tool_call_counter: List[int] = [0]
for event in _iter_sse_events(response):
for chunk in _translate_stream_event(event, model, tool_call_counter):
yield chunk
except httpx.HTTPError as exc:
raise CodeAssistError(
f"Streaming request failed: {exc}",
code="code_assist_stream_error",
) from exc
return _generator()
def _gemini_http_error(response: httpx.Response) -> CodeAssistError:
"""Translate an httpx response into a CodeAssistError with rich metadata.
Parses Google's error envelope (``{"error": {"code", "message", "status",
"details": [...]}}``) so the agent's error classifier can reason about
the failure — ``status_code`` enables the rate_limit / auth classification
paths, and ``response`` lets the main loop honor ``Retry-After`` just
like it does for OpenAI SDK exceptions.
Also lifts a few recognizable Google conditions into human-readable
messages so the user sees something better than a 500-char JSON dump:
MODEL_CAPACITY_EXHAUSTED → "Gemini model capacity exhausted for
<model>. This is a Google-side throttle..."
RESOURCE_EXHAUSTED w/o reason → quota-style message
404 → "Model <name> not found at cloudcode-pa..."
"""
status = response.status_code
# Parse the body once, surviving any weird encodings.
body_text = ""
body_json: Dict[str, Any] = {}
try:
body_text = response.text
except Exception:
body_text = ""
if body_text:
try:
parsed = json.loads(body_text)
if isinstance(parsed, dict):
body_json = parsed
except (ValueError, TypeError):
body_json = {}
# Dig into Google's error envelope. Shape is:
# {"error": {"code": 429, "message": "...", "status": "RESOURCE_EXHAUSTED",
# "details": [{"@type": ".../ErrorInfo", "reason": "MODEL_CAPACITY_EXHAUSTED",
# "metadata": {...}},
# {"@type": ".../RetryInfo", "retryDelay": "30s"}]}}
err_obj = body_json.get("error") if isinstance(body_json, dict) else None
if not isinstance(err_obj, dict):
err_obj = {}
err_status = str(err_obj.get("status") or "").strip()
err_message = str(err_obj.get("message") or "").strip()
_raw_details = err_obj.get("details")
err_details_list = _raw_details if isinstance(_raw_details, list) else []
# Extract google.rpc.ErrorInfo reason + metadata. There may be more
# than one ErrorInfo (rare), so we pick the first one with a reason.
error_reason = ""
error_metadata: Dict[str, Any] = {}
retry_delay_seconds: Optional[float] = None
for detail in err_details_list:
if not isinstance(detail, dict):
continue
type_url = str(detail.get("@type") or "")
if not error_reason and type_url.endswith("/google.rpc.ErrorInfo"):
reason = detail.get("reason")
if isinstance(reason, str) and reason:
error_reason = reason
md = detail.get("metadata")
if isinstance(md, dict):
error_metadata = md
elif retry_delay_seconds is None and type_url.endswith("/google.rpc.RetryInfo"):
# retryDelay is a google.protobuf.Duration string like "30s" or "1.5s".
delay_raw = detail.get("retryDelay")
if isinstance(delay_raw, str) and delay_raw.endswith("s"):
try:
retry_delay_seconds = float(delay_raw[:-1])
except ValueError:
pass
elif isinstance(delay_raw, (int, float)):
retry_delay_seconds = float(delay_raw)
# Fall back to the Retry-After header if the body didn't include RetryInfo.
if retry_delay_seconds is None:
try:
header_val = response.headers.get("Retry-After") or response.headers.get("retry-after")
except Exception:
header_val = None
if header_val:
try:
retry_delay_seconds = float(header_val)
except (TypeError, ValueError):
retry_delay_seconds = None
# Classify the error code. ``code_assist_rate_limited`` stays the default
# for 429s; a more specific reason tag helps downstream callers (e.g. tests,
# logs) without changing the rate_limit classification path.
code = f"code_assist_http_{status}"
if status == 401:
code = "code_assist_unauthorized"
elif status == 429:
code = "code_assist_rate_limited"
if error_reason == "MODEL_CAPACITY_EXHAUSTED":
code = "code_assist_capacity_exhausted"
# Build a human-readable message. Keep the status + a raw-body tail for
# debugging, but lead with a friendlier summary when we recognize the
# Google signal.
model_hint = ""
if isinstance(error_metadata, dict):
model_hint = str(error_metadata.get("model") or error_metadata.get("modelId") or "").strip()
if status == 429 and error_reason == "MODEL_CAPACITY_EXHAUSTED":
target = model_hint or "this Gemini model"
message = (
f"Gemini capacity exhausted for {target} (Google-side throttle, "
f"not a Hermes issue). Try a different Gemini model or set a "
f"fallback_providers entry to a non-Gemini provider."
)
if retry_delay_seconds is not None:
message += f" Google suggests retrying in {retry_delay_seconds:g}s."
elif status == 429 and err_status == "RESOURCE_EXHAUSTED":
message = (
f"Gemini quota exhausted ({err_message or 'RESOURCE_EXHAUSTED'}). "
f"Check /gquota for remaining daily requests."
)
if retry_delay_seconds is not None:
message += f" Retry suggested in {retry_delay_seconds:g}s."
elif status == 404:
# Google returns 404 when a model has been retired or renamed.
target = model_hint or (err_message or "model")
message = (
f"Code Assist 404: {target} is not available at "
f"cloudcode-pa.googleapis.com. It may have been renamed or "
f"retired. Check hermes_cli/models.py for the current list."
)
elif err_message:
# Generic fallback with the parsed message.
message = f"Code Assist HTTP {status} ({err_status or 'error'}): {err_message}"
else:
# Last-ditch fallback — raw body snippet.
message = f"Code Assist returned HTTP {status}: {body_text[:500]}"
return CodeAssistError(
message,
code=code,
status_code=status,
response=response,
retry_after=retry_delay_seconds,
details={
"status": err_status,
"reason": error_reason,
"metadata": error_metadata,
"message": err_message,
},
)
+1 -31
View File
@@ -33,23 +33,6 @@ logger = logging.getLogger(__name__)
DEFAULT_GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
# Published max output-token ceiling shared by every current Gemini text model
# (2.5 + 3.x: flash, flash-lite, pro). Used as the default when the caller
# passes max_tokens=None, because Gemini's native API otherwise applies a low
# internal default and truncates output (unlike OpenAI-compat endpoints where
# an omitted limit means full budget).
GEMINI_DEFAULT_MAX_OUTPUT_TOKENS = 65535
def bare_gemini_model_id(model: str) -> str:
"""Strip Gemini's own provider prefix from an aggregator-style model id."""
name = (model or "").strip()
lowered = name.lower()
for prefix in ("google/", "gemini/"):
if lowered.startswith(prefix):
return name[len(prefix):].strip() or name
return name
def is_native_gemini_base_url(base_url: str) -> bool:
"""Return True when the endpoint speaks Gemini's native REST API."""
@@ -340,7 +323,7 @@ def _build_gemini_contents(messages: List[Dict[str, Any]]) -> tuple[List[Dict[st
system_instruction = None
joined_system = "\n".join(part for part in system_text_parts if part).strip()
if joined_system:
system_instruction = {"role": "system", "parts": [{"text": joined_system}]}
system_instruction = {"parts": [{"text": joined_system}]}
return contents, system_instruction
@@ -431,18 +414,6 @@ def build_gemini_request(
generation_config["temperature"] = temperature
if max_tokens is not None:
generation_config["maxOutputTokens"] = max_tokens
else:
# Gemini's native generateContent does NOT treat an omitted
# maxOutputTokens as "use the model's full output budget" — it applies
# a low internal default and the model stops early with
# finishReason=MAX_TOKENS, truncating tool calls mid-stream (Hermes
# then retries 3× and refuses the incomplete call). Every current
# Gemini text model (2.5 + 3.x, flash / flash-lite / pro) caps at
# 65,535 output tokens, so default to that ceiling when the caller
# passes None ("unlimited"). See the OpenAI-compat path where omitting
# the field genuinely means full budget — that assumption does not
# hold on the native API.
generation_config["maxOutputTokens"] = GEMINI_DEFAULT_MAX_OUTPUT_TOKENS
if top_p is not None:
generation_config["topP"] = top_p
if stop:
@@ -924,7 +895,6 @@ class GeminiNativeClient:
thinking_config=thinking_config,
)
model = bare_gemini_model_id(model)
if stream:
return self._stream_completion(model=model, request=request, timeout=timeout)
+451
View File
@@ -0,0 +1,451 @@
"""Google Code Assist API client — project discovery, onboarding, quota.
The Code Assist API powers Google's official gemini-cli. It sits at
``cloudcode-pa.googleapis.com`` and provides:
- Free tier access (generous daily quota) for personal Google accounts
- Paid tier access via GCP projects with billing / Workspace / Standard / Enterprise
This module handles the control-plane dance needed before inference:
1. ``load_code_assist()`` — probe the user's account to learn what tier they're on
and whether a ``cloudaicompanionProject`` is already assigned.
2. ``onboard_user()`` — if the user hasn't been onboarded yet (new account, fresh
free tier, etc.), call this with the chosen tier + project id. Supports LRO
polling for slow provisioning.
3. ``retrieve_user_quota()`` — fetch the ``buckets[]`` array showing remaining
quota per model, used by the ``/gquota`` slash command.
VPC-SC handling: enterprise accounts under a VPC Service Controls perimeter
will get ``SECURITY_POLICY_VIOLATED`` on ``load_code_assist``. We catch this
and force the account to ``standard-tier`` so the call chain still succeeds.
Derived from opencode-gemini-auth (MIT) and clawdbot/extensions/google. The
request/response shapes are specific to Google's internal Code Assist API,
documented nowhere public — we copy them from the reference implementations.
"""
from __future__ import annotations
import json
import logging
import time
import urllib.error
import urllib.request
import uuid
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
# =============================================================================
# Constants
# =============================================================================
CODE_ASSIST_ENDPOINT = "https://cloudcode-pa.googleapis.com"
# Fallback endpoints tried when prod returns an error during project discovery
FALLBACK_ENDPOINTS = [
"https://daily-cloudcode-pa.sandbox.googleapis.com",
"https://autopush-cloudcode-pa.sandbox.googleapis.com",
]
# Tier identifiers that Google's API uses
FREE_TIER_ID = "free-tier"
LEGACY_TIER_ID = "legacy-tier"
STANDARD_TIER_ID = "standard-tier"
# Default HTTP headers matching gemini-cli's fingerprint.
# Google may reject unrecognized User-Agents on these internal endpoints.
_GEMINI_CLI_USER_AGENT = "google-api-nodejs-client/9.15.1 (gzip)"
_X_GOOG_API_CLIENT = "gl-node/24.0.0"
_DEFAULT_REQUEST_TIMEOUT = 30.0
_ONBOARDING_POLL_ATTEMPTS = 12
_ONBOARDING_POLL_INTERVAL_SECONDS = 5.0
class CodeAssistError(RuntimeError):
"""Exception raised by the Code Assist (``cloudcode-pa``) integration.
Carries HTTP status / response / retry-after metadata so the agent's
``error_classifier._extract_status_code`` and the main loop's Retry-After
handling (which walks ``error.response.headers``) pick up the right
signals. Without these, 429s from the OAuth path look like opaque
``RuntimeError`` and skip the rate-limit path.
"""
def __init__(
self,
message: str,
*,
code: str = "code_assist_error",
status_code: Optional[int] = None,
response: Any = None,
retry_after: Optional[float] = None,
details: Optional[Dict[str, Any]] = None,
) -> None:
super().__init__(message)
self.code = code
# ``status_code`` is picked up by ``agent.error_classifier._extract_status_code``
# so a 429 from Code Assist classifies as FailoverReason.rate_limit and
# triggers the main loop's fallback_providers chain the same way SDK
# errors do.
self.status_code = status_code
# ``response`` is the underlying ``httpx.Response`` (or a shim with a
# ``.headers`` mapping and ``.json()`` method). The main loop reads
# ``error.response.headers["Retry-After"]`` to honor Google's retry
# hints when the backend throttles us.
self.response = response
# Parsed ``Retry-After`` seconds (kept separately for convenience —
# Google returns retry hints in both the header and the error body's
# ``google.rpc.RetryInfo`` details, and we pick whichever we found).
self.retry_after = retry_after
# Parsed structured error details from the Google error envelope
# (e.g. ``{"reason": "MODEL_CAPACITY_EXHAUSTED", "status": "RESOURCE_EXHAUSTED"}``).
# Useful for logging and for tests that want to assert on specifics.
self.details = details or {}
class ProjectIdRequiredError(CodeAssistError):
def __init__(self, message: str = "GCP project id required for this tier") -> None:
super().__init__(message, code="code_assist_project_id_required")
# =============================================================================
# HTTP primitive (auth via Bearer token passed per-call)
# =============================================================================
def _build_headers(access_token: str, *, user_agent_model: str = "") -> Dict[str, str]:
ua = _GEMINI_CLI_USER_AGENT
if user_agent_model:
ua = f"{ua} model/{user_agent_model}"
return {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"Bearer {access_token}",
"User-Agent": ua,
"X-Goog-Api-Client": _X_GOOG_API_CLIENT,
"x-activity-request-id": str(uuid.uuid4()),
}
def _client_metadata() -> Dict[str, str]:
"""Match Google's gemini-cli exactly — unrecognized metadata may be rejected."""
return {
"ideType": "IDE_UNSPECIFIED",
"platform": "PLATFORM_UNSPECIFIED",
"pluginType": "GEMINI",
}
def _post_json(
url: str,
body: Dict[str, Any],
access_token: str,
*,
timeout: float = _DEFAULT_REQUEST_TIMEOUT,
user_agent_model: str = "",
) -> Dict[str, Any]:
data = json.dumps(body).encode("utf-8")
request = urllib.request.Request(
url, data=data, method="POST",
headers=_build_headers(access_token, user_agent_model=user_agent_model),
)
try:
with urllib.request.urlopen(request, timeout=timeout) as response:
raw = response.read().decode("utf-8", errors="replace")
return json.loads(raw) if raw else {}
except urllib.error.HTTPError as exc:
detail = ""
try:
detail = exc.read().decode("utf-8", errors="replace")
except Exception:
pass
# Special case: VPC-SC violation should be distinguishable
if _is_vpc_sc_violation(detail):
raise CodeAssistError(
f"VPC-SC policy violation: {detail}",
code="code_assist_vpc_sc",
) from exc
raise CodeAssistError(
f"Code Assist HTTP {exc.code}: {detail or exc.reason}",
code=f"code_assist_http_{exc.code}",
) from exc
except urllib.error.URLError as exc:
raise CodeAssistError(
f"Code Assist request failed: {exc}",
code="code_assist_network_error",
) from exc
def _is_vpc_sc_violation(body: str) -> bool:
"""Detect a VPC Service Controls violation from a response body."""
if not body:
return False
try:
parsed = json.loads(body)
except (json.JSONDecodeError, ValueError):
return "SECURITY_POLICY_VIOLATED" in body
# Walk the nested error structure Google uses
error = parsed.get("error") if isinstance(parsed, dict) else None
if not isinstance(error, dict):
return False
details = error.get("details") or []
if isinstance(details, list):
for item in details:
if isinstance(item, dict):
reason = item.get("reason") or ""
if reason == "SECURITY_POLICY_VIOLATED":
return True
msg = str(error.get("message", ""))
return "SECURITY_POLICY_VIOLATED" in msg
# =============================================================================
# load_code_assist — discovers current tier + assigned project
# =============================================================================
@dataclass
class CodeAssistProjectInfo:
"""Result from ``load_code_assist``."""
current_tier_id: str = ""
cloudaicompanion_project: str = "" # Google-managed project (free tier)
allowed_tiers: List[str] = field(default_factory=list)
raw: Dict[str, Any] = field(default_factory=dict)
def load_code_assist(
access_token: str,
*,
project_id: str = "",
user_agent_model: str = "",
) -> CodeAssistProjectInfo:
"""Call ``POST /v1internal:loadCodeAssist`` with prod → sandbox fallback.
Returns whatever tier + project info Google reports. On VPC-SC violations,
returns a synthetic ``standard-tier`` result so the chain can continue.
"""
body: Dict[str, Any] = {
"metadata": {
"duetProject": project_id,
**_client_metadata(),
},
}
if project_id:
body["cloudaicompanionProject"] = project_id
endpoints = [CODE_ASSIST_ENDPOINT] + FALLBACK_ENDPOINTS
last_err: Optional[Exception] = None
for endpoint in endpoints:
url = f"{endpoint}/v1internal:loadCodeAssist"
try:
resp = _post_json(url, body, access_token, user_agent_model=user_agent_model)
return _parse_load_response(resp)
except CodeAssistError as exc:
if exc.code == "code_assist_vpc_sc":
logger.info("VPC-SC violation on %s — defaulting to standard-tier", endpoint)
return CodeAssistProjectInfo(
current_tier_id=STANDARD_TIER_ID,
cloudaicompanion_project=project_id,
)
last_err = exc
logger.warning("loadCodeAssist failed on %s: %s", endpoint, exc)
continue
if last_err:
raise last_err
return CodeAssistProjectInfo()
def _parse_load_response(resp: Dict[str, Any]) -> CodeAssistProjectInfo:
current_tier = resp.get("currentTier") or {}
tier_id = str(current_tier.get("id") or "") if isinstance(current_tier, dict) else ""
project = str(resp.get("cloudaicompanionProject") or "")
allowed = resp.get("allowedTiers") or []
allowed_ids: List[str] = []
if isinstance(allowed, list):
for t in allowed:
if isinstance(t, dict):
tid = str(t.get("id") or "")
if tid:
allowed_ids.append(tid)
return CodeAssistProjectInfo(
current_tier_id=tier_id,
cloudaicompanion_project=project,
allowed_tiers=allowed_ids,
raw=resp,
)
# =============================================================================
# onboard_user — provisions a new user on a tier (with LRO polling)
# =============================================================================
def onboard_user(
access_token: str,
*,
tier_id: str,
project_id: str = "",
user_agent_model: str = "",
) -> Dict[str, Any]:
"""Call ``POST /v1internal:onboardUser`` to provision the user.
For paid tiers, ``project_id`` is REQUIRED (raises ProjectIdRequiredError).
For free tiers, ``project_id`` is optional — Google will assign one.
Returns the final operation response. Polls ``/v1internal/<name>`` for up
to ``_ONBOARDING_POLL_ATTEMPTS`` × ``_ONBOARDING_POLL_INTERVAL_SECONDS``
(default: 12 × 5s = 1 min).
"""
if tier_id != FREE_TIER_ID and tier_id != LEGACY_TIER_ID and not project_id:
raise ProjectIdRequiredError(
f"Tier {tier_id!r} requires a GCP project id. "
"Set HERMES_GEMINI_PROJECT_ID or GOOGLE_CLOUD_PROJECT."
)
body: Dict[str, Any] = {
"tierId": tier_id,
"metadata": _client_metadata(),
}
if project_id:
body["cloudaicompanionProject"] = project_id
endpoint = CODE_ASSIST_ENDPOINT
url = f"{endpoint}/v1internal:onboardUser"
resp = _post_json(url, body, access_token, user_agent_model=user_agent_model)
# Poll if LRO (long-running operation)
if not resp.get("done"):
op_name = resp.get("name", "")
if not op_name:
return resp
for attempt in range(_ONBOARDING_POLL_ATTEMPTS):
time.sleep(_ONBOARDING_POLL_INTERVAL_SECONDS)
poll_url = f"{endpoint}/v1internal/{op_name}"
try:
poll_resp = _post_json(poll_url, {}, access_token, user_agent_model=user_agent_model)
except CodeAssistError as exc:
logger.warning("Onboarding poll attempt %d failed: %s", attempt + 1, exc)
continue
if poll_resp.get("done"):
return poll_resp
logger.warning("Onboarding did not complete within %d attempts", _ONBOARDING_POLL_ATTEMPTS)
return resp
# =============================================================================
# retrieve_user_quota — for /gquota
# =============================================================================
@dataclass
class QuotaBucket:
model_id: str
token_type: str = ""
remaining_fraction: float = 0.0
reset_time_iso: str = ""
raw: Dict[str, Any] = field(default_factory=dict)
def retrieve_user_quota(
access_token: str,
*,
project_id: str = "",
user_agent_model: str = "",
) -> List[QuotaBucket]:
"""Call ``POST /v1internal:retrieveUserQuota`` and parse ``buckets[]``."""
body: Dict[str, Any] = {}
if project_id:
body["project"] = project_id
url = f"{CODE_ASSIST_ENDPOINT}/v1internal:retrieveUserQuota"
resp = _post_json(url, body, access_token, user_agent_model=user_agent_model)
raw_buckets = resp.get("buckets") or []
buckets: List[QuotaBucket] = []
if not isinstance(raw_buckets, list):
return buckets
for b in raw_buckets:
if not isinstance(b, dict):
continue
buckets.append(QuotaBucket(
model_id=str(b.get("modelId") or ""),
token_type=str(b.get("tokenType") or ""),
remaining_fraction=float(b.get("remainingFraction") or 0.0),
reset_time_iso=str(b.get("resetTime") or ""),
raw=b,
))
return buckets
# =============================================================================
# Project context resolution
# =============================================================================
@dataclass
class ProjectContext:
"""Resolved state for a given OAuth session."""
project_id: str = "" # effective project id sent on requests
managed_project_id: str = "" # Google-assigned project (free tier)
tier_id: str = ""
source: str = "" # "env", "config", "discovered", "onboarded"
def resolve_project_context(
access_token: str,
*,
configured_project_id: str = "",
env_project_id: str = "",
user_agent_model: str = "",
) -> ProjectContext:
"""Figure out what project id + tier to use for requests.
Priority:
1. If configured_project_id or env_project_id is set, use that directly
and short-circuit (no discovery needed).
2. Otherwise call loadCodeAssist to see what Google says.
3. If no tier assigned yet, onboard the user (free tier default).
"""
# Short-circuit: caller provided a project id
if configured_project_id:
return ProjectContext(
project_id=configured_project_id,
tier_id=STANDARD_TIER_ID, # assume paid since they specified one
source="config",
)
if env_project_id:
return ProjectContext(
project_id=env_project_id,
tier_id=STANDARD_TIER_ID,
source="env",
)
# Discover via loadCodeAssist
info = load_code_assist(access_token, user_agent_model=user_agent_model)
effective_project = info.cloudaicompanion_project
tier = info.current_tier_id
if not tier:
# User hasn't been onboarded — provision them on free tier
onboard_resp = onboard_user(
access_token,
tier_id=FREE_TIER_ID,
project_id="",
user_agent_model=user_agent_model,
)
# Re-parse from the onboard response
response_body = onboard_resp.get("response") or {}
if isinstance(response_body, dict):
effective_project = (
effective_project
or str(response_body.get("cloudaicompanionProject") or "")
)
tier = FREE_TIER_ID
source = "onboarded"
else:
source = "discovered"
return ProjectContext(
project_id=effective_project,
managed_project_id=effective_project if tier == FREE_TIER_ID else "",
tier_id=tier,
source=source,
)
File diff suppressed because it is too large Load Diff
+5 -74
View File
@@ -11,18 +11,6 @@ Providers live in ``<repo>/plugins/image_gen/<name>/`` (built-in, auto-loaded
as ``kind: backend``) or ``~/.hermes/plugins/image_gen/<name>/`` (user, opt-in
via ``plugins.enabled``).
Unified surface
---------------
One tool — ``image_generate`` — covers **text-to-image** and
**image-to-image / image editing**. The router is the presence of
``image_url`` (and/or ``reference_image_urls``): if any source image is
provided, the provider routes to its image-to-image / edit endpoint; if
omitted, the provider routes to text-to-image. Users pick one **model**
(e.g. nano-banana-pro, gpt-image-2, grok-imagine-image); the provider
handles which underlying endpoint to hit. This mirrors the ``video_gen``
provider design (``agent/video_gen_provider.py``) so the two surfaces
stay learnable together.
Response shape
--------------
All providers return a dict that :func:`success_response` / :func:`error_response`
@@ -33,7 +21,6 @@ produce. The tool wrapper JSON-serializes it. Keys:
model str provider-specific model identifier
prompt str echoed prompt
aspect_ratio str "landscape" | "square" | "portrait"
modality str "text" | "image" (which mode was used)
provider str provider name (for diagnostics)
error str only when success=False
error_type str only when success=False
@@ -140,51 +127,19 @@ class ImageGenProvider(abc.ABC):
return models[0].get("id")
return None
def capabilities(self) -> Dict[str, Any]:
"""Return what this provider supports.
Returned dict (all keys optional)::
{
"modalities": ["text", "image"], # which inputs the backend accepts
"max_reference_images": 9, # cap for reference_image_urls
}
``modalities`` declares whether the active backend/model supports
text-to-image (``"text"``), image-to-image / editing (``"image"``),
or both. The tool layer surfaces this in the dynamic schema so the
model knows when ``image_url`` is honored. Used by ``hermes tools``
for the picker too. Default: text-only (backward compatible — a
provider that doesn't override this advertises text-to-image only).
"""
return {
"modalities": ["text"],
"max_reference_images": 0,
}
@abc.abstractmethod
def generate(
self,
prompt: str,
aspect_ratio: str = DEFAULT_ASPECT_RATIO,
*,
image_url: Optional[str] = None,
reference_image_urls: Optional[List[str]] = None,
**kwargs: Any,
) -> Dict[str, Any]:
"""Generate an image from a text prompt, or edit/transform a source image.
Routing: if ``image_url`` (or any ``reference_image_urls``) is
provided, the provider should route to its image-to-image / edit
endpoint; otherwise text-to-image. ``image_url`` is the primary
source image to edit; ``reference_image_urls`` are additional
style/composition references (provider clamps to its declared
``max_reference_images``).
"""Generate an image.
Implementations should return the dict from :func:`success_response`
or :func:`error_response`. ``kwargs`` may contain forward-compat
parameters future versions of the schema will expose —
implementations MUST ignore unknown keys (no TypeError).
parameters future versions of the schema will expose — implementations
should ignore unknown keys.
"""
@@ -207,26 +162,6 @@ def resolve_aspect_ratio(value: Optional[str]) -> str:
return DEFAULT_ASPECT_RATIO
def normalize_reference_images(value: Any) -> Optional[List[str]]:
"""Coerce a reference-image argument into a clean list of URL/path strings.
Accepts a single string or a list; strips blanks and whitespace. Returns
``None`` when nothing usable remains so providers can treat "no refs" as a
single sentinel.
"""
if value is None:
return None
if isinstance(value, str):
value = [value]
if not isinstance(value, (list, tuple)):
return None
out: List[str] = []
for item in value:
if isinstance(item, str) and item.strip():
out.append(item.strip())
return out or None
def _images_cache_dir() -> Path:
"""Return ``$HERMES_HOME/cache/images/``, creating parents as needed."""
from hermes_constants import get_hermes_home
@@ -345,16 +280,13 @@ def success_response(
prompt: str,
aspect_ratio: str,
provider: str,
modality: str = "text",
extra: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Build a uniform success response dict.
``image`` may be an HTTP URL or an absolute filesystem path (for b64
providers like OpenAI). ``modality`` is ``"text"`` (text-to-image) or
``"image"`` (image-to-image / editing) — indicates which endpoint was
actually hit, useful for diagnostics. Callers that need to pass through
additional backend-specific fields can supply ``extra``.
providers like OpenAI). Callers that need to pass through additional
backend-specific fields can supply ``extra``.
"""
payload: Dict[str, Any] = {
"success": True,
@@ -362,7 +294,6 @@ def success_response(
"model": model,
"prompt": prompt,
"aspect_ratio": aspect_ratio,
"modality": modality,
"provider": provider,
}
if extra:
+11 -240
View File
@@ -219,110 +219,9 @@ def _supports_vision_override(
coerced = _coerce_capability_bool(per_model.get("supports_vision"))
if coerced is not None:
return coerced
# 2b. Legacy list-style custom_providers. Entries are dicts with a
# "name" key and a nested "models" dict. Match by provider name (which
# may appear as the raw name or "custom:<name>" at runtime).
custom_providers = cfg.get("custom_providers")
if isinstance(custom_providers, list):
# Build candidate names: the provider value and the config provider
# value, both raw and with "custom:" prefix stripped/added.
candidate_names: set = set()
for p in filter(None, (provider, config_provider)):
candidate_names.add(p)
if p.startswith("custom:"):
candidate_names.add(p[len("custom:"):])
else:
candidate_names.add(f"custom:{p}")
for entry_raw in custom_providers:
if not isinstance(entry_raw, dict):
continue
entry_name = str(entry_raw.get("name") or "").strip()
if entry_name not in candidate_names:
continue
models_raw = entry_raw.get("models")
models_cfg = models_raw if isinstance(models_raw, dict) else {}
per_model_raw = models_cfg.get(model)
per_model = per_model_raw if isinstance(per_model_raw, dict) else {}
coerced = _coerce_capability_bool(per_model.get("supports_vision"))
if coerced is not None:
return coerced
return None
def _resolve_inference_base_url(
cfg: Optional[Dict[str, Any]],
provider: str,
) -> str:
"""Best-effort base URL for the active inference provider."""
try:
from agent.auxiliary_client import _RUNTIME_MAIN_BASE_URL
runtime = str(_RUNTIME_MAIN_BASE_URL or "").strip()
if runtime:
return runtime
except Exception:
pass
if not isinstance(cfg, dict):
return ""
model_cfg_raw = cfg.get("model")
model_cfg: Dict[str, Any] = model_cfg_raw if isinstance(model_cfg_raw, dict) else {}
base_url = str(model_cfg.get("base_url") or "").strip()
if base_url:
return base_url
config_provider = str(model_cfg.get("provider") or "").strip()
candidate_names: set[str] = set()
for p in filter(None, (provider, config_provider)):
candidate_names.add(p)
if p.lower().startswith("custom:"):
candidate_names.add(p.split(":", 1)[1])
else:
candidate_names.add(f"custom:{p}")
providers_cfg = cfg.get("providers")
if isinstance(providers_cfg, dict):
for name in candidate_names:
entry = providers_cfg.get(name)
if isinstance(entry, dict):
bu = str(entry.get("base_url") or "").strip()
if bu:
return bu
custom_providers = cfg.get("custom_providers")
if isinstance(custom_providers, list):
lowered = {n.lower() for n in candidate_names}
for entry_raw in custom_providers:
if not isinstance(entry_raw, dict):
continue
entry_name = str(entry_raw.get("name") or "").strip()
if entry_name not in candidate_names and entry_name.lower() not in lowered:
continue
bu = str(entry_raw.get("base_url") or "").strip()
if bu:
return bu
return ""
def _should_probe_ollama_vision(provider: str, base_url: str) -> bool:
"""True when the active provider likely fronts a local Ollama server."""
p = (provider or "").strip().lower()
if p == "ollama":
return True
if not base_url:
return False
try:
from agent.model_metadata import detect_local_server_type
return detect_local_server_type(base_url) == "ollama"
except Exception:
return False
def _coerce_mode(raw: Any) -> str:
"""Normalize a config value into one of the valid modes."""
if not isinstance(raw, str):
@@ -374,33 +273,15 @@ def _lookup_supports_vision(
return override
if not provider or not model:
return None
caps = None
try:
from agent.models_dev import get_model_capabilities
caps = get_model_capabilities(provider, model)
except Exception as exc: # pragma: no cover - defensive
logger.debug("image_routing: caps lookup failed for %s:%s — %s", provider, model, exc)
if caps is not None:
return bool(caps.supports_vision)
base_url = _resolve_inference_base_url(cfg, provider)
if not base_url and (provider or "").strip().lower() == "ollama":
base_url = "http://localhost:11434/v1"
if _should_probe_ollama_vision(provider, base_url):
try:
from agent.model_metadata import query_ollama_supports_vision
ollama_vision = query_ollama_supports_vision(model, base_url)
if ollama_vision is not None:
return ollama_vision
except Exception as exc: # pragma: no cover - defensive
logger.debug(
"image_routing: ollama vision probe failed for %s:%s — %s",
provider,
model,
exc,
)
return None
return None
if caps is None:
return None
return bool(caps.supports_vision)
def decide_image_input_mode(
@@ -478,98 +359,14 @@ def _sniff_mime_from_bytes(raw: bytes) -> Optional[str]:
# BMP: "BM"
if raw.startswith(b"BM"):
return "image/bmp"
# ISO-BMFF family (HEIC/HEIF/AVIF): bytes 4..8 == 'ftyp', major brand at 8..12
if len(raw) >= 12 and raw[4:8] == b"ftyp":
brand = raw[8:12]
if brand in {b"avif", b"avis"}:
return "image/avif"
if brand in {
b"heic", b"heix", b"hevc", b"hevx",
b"mif1", b"msf1", b"heim", b"heis",
}:
return "image/heic"
# TIFF: II*\0 (little-endian) or MM\0* (big-endian)
if raw[:4] in {b"II*\x00", b"MM\x00*"}:
return "image/tiff"
# ICO: 00 00 01 00 (reserved=0, type=1=icon)
if raw[:4] == b"\x00\x00\x01\x00":
return "image/x-icon"
# SVG: text-based, look for an <svg tag near the start (skip BOM/whitespace)
head = raw[:512].lstrip().lower()
if head.startswith(b"<?xml") or head.startswith(b"<svg"):
if b"<svg" in head:
return "image/svg+xml"
# HEIC/HEIF: ftypheic / ftypheix / ftypmif1 / ftypmsf1 etc.
if len(raw) >= 12 and raw[4:8] == b"ftyp" and raw[8:12] in {
b"heic", b"heix", b"hevc", b"hevx", b"mif1", b"msf1", b"heim", b"heis",
}:
return "image/heic"
return None
# Formats every major vision provider (Anthropic, OpenAI, Gemini, Bedrock)
# accepts natively. Anything outside this set has to be transcoded to PNG
# before we declare media_type, otherwise the provider returns HTTP 400
# ("Could not process image" / "Unsupported image media type") and the
# whole turn fails with no salvage path.
#
# Discord (and a few other chat platforms) freely accept attachments in
# formats outside this set -- AVIF screenshots from Chromium, HEIC from
# iPhones, TIFF from scanners, BMP from old Windows tools, ICO -- so users
# do hit this in practice. SVG is vector and Pillow cannot rasterize it;
# it is skipped (logged) rather than transcoded.
_UNIVERSALLY_SUPPORTED_MIMES = frozenset({
"image/png", "image/jpeg", "image/gif", "image/webp",
})
def _transcode_to_png(raw: bytes) -> Optional[bytes]:
"""Decode arbitrary image bytes with Pillow and re-encode as PNG.
Returns None if Pillow isn't installed or can't decode the input
(rare formats, corrupted bytes, missing optional decoder plugin for
HEIC/AVIF, or vector formats like SVG). Caller falls back to skipping
the image so the rest of the turn still works.
HEIC/HEIF and AVIF need optional Pillow plugins; we try to register
them on demand and swallow ImportError so a missing plugin just
looks like 'Pillow can't decode this' rather than crashing.
"""
try:
from PIL import Image
except ImportError:
logger.info(
"image_routing: Pillow not installed; cannot transcode "
"non-standard image format to PNG. Install with `pip install Pillow` "
"(and `pillow-heif` / `pillow-avif-plugin` for those formats)."
)
return None
# Optional plugin registration. Silent on failure: an unsupported
# format will just fall through to Image.open raising below.
try:
import pillow_heif # type: ignore
pillow_heif.register_heif_opener()
except Exception:
pass
try:
import pillow_avif # type: ignore # noqa: F401 -- registers AVIF on import
except Exception:
pass
try:
from io import BytesIO
with Image.open(BytesIO(raw)) as im:
# Pick an output mode PNG can serialise. Anything other than
# the standard set gets normalised to RGBA so transparency is
# preserved where the source had it.
if im.mode not in {"RGB", "RGBA", "L", "LA", "P"}:
im = im.convert("RGBA")
buf = BytesIO()
im.save(buf, format="PNG", optimize=False)
return buf.getvalue()
except Exception as exc:
logger.info(
"image_routing: Pillow could not transcode image to PNG -- %s", exc
)
return None
def _guess_mime(path: Path, raw: Optional[bytes] = None) -> str:
"""Return image MIME type for *path*.
@@ -605,18 +402,8 @@ def _file_to_data_url(path: Path) -> Optional[str]:
accept large images (OpenAI 49 MB+, Gemini 100 MB) don't pay a silent
quality tax just because one other provider is stricter.
Format compatibility IS handled here: if the sniffed MIME isn't one
of ``_UNIVERSALLY_SUPPORTED_MIMES`` (i.e. it's something like AVIF,
HEIC, BMP, TIFF, or ICO that some providers reject outright), we
transcode to PNG with Pillow before declaring media_type. This fixes
the user-visible "Could not process image" HTTP 400 from Anthropic on
Discord-attached AVIF/HEIC/BMP files.
Returns None if the file can't be read OR if the format isn't
universally supported AND Pillow can't transcode it (Pillow missing,
HEIC/AVIF plugin missing, vector format like SVG, corrupt bytes). The
caller reports those paths in ``skipped`` and the rest of the turn
proceeds.
Returns None only if the file can't be read (missing, permission
denied, etc.); the caller reports those paths in ``skipped``.
"""
try:
raw = path.read_bytes()
@@ -624,22 +411,6 @@ def _file_to_data_url(path: Path) -> Optional[str]:
logger.warning("image_routing: failed to read %s — %s", path, exc)
return None
mime = _guess_mime(path, raw=raw)
if mime not in _UNIVERSALLY_SUPPORTED_MIMES:
transcoded = _transcode_to_png(raw)
if transcoded is None:
logger.warning(
"image_routing: %s is %s which is not accepted by all major "
"vision providers and could not be transcoded to PNG; "
"skipping this attachment.",
path, mime,
)
return None
logger.info(
"image_routing: transcoded %s (%s) -> image/png for provider compatibility",
path.name, mime,
)
raw = transcoded
mime = "image/png"
b64 = base64.b64encode(raw).decode("ascii")
return f"data:{mime};base64,{b64}"
+17 -8
View File
@@ -20,17 +20,23 @@ import json
import time
from collections import Counter, defaultdict
from datetime import datetime
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List
from agent.usage_pricing import (
CanonicalUsage,
DEFAULT_PRICING,
estimate_usage_cost,
format_duration_compact,
has_known_pricing,
)
_DEFAULT_PRICING = DEFAULT_PRICING
def _has_known_pricing(model_name: str, provider: str = None, base_url: str = None) -> bool:
"""Check if a model has known pricing (vs unknown/custom endpoint)."""
return has_known_pricing(model_name, provider=provider, base_url=base_url)
def _estimate_cost(
session_or_model: Dict[str, Any] | str,
@@ -39,8 +45,8 @@ def _estimate_cost(
*,
cache_read_tokens: int = 0,
cache_write_tokens: int = 0,
provider: Optional[str] = None,
base_url: Optional[str] = None,
provider: str = None,
base_url: str = None,
) -> tuple[float, str]:
"""Estimate the USD cost for a session row or a model/token tuple."""
if isinstance(session_or_model, dict):
@@ -71,6 +77,9 @@ def _estimate_cost(
return float(result.amount_usd or 0.0), result.status
def _format_duration(seconds: float) -> str:
"""Format seconds into a human-readable duration string."""
return format_duration_compact(seconds)
def _bar_chart(values: List[int], max_width: int = 20) -> List[str]:
@@ -426,7 +435,7 @@ class InsightsEngine:
included_cost_sessions += 1
elif status == "unknown":
unknown_cost_sessions += 1
if has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url")):
if _has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url")):
models_with_pricing.add(display)
else:
models_without_pricing.add(display)
@@ -499,7 +508,7 @@ class InsightsEngine:
d["tool_calls"] += s.get("tool_call_count") or 0
estimate, status = _estimate_cost(s)
d["cost"] += estimate
d["has_pricing"] = has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
d["has_pricing"] = _has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
d["cost_status"] = status
result = [
@@ -670,7 +679,7 @@ class InsightsEngine:
top.append({
"label": "Longest session",
"session_id": longest["id"][:16],
"value": format_duration_compact(dur),
"value": _format_duration(dur),
"date": datetime.fromtimestamp(longest["started_at"]).strftime("%b %d"),
})
@@ -755,7 +764,7 @@ class InsightsEngine:
lines.append(f" Input tokens: {o['total_input_tokens']:<12,} Output tokens: {o['total_output_tokens']:,}")
lines.append(f" Total tokens: {o['total_tokens']:,}")
if o["total_hours"] > 0:
lines.append(f" Active time: ~{format_duration_compact(o['total_hours'] * 3600):<11} Avg session: ~{format_duration_compact(o['avg_session_duration'])}")
lines.append(f" Active time: ~{_format_duration(o['total_hours'] * 3600):<11} Avg session: ~{_format_duration(o['avg_session_duration'])}")
lines.append(f" Avg msgs/session: {o['avg_messages_per_session']:.1f}")
lines.append("")
@@ -870,7 +879,7 @@ class InsightsEngine:
lines.append(f"**Sessions:** {o['total_sessions']} | **Messages:** {o['total_messages']:,} | **Tool calls:** {o['total_tool_calls']:,}")
lines.append(f"**Tokens:** {o['total_tokens']:,} (in: {o['total_input_tokens']:,} / out: {o['total_output_tokens']:,})")
if o["total_hours"] > 0:
lines.append(f"**Active time:** ~{format_duration_compact(o['total_hours'] * 3600)} | **Avg session:** ~{format_duration_compact(o['avg_session_duration'])}")
lines.append(f"**Active time:** ~{_format_duration(o['total_hours'] * 3600)} | **Avg session:** ~{_format_duration(o['avg_session_duration'])}")
lines.append("")
# Models (top 5)
-136
View File
@@ -1,136 +0,0 @@
#!/usr/bin/env python3
"""``/learn`` — build the standards-guided prompt that turns whatever the user
described into a reusable skill.
``/learn`` is open-ended. The user can point it at anything they can describe:
a directory of code, an API doc URL, a workflow they just walked the agent
through in this conversation, or pasted notes. This module builds ONE prompt
that instructs the live agent to:
1. Gather the sources the user named, using the tools it already has
(``read_file`` / ``search_files`` for dirs, ``web_extract`` for URLs, the
current conversation for "what I just did", the user's text for pasted
material).
2. Author a single ``SKILL.md`` via ``skill_manage`` that follows the Hermes
skill-authoring standards (description <=60 chars, the modern section
order, Hermes-tool framing, no invented commands).
There is no separate distillation engine and no model-tool footprint: the
agent does the work with its existing toolset, so this works identically on
local, Docker, and remote terminal backends. Every surface (CLI ``/learn``,
gateway ``/learn``, the dashboard "Learn a skill" panel) calls
:func:`build_learn_prompt` and feeds the result to the agent as a normal turn.
"""
from __future__ import annotations
# The house-style rules, distilled from AGENTS.md "Skill authoring standards
# (HARDLINE)" and the hermes-agent-dev new-skill salvage reference. Embedded in
# the prompt so the agent authors skills the way a maintainer would by hand.
_AUTHORING_STANDARDS = """\
Follow the Hermes skill-authoring standards exactly. These are the same
HARDLINE rules a maintainer enforces in review:
Frontmatter:
- name: lowercase-hyphenated, <=64 chars, no spaces.
- description: ONE sentence, **<=60 characters**, ends with a period. State the
capability, not the implementation. No marketing words (powerful,
comprehensive, seamless, advanced, robust). Do NOT repeat the skill name. If
the description contains a colon, wrap the whole value in double quotes.
This is the most-violated rule and it is NOT cosmetic: the system-prompt
skill index truncates the description to 60 chars and loads it every
session, so anything past char 60 is silently cut and never routes. After
you write the description, COUNT the characters; if it is over 60, cut it
down before saving — do not ship a sentence and hope.
Good (<=60): `Search arXiv papers by keyword, author, or ID.`
Bad (123): `A comprehensive skill that lets the agent search arXiv for
academic papers using keywords, authors, and categories.`
- version: 0.1.0
- author: always the literal value `Hermes`. NEVER fill it from the host
environment — the OS/login username (e.g. the `user=` line in your
environment hints), git config, or any identity you can probe must not be
written. Skills get shared and published, so an environment-derived name is
a privacy leak the user never opted into; the skill names itself as Hermes.
- platforms: declare `[macos]`, `[linux]`, and/or `[windows]` IF the skill
uses OS-bound primitives (osascript/apt/systemctl => the matching OS; /proc,
os.setsid, signal.SIGKILL => linux; fcntl/termios => POSIX). Prefer fixing it
cross-platform first (tempfile.gettempdir(), pathlib.Path, psutil); gate only
when the dependency is genuinely platform-bound. Omit the field for portable
skills.
- metadata.hermes.tags: a few Capitalized, Relevant, Tags.
Body section order (omit a section only if it genuinely has no content):
1. "# <Human Title>" then a 2-3 sentence intro: what it does, what it does NOT
do, and the key dependency stance (e.g. "stdlib only").
2. "## When to Use" — bullet list of concrete trigger phrases.
3. "## Prerequisites" — exact env vars, install steps, credentials.
4. "## How to Run" — the canonical invocation, framed through Hermes tools.
5. "## Quick Reference" — a flat command/endpoint list, no narration.
6. "## Procedure" — numbered steps with copy-paste-exact commands.
7. "## Pitfalls" — known limits, rate limits, things that look broken but aren't.
8. "## Verification" — a single command/check that proves the skill worked.
Hermes-tool framing (this is what makes it a skill, not shell docs):
- Frame running scripts as "invoke through the `terminal` tool".
- Reference Hermes tools by name in backticks: `terminal`, `read_file`,
`write_file`, `search_files`, `patch`, `web_extract`, `web_search`,
`vision_analyze`, `browser_navigate`, `delegate_task`, `image_generate`,
`text_to_speech`, `cronjob`, `memory`, `skill_view`, `execute_code`.
- Do NOT name shell utilities the agent already has wrapped: say `read_file`
not cat/head/tail, `search_files` not grep/rg/find/ls, `patch` not sed/awk,
`web_extract` not curl-to-scrape, `write_file` not echo>file or heredocs.
- Third-party CLIs (ffmpeg, gh, an SDK) are fine inside a script file, but the
prose still frames them as "invoke through the `terminal` tool". If the
skill needs an MCP server, name it and document its setup in Prerequisites.
Quality bar:
- Prefer exact commands, endpoint URLs, function signatures, and config keys
that appear VERBATIM in the source. NEVER invent flags, paths, or APIs — if
you didn't see it in the source, don't write it.
- Keep it tight and scannable: ~100 lines for a simple skill, ~200 for a
complex one. Don't re-paste the source docs.
- Don't write a router/index/hub skill that only points at other skills.
- Larger scripts/parsers belong in a `scripts/` file (add via
`skill_manage` write_file), referenced from SKILL.md by relative path — not
inlined for the agent to re-type every run. References go in `references/`,
templates in `templates/`."""
def build_learn_prompt(user_request: str) -> str:
"""Build the agent prompt for an open-ended ``/learn`` request.
Args:
user_request: the free-text the user gave after ``/learn`` — a
description of the workflow, paths, URLs, or "what I just did".
Returns:
A complete instruction the agent runs as a normal turn. The agent
gathers the described sources with its existing tools and authors the
skill via ``skill_manage``.
"""
req = (user_request or "").strip()
if not req:
req = (
"the workflow we just went through in this conversation — review "
"the steps taken and distill them into a reusable skill"
)
return (
"[/learn] The user wants you to learn a reusable skill from the "
"source(s) they described below, and save it.\n\n"
f"WHAT TO LEARN FROM:\n{req}\n\n"
"Do this:\n"
"1. Gather the material. Resolve whatever the user named using the "
"tools you already have — `read_file`/`search_files` for local files "
"or directories, `web_extract` for URLs, the current conversation "
"history if they referred to something you just did, and the text "
"they pasted as-is. If the request is ambiguous about scope, make a "
"reasonable choice and note it; do not stall.\n"
"2. Author ONE SKILL.md and save it with the `skill_manage` tool "
"(action=\"create\"). Pick a sensible category. If the procedure needs "
"a non-trivial script, add it under the skill's `scripts/` with "
"`skill_manage` write_file and reference it by relative path.\n\n"
f"{_AUTHORING_STANDARDS}\n\n"
"When done, tell the user the skill name, its category, and a "
"one-line summary of what it captured."
)
-3
View File
@@ -262,7 +262,6 @@ def _install_npm(
capture_output=True,
text=True,
timeout=300,
stdin=subprocess.DEVNULL,
)
if proc.returncode != 0:
logger.warning(
@@ -311,7 +310,6 @@ def _install_go(pkg: str, bin_name: str) -> Optional[str]:
text=True,
timeout=600,
env=env,
stdin=subprocess.DEVNULL,
)
if proc.returncode != 0:
logger.warning(
@@ -349,7 +347,6 @@ def _install_pip(pkg: str, bin_name: str) -> Optional[str]:
capture_output=True,
text=True,
timeout=300,
stdin=subprocess.DEVNULL,
)
if proc.returncode != 0:
logger.warning(
+38 -466
View File
@@ -25,125 +25,16 @@ Usage in run_agent.py:
from __future__ import annotations
import json
import logging
import re
import inspect
import threading
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Callable, Dict, List, Optional
from typing import Any, Dict, List, Optional
from agent.memory_provider import MemoryProvider
from agent.skill_commands import extract_user_instruction_from_skill_message
from tools.registry import tool_error
logger = logging.getLogger(__name__)
# How long shutdown_all() waits for in-flight background sync/prefetch work
# to drain before abandoning it. A wedged provider must never block process
# teardown indefinitely — the worker threads are daemon, so anything still
# running past this window dies with the interpreter.
_SYNC_DRAIN_TIMEOUT_S = 5.0
def normalize_tool_schema(schema: Any) -> Optional[Dict[str, Any]]:
"""Return a function-tool dict with a resolvable top-level ``name``.
Context engines and memory providers expose tool schemas via
``get_tool_schemas()``. The expected shape is a bare function schema
(``{"name": ..., "description": ..., "parameters": ...}``) which callers
wrap as ``{"type": "function", "function": schema}``.
Some providers instead return an entry that is *already* in OpenAI tool
form (``{"type": "function", "function": {"name": ...}}``). Wrapping that
a second time produces ``{"type": "function", "function": {"type":
"function", "function": {...}}}`` whose ``function`` has no top-level
``name``. Strict providers (e.g. DeepSeek) reject the *entire* request
with ``tools[N].function: missing field name`` (HTTP 400), so one bad
schema disables the whole toolset and breaks every turn (#47707).
This helper normalizes both shapes to the bare function schema and
returns ``None`` for anything without a resolvable name, so callers can
skip-with-warning rather than appending a nameless tool.
"""
if not isinstance(schema, dict):
return None
# Unwrap an already-wrapped OpenAI tool entry.
if schema.get("type") == "function" and isinstance(schema.get("function"), dict):
schema = schema["function"]
if not isinstance(schema, dict):
return None
name = schema.get("name", "")
if not name or not isinstance(name, str):
return None
return schema
def memory_provider_tools_enabled(enabled_toolsets: Optional[List[str]]) -> bool:
"""Return whether external memory-provider tools should be exposed."""
if enabled_toolsets is None:
return True
if not enabled_toolsets:
return False
if "memory" in enabled_toolsets:
return True
try:
from toolsets import resolve_toolset
return any("memory" in resolve_toolset(name) for name in enabled_toolsets)
except Exception:
logger.debug("Failed to resolve enabled toolsets for memory-provider tools", exc_info=True)
return False
def inject_memory_provider_tools(agent: Any) -> int:
"""Append external memory-provider tool schemas to an agent tool surface."""
memory_manager = getattr(agent, "_memory_manager", None)
tools = getattr(agent, "tools", None)
if not memory_manager or tools is None:
return 0
existing_tool_names = {
tool.get("function", {}).get("name")
for tool in tools
if isinstance(tool, dict)
}
if (
"memory" not in existing_tool_names
and not memory_provider_tools_enabled(getattr(agent, "enabled_toolsets", None))
):
return 0
get_schemas = getattr(memory_manager, "get_all_tool_schemas", None)
if not callable(get_schemas):
return 0
valid_tool_names = getattr(agent, "valid_tool_names", None)
if valid_tool_names is None:
valid_tool_names = set()
agent.valid_tool_names = valid_tool_names
added = 0
for raw_schema in get_schemas():
schema = normalize_tool_schema(raw_schema)
if schema is None:
logger.warning(
"Memory provider returned a tool schema with no resolvable "
"name; skipping to avoid poisoning the request (%r)",
raw_schema,
)
continue
tool_name = schema["name"]
if tool_name in existing_tool_names:
continue
tools.append({"type": "function", "function": schema})
valid_tool_names.add(tool_name)
existing_tool_names.add(tool_name)
added += 1
return added
# ---------------------------------------------------------------------------
# Context fencing helpers
@@ -361,13 +252,6 @@ class MemoryManager:
self._providers: List[MemoryProvider] = []
self._tool_to_provider: Dict[str, MemoryProvider] = {}
self._has_external: bool = False # True once a non-builtin provider is added
# Background executor for end-of-turn sync/prefetch. Lazily created on
# first use so the common builtin-only path spawns no extra threads.
# A single worker serializes a provider's writes (turn N must land
# before turn N+1) and caps thread growth at one per manager. See
# _submit_background() and the sync_all/queue_prefetch_all rationale.
self._sync_executor: Optional[ThreadPoolExecutor] = None
self._sync_executor_lock = threading.Lock()
# -- Registration --------------------------------------------------------
@@ -397,31 +281,9 @@ class MemoryManager:
self._providers.append(provider)
# Core tool names are reserved — a memory provider must never register
# a tool that shadows a built-in (e.g. ``clarify``, ``delegate_task``).
# Built-ins always win, so such a tool is dropped at agent init and
# would otherwise linger in ``_tool_to_provider`` and hijack dispatch
# (#40466). Reject it here, at the door, so it never enters the routing
# table at all — matching the built-ins-always-win invariant used by
# the TTS/browser/search provider registries.
from toolsets import _HERMES_CORE_TOOLS
_core_tool_names = set(_HERMES_CORE_TOOLS)
# Index tool names → provider for routing
for raw_schema in provider.get_tool_schemas():
schema = normalize_tool_schema(raw_schema)
if schema is None:
continue
tool_name = schema["name"]
if tool_name in _core_tool_names:
logger.warning(
"Memory provider '%s' tool '%s' shadows a reserved core "
"tool name; registration ignored. Core tools always win — "
"rename the provider's tool to something unique.",
provider.name, tool_name,
)
continue
for schema in provider.get_tool_schemas():
tool_name = schema.get("name", "")
if tool_name and tool_name not in self._tool_to_provider:
self._tool_to_provider[tool_name] = provider
elif tool_name in self._tool_to_provider:
@@ -474,37 +336,16 @@ class MemoryManager:
# -- Prefetch / recall ---------------------------------------------------
@staticmethod
def _strip_skill_scaffolding(text: str) -> Optional[str]:
"""Return memory-worthy user text, or None to skip the turn.
When a user invokes a /skill or /bundle, Hermes expands the turn into
a model-facing message that embeds the entire skill body. Feeding that
verbatim to memory providers pollutes their stores/embeddings with
prompt scaffolding instead of what the user actually asked. We recover
just the user's instruction here, once, for every provider — so this
is fixed for the whole provider fan-out, not per backend.
- Non-skill messages pass through unchanged.
- Skill turns with a user instruction return that instruction.
- Bare skill invocations (no instruction) return None → callers skip
the turn, since there is no user content worth remembering.
"""
return extract_user_instruction_from_skill_message(text)
def prefetch_all(self, query: str, *, session_id: str = "") -> str:
"""Collect prefetch context from all providers.
Returns merged context text labeled by provider. Empty providers
are skipped. Failures in one provider don't block others.
"""
clean_query = self._strip_skill_scaffolding(query)
if not clean_query:
return ""
parts = []
for provider in self._providers:
try:
result = provider.prefetch(clean_query, session_id=session_id)
result = provider.prefetch(query, session_id=session_id)
if result and result.strip():
parts.append(result)
except Exception as e:
@@ -515,31 +356,15 @@ class MemoryManager:
return "\n\n".join(parts)
def queue_prefetch_all(self, query: str, *, session_id: str = "") -> None:
"""Queue background prefetch on all providers for the next turn.
Provider work is dispatched to a background worker so a slow or
wedged provider can never block the caller. See ``sync_all`` for
the full rationale (agent stuck "running" minutes after a turn).
"""
providers = list(self._providers)
if not providers:
return
clean_query = self._strip_skill_scaffolding(query)
if not clean_query:
return
def _run() -> None:
for provider in providers:
try:
provider.queue_prefetch(clean_query, session_id=session_id)
except Exception as e:
logger.debug(
"Memory provider '%s' queue_prefetch failed (non-fatal): %s",
provider.name, e,
)
self._submit_background(_run)
"""Queue background prefetch on all providers for the next turn."""
for provider in self._providers:
try:
provider.queue_prefetch(query, session_id=session_id)
except Exception as e:
logger.debug(
"Memory provider '%s' queue_prefetch failed (non-fatal): %s",
provider.name, e,
)
# -- Sync ----------------------------------------------------------------
@@ -563,156 +388,39 @@ class MemoryManager:
session_id: str = "",
messages: Optional[List[Dict[str, Any]]] = None,
) -> None:
"""Sync a completed turn to all providers.
Runs on a background worker thread, NOT inline on the
turn-completion path. A provider's ``sync_turn`` may make a
blocking network/daemon call (a misconfigured Hindsight daemon
was observed blocking ~298s before failing); doing that inline
held ``run_conversation`` open long after the user saw their
response, so every interface (CLI, TUI, gateway) kept the agent
marked "running" for minutes and any follow-up message triggered
an aggressive interrupt. Dispatching off-thread means a slow or
broken provider can never stall the turn — the sync simply
completes (or fails, logged) in the background.
Writes are serialized through a single worker so turn N lands
before turn N+1; provider implementations don't need their own
ordering guarantees.
"""
providers = list(self._providers)
if not providers:
return
clean_user_content = self._strip_skill_scaffolding(user_content)
if not clean_user_content:
return
user_content = clean_user_content
def _run() -> None:
for provider in providers:
try:
if messages is not None and self._provider_sync_accepts_messages(provider):
provider.sync_turn(
user_content,
assistant_content,
session_id=session_id,
messages=messages,
)
else:
provider.sync_turn(
user_content,
assistant_content,
session_id=session_id,
)
except Exception as e:
logger.warning(
"Memory provider '%s' sync_turn failed: %s",
provider.name, e,
)
self._submit_background(_run)
# -- Background dispatch -------------------------------------------------
def _submit_background(self, fn) -> None:
"""Run ``fn`` on the manager's background worker.
The executor is created lazily and shared across calls. If the
executor can't be created or has already been shut down, ``fn``
runs inline as a last-resort fallback — losing the async benefit
but never losing the write itself. ``fn`` must do its own
per-provider error handling; this wrapper only guards executor
plumbing.
"""
executor = self._get_sync_executor()
if executor is None:
# Executor unavailable (shut down / creation failed) — run
# inline rather than drop the work. Slow, but correct.
"""Sync a completed turn to all providers."""
for provider in self._providers:
try:
fn()
except Exception as e: # pragma: no cover - fn guards internally
logger.debug("Inline memory background task failed: %s", e)
return
try:
executor.submit(fn)
except RuntimeError:
# Executor was shut down between the get and the submit
# (teardown race). Fall back to inline.
try:
fn()
except Exception as e: # pragma: no cover - fn guards internally
logger.debug("Inline memory background task failed: %s", e)
def _get_sync_executor(self) -> Optional[ThreadPoolExecutor]:
"""Lazily create the single-worker background executor."""
if self._sync_executor is not None:
return self._sync_executor
with self._sync_executor_lock:
if self._sync_executor is None:
try:
self._sync_executor = ThreadPoolExecutor(
max_workers=1,
thread_name_prefix="mem-sync",
if messages is not None and self._provider_sync_accepts_messages(provider):
provider.sync_turn(
user_content,
assistant_content,
session_id=session_id,
messages=messages,
)
except Exception as e: # pragma: no cover - resource exhaustion
logger.warning("Failed to create memory sync executor: %s", e)
return None
return self._sync_executor
def flush_pending(self, timeout: Optional[float] = None) -> bool:
"""Block until queued sync/prefetch work has drained.
Single-worker executor means submitting a sentinel and waiting on
it guarantees every previously-submitted task has run. Returns
True if the barrier completed within ``timeout`` (or no executor
exists), False on timeout. Used at real session boundaries and by
tests that need to assert provider state deterministically.
"""
executor = self._sync_executor
if executor is None:
return True
try:
fut = executor.submit(lambda: None)
except RuntimeError:
# Executor already shut down — nothing pending.
return True
try:
fut.result(timeout=timeout)
return True
except Exception:
return False
else:
provider.sync_turn(
user_content,
assistant_content,
session_id=session_id,
)
except Exception as e:
logger.warning(
"Memory provider '%s' sync_turn failed: %s",
provider.name, e,
)
# -- Tools ---------------------------------------------------------------
def get_all_tool_schemas(self) -> List[Dict[str, Any]]:
"""Collect tool schemas from all providers.
Reserved core tool names (``clarify``, ``delegate_task``, etc.) are
skipped — they are rejected from the routing table in
:meth:`add_provider`, so the manager must not advertise a schema it
will never route. Built-ins always win (#40466).
"""
from toolsets import _HERMES_CORE_TOOLS
_core_tool_names = set(_HERMES_CORE_TOOLS)
"""Collect tool schemas from all providers."""
schemas = []
seen = set()
for provider in self._providers:
try:
for raw_schema in provider.get_tool_schemas():
schema = normalize_tool_schema(raw_schema)
if schema is None:
logger.warning(
"Memory provider '%s' returned a tool schema with "
"no resolvable name; skipping (%r)",
provider.name, raw_schema,
)
continue
name = schema["name"]
if name in _core_tool_names:
continue
if name not in seen:
for schema in provider.get_tool_schemas():
name = schema.get("name", "")
if name and name not in seen:
schemas.append(schema)
seen.add(name)
except Exception as e:
@@ -772,10 +480,9 @@ class MemoryManager:
try:
provider.on_session_end(messages)
except Exception as e:
logger.warning(
logger.debug(
"Memory provider '%s' on_session_end failed: %s",
provider.name, e,
exc_info=True,
)
def on_session_switch(
@@ -901,87 +608,6 @@ class MemoryManager:
provider.name, e,
)
# Actions the bridge mirrors to external providers. The built-in memory
# tool can also return non-mutating shapes (errors, staged-for-approval
# records); those are filtered out by ``notify_memory_tool_write`` before
# we ever reach a provider.
_MIRRORED_MEMORY_ACTIONS = {"add", "replace", "remove"}
@staticmethod
def _memory_tool_result_succeeded(result: Any) -> bool:
"""True only when the built-in memory tool actually committed a write.
Fails closed: a string that isn't JSON, a non-dict result, a missing
``success``, or a write staged for approval (``staged is True``) all
return False so external providers are never told about a write that
did not land.
"""
if isinstance(result, str):
try:
result = json.loads(result)
except Exception:
return False
if not isinstance(result, dict):
return False
return result.get("success") is True and result.get("staged") is not True
def notify_memory_tool_write(
self,
tool_result: Any,
tool_args: Dict[str, Any],
*,
build_metadata: Optional[Callable[[], Dict[str, Any]]] = None,
) -> None:
"""Mirror a built-in memory tool call to external providers.
This is the single entry point the agent loop calls after running the
built-in ``memory`` tool. All the decisions about *whether* and *what*
to mirror live here, behind the manager interface — the loop only hands
over the raw tool result and args:
* gate on a committed (non-staged, successful) write,
* expand the single-op and batched (``operations``) shapes,
* keep only mutating actions (add/replace/remove),
* build per-op provenance metadata and forward ``old_text``.
``build_metadata`` is an optional agent-side callable (the loop knows
session/task/tool-call provenance the manager does not) invoked once per
mirrored op.
"""
if not self._memory_tool_result_succeeded(tool_result):
return
target = str(tool_args.get("target") or "memory")
operations = tool_args.get("operations")
if isinstance(operations, list) and operations:
raw_operations = operations
else:
raw_operations = [{
"action": tool_args.get("action"),
"content": tool_args.get("content"),
"old_text": tool_args.get("old_text"),
}]
for op in raw_operations:
if not isinstance(op, dict):
continue
action = str(op.get("action") or "")
if action not in self._MIRRORED_MEMORY_ACTIONS:
continue
try:
metadata = dict(build_metadata() if build_metadata else {})
old_text = op.get("old_text")
if old_text:
metadata["old_text"] = str(old_text)
self.on_memory_write(
action,
target,
str(op.get("content") or ""),
metadata=metadata,
)
except Exception as e:
logger.debug("notify_memory_tool_write failed for op %s: %s", action, e)
def on_delegation(self, task: str, result: str, *,
child_session_id: str = "", **kwargs) -> None:
"""Notify all providers that a subagent completed."""
@@ -997,15 +623,7 @@ class MemoryManager:
)
def shutdown_all(self) -> None:
"""Shut down all providers (reverse order for clean teardown).
Drains the background sync/prefetch executor first (bounded by
``_SYNC_DRAIN_TIMEOUT_S``) so a turn's final sync has a chance to
land before providers are torn down. The worker threads are
daemon, so anything still wedged past the drain window dies with
the interpreter rather than blocking exit.
"""
self._drain_sync_executor()
"""Shut down all providers (reverse order for clean teardown)."""
for provider in reversed(self._providers):
try:
provider.shutdown()
@@ -1015,52 +633,6 @@ class MemoryManager:
provider.name, e,
)
def _drain_sync_executor(self) -> None:
"""Shut down the background executor, waiting briefly for drain.
Bounded by ``_SYNC_DRAIN_TIMEOUT_S``: a wedged provider must never
hang process/session teardown. We stop accepting new work and
cancel anything still queued, then wait at most the drain timeout
for the currently-running task on a watcher thread. The worker is
daemon, so an over-running task dies with the interpreter.
"""
with self._sync_executor_lock:
executor = self._sync_executor
self._sync_executor = None
if executor is None:
return
try:
# Stop accepting new work and drop anything still queued, but
# do NOT block here — cancel_futures cancels not-yet-started
# tasks; the in-flight one keeps running on its daemon thread.
executor.shutdown(wait=False, cancel_futures=True)
except TypeError:
# Older Python without cancel_futures kwarg.
try:
executor.shutdown(wait=False)
except Exception as e: # pragma: no cover
logger.debug("Memory sync executor shutdown failed: %s", e)
return
except Exception as e: # pragma: no cover
logger.debug("Memory sync executor shutdown failed: %s", e)
return
# Give an in-flight sync a bounded chance to finish on a watcher
# thread so we don't block the caller past the drain timeout.
drainer = threading.Thread(
target=lambda: self._bounded_executor_wait(executor),
daemon=True,
name="mem-sync-drain",
)
drainer.start()
drainer.join(timeout=_SYNC_DRAIN_TIMEOUT_S)
@staticmethod
def _bounded_executor_wait(executor: ThreadPoolExecutor) -> None:
try:
executor.shutdown(wait=True)
except Exception as e: # pragma: no cover
logger.debug("Memory sync executor drain wait failed: %s", e)
def initialize_all(self, session_id: str, **kwargs) -> None:
"""Initialize all providers.
-19
View File
@@ -28,7 +28,6 @@ Optional hooks (override to opt in):
on_pre_compress(messages) -> str — extract before context compression
on_memory_write(action, target, content, metadata=None) — mirror built-in memory writes
on_delegation(task, result, **kwargs) — parent-side observation of subagent work
backup_paths() -> list[str] — extra on-disk paths to include in `hermes backup`
"""
from __future__ import annotations
@@ -295,21 +294,3 @@ class MemoryProvider(ABC):
Use to mirror built-in memory writes to your backend.
"""
def backup_paths(self) -> List[str]:
"""Return extra on-disk paths this provider stores OUTSIDE HERMES_HOME.
``hermes backup`` only walks HERMES_HOME, so any provider state kept
under ``~/.honcho``, ``~/.hindsight``, ``~/.openviking``, etc. is lost
across a backup/import cycle unless it's declared here.
Return a list of absolute path strings (files or directories). The
backup command resolves each, captures the ones that exist and live
under the user's home directory into a reserved ``_external/`` subtree
of the archive, and ``hermes import`` restores them to their original
locations. Paths outside the home directory are skipped for safety.
MUST be callable without ``initialize()`` and without network — resolve
from config/env only. Default returns an empty list (nothing external).
"""
return []
-50
View File
@@ -1,50 +0,0 @@
from __future__ import annotations
from collections.abc import Mapping
from typing import Any
_NON_TEXT_PART_TYPES = {"image", "image_url", "input_image", "audio", "input_audio"}
_TEXT_KEYS = ("text", "content", "input_text", "output_text", "summary_text")
def _field(value: Any, key: str) -> Any:
if isinstance(value, Mapping):
return value.get(key)
return getattr(value, key, None)
def _text_from_part(part: Any) -> str:
if part is None:
return ""
if isinstance(part, str):
return part
part_type = str(_field(part, "type") or "").strip().lower()
if part_type in _NON_TEXT_PART_TYPES:
return ""
for key in _TEXT_KEYS:
text = _field(part, key)
if isinstance(text, str):
return text
return ""
def flatten_message_text(content: Any, *, sep: str = "\n") -> str:
"""Return the visible text from common chat/Responses message content shapes."""
if content is None:
return ""
if isinstance(content, str):
return content
if isinstance(content, list):
chunks = [_text_from_part(part) for part in content]
return sep.join(chunk for chunk in chunks if chunk)
text = _text_from_part(content)
if text:
return text
try:
return str(content)
except Exception:
return ""
-33
View File
@@ -279,38 +279,6 @@ def _repair_tool_call_arguments(raw_args: str, tool_name: str = "?") -> str:
return "{}"
def close_interrupted_tool_sequence(messages: list, final_response: Any = None) -> bool:
"""Append a synthetic assistant turn when an interrupted tail is a tool result.
A turn cut short by ``/stop`` can leave the transcript ending on a raw
``tool`` message (a tool finished, or its execution was cancelled, but the
model never streamed a closing assistant turn). Persisting that tail means
the next user message lands as ``… tool → user`` — a role-alternation
violation that strict providers (Gemini, Claude) react to by hallucinating
a continuation of the user's message and ignoring prior context, which
reads to the user as "lost context" (#48879).
``finalize_turn`` closes this on the happy interrupt path, but the
retry/backoff/error interrupt aborts in ``conversation_loop`` ``return``
early and never reach it — this shared helper closes the sequence on all of
them. ``final_response`` is usually empty on an interrupt, so an explicit
placeholder is used rather than an empty-content assistant turn.
Mutates ``messages`` in place. Returns True if a closing turn was appended.
"""
if not messages:
return False
last = messages[-1]
if not isinstance(last, dict) or last.get("role") != "tool":
return False
text = final_response if isinstance(final_response, str) else ""
messages.append({
"role": "assistant",
"content": text.strip() or "Operation interrupted.",
})
return True
def _strip_non_ascii(text: str) -> str:
"""Remove non-ASCII characters, replacing with closest ASCII equivalent or removing.
@@ -463,7 +431,6 @@ def _sanitize_structure_non_ascii(payload: Any) -> bool:
__all__ = [
"_SURROGATE_RE",
"close_interrupted_tool_sequence",
"_sanitize_surrogates",
"_sanitize_structure_surrogates",
"_sanitize_messages_surrogates",
-586
View File
@@ -1,586 +0,0 @@
"""Mixture-of-Agents runtime helpers for /moa turns.
The slash command is deliberately not a model tool. It marks one user turn as
MoA-enabled; the normal Hermes agent loop still owns tool calling and turn
termination, while this module gathers reference-model context before each model
iteration.
"""
from __future__ import annotations
import hashlib
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import Any
from agent.auxiliary_client import call_llm
from agent.transports import get_transport
logger = logging.getLogger(__name__)
# Upper bound on concurrent reference-model calls. References are independent
# advisory calls (no tools, no inter-dependence), so we fan them out the same
# way delegate_task runs a batch: all in flight at once, results collected when
# every reference finishes. Presets rarely list more than a handful of
# references; this cap just protects against a pathologically large preset
# opening dozens of sockets at once.
_MAX_REFERENCE_WORKERS = 8
# Per-tool-result character budget for the advisory reference view. Tool
# results can be huge (a full diff, a 5000-line file dump); replaying them
# verbatim per reference per tool-loop step would blow the reference model's
# context window and cost. We keep the agent's *actions* (tool calls) in full —
# they are cheap, high-signal, and tell the reference what the agent did — but
# preview each tool *result* head+tail so the reference still sees what came
# back without replaying megabytes. The acting aggregator always gets the full,
# untrimmed transcript; this budget only shapes the advisory copy.
_REFERENCE_TOOL_RESULT_BUDGET = 4000
# System prompt prepended to every reference-model call. References are
# advisory — they do NOT act, call tools, or own the task. Without this
# framing a reference receives the bare trimmed conversation and assumes it is
# the acting agent: it then refuses ("I can't access repositories / URLs from
# here") or tries to call tools it doesn't have. The prompt reframes the model
# as an analyst whose job is to reason about the presented state and hand its
# best thinking to the aggregator/orchestrator that will actually act.
_REFERENCE_SYSTEM_PROMPT = (
"You are a reference advisor in a Mixture of Agents (MoA) process. You are "
"NOT the acting agent and you do NOT execute anything: you cannot call "
"tools, run commands, browse, or access files, repositories, or URLs, and "
"you should not try to or apologize for being unable to. A separate "
"aggregator/orchestrator model holds those capabilities and will take the "
"actual actions.\n\n"
"The conversation below is the current state of a task handled by that "
"acting agent. Your job is to give your most intelligent analysis of that "
"state: understand the goal, reason about the problem, and advise on what "
"to do next. Surface the best approach, concrete next steps and tool-use "
"strategy, likely pitfalls and risks, and anything the acting agent may "
"have missed or gotten wrong. Assume any referenced files, URLs, or "
"systems exist and reason about them from the context given rather than "
"asking for access.\n\n"
"Respond with your advice directly — no preamble, no disclaimers about "
"tools or access. Your response is private guidance handed to the "
"aggregator, not an answer shown to the user."
)
def _slot_label(slot: dict[str, str]) -> str:
return f"{slot.get('provider', '').strip()}:{slot.get('model', '').strip()}"
def _slot_runtime(slot: dict[str, str]) -> dict[str, Any]:
"""Resolve a reference/aggregator slot to real runtime call kwargs.
A MoA slot is just a model selection — it must be called the same way any
model is called elsewhere, not through a bare ``call_llm(provider=...,
model=...)`` that leaves base_url/api_key/api_mode unresolved and lets the
auxiliary auto-detector guess. We route the slot's provider through
``resolve_runtime_provider`` (the canonical provider→api_mode/base_url/
api_key resolver the CLI, gateway, and delegate_task all use), so the slot
gets its provider's real API surface — e.g. MiniMax → anthropic_messages,
GPT-5/o-series → max_completion_tokens, custom endpoints → their base_url.
Returns the kwargs to pass through to ``call_llm`` (provider/model plus the
resolved base_url/api_key when available). Falls back to the bare
provider/model on any resolution error so a misconfigured slot still
attempts the call rather than aborting the whole MoA turn.
"""
provider = str(slot.get("provider") or "").strip()
model = str(slot.get("model") or "").strip()
out: dict[str, Any] = {"provider": provider, "model": model}
try:
from hermes_cli.runtime_provider import resolve_runtime_provider
rt = resolve_runtime_provider(requested=provider, target_model=model)
resolved_provider = str(rt.get("provider") or provider).strip().lower()
# call_llm treats an explicit base_url as a custom endpoint. That is
# correct for ordinary OpenAI-compatible targets, but wrong for OAuth /
# provider-backed targets whose provider branch adds auth refresh,
# request metadata, or request-shape adapters. Keep those providers
# identified by name.
if resolved_provider in {"nous", "openai-codex", "xai-oauth"}:
return out
# Pass the resolved endpoint through so call_llm builds the request for
# the provider's actual API surface instead of auto-detecting. base_url
# routes call_llm to the right adapter (incl. anthropic_messages mode);
# api_key is the resolved credential for that provider.
if rt.get("base_url"):
out["base_url"] = rt["base_url"]
if rt.get("api_key"):
out["api_key"] = rt["api_key"]
except Exception as exc: # pragma: no cover - defensive
logger.debug("MoA slot runtime resolution failed for %s: %s", _slot_label(slot), exc)
return out
def _run_reference(
slot: dict[str, str],
ref_messages: list[dict[str, Any]],
*,
temperature: float | None = None,
max_tokens: int | None = None,
) -> tuple[str, str]:
"""Call one reference model and return ``(label, text)``.
The slot is resolved to its provider's real runtime (via ``_slot_runtime``)
and called through the same ``call_llm`` request-building path any model
uses, so per-model wire-format handling (anthropic_messages,
max_completion_tokens, fixed/forbidden temperature) applies identically to
a reference as it would if that model were the acting model. MoA imposes no
cap of its own (``max_tokens`` defaults to ``None`` → omitted → the model's
real maximum); ``temperature`` is only the user's configured preset value,
which call_llm may still override per model.
Never raises: a failed reference becomes a labelled note so the aggregator
can still act with partial context. Designed to run inside a thread pool —
``call_llm`` is synchronous/blocking, so threads (not asyncio) are the right
concurrency primitive, mirroring ``delegate_task``'s batch fan-out.
"""
label = _slot_label(slot)
try:
# Prepend the advisory-role system prompt so the reference understands
# it is analyzing state for an aggregator, not acting on the task. The
# trimmed view (_reference_messages) already strips the agent's own
# system prompt, so this is the only system message the reference sees.
messages = [{"role": "system", "content": _REFERENCE_SYSTEM_PROMPT}, *ref_messages]
response = call_llm(
task="moa_reference",
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
**_slot_runtime(slot),
)
return label, _extract_text(response) or "(empty response)"
except Exception as exc:
logger.warning("MoA reference model %s failed: %s", label, exc)
return label, f"[failed: {exc}]"
def _run_references_parallel(
reference_models: list[dict[str, str]],
ref_messages: list[dict[str, Any]],
*,
temperature: float | None = None,
max_tokens: int | None = None,
) -> list[tuple[str, str]]:
"""Fan out all reference models in parallel, returning outputs in order.
Like ``delegate_task``'s batch mode, every reference is dispatched at once
and we block until all of them finish before handing the joined results to
the aggregator. Output order matches ``reference_models`` so the
``Reference {idx}`` labelling stays stable. MoA presets that reference
another MoA preset are skipped here (recursion guard) with a labelled note.
"""
if not reference_models:
return []
results: list[tuple[str, str] | None] = [None] * len(reference_models)
futures = {}
workers = min(_MAX_REFERENCE_WORKERS, len(reference_models))
with ThreadPoolExecutor(max_workers=workers) as executor:
for idx, slot in enumerate(reference_models):
if slot.get("provider") == "moa":
results[idx] = (
_slot_label(slot),
"[skipped: MoA presets cannot recursively reference MoA]",
)
continue
futures[
executor.submit(
_run_reference,
slot,
ref_messages,
temperature=temperature,
max_tokens=max_tokens,
)
] = idx
# Collect every reference before returning — the aggregator needs the
# complete set, so there is no early-exit / first-completed path here.
for future, idx in futures.items():
results[idx] = future.result()
return [r for r in results if r is not None]
def _truncate_tool_result(text: str, budget: int = _REFERENCE_TOOL_RESULT_BUDGET) -> str:
"""Head+tail preview of a tool result for the advisory view.
Keeps the first and last halves of the budget with a ``[... N chars
omitted ...]`` marker between them, so a reference sees both how the result
started and how it ended without replaying the whole payload.
"""
if not text or len(text) <= budget:
return text
half = budget // 2
omitted = len(text) - 2 * half
return f"{text[:half]}\n[... {omitted} chars omitted ...]\n{text[-half:]}"
def _render_tool_calls(tool_calls: Any) -> str:
"""Render an assistant turn's tool_calls as readable text lines.
The advisory view cannot carry real ``tool_calls`` payloads (strict
providers reject tool_calls the reference never produced), so the agent's
actions are flattened to text the reference can read and reason about.
"""
lines: list[str] = []
for tc in tool_calls or []:
fn = (tc.get("function") or {}) if isinstance(tc, dict) else {}
name = fn.get("name") or (tc.get("name") if isinstance(tc, dict) else "") or "tool"
args = fn.get("arguments")
if isinstance(args, str):
args_text = args
elif args is not None:
try:
import json
args_text = json.dumps(args, ensure_ascii=False)
except Exception:
args_text = str(args)
else:
args_text = ""
lines.append(f"[called tool: {name}({args_text})]" if args_text else f"[called tool: {name}]")
return "\n".join(lines)
def _reference_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Build an advisory view of the conversation for reference models.
A reference gives an INFORMED judgement on the current state, so it must
see what the agent actually did — its tool calls AND the tool results that
came back — not just the agent's narration. We therefore preserve the whole
conversation flow, but flatten it into clean user/assistant *text* turns:
- system prompt: dropped (8K of Hermes boilerplate, not advisory signal).
- assistant turns: kept; any ``tool_calls`` are rendered inline as
``[called tool: name(args)]`` text lines appended to the turn's text.
- ``tool``-role results: NOT dropped. Each is folded (head+tail preview,
see ``_truncate_tool_result``) into the *preceding* assistant turn as a
``[tool result: ...]`` block, so the reference sees what came back.
This emits ZERO ``tool``-role messages and ZERO ``tool_calls`` arrays — only
plain user/assistant text — so strict providers (Mistral, Fireworks) that
reject orphan tool messages / unproduced tool_calls don't 400, while the
reference still has the full picture.
The view MUST end with a ``user`` turn. Anthropic (and OpenRouter→Anthropic)
interpret a trailing assistant turn as an assistant *prefill* to continue,
and no-prefill models (e.g. Claude Opus 4.8) reject it with
``400 ... must end with a user message``. Rather than DELETE the agent's
latest context to satisfy that (which would blind the reference to the
current state), we APPEND a synthetic user turn asking the reference to
judge the state above. End-on-user is satisfied and no context is lost.
The acting aggregator always receives the full, untrimmed transcript; this
function only shapes the disposable advisory copy.
"""
advisory_instruction = (
"[The conversation above is the current state of the task. Give your "
"most intelligent judgement: what is going on, what should happen next, "
"what risks or mistakes you see, and how the acting agent should "
"proceed.]"
)
rendered: list[dict[str, Any]] = []
last_user_content: str | None = None
for msg in messages:
role = msg.get("role")
content = msg.get("content")
text = content if isinstance(content, str) else ""
if role == "system":
continue
if role == "user":
if text.strip():
last_user_content = text
rendered.append({"role": "user", "content": text})
elif role == "assistant":
parts: list[str] = []
if text.strip():
parts.append(text.strip())
calls_text = _render_tool_calls(msg.get("tool_calls"))
if calls_text:
parts.append(calls_text)
# Empty assistant turns (no text, no calls) carry nothing advisory.
if parts:
rendered.append({"role": "assistant", "content": "\n".join(parts)})
elif role == "tool":
# Fold the tool result into the preceding assistant turn as text so
# the reference sees what came back, without emitting a tool-role
# message a reference never produced.
result_text = _truncate_tool_result(text)
block = f"[tool result: {result_text}]"
if rendered and rendered[-1].get("role") == "assistant":
rendered[-1]["content"] = rendered[-1]["content"] + "\n" + block
else:
# No assistant turn to attach to (e.g. a leading tool result);
# keep it as advisory context on its own assistant-role line.
rendered.append({"role": "assistant", "content": block})
# Any other role is ignored.
# End on a user turn: append a synthetic advisory request rather than
# deleting the agent's latest assistant context. This satisfies Anthropic's
# no-trailing-assistant-prefill rule while preserving full state.
if rendered and rendered[-1].get("role") == "assistant":
rendered.append({"role": "user", "content": advisory_instruction})
elif rendered and rendered[-1].get("role") == "user":
# Already ends on a user turn (fresh user prompt, no agent action yet).
# Leave it — the reference answers that prompt directly.
pass
if not rendered:
# Degenerate case: nothing rendered. Fall back to the latest user turn.
if last_user_content is not None:
return [{"role": "user", "content": last_user_content}]
for msg in reversed(messages):
if msg.get("role") == "user" and isinstance(msg.get("content"), str):
return [{"role": "user", "content": msg["content"]}]
return rendered
def _extract_text(response: Any) -> str:
try:
transport = get_transport("chat_completions")
if transport is None:
raise RuntimeError("chat_completions transport unavailable")
normalized = transport.normalize_response(response)
text = (normalized.content or "").strip()
if text:
return text
except Exception:
pass
try:
content = response.choices[0].message.content
return (content or "").strip()
except Exception:
return ""
def aggregate_moa_context(
*,
user_prompt: str,
api_messages: list[dict[str, Any]],
reference_models: list[dict[str, str]],
aggregator: dict[str, str],
temperature: float = 0.6,
aggregator_temperature: float = 0.4,
max_tokens: int | None = None,
) -> str:
"""Run configured reference models and synthesize their advice.
Failures are returned as model-specific notes instead of aborting the normal
agent loop; the main model can still act with partial context.
``max_tokens`` is ``None`` by default: MoA does not cap reference or
aggregator output, so each model uses its own maximum. ``call_llm`` omits
the parameter entirely when it is ``None`` (see its docstring), which also
sidesteps providers that reject ``max_tokens`` outright. A hardcoded cap
here previously truncated long aggregator syntheses.
"""
reference_outputs: list[tuple[str, str]] = []
ref_messages = _reference_messages(api_messages)
reference_outputs = _run_references_parallel(
reference_models,
ref_messages,
temperature=temperature,
max_tokens=max_tokens,
)
joined = "\n\n".join(
f"Reference {idx} — {label}:\n{text}"
for idx, (label, text) in enumerate(reference_outputs, start=1)
)
synth_prompt = (
"You are the aggregator in a Mixture of Agents process. Synthesize the "
"reference responses into concise, actionable guidance for the main "
"Hermes agent. Focus on next steps, tool-use strategy, risks, and any "
"disagreements. Do not answer the user directly unless that is all that "
"is needed; produce context the main agent should use in its normal loop.\n\n"
f"Original user prompt:\n{user_prompt}\n\n"
f"Reference responses:\n{joined}"
)
agg_label = _slot_label(aggregator)
try:
response = call_llm(
task="moa_aggregator",
messages=[{"role": "user", "content": synth_prompt}],
temperature=aggregator_temperature,
max_tokens=max_tokens,
**_slot_runtime(aggregator),
)
synthesis = _extract_text(response)
except Exception as exc:
logger.warning("MoA aggregator model %s failed: %s", agg_label, exc)
synthesis = ""
if not synthesis:
synthesis = joined
return (
"[Mixture of Agents context — use this as private guidance for the "
"normal Hermes agent loop. You may call tools, continue reasoning, or "
"finish normally.]\n"
f"Aggregator: {agg_label}\n"
f"References: {', '.join(_slot_label(slot) for slot in reference_models)}\n\n"
f"{synthesis.strip()}"
)
class MoAChatCompletions:
"""OpenAI-chat-compatible facade where the aggregator is the acting model."""
def __init__(self, preset_name: str, reference_callback: Any = None):
self.preset_name = preset_name or "default"
# Optional display hook. Called as reference outputs become available so
# frontends can show each reference model's answer as a labelled block
# before the aggregator acts. Signature:
# reference_callback(event, **kwargs)
# where event is one of:
# "moa.reference" kwargs: index, count, label, text
# "moa.aggregating" kwargs: aggregator (label), ref_count
# Never raises into the model call — display is best-effort.
self.reference_callback = reference_callback
# State-scoped reference cache. The agent loop calls create() once per
# tool-loop iteration; references should re-run whenever the task STATE
# advances — i.e. on every new user message AND every new tool result —
# so each reference judges the latest state. The advisory view
# (_reference_messages) now renders tool calls + results as text, so its
# signature changes on every new tool response; the cache key is that
# signature, so a new tool result is a cache MISS (references re-run)
# while a redundant create() call with identical state is a HIT (no
# re-run, no re-emit). This gives "fire on every user/tool response"
# for free, without re-firing on a pure no-op re-call.
self._ref_cache_key: tuple | None = None
self._ref_cache_outputs: list[tuple[str, str]] = []
def _emit(self, event: str, **kwargs: Any) -> None:
cb = self.reference_callback
if cb is None:
return
try:
cb(event, **kwargs)
except Exception as exc: # pragma: no cover - display must never break the turn
logger.debug("MoA reference_callback failed for %s: %s", event, exc)
def create(self, **api_kwargs: Any) -> Any:
from hermes_cli.config import load_config
from hermes_cli.moa_config import resolve_moa_preset
preset = resolve_moa_preset(load_config().get("moa") or {}, self.preset_name)
messages = list(api_kwargs.get("messages") or [])
reference_models = preset.get("reference_models") or []
aggregator = preset.get("aggregator") or {}
# MoA does not cap reference or aggregator output: each model uses its
# own maximum. Passing max_tokens=None makes call_llm omit the parameter
# (it never caps by default), so a long aggregator synthesis is never
# truncated and providers that reject max_tokens don't 400.
temperature = float(preset.get("reference_temperature", 0.6) or 0.6)
aggregator_temperature = float(preset.get("aggregator_temperature", api_kwargs.get("temperature") or 0.4) or 0.4)
# When the preset is disabled, skip the reference fan-out and let the
# configured aggregator act alone — it is the preset's acting model, so
# a disabled MoA preset is simply "use the aggregator directly."
if not preset.get("enabled", True):
reference_models = []
reference_outputs: list[tuple[str, str]] = []
ref_messages = _reference_messages(messages)
# Turn-scoped cache: only run + display references when the advisory
# view changed (i.e. a new user turn). Within one turn the agent loop
# calls create() once per tool iteration with the same advisory view;
# reuse the cached outputs and skip both the re-run and the re-emit.
_sig = hashlib.sha256(
"\u0000".join(
f"{m.get('role')}:{m.get('content')}" for m in ref_messages
).encode("utf-8", "replace")
).hexdigest()
_cache_key = (self.preset_name, _sig, tuple(_slot_label(s) for s in reference_models))
_refs_from_cache = _cache_key == self._ref_cache_key and bool(self._ref_cache_outputs)
if _refs_from_cache:
reference_outputs = list(self._ref_cache_outputs)
else:
reference_outputs = _run_references_parallel(
reference_models,
ref_messages,
temperature=temperature,
max_tokens=None,
)
self._ref_cache_key = _cache_key
self._ref_cache_outputs = list(reference_outputs)
# Surface each reference model's answer to the display BEFORE the
# aggregator acts — once per turn (only on the iteration that
# actually ran them). The user sees one labelled block per
# reference (rendered like a thinking block) so the MoA process is
# visible rather than a silent pause. Best-effort: never blocks the
# turn.
_ref_count = len(reference_outputs)
for _idx, (_label, _text) in enumerate(reference_outputs, start=1):
self._emit(
"moa.reference",
index=_idx,
count=_ref_count,
label=_label,
text=_text,
)
if _ref_count:
self._emit(
"moa.aggregating",
aggregator=_slot_label(aggregator),
ref_count=_ref_count,
)
agg_messages = [dict(m) for m in messages]
if reference_outputs:
joined = "\n\n".join(
f"Reference {idx} — {label}:\n{text}"
for idx, (label, text) in enumerate(reference_outputs, start=1)
)
guidance = (
"[Mixture of Agents reference context]\n"
f"Preset: {self.preset_name}\n"
f"Aggregator/acting model: {_slot_label(aggregator)}\n"
f"References: {', '.join(label for label, _ in reference_outputs)}\n\n"
"Use the reference responses below as private context. You are the aggregator and acting model: "
"answer the user directly or call tools as needed.\n\n"
f"{joined}"
)
for msg in reversed(agg_messages):
if msg.get("role") == "user" and isinstance(msg.get("content"), str):
msg["content"] = msg["content"] + "\n\n" + guidance
break
else:
agg_messages.append({"role": "user", "content": guidance})
if aggregator.get("provider") == "moa":
raise RuntimeError("MoA aggregator cannot be another MoA preset")
agg_kwargs = dict(api_kwargs)
agg_kwargs["messages"] = agg_messages
# The aggregator is the acting model. Resolve its slot to the provider's
# real runtime (base_url/api_key/api_mode) and call it through the same
# request-building path any model uses — so per-model wire-format
# handling (anthropic_messages, max_completion_tokens, fixed/forbidden
# temperature) applies identically to it. MoA imposes no output cap:
# max_tokens is passed through from the caller (normally None → omitted
# → the model's real maximum). The preset's old hardcoded 4096 default
# is gone — it truncated long syntheses.
return call_llm(
task="moa_aggregator",
messages=agg_messages,
temperature=aggregator_temperature,
max_tokens=agg_kwargs.get("max_tokens"),
tools=agg_kwargs.get("tools"),
extra_body=agg_kwargs.get("extra_body"),
**_slot_runtime(aggregator),
)
class MoAClient:
def __init__(self, preset_name: str, reference_callback: Any = None):
self.chat = type("_MoAChat", (), {})()
self.chat.completions = MoAChatCompletions(preset_name, reference_callback=reference_callback)
+6 -278
View File
@@ -5,7 +5,6 @@ and run_agent.py for pre-flight context checks.
"""
import ipaddress
import json
import logging
import os
import re
@@ -17,7 +16,7 @@ from urllib.parse import urlparse
import requests
import yaml
from utils import atomic_json_write, base_url_host_matches, base_url_hostname
from utils import base_url_host_matches, base_url_hostname
from hermes_constants import OPENROUTER_MODELS_URL
@@ -112,57 +111,6 @@ _endpoint_model_metadata_cache: Dict[str, Dict[str, Dict[str, Any]]] = {}
_endpoint_model_metadata_cache_time: Dict[str, float] = {}
_ENDPOINT_MODEL_CACHE_TTL = 300
def _get_model_metadata_cache_path() -> Path:
"""Return path to the OpenRouter model metadata disk cache."""
from hermes_constants import get_hermes_home
return get_hermes_home() / "cache" / "openrouter_model_metadata.json"
def _model_metadata_disk_cache_age_seconds() -> Optional[float]:
"""Return disk-cache age in seconds, or None if freshness is unknown."""
try:
cache_path = _get_model_metadata_cache_path()
if not cache_path.exists():
return None
age = time.time() - cache_path.stat().st_mtime
if age < 0:
return None
return age
except Exception:
return None
def _load_model_metadata_disk_cache() -> Dict[str, Dict[str, Any]]:
"""Load processed OpenRouter metadata cache from disk."""
try:
cache_path = _get_model_metadata_cache_path()
with cache_path.open("r", encoding="utf-8") as f:
data = json.load(f)
if not isinstance(data, dict):
return {}
return {
str(key): value
for key, value in data.items()
if isinstance(value, dict)
}
except Exception as e:
logger.debug("Failed to load OpenRouter model metadata disk cache: %s", e)
return {}
def _save_model_metadata_disk_cache(data: Dict[str, Dict[str, Any]]) -> None:
"""Save processed OpenRouter metadata cache to disk atomically."""
try:
atomic_json_write(
_get_model_metadata_cache_path(),
data,
indent=0,
separators=(",", ":"),
)
except Exception as e:
logger.debug("Failed to save OpenRouter model metadata disk cache: %s", e)
# Descending tiers for context length probing when the model is unknown.
# We start at 256K (covers GPT-5.x, many current large-context models) and
# step down on context-length errors until one works. Tier[0] is also the
@@ -193,8 +141,6 @@ DEFAULT_CONTEXT_LENGTHS = {
# fuzzy-match collisions (e.g. "anthropic/claude-sonnet-4" is a
# substring of "anthropic/claude-sonnet-4.6").
# OpenRouter-prefixed models resolve via OpenRouter live API or models.dev.
"claude-fable-5": 1000000,
"claude-fable": 1000000,
"claude-opus-4-8": 1000000,
"claude-opus-4.8": 1000000,
"claude-opus-4-7": 1000000,
@@ -261,13 +207,7 @@ DEFAULT_CONTEXT_LENGTHS = {
# https://platform.minimax.io/docs/api-reference/text-chat-openai
"minimax-m3": 1000000,
"minimax": 204800,
# GLM — GLM-5.2 ships with a 1M context window (verified empirically:
# needle-in-a-haystack retrieval at 789K prompt tokens succeeded with
# zero errors on api.z.ai/api/coding/paas/v4). Older GLM models
# (5, 5.1, 5-turbo) are ~202K. Longest-key-first substring matching
# ensures "glm-5.2" resolves to 1M while older variants still hit the
# generic 202K fallback.
"glm-5.2": 1_048_576,
# GLM
"glm": 202752,
# xAI Grok — xAI /v1/models does not return context_length metadata,
# so these hardcoded fallbacks prevent Hermes from probing-down to
@@ -275,11 +215,6 @@ DEFAULT_CONTEXT_LENGTHS = {
# via a custom provider. Values sourced from models.dev (2026-04).
# Keys use substring matching (longest-first), so e.g. "grok-4.20"
# matches "grok-4.20-0309-reasoning" / "-non-reasoning" / "-multi-agent-0309".
# OAuth-only slug; absent from GET /v1/models. xAI publishes a 200k
# usable context window for Composer 2.5 on Grok Build (SuperGrok /
# Premium+); /v1/responses additionally enforces a ~262144 input+output
# budget, but the usable context (what we track here) is 200k.
"grok-composer": 200000, # grok-composer-2.5-fast (Grok Build CLI)
"grok-build": 256000, # grok-build-0.1
"grok-code-fast": 256000, # grok-code-fast-1
"grok-2-vision": 8192, # grok-2-vision, -1212, -latest
@@ -478,16 +413,6 @@ def _infer_provider_from_url(base_url: str) -> Optional[str]:
return None
def _lmstudio_server_root(base_url: str) -> str:
"""Return the LM Studio server root for native ``/api/v1`` endpoints."""
root = _normalize_base_url(base_url).rstrip("/")
for suffix in ("/api/v1", "/api", "/v1"):
if root.endswith(suffix):
root = root[: -len(suffix)].rstrip("/")
break
return root
def _is_known_provider_base_url(base_url: str) -> bool:
return _infer_provider_from_url(base_url) is not None
@@ -559,7 +484,6 @@ def detect_local_server_type(base_url: str, api_key: str = "") -> Optional[str]:
server_url = normalized
if server_url.endswith("/v1"):
server_url = server_url[:-3]
lmstudio_url = _lmstudio_server_root(base_url)
headers = _auth_headers(api_key)
@@ -567,7 +491,7 @@ def detect_local_server_type(base_url: str, api_key: str = "") -> Optional[str]:
with httpx.Client(timeout=2.0, headers=headers) as client:
# LM Studio exposes /api/v1/models — check first (most specific)
try:
r = client.get(f"{lmstudio_url}/api/v1/models")
r = client.get(f"{server_url}/api/v1/models")
if r.status_code == 200:
return "lm-studio"
except Exception:
@@ -701,15 +625,6 @@ def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any
if not force_refresh and _model_metadata_cache and (time.time() - _model_metadata_cache_time) < _MODEL_CACHE_TTL:
return _model_metadata_cache
if not force_refresh:
disk_age = _model_metadata_disk_cache_age_seconds()
if disk_age is not None and disk_age < _MODEL_CACHE_TTL:
disk_cache = _load_model_metadata_disk_cache()
if disk_cache:
_model_metadata_cache = disk_cache
_model_metadata_cache_time = time.time() - disk_age
return _model_metadata_cache
try:
response = requests.get(OPENROUTER_MODELS_URL, timeout=10, verify=_resolve_requests_verify())
response.raise_for_status()
@@ -731,24 +646,12 @@ def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any
_model_metadata_cache = cache
_model_metadata_cache_time = time.time()
_save_model_metadata_disk_cache(cache)
logger.debug("Fetched metadata for %s models from OpenRouter", len(cache))
return cache
except Exception as e:
logger.warning(f"Failed to fetch model metadata from OpenRouter: {e}")
if _model_metadata_cache:
return _model_metadata_cache
disk_cache = _load_model_metadata_disk_cache()
if disk_cache:
_model_metadata_cache = disk_cache
disk_age = _model_metadata_disk_cache_age_seconds()
if disk_age is not None:
_model_metadata_cache_time = time.time() - min(disk_age, _MODEL_CACHE_TTL)
else:
_model_metadata_cache_time = time.time() - _MODEL_CACHE_TTL + 1
return _model_metadata_cache
return {}
return _model_metadata_cache or {}
def fetch_endpoint_model_metadata(
@@ -785,7 +688,7 @@ def fetch_endpoint_model_metadata(
if is_local_endpoint(normalized):
try:
if detect_local_server_type(normalized, api_key=api_key) == "lm-studio":
server_url = _lmstudio_server_root(normalized)
server_url = normalized[:-3].rstrip("/") if normalized.endswith("/v1") else normalized
response = requests.get(
server_url.rstrip("/") + "/api/v1/models",
headers=headers,
@@ -1061,20 +964,6 @@ def parse_available_output_tokens_from_error(error_msg: str) -> Optional[int]:
is_output_cap_error = (
"max_tokens" in error_lower
and ("available_tokens" in error_lower or "available tokens" in error_lower)
) or (
# OpenRouter/Nous phrasing of the same condition.
"in the output" in error_lower
and "maximum context length" in error_lower
) or (
# LM Studio / llama.cpp / some OpenAI-compatible servers:
# "This model's maximum context length is 65536 tokens. However, you
# requested 65536 output tokens and your prompt contains 77409
# characters ..."
# The "requested N output tokens" phrasing means the OUTPUT cap is the
# problem (the input itself fits) — reduce max_tokens, don't compress.
"maximum context length" in error_lower
and "requested" in error_lower
and "output tokens" in error_lower
)
if not is_output_cap_error:
return None
@@ -1093,35 +982,6 @@ def parse_available_output_tokens_from_error(error_msg: str) -> Optional[int]:
tokens = int(match.group(1))
if tokens >= 1:
return tokens
# OpenRouter/Nous format: "maximum context length is N … (A of text input,
# B of tool input, C in the output)". Available output = ctx - text - tool.
_m_ctx = re.search(r'maximum context length is (\d+)', error_lower)
_m_parts = re.search(
r'\((\d+)\s+of text input,\s*(\d+)\s+of tool input,\s*(\d+)\s+in the output\)',
error_lower,
)
if _m_ctx and _m_parts:
_available = int(_m_ctx.group(1)) - int(_m_parts.group(1)) - int(_m_parts.group(2))
if _available >= 1:
return _available
# LM Studio / llama.cpp style: context window is reported in tokens but the
# prompt size is reported in CHARACTERS, e.g.
# "maximum context length is 65536 tokens ... your prompt contains 77409
# characters ...".
# Estimate the input tokens conservatively (~3 chars/token, which
# over-reserves the input so the retried output cap stays safely inside the
# window) and leave the remainder of the window for output.
_m_ctx_tok = re.search(r'maximum context length is (\d+)\s*token', error_lower)
_m_chars = re.search(r'prompt contains (\d+)\s*character', error_lower)
if _m_ctx_tok and _m_chars:
_ctx = int(_m_ctx_tok.group(1))
_est_input = (int(_m_chars.group(1)) + 2) // 3
_available = _ctx - _est_input
if _available >= 1:
return _available
return None
@@ -1199,56 +1059,6 @@ def query_ollama_num_ctx(model: str, base_url: str, api_key: str = "") -> Option
return None
def query_ollama_supports_vision(model: str, base_url: str, api_key: str = "") -> Optional[bool]:
"""Return True/False when Ollama ``/api/show`` reports vision support.
Uses the ``capabilities`` field on Ollama 0.6.0+ and falls back to
``model_info.*.vision.block_count`` on older servers. Returns None when
the server is unreachable, not Ollama, or the model is unknown.
"""
import httpx
bare_model = _strip_provider_prefix(model)
if not bare_model or not base_url:
return None
try:
if detect_local_server_type(base_url, api_key=api_key) != "ollama":
return None
except Exception:
return None
server_url = base_url.rstrip("/")
if server_url.endswith("/v1"):
server_url = server_url[:-3]
headers = _auth_headers(api_key)
try:
with httpx.Client(timeout=3.0, headers=headers) as client:
resp = client.post(f"{server_url}/api/show", json={"name": bare_model})
if resp.status_code != 200:
return None
data = resp.json()
except Exception:
return None
caps = data.get("capabilities")
if isinstance(caps, list):
if any(str(cap).lower() == "vision" for cap in caps):
return True
if caps:
return False
model_info = data.get("model_info")
if isinstance(model_info, dict):
for key in model_info:
if "vision.block_count" in str(key).lower():
return True
return None
def _query_ollama_api_show(model: str, base_url: str, api_key: str = "") -> Optional[int]:
"""Query an Ollama server's native ``/api/show`` for context length.
@@ -1358,7 +1168,6 @@ def _query_local_context_length(model: str, base_url: str, api_key: str = "") ->
server_url = base_url.rstrip("/")
if server_url.endswith("/v1"):
server_url = server_url[:-3]
lmstudio_url = _lmstudio_server_root(base_url)
headers = _auth_headers(api_key)
@@ -1402,7 +1211,7 @@ def _query_local_context_length(model: str, base_url: str, api_key: str = "") ->
# Use _model_id_matches for fuzzy matching: LM Studio stores models as
# "publisher/slug" but users configure only "slug" after "local:" prefix.
if server_type == "lm-studio":
resp = client.get(f"{lmstudio_url}/api/v1/models")
resp = client.get(f"{server_url}/api/v1/models")
if resp.status_code == 200:
data = resp.json()
for m in data.get("models", []):
@@ -1708,34 +1517,6 @@ def get_model_context_length(
if config_context_length is not None and isinstance(config_context_length, int) and config_context_length > 0:
return config_context_length
# 0a. MoA virtual provider — ``model`` is a preset name, not a real model,
# and ``base_url`` is the local virtual endpoint, so every probe below would
# miss and fall through to the 256K default. The aggregator is the acting
# model, so resolve the context window from the aggregator slot's real
# provider+model instead. References are advisory-only and never bound the
# acting context, so they're ignored here.
if (provider or "").strip().lower() == "moa":
try:
from hermes_cli.config import load_config
from hermes_cli.moa_config import resolve_moa_preset
from hermes_cli.runtime_provider import resolve_runtime_provider
preset = resolve_moa_preset(load_config().get("moa") or {}, model)
agg = preset.get("aggregator") or {}
agg_provider = str(agg.get("provider") or "").strip()
agg_model = str(agg.get("model") or "").strip()
if agg_model and agg_provider and agg_provider.lower() != "moa":
rt = resolve_runtime_provider(requested=agg_provider, target_model=agg_model)
return get_model_context_length(
agg_model,
base_url=rt.get("base_url", "") or "",
api_key=rt.get("api_key", "") or "",
provider=agg_provider,
)
except Exception:
logger.debug("MoA aggregator context-length resolution failed", exc_info=True)
# Fall through to the generic default if aggregator resolution failed.
# 0b. custom_providers per-model override — check before any probe.
# This closes the gap where /model switch and display paths used to fall
# back to 128K despite the user having a per-model context_length set.
@@ -1886,26 +1667,6 @@ def get_model_context_length(
"in config.yaml to override.",
model, base_url, f"{DEFAULT_FALLBACK_CONTEXT:,}",
)
# 3b. Before falling back to the hard 256K default, consult the
# hardcoded catalog as a last resort. A proxied/custom Anthropic
# gateway (e.g. corporate proxy) fails the Ollama/local probes
# above, but the model name may still match an entry in
# DEFAULT_CONTEXT_LENGTHS (e.g. "claude-opus-4-8" → 1M).
# Without this, the early return here short-circuits the catalog
# lookup at step 8 and silently caps context at 256K.
model_lower = model.lower()
for default_model, length in sorted(
DEFAULT_CONTEXT_LENGTHS.items(),
key=lambda x: len(x[0]),
reverse=True,
):
if default_model in model_lower:
logger.info(
"Using hardcoded context length %s for model %r "
"(custom endpoint, catalog match on %r)",
f"{length:,}", model, default_model,
)
return length
return DEFAULT_FALLBACK_CONTEXT
# 4. Anthropic /v1/models API (only for regular API keys, not OAuth)
@@ -1986,43 +1747,10 @@ def get_model_context_length(
if ctx is not None:
save_context_length(model, base_url, ctx)
return ctx
# 5f. OpenRouter live /models metadata — authoritative for OpenRouter-routed
# models. OpenRouter's catalog carries per-model context_length (e.g.
# anthropic/claude-fable-5 -> 1M) and refreshes as new slugs ship, so it
# must win over both models.dev (step 5g) and the hardcoded family catch-all
# (step 8). Before this branch, an OpenRouter selection set
# effective_provider="openrouter", which (a) made the models.dev lookup miss
# brand-new slugs and (b) skipped the step-6 OR fallback (gated on `not
# effective_provider`), so a fresh slug like claude-fable-5 fell through to
# the generic "claude": 200K entry and under-reported a 1M window. Mirrors
# the dedicated Nous/Copilot/GMI branches above.
if effective_provider == "openrouter":
metadata = fetch_model_metadata()
entry = metadata.get(model)
if entry:
or_ctx = entry.get("context_length")
# Guard against the known OpenRouter Kimi-family 32k underreport
# (same class the hardcoded overrides exist to mitigate).
if isinstance(or_ctx, int) and or_ctx > 0 and not (
or_ctx == 32768 and _model_name_suggests_kimi(model)
):
return or_ctx
if effective_provider:
from agent.models_dev import lookup_models_dev_context
ctx = lookup_models_dev_context(effective_provider, model)
if ctx:
# MiniMax M3: models.dev reports 512K but actual context is 1M.
# Prefer hardcoded catalog over stale probe value.
if _model_name_suggests_minimax_m3(model):
catalog = DEFAULT_CONTEXT_LENGTHS.get("minimax-m3")
if catalog and ctx < catalog:
logger.info(
"Rejecting models.dev context=%s for %r "
"(MiniMax-M3 underreport); using hardcoded default %s",
ctx, model, f"{catalog:,}",
)
ctx = catalog
return ctx
# 6. OpenRouter live API metadata — provider-unaware fallback.
+1 -8
View File
@@ -135,14 +135,7 @@ def _repair_schema(node: Any, is_schema: bool = True) -> Any:
def _fill_missing_type(node: Dict[str, Any]) -> Dict[str, Any]:
"""Infer a reasonable ``type`` if this schema node has none."""
node_type = node.get("type")
if isinstance(node_type, list):
concrete = next(
(t for t in node_type if isinstance(t, str) and t not in {"", "null"}),
"string",
)
return {**node, "type": concrete}
if "type" in node and node_type not in {None, ""}:
if "type" in node and node["type"] not in {None, ""}:
return node
# Heuristic: presence of ``properties`` → object, ``items`` → array, ``enum``
-60
View File
@@ -26,7 +26,6 @@ logger = logging.getLogger(__name__)
BUSY_INPUT_FLAG = "busy_input_prompt"
TOOL_PROGRESS_FLAG = "tool_progress_prompt"
OPENCLAW_RESIDUE_FLAG = "openclaw_residue_cleanup"
PROFILE_BUILD_FLAG = "profile_build_offered"
# -------------------------------------------------------------------------
@@ -127,62 +126,6 @@ def detect_openclaw_residue(home: Optional[Path] = None) -> bool:
return False
# -------------------------------------------------------------------------
# Onboarding profile-build path (opt-in, consent-gated)
# -------------------------------------------------------------------------
def profile_build_mode(config: Mapping[str, Any]) -> str:
"""Resolve the onboarding profile-build mode from config.
Returns one of:
``"ask"`` — on first contact, OFFER to build a profile (default).
``"off"`` — never offer; the first-message note stays a plain intro.
Read from ``config.onboarding.profile_build``. Unknown / missing values
fall back to ``"ask"`` so the default experience offers the flow. Any
network/account lookups inside the flow are separately consented to in
conversation — this setting only governs whether the offer is made.
"""
if not isinstance(config, Mapping):
return "ask"
onboarding = config.get("onboarding")
if not isinstance(onboarding, Mapping):
return "ask"
mode = onboarding.get("profile_build")
if isinstance(mode, str) and mode.strip().lower() == "off":
return "off"
return "ask"
def profile_build_directive() -> str:
"""System-note directive appended to the very first message ever.
Instructs the agent to run a short, opt-in, consent-gated profile-build
flow and persist confirmed facts to the user-profile memory store
(``memory`` tool, ``target="user"``). Phrased so the agent ASKS before any
lookup and never silently reads connected accounts — directly addressing
the privacy concern that reading email/accounts unprompted feels invasive.
"""
return (
"\n\n[System note: This is the user's very first message ever. "
"After a one-sentence introduction (mention /help shows commands), "
"OFFER — do not assume — to build a short profile of them so you can "
"be more useful, and explain they can decline or do it later. If and "
"ONLY IF they accept:\n"
" 1. Ask for whatever they're comfortable sharing (name, what they "
"do, how they like you to work). Volunteered facts come first.\n"
" 2. Before ANY external lookup, say what you intend to look up and "
"get explicit consent for that step. Never read their connected "
"accounts (email, calendar, etc.) silently — ask each time.\n"
" 3. With consent, you may use web_search to confirm public details "
"(e.g. employer, public profiles) from the data points they gave.\n"
" 4. Save each confirmed, durable fact with the memory tool using "
"target=\"user\" — keep entries compact and high-signal.\n"
"If they decline at any point, stop immediately and continue normally. "
"Keep the whole exchange light and conversational, not an interrogation.]"
)
# -------------------------------------------------------------------------
# State read / write
# -------------------------------------------------------------------------
@@ -239,15 +182,12 @@ __all__ = [
"BUSY_INPUT_FLAG",
"TOOL_PROGRESS_FLAG",
"OPENCLAW_RESIDUE_FLAG",
"PROFILE_BUILD_FLAG",
"busy_input_hint_gateway",
"busy_input_hint_cli",
"tool_progress_hint_gateway",
"tool_progress_hint_cli",
"openclaw_residue_hint_cli",
"detect_openclaw_residue",
"profile_build_mode",
"profile_build_directive",
"is_seen",
"mark_seen",
]
-158
View File
@@ -1,158 +0,0 @@
"""Shared one-off LLM requests for non-conversational helpers.
A "one-shot" is a single, stateless model call that runs *outside* any
conversation: it never touches a session's history, never breaks prompt
caching, and returns plain text. UI surfaces use it for small generative
chores — a commit message from a diff, a rename suggestion, a summary —
where spinning up an agent turn would be wrong (it would pollute the thread)
and hand-rolling an LLM call at every call site would be worse.
Two ways to call it:
* ``run_oneshot(instructions=..., user_input=...)`` — caller supplies the
full prompt.
* ``run_oneshot(template="commit_message", variables={...})`` — caller
names a registered template and passes its variables; the template owns
the prompt engineering so it stays consistent across CLI/TUI/desktop.
Model selection rides the same auxiliary plumbing as title generation
(:func:`agent.auxiliary_client.call_llm`): pass ``main_runtime`` to inherit
the live session's provider/model, otherwise the configured ``task`` (default
``title_generation``) resolves a cheap/fast backend.
"""
import logging
from typing import Any, Callable, Dict, Optional, Tuple
from agent.auxiliary_client import call_llm, extract_content_or_reasoning
logger = logging.getLogger(__name__)
# A template turns a variables dict into a (instructions, user_input) pair.
# Templates are plain callables (not str.format) so diff/code payloads with
# literal "{" / "}" pass through untouched.
PromptTemplate = Callable[[Dict[str, Any]], Tuple[str, str]]
def _truncate(text: str, limit: int) -> str:
text = text or ""
if len(text) <= limit:
return text
return text[:limit].rstrip() + "\n…(truncated)"
_COMMIT_INSTRUCTIONS = (
"You write git commit messages. Given a diff of staged changes, write ONE "
"concise Conventional Commits message describing what the change does and why.\n"
"Rules:\n"
"- Subject line: type(scope): summary — imperative mood, lower-case, no "
"trailing period, ≤ 72 characters. Types: feat, fix, refactor, perf, docs, "
"test, build, chore, style, ci.\n"
"- Omit the scope if it isn't obvious.\n"
"- Add a short body (wrapped at ~72 cols) ONLY when the change needs "
"explanation; skip it for small/obvious changes.\n"
"- Describe the actual change, never restate the diff line-by-line.\n"
"- Return ONLY the commit message text — no quotes, no markdown fences, no "
"preamble."
)
def _commit_message_template(variables: Dict[str, Any]) -> Tuple[str, str]:
diff = _truncate(str(variables.get("diff") or ""), 12000)
recent = _truncate(str(variables.get("recent_commits") or ""), 1500)
parts = []
if recent.strip():
parts.append(
"Recent commit subjects from this repo (match their style/conventions):\n"
f"{recent}"
)
parts.append("Diff to describe:\n" + (diff or "(no textual diff available)"))
# "Regenerate" must yield something new even on models that decode greedily
# / pin temperature server-side. A trailing nonce isn't enough, so we hand
# back the previous message and require a genuinely different one.
avoid = _truncate(str(variables.get("avoid") or "").strip(), 1000)
if avoid:
parts.append(
"You already proposed the message below and the user wants a "
"different one. Write a NEW message with different wording (and, if "
"reasonable, a different emphasis or scope framing) — do not repeat "
f"it:\n{avoid}"
)
return _COMMIT_INSTRUCTIONS, "\n\n".join(parts)
# Registry of named templates. Add an entry here to give a new surface a
# consistent, reusable prompt without teaching every caller the prompt text.
PROMPT_TEMPLATES: Dict[str, PromptTemplate] = {
"commit_message": _commit_message_template,
}
def render_template(name: str, variables: Optional[Dict[str, Any]] = None) -> Tuple[str, str]:
"""Resolve a registered template into (instructions, user_input).
Raises KeyError if the template name is unknown so callers fail loudly
instead of silently sending an empty prompt.
"""
template = PROMPT_TEMPLATES.get(name)
if template is None:
raise KeyError(f"unknown one-shot template: {name}")
return template(variables or {})
def run_oneshot(
*,
instructions: str = "",
user_input: str = "",
template: Optional[str] = None,
variables: Optional[Dict[str, Any]] = None,
task: str = "title_generation",
max_tokens: int = 1024,
temperature: Optional[float] = 0.3,
timeout: float = 60.0,
main_runtime: Optional[Dict[str, Any]] = None,
) -> str:
"""Run a single stateless LLM request and return its text.
Provide either a registered ``template`` (+ ``variables``) or an explicit
``instructions`` / ``user_input`` pair. Returns the model's text answer,
stripped of surrounding whitespace and any wrapping code fence.
Raises RuntimeError when no LLM provider is configured (surfaced from
:func:`call_llm`) and KeyError for an unknown template name.
"""
if template:
instructions, user_input = render_template(template, variables)
if not (instructions or "").strip() and not (user_input or "").strip():
raise ValueError("run_oneshot requires a template or instructions/user_input")
messages = []
if (instructions or "").strip():
messages.append({"role": "system", "content": instructions})
messages.append({"role": "user", "content": user_input or ""})
response = call_llm(
task=task,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
timeout=timeout,
main_runtime=main_runtime,
)
text = (extract_content_or_reasoning(response) or "").strip()
return _strip_code_fence(text)
def _strip_code_fence(text: str) -> str:
"""Drop a single wrapping ``` fence the model may have added."""
if not text.startswith("```"):
return text
lines = text.splitlines()
if len(lines) >= 2 and lines[0].startswith("```") and lines[-1].strip() == "```":
return "\n".join(lines[1:-1]).strip()
return text
-51
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"""Petdex pet engine — shared core for the CLI, TUI, and desktop surfaces.
Petdex (https://github.com/crafter-station/petdex) is a public gallery of
animated sprite "pets" for coding agents. Each pet is a ``pet.json`` plus a
``spritesheet.{webp,png}`` of 192×208 px cells. Current Codex/petdex sheets use
an 8-column × 9-row atlas; older Hermes/petdex sheets used an 8-row atlas.
Hermes infers the row taxonomy from the sheet and maps agent activity onto
idle/run/review/failed/wave/jump.
This package is the **single source of truth** for the feature so the base
CLI (Python) and TUI (Ink, via ``tui_gateway``) never duplicate the hard
parts:
- :mod:`agent.pet.constants` — frame geometry + the :class:`PetState` enum.
- :mod:`agent.pet.state` — map agent activity → a :class:`PetState`.
- :mod:`agent.pet.manifest` — fetch the public petdex manifest.
- :mod:`agent.pet.store` — install / list / resolve pets on disk
(profile-aware via ``get_hermes_home()``).
- :mod:`agent.pet.render` — decode a spritesheet and encode frames for a
terminal (kitty / iTerm2 / sixel graphics
protocols, with a Unicode half-block
fallback).
Rendering in the Electron desktop is necessarily TypeScript (canvas), but it
reuses the same on-disk store and the same state semantics.
The whole feature is a *display* concern: it adds no model tool, mutates no
system prompt or toolset, and therefore has zero effect on prompt caching.
"""
from agent.pet.constants import (
DEFAULT_SCALE,
FRAME_H,
FRAME_W,
FRAMES_PER_STATE,
LOOP_MS,
STATE_ROWS,
PetState,
)
from agent.pet.state import derive_pet_state
__all__ = [
"DEFAULT_SCALE",
"FRAME_H",
"FRAME_W",
"FRAMES_PER_STATE",
"LOOP_MS",
"STATE_ROWS",
"PetState",
"derive_pet_state",
]
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"""Pet sprite geometry + animation-state taxonomy.
These values are the common petdex/Codex pet geometry. The real ``pet.json``
usually only carries ``id``/``displayName``/``description``/``spritesheetPath``;
row taxonomy is inferred from the atlas shape so Hermes can render both legacy
8-row sheets and current 9-row Codex sheets.
"""
from __future__ import annotations
from enum import Enum
# Frame geometry (pixels). Current Codex/petdex spritesheets are 8 columns x 9
# rows (1536x1872), while older Hermes/petdex sheets used 9 columns x 8 rows
# (1728x1664). Renderers derive both row taxonomy and real column count from the
# concrete sheet, so either shape works.
FRAME_W = 192
FRAME_H = 208
# Frames consumed per animation state (the petdex web app uses CSS
# ``steps(6)``). A sheet may physically contain more columns; we only step
# through the first ``FRAMES_PER_STATE``.
FRAMES_PER_STATE = 6
# Full-loop duration for one state, milliseconds (petdex default).
LOOP_MS = 1100
# Default on-screen scale relative to native frame size. ``display.pet.scale``
# is the single master scalar: the desktop canvas multiplies its native pixels
# by it and every terminal surface derives its half-block/kitty column width
# from it (see :func:`cols_for_scale`), so one number shrinks all three
# interfaces together. (petdex's own clients render at 0.7; we default smaller
# so the kitty/GUI mascot stays a glanceable corner sprite. The half-block
# fallback can't shrink as far — see ``UNICODE_MIN_COLS`` — and clamps to its
# legibility floor instead.)
DEFAULT_SCALE = 0.33
# User-settable scale bounds (``/pet scale``, desktop slider). Floor keeps the
# pet clickable/visible; ceiling stops a fat-fingered value from filling the
# screen. The unicode fallback additionally clamps to ``UNICODE_MIN_COLS``.
MIN_SCALE = 0.1
MAX_SCALE = 3.0
def clamp_scale(scale: float) -> float:
"""Clamp *scale* to ``[MIN_SCALE, MAX_SCALE]`` (the single validation point)."""
return max(MIN_SCALE, min(MAX_SCALE, scale))
# Terminal cells one native frame spans at ``scale == 1.0``. A cell is ~8px
# wide, a frame is ``FRAME_W`` (192) px → 24 cells. This mirrors the kitty
# graphics placement (``scaled_px // 8``) so at full scale every renderer agrees.
BASE_UNICODE_COLS = FRAME_W // 8
# Legibility floor for the half-block fallback. A half-block cell samples the
# sprite at only 1 horizontal + 2 vertical taps, so below this width a 192×208
# pet collapses into an unreadable blob *regardless* of scale. kitty/GUI draw
# true pixels and have no such floor — that's why the same ``scale: 0.33`` is
# crisp there but mush in half-blocks. ``scale`` shrinks the unicode pet down
# TO this floor (and grows it above), instead of past it into noise.
UNICODE_MIN_COLS = 16
def cols_for_scale(scale: float) -> int:
"""Half-block width implied by *scale*, clamped to the legibility floor.
Above the floor it tracks the kitty cell box (``scaled_px // 8``) so the two
renderers converge at larger sizes; below it the floor keeps the sprite
readable rather than letting it devolve into a blob.
"""
return max(UNICODE_MIN_COLS, round(BASE_UNICODE_COLS * (scale or DEFAULT_SCALE)))
def resolve_cols(scale: float, unicode_cols: int = 0) -> int:
"""Resolve terminal width: explicit *unicode_cols* override, else from *scale*."""
return int(unicode_cols) if unicode_cols and int(unicode_cols) > 0 else cols_for_scale(scale)
class PetState(str, Enum):
"""Animation state a pet can be shown in.
These are Hermes' activity state names. They are not always identical to the
source atlas row names: Codex-format pets use rows like ``jumping`` /
``running`` while the UI keeps the shorter ``jump`` / ``run`` names.
"""
IDLE = "idle"
WAVE = "wave"
RUN = "run"
FAILED = "failed"
REVIEW = "review"
JUMP = "jump"
WAITING = "waiting"
# Legacy Hermes/petdex row order (top -> bottom) used by the older 8-row,
# 9-column atlas shape.
LEGACY_STATE_ROWS: list[str] = [
PetState.IDLE.value,
PetState.WAVE.value,
PetState.RUN.value,
PetState.FAILED.value,
PetState.REVIEW.value,
PetState.JUMP.value,
"extra1",
"extra2",
]
# Current Petdex row order (top -> bottom) used by 1536x1872 atlases:
# 8 columns x 9 rows of 192x208 cells.
CODEX_STATE_ROWS: list[str] = [
PetState.IDLE.value,
"running-right",
"running-left",
"waving",
"jumping",
PetState.FAILED.value,
PetState.WAITING.value,
"running",
PetState.REVIEW.value,
]
# Default/fallback for callers without a sheet. Prefer the current 9-row Codex
# format because generated pets and the public Codex pet contract use it.
STATE_ROWS: list[str] = CODEX_STATE_ROWS
# Canonical Hermes activity names -> accepted row-name aliases in descending
# preference. This keeps our internal state names stable (`wave`/`jump`/`run`)
# while matching Petdex's current `waving`/`jumping`/`running` taxonomy.
STATE_ALIASES: dict[str, tuple[str, ...]] = {
PetState.IDLE.value: (PetState.IDLE.value,),
PetState.WAVE.value: (PetState.WAVE.value, "waving"),
PetState.JUMP.value: (PetState.JUMP.value, "jumping"),
PetState.RUN.value: (PetState.RUN.value, "running"),
PetState.FAILED.value: (PetState.FAILED.value,),
PetState.REVIEW.value: (PetState.REVIEW.value,),
PetState.WAITING.value: (PetState.WAITING.value,),
}
def state_aliases_for(state: "PetState | str") -> tuple[str, ...]:
"""Return accepted row-name aliases for *state* (always non-empty)."""
value = state.value if isinstance(state, PetState) else str(state)
aliases = STATE_ALIASES.get(value)
return aliases if aliases else (value,)
def state_rows_for_grid(row_count: int | None) -> list[str]:
"""Return the row taxonomy for a spritesheet with *row_count* rows."""
try:
rows = int(row_count or 0)
except (TypeError, ValueError):
rows = 0
if rows >= len(CODEX_STATE_ROWS):
return CODEX_STATE_ROWS
return LEGACY_STATE_ROWS
def state_row_index(state: "PetState | str", row_count: int | None = None) -> int:
"""Return the spritesheet row index for *state* (clamped, never raises)."""
rows = state_rows_for_grid(row_count)
for name in state_aliases_for(state):
try:
return rows.index(name)
except ValueError:
continue
return 0 # fall back to the idle row
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"""Pet generation — base-draft → hatch pipeline.
Public surface used by the gateway RPCs, the CLI ``hermes pets generate``
command, and tests:
- :func:`generate_base_drafts` / :func:`hatch_pet` — the two-step flow.
- :class:`HatchResult`, :class:`GenerationError`.
- :mod:`atlas` — deterministic frame extraction + atlas composition/validation.
Image generation is delegated to the active reference-capable
:class:`~agent.image_gen_provider.ImageGenProvider` (OpenAI gpt-image-2 or Krea);
atlas assembly is fully deterministic so it's testable without any API calls.
"""
from __future__ import annotations
from agent.pet.generate.imagegen import GenerationError
from agent.pet.generate.orchestrate import (
HatchResult,
generate_base_drafts,
hatch_pet,
)
__all__ = [
"GenerationError",
"HatchResult",
"generate_base_drafts",
"hatch_pet",
]
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"""Thin image-generation layer for pet sprites.
Wraps the active :class:`~agent.image_gen_provider.ImageGenProvider` with the
two things sprite generation needs that the agent-facing ``image_generate`` tool
doesn't expose: **N variants** (loop) and **reference-image grounding** (so each
animation row stays the same character as the chosen base).
Reference grounding only works on providers that support it — currently OpenAI
``gpt-image-2`` (image edits) and Krea (style references). We resolve to one of
those and surface a clear, actionable error otherwise rather than silently
producing an ungrounded, drifting pet.
"""
from __future__ import annotations
import logging
import os
from dataclasses import dataclass
from pathlib import Path
logger = logging.getLogger(__name__)
# Providers that can ground generation on a reference image, in preference order
# (Nous Portal → OpenAI → OpenRouter → …). OpenRouter/Nous run a quality-first
# model chain and may fall back depending on account access and endpoint behavior,
# so fidelity can vary by configured backend + model availability.
_REF_CAPABLE = ("nous", "openai", "openai-codex", "openrouter", "krea")
# Friendly display label per reference-capable provider, surfaced in the desktop
# pet-gen picker.
_PROVIDER_LABELS: dict[str, str] = {
"nous": "Nous Portal",
"openrouter": "OpenRouter",
"openai": "OpenAI",
"openai-codex": "OpenAI (Codex)",
"krea": "Krea",
}
def _forced_provider_from_env() -> str | None:
"""Optional QA override to force a pet-gen backend.
`HERMES_PET_IMAGE_PROVIDER=<name>` (e.g. `openrouter`) bypasses the normal
active/default provider resolution for pet generation only. Unknown values are
ignored so existing users are unaffected.
"""
forced = os.environ.get("HERMES_PET_IMAGE_PROVIDER", "").strip().lower()
return forced if forced in _REF_CAPABLE else None
class GenerationError(RuntimeError):
"""Raised on any image-generation failure (no provider, API error, IO)."""
@dataclass(frozen=True)
class SpriteProvider:
"""Resolved provider plus whether it can take reference images."""
name: str
provider: object
supports_references: bool
def _discover() -> None:
try:
from hermes_cli.plugins import _ensure_plugins_discovered
_ensure_plugins_discovered()
except Exception as exc: # noqa: BLE001 - discovery is best-effort
logger.debug("image-gen plugin discovery failed: %s", exc)
def resolve_provider(*, require_references: bool = True, prefer: str | None = None) -> SpriteProvider:
"""Pick the image provider to use for sprite work.
Preference: an explicit *prefer* choice (the desktop pet-gen picker) when it's
reference-capable and configured, then the configured/active provider when
it's reference-capable, else the first available reference-capable provider.
With *require_references* off we fall back to any available provider (used for
prompt-only base drafts).
"""
_discover()
from agent.image_gen_registry import get_active_provider, get_provider
# QA override: force one provider for pet-gen iteration regardless of the
# globally active image_gen backend.
forced = _forced_provider_from_env()
if forced:
chosen = get_provider(forced)
if chosen is not None and chosen.is_available():
return SpriteProvider(name=forced, provider=chosen, supports_references=True)
# An explicit user pick wins when it's reference-capable and has credentials;
# otherwise we ignore it and fall through to the normal resolution.
if prefer:
chosen = get_provider(prefer)
if prefer in _REF_CAPABLE and chosen is not None and chosen.is_available():
return SpriteProvider(name=prefer, provider=chosen, supports_references=True)
# Configured / active provider first.
active = None
try:
active = get_active_provider()
except Exception: # noqa: BLE001
active = None
if active is not None:
name = getattr(active, "name", "")
if name in _REF_CAPABLE and active.is_available():
return SpriteProvider(name=name, provider=active, supports_references=True)
# Any available reference-capable provider.
for name in _REF_CAPABLE:
provider = get_provider(name)
if provider is not None and provider.is_available():
return SpriteProvider(name=name, provider=provider, supports_references=True)
if not require_references and active is not None and active.is_available():
return SpriteProvider(
name=getattr(active, "name", "unknown"), provider=active, supports_references=False
)
raise GenerationError(
"Pet generation needs an image backend that supports reference images. "
"Open `hermes tools` → Image Generation and configure Nous Portal, "
"OpenRouter, or OpenAI (gpt-image-2) with an API key."
)
def list_sprite_providers() -> list[dict]:
"""The reference-capable providers available to pick for pet generation.
Returns ``[{name, label, default}]`` for every ref-capable provider the user
actually has credentials for, in preference order, marking the one
:func:`resolve_provider` would choose with no explicit preference. Empty when
none is configured (the picker hides itself). Best-effort: discovery hiccups
yield an empty list.
"""
_discover()
from agent.image_gen_registry import get_provider
try:
default_name = resolve_provider(require_references=True).name
except GenerationError:
default_name = ""
out: list[dict] = []
for name in _REF_CAPABLE:
provider = get_provider(name)
if provider is None or not provider.is_available():
continue
out.append(
{
"name": name,
"label": _PROVIDER_LABELS.get(name, name),
"default": name == default_name,
}
)
return out
def _save_local(image_ref: str, *, prefix: str) -> Path:
"""Return a local path for *image_ref*, downloading it if it's a URL."""
if image_ref.startswith(("http://", "https://")):
from agent.image_gen_provider import save_url_image
return Path(save_url_image(image_ref, prefix=prefix))
return Path(image_ref)
def _rejected_background(error: str) -> bool:
"""True when a provider error is specifically about the ``background`` param.
Transparent backgrounds are a per-model capability (e.g. some gpt-image tiers
reject ``background=transparent`` outright). We detect that one rejection so
we can retry without the flag rather than failing the whole pet — our chroma
key pass makes the result transparent regardless.
"""
lowered = (error or "").lower()
return "background" in lowered and ("not supported" in lowered or "transparent" in lowered)
def generate(
prompt: str,
*,
n: int = 1,
reference_images: list[Path] | None = None,
provider: SpriteProvider | None = None,
prefix: str = "pet_gen",
aspect_ratio: str = "square",
) -> list[Path]:
"""Generate *n* sprite images and return their local paths.
*reference_images* grounds the output on a base image (required for rows).
*aspect_ratio* picks the canvas: ``"square"`` for single-character base
drafts, ``"landscape"`` for multi-frame row strips (the wider 1536px canvas
gives every frame real horizontal room so winged poses don't have to be
shrunk to avoid touching their neighbors).
We *ask* for a transparent background, but fall back to an opaque generation
(cleaned up downstream by the chroma-key pass) on models that reject the
flag. Raises :class:`GenerationError` if nothing usable comes back.
"""
sprite = provider or resolve_provider(require_references=bool(reference_images))
if reference_images and not sprite.supports_references:
raise GenerationError(
f"image backend '{sprite.name}' cannot use reference images; "
"configure OpenAI gpt-image-2 or Krea for pet generation"
)
refs = [str(p) for p in (reference_images or [])]
def _run(extra: dict) -> tuple[Path | None, str]:
kwargs: dict = {"aspect_ratio": aspect_ratio, **extra}
if refs:
# Providers disagree on the ref kwarg name: our OpenRouter/Nous
# backends read ``reference_images``, OpenAI's gpt-image-2 reads
# ``reference_image_urls``. Send both; each ignores the other.
kwargs["reference_images"] = refs
kwargs["reference_image_urls"] = refs
try:
result = sprite.provider.generate(prompt, **kwargs)
except Exception as exc: # noqa: BLE001 - normalize provider crashes
logger.debug("provider.generate crashed: %s", exc)
return None, str(exc)
if not isinstance(result, dict) or not result.get("success"):
return None, (result or {}).get("error", "unknown error") if isinstance(result, dict) else "no result"
image_ref = result.get("image")
if not image_ref:
return None, "provider returned no image"
try:
return _save_local(str(image_ref), prefix=prefix), ""
except Exception as exc: # noqa: BLE001
return None, f"could not save generated image: {exc}"
out: list[Path] = []
last_error = ""
allow_transparent = True
for _ in range(max(1, n)):
path, err = _run({"background": "transparent"} if allow_transparent else {})
# Model doesn't support the transparent flag → drop it for this and every
# remaining variant (no point re-probing a capability we just disproved).
if path is None and allow_transparent and _rejected_background(err):
allow_transparent = False
path, err = _run({})
if path is not None:
out.append(path)
else:
last_error = err
if not out:
raise GenerationError(last_error or "image generation produced no output")
return out
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"""Pet generation orchestration — the base-draft → hatch flow.
Two steps, mirroring the UX across every surface:
1. :func:`generate_base_drafts` — a handful of prompt-only "what should this pet
look like" variants. Cheap; the user picks one (or retries for a fresh set).
2. :func:`hatch_pet` — takes the chosen base and generates one grounded row
strip per Hermes state, slices each into frames, composes the atlas, validates
it, and writes the pet into the store.
Splitting it this way bounds cost (4 cheap base calls per round; the ~6 row
calls happen once, on the pet you actually keep) and gives each UI a natural
preview/loading point.
"""
from __future__ import annotations
import logging
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
from pathlib import Path
from typing import Callable
from agent.pet.generate import atlas, imagegen, prompts
from agent.pet.generate.imagegen import GenerationError, SpriteProvider
logger = logging.getLogger(__name__)
# (event, detail) — e.g. ("row", "idle"), ("compose", ""), ("save", "<slug>").
ProgressFn = Callable[[str, str], None]
# Image generations are independent network calls, so we fan them out instead of
# blocking on each in turn — a hatch is ~8 row calls that would otherwise run
# back-to-back and routinely blow past the client's RPC timeout. Capped so we
# don't hammer the provider's rate limit (one cold call can still be slow).
_MAX_PARALLEL_GENERATIONS = 4
# How many times to (re)generate a single row before accepting a best-effort
# slice. Early attempts demand clean per-pose gutters; the last is lenient so a
# stubborn row still yields frames instead of dropping out entirely.
_ROW_GEN_ATTEMPTS = 3
_MIN_FILLED_STATES = 6
_REQUIRED_STATES = frozenset({"idle", "running-right", "waving"})
@dataclass(frozen=True)
class HatchResult:
"""Outcome of a successful :func:`hatch_pet`."""
slug: str
display_name: str
spritesheet: Path
states: list[str]
validation: dict
def _harden_transparency(path: Path) -> Path:
"""Key out any solid backdrop the provider painted; save as an RGBA PNG.
``background=transparent`` is requested on every call, but image models honor
it inconsistently — some still paint a flat (often near-white) backdrop. We
run the same chroma-key pass the row extractor uses so every base draft the
user picks between (and the reference the rows are grounded on) is a clean
cutout. Best-effort: a decode failure leaves the original untouched.
"""
from PIL import Image
try:
with Image.open(path) as opened:
keyed = atlas.remove_background(opened.convert("RGBA"))
# Zero the RGB of any leftover semi-transparent edge pixels so a keyed
# draft has no colored halo when composited on the dark UI.
keyed = atlas._clear_transparent_rgb(keyed)
out = path.with_suffix(".png")
keyed.save(out, format="PNG")
return out
except Exception as exc: # noqa: BLE001 - cosmetic; fall back to the raw image
logger.debug("base draft transparency hardening failed for %s: %s", path, exc)
return path
def generate_base_drafts(
concept: str,
*,
n: int = 4,
style: str = "auto",
reference_images: list[Path] | None = None,
provider: SpriteProvider | None = None,
on_draft: Callable[[int, Path], None] | None = None,
is_cancelled: Callable[[], bool] | None = None,
) -> list[Path]:
"""Generate *n* candidate base looks for *concept*; returns image paths.
Each draft is hardened to a transparent cutout (see :func:`_harden_transparency`).
Drafts are generated concurrently and *on_draft(index, path)* fires as each
one finishes (not at the end) so callers can stream previews to the UI
instead of leaving it blank until the whole batch is done.
*is_cancelled*, when supplied, is polled cooperatively: a draft that hasn't
started yet is skipped, and once it trips we stop staging/streaming further
drafts and cancel any queued work (already-in-flight provider calls can't be
hard-killed, but their results are dropped).
"""
# A user reference image (e.g. their own pet) grounds every draft, so it
# needs a reference-capable provider — same requirement as the row passes.
refs = reference_images or None
sprite = provider or imagegen.resolve_provider(require_references=bool(refs))
cancelled = is_cancelled or (lambda: False)
# Each draft is its own one-shot generation, run concurrently so the user
# waits for one image, not N. A single draft failing must not sink the set.
# Each gets a distinct variation nudge so the options aren't near-duplicates.
logger.info("pet generate: drafting %d base looks for %r (style=%s)", n, concept, style)
def _one(index: int) -> tuple[int, Path | None, str | None]:
if cancelled():
return index, None, None
t0 = time.monotonic()
variation = prompts.BASE_VARIATIONS[index % len(prompts.BASE_VARIATIONS)]
prompt = prompts.build_base_prompt(concept, style=style, variation=variation)
try:
out = imagegen.generate(prompt, n=1, reference_images=refs, provider=sprite, prefix="pet_base")
except Exception as exc: # noqa: BLE001 - tolerate a single failed draft
logger.warning("pet generate: draft %d failed after %.1fs: %s", index, time.monotonic() - t0, exc)
return index, None, str(exc)
if not out:
logger.warning("pet generate: draft %d produced no image", index)
return index, None, "the image provider returned no image"
logger.info("pet generate: draft %d ready in %.1fs", index, time.monotonic() - t0)
return index, _harden_transparency(out[0]), None
workers = max(1, min(n, _MAX_PARALLEL_GENERATIONS))
results: dict[int, Path] = {}
errors: list[str] = []
with ThreadPoolExecutor(max_workers=workers) as pool:
futures = [pool.submit(_one, i) for i in range(n)]
# as_completed runs in *this* (the caller's) thread, so on_draft — and any
# gateway event it emits — inherits the request's bound transport, unlike
# the worker threads above.
for fut in as_completed(futures):
if cancelled():
logger.info("pet generate: cancelled — dropping remaining drafts")
for pending in futures:
pending.cancel()
break
index, path, err = fut.result()
if path is None:
if err:
errors.append(err)
continue
results[index] = path
if on_draft is not None:
try:
on_draft(index, path)
except Exception as exc: # noqa: BLE001 - progress is best-effort
logger.debug("on_draft callback failed: %s", exc)
drafts = [results[i] for i in sorted(results)]
if not drafts and not cancelled():
# Surface *why* — every draft failed for a reason (a content-policy refusal
# on a name like "minion", a provider/auth error, …); the most common one
# is the representative cause. Far more useful than "no usable drafts".
raise GenerationError(_drafts_failed_reason(errors))
return drafts
def _drafts_failed_reason(errors: list[str]) -> str:
"""The representative reason a draft round produced nothing, humanized."""
if not errors:
return "image generation produced no usable drafts"
from collections import Counter
return _humanize_image_error(Counter(errors).most_common(1)[0][0])
def _humanize_image_error(error: str) -> str:
"""Turn a raw provider error into a friendly, actionable sentence.
The big one is moderation: image models refuse trademarked characters and
real people (e.g. "minion"), which reads as an opaque 400 otherwise.
"""
low = error.lower()
if any(s in low for s in ("moderation_blocked", "safety system", "content policy", "content_policy")):
return (
"The image provider blocked this prompt — its safety filter rejects "
"trademarked characters and real people. Try an original description."
)
if any(s in low for s in ("api key", "unauthorized", "401", "auth")):
return "The image provider rejected the request — check your API key in Settings → Providers."
if "rate limit" in low or "429" in low:
return "The image provider is rate-limiting — wait a moment and try again."
# Otherwise the first line, trimmed of the noisy provider envelope.
return error.splitlines()[0].strip()[:200]
def hatch_pet(
*,
base_image: str | Path,
slug: str,
display_name: str = "",
description: str = "",
concept: str = "",
style: str = "auto",
on_progress: ProgressFn | None = None,
provider: SpriteProvider | None = None,
is_cancelled: Callable[[], bool] | None = None,
) -> HatchResult:
"""Turn an approved base image into a full, installed Hermes pet.
Generates a grounded row strip per state, extracts frames, composes +
validates the atlas, and registers it. The idle row falls back to the base
look so the pet always renders. Raises :class:`GenerationError` on failure.
*is_cancelled*, when supplied, is polled cooperatively: rows that haven't
started are skipped, queued rows are cancelled, and once every row is done we
abort (raising :class:`GenerationError`) before composing/saving so a stopped
hatch never writes a half-built pet.
"""
base = Path(base_image)
if not base.is_file():
raise GenerationError(f"base image not found: {base}")
sprite = provider or imagegen.resolve_provider(require_references=True)
progress = on_progress or (lambda *_: None)
cancelled = is_cancelled or (lambda: False)
label = concept or display_name or slug
frames_by_state: dict[str, list] = {}
total_rows = len(atlas.ROW_SPECS)
logger.info("pet hatch %r: generating %d animation rows", slug, total_rows)
# Generate every state's row strip concurrently — they're independent
# grounded calls, so the hatch waits for the slowest row, not their sum. A
# single row failing is tolerated (idle is guaranteed below).
def _gen_row(spec: tuple[str, int, int]) -> tuple[str, list | None]:
state, _row, count = spec
if cancelled():
return state, None
t0 = time.monotonic()
last_exc: Exception | None = None
# Self-healing: a model occasionally returns a row whose poses are touching
# (no clean gutters), which slices badly. We retry such rolls; only the
# final attempt falls back to lenient ``auto`` slicing so a stubborn row
# still yields *something* rather than dropping the whole row.
for attempt in range(_ROW_GEN_ATTEMPTS):
if cancelled():
return state, None
strict = attempt < _ROW_GEN_ATTEMPTS - 1
try:
strips = imagegen.generate(
prompts.build_row_prompt(state, count, label, style=style),
n=1,
reference_images=[base],
provider=sprite,
prefix=f"pet_row_{state}",
# Wider canvas → each frame gets real horizontal room, so winged
# poses keep a full, healthy size and still leave clean gutters.
aspect_ratio="landscape",
)
# ``components`` requires clean per-pose gutters (raises otherwise),
# so a touching roll is rejected and regenerated; the last attempt
# uses ``auto`` (equal-slot fallback, never raises). Raw (fit=False)
# so normalize_cells registers the whole pet at once.
method = "components" if strict else "auto"
frames = atlas.extract_strip_frames(strips[0], count, method=method, fit=False)
logger.info(
"pet hatch %r: row %r ready in %.1fs (attempt %d)",
slug, state, time.monotonic() - t0, attempt + 1,
)
return state, frames
except Exception as exc: # noqa: BLE001 - retried; one bad row is tolerated
last_exc = exc
logger.warning(
"pet hatch %r: row %r attempt %d/%d failed: %s",
slug, state, attempt + 1, _ROW_GEN_ATTEMPTS, exc,
)
logger.warning(
"pet hatch %r: row %r gave up after %.1fs: %s",
slug, state, time.monotonic() - t0, last_exc,
)
return state, None
# running-left is derived by mirroring running-right (guaranteed-consistent
# and one fewer generation), so we don't generate it directly.
generated_specs = [spec for spec in atlas.ROW_SPECS if spec[0] != "running-left"]
workers = max(1, min(len(generated_specs), _MAX_PARALLEL_GENERATIONS))
done = 0
with ThreadPoolExecutor(max_workers=workers) as pool:
futures = [pool.submit(_gen_row, spec) for spec in generated_specs]
# as_completed runs on the caller (request) thread, so progress events
# emitted here inherit the request transport — unlike the worker threads.
for fut in as_completed(futures):
if cancelled():
logger.info("pet hatch %r: cancelled — dropping remaining rows", slug)
for pending in futures:
pending.cancel()
break
state, frames = fut.result()
done += 1
progress("row", f"{state}:{done}:{total_rows}")
if frames:
frames_by_state[state] = frames
if cancelled():
raise GenerationError("hatch cancelled")
# Derive running-left from the approved running-right row (per-frame mirror,
# preserving order/timing). Missing running-right is rejected below; a pet
# without its canonical walk cycle is a failed hatch, not a shippable mascot.
right = frames_by_state.get("running-right")
if right:
done += 1
progress("row", f"running-left:{done}:{total_rows}")
frames_by_state["running-left"] = atlas.mirror_frames(right)
logger.info("pet hatch %r: row 'running-left' mirrored from running-right", slug)
else:
logger.warning("pet hatch %r: no running-right to mirror; left walk left empty", slug)
# Idle is the resting state the renderer falls back to — guarantee it.
if not frames_by_state.get("idle"):
progress("row", "idle-fallback")
frames_by_state["idle"] = [atlas.single_frame(base, fit=False)]
progress("compose", "")
logger.info("pet hatch %r: composing atlas from %d states", slug, len(frames_by_state))
# One shared scale + baseline across every state so the pet never slides or
# pulses size between frames; compose just packs the normalized cells.
sheet = atlas.compose_atlas(atlas.normalize_cells(frames_by_state))
validation = atlas.validate_atlas(sheet)
if not validation["ok"]:
raise GenerationError("; ".join(validation["errors"]) or "atlas validation failed")
filled_states = set(validation["filled_states"])
missing_required = sorted(_REQUIRED_STATES - filled_states)
if missing_required:
raise GenerationError(f"missing required animation row(s): {', '.join(missing_required)}")
if len(filled_states) < _MIN_FILLED_STATES:
raise GenerationError(
f"only {len(filled_states)}/{len(atlas.ROW_SPECS)} animation rows were usable; regenerate"
)
from agent.pet import store
progress("save", slug)
logger.info("pet hatch %r: saving pet", slug)
pet = store.register_local_pet(
sheet,
slug=slug,
display_name=display_name or slug,
description=description,
)
return HatchResult(
slug=pet.slug,
display_name=pet.display_name,
spritesheet=pet.spritesheet,
states=validation["filled_states"],
validation=validation,
)
-183
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"""Prompt builders for pet generation.
Two prompt shapes: a *base* prompt (prompt-only, produces the canonical look the
user picks between) and per-*state* *row* prompts (grounded on the chosen base,
produce one horizontal strip of N poses). Prompts stay concise and
sprite-production oriented; the identity lock and "one transparent row" framing
matter more than flowery description.
We generate the full petdex/Codex nine-state set (see
:data:`agent.pet.generate.atlas.ROW_SPECS`) so a hatched pet is a valid
``petdex submit`` spritesheet.
"""
from __future__ import annotations
# What each petdex/Codex state should depict (kept short — these go straight into
# the row prompt). Phrased to avoid the common sprite-gen failure modes (detached
# effects, motion lines, shadows). Critical distinction: ``running`` is the
# *working* state (in place), while ``running-right`` / ``running-left`` are the
# actual directional walk/run cycles.
STATE_ACTIONS: dict[str, str] = {
"idle": "a calm idle loop: subtle breathing, a tiny blink or gentle bob, no big gestures",
"running-right": (
"a sideways walk/run locomotion cycle moving to the RIGHT: the character "
"faces and travels right with clear directional steps, a smooth gait loop"
),
"running-left": (
"a sideways walk/run locomotion cycle moving to the LEFT: the character "
"faces and travels left with clear directional steps (the mirror of the "
"right-facing run)"
),
"waving": "a friendly greeting: raising a paw/hand/limb to wave, clear up-and-down gesture",
"jumping": "a happy celebration jump: anticipation, lift off the ground, peak, and land",
"failed": "a sad or deflated reaction: slumped, dejected, small frown — readable but not noisy",
"waiting": (
"an expectant 'waiting on you' pose: looking up/out as if asking for input "
"or approval — distinct from idle and review"
),
"running": (
"focused active work, staying IN PLACE (NOT walking or foot-running): "
"leaning in, concentrating, busy 'thinking / processing / typing' energy"
),
"review": "careful inspection: a focused lean, head tilt, studying something intently",
}
_STYLE_HINTS: dict[str, str] = {
# Default to the popular petdex look: crisp 16-bit PIXEL ART, not the smooth
# 2D illustration (let alone 3D render) gpt-image reaches for by default.
"auto": (
" Style: crisp 16-bit PIXEL-ART game sprite — visible square pixels, a small "
"limited palette, clean dark outline, flat cel shading, chunky chibi "
"proportions, like a classic SNES/JRPG party member or a petdex.dev mascot. "
"Absolutely NOT 3D-rendered, NOT a smooth painted or vector illustration, "
"NOT photorealistic — no soft gradients, no realistic lighting, no figurine look."
),
"pixel": " Render in clean 16-bit pixel-art style with visible square pixels and a limited palette.",
"plush": " Render as a soft plush toy.",
"clay": " Render as a claymation / soft 3D clay figure.",
"sticker": " Render as a glossy die-cut sticker.",
"flat-vector": " Render in flat vector mascot style.",
"3d-toy": " Render as a glossy 3D toy.",
"painterly": " Render in a soft painterly style.",
}
_BACKGROUND = (
"Center the character on a SINGLE flat, uniform, high-contrast chroma-key "
"background — pure hot magenta #FF00FF (only if magenta appears on the "
"character, use pure green #00FF00 instead). The background is ONE continuous "
"even color that completely surrounds the character with NO gradient, "
"vignette, texture, pattern, scenery, shadow, ground line, frame, border, "
"panel, comic cell, gutter line, grid, or divider of any kind, so it keys out "
"cleanly. The background color must not appear anywhere on the character. "
"No text, no labels, no speech bubbles, no UI."
)
def style_hint(style: str | None) -> str:
return _STYLE_HINTS.get((style or "auto").strip().lower(), "")
# Row strips are generated on the wider landscape canvas (see imagegen.generate /
# orchestrate). The extra width is what lets each pose stay a healthy size AND
# leave a real gutter — used here only to cite concrete pixel numbers.
_ASSUMED_STRIP_WIDTH = 1536
def _spacing_spec(frame_count: int) -> tuple[int, int]:
"""(per-pose width px, gap px) for a row of *frame_count* poses.
Pixel counts alone don't hold — the model fills each slot edge-to-edge with
the full wingspan, so neighbors touch even when bodies are spaced. The lever
that works is proportional containment on a wide canvas: give each pose its
own equal cell and keep the ENTIRE silhouette (wings/tail/halo included)
inside it. On the 1536px landscape strip ~70% occupancy still leaves a
generous gutter, so the pet stays a normal, good-looking size — no shrinking.
"""
slots = max(1, frame_count)
slot_w = _ASSUMED_STRIP_WIDTH / slots
pose_px = round(slot_w * 0.7)
gap_px = max(48, round(slot_w * 0.3))
return pose_px, gap_px
# Per-draft nudges so the 4 base options are actually distinct — gpt-image returns
# near-duplicates for a single prompt. We vary the *look* (palette, build,
# expression, accents), NOT the pose, so the chosen base still grounds clean,
# consistent animation rows.
BASE_VARIATIONS: tuple[str, ...] = (
"",
"a distinctly different colour palette and markings",
"a heavier, broader silhouette with sturdier proportions",
"a different facial structure and expression matching the concept tone, with unique accent/accessory details",
"a leaner, taller build and an alternate colour scheme",
"bolder, more saturated colours and a stronger expression matching the concept tone",
)
def build_base_prompt(concept: str, *, style: str | None = "auto", variation: str = "") -> str:
"""The base look: a single, clean, centered full-body mascot.
*variation* differentiates one draft from the next (see :data:`BASE_VARIATIONS`).
"""
concept = (concept or "a distinctive mascot creature").strip()
nudge = f" Make this design distinct: {variation}." if variation else ""
return (
f"A stylized mascot pet character: {concept}. "
"Honor the requested tone and mood exactly (cute, eerie, scary, menacing, whimsical, etc.) "
"while staying non-graphic. "
"Compact, whole-body silhouette that reads clearly at small size, "
"clear readable facial features, simple consistent palette. "
# A neutral, symmetric, at-rest stance makes the cleanest identity anchor
"Neutral front-facing standing pose, upright and symmetric, arms/limbs "
"relaxed at the sides, feet together on the ground, any cape/accessories "
"hanging straight and still."
f"{nudge} "
f"{_BACKGROUND}{style_hint(style)}"
)
def build_row_prompt(state: str, frame_count: int, concept: str, *, style: str | None = "auto") -> str:
"""A row strip: *frame_count* poses of the SAME character, left→right.
The attached base image is the identity source of truth; the prompt locks
species, palette, face, and props to it.
"""
action = STATE_ACTIONS.get(state, "a simple idle pose")
concept = (concept or "the mascot").strip()
pose_px, gap_px = _spacing_spec(frame_count)
return (
f"Using the attached reference image as the exact same character "
f"(same species, face, colors, markings, proportions, and props), "
"preserving the same emotional tone/mood (e.g., scary stays scary, cute stays cute), "
f"draw a single WIDE horizontal strip of {frame_count} animation frames showing {action}. "
f"LAYOUT: arrange {frame_count} poses in ONE horizontal row at equal spacing, "
"each pose centered in its own imaginary equal region. Draw NO panel borders, "
"NO comic cells, NO boxes, NO vertical divider/gutter lines, NO grid, NO frame "
"outlines between poses — the backdrop is one unbroken flat field behind all of them. "
"Fill the WHOLE strip with the SAME single flat chroma-key color as the attached "
"reference image's background (identical hue in every frame, no per-pose color shifts). "
f"SPACING (critical): draw each pose at a consistent, healthy, clearly "
f"visible size (roughly {pose_px}px wide on a {_ASSUMED_STRIP_WIDTH}px "
f"strip) — do NOT shrink it tiny — but keep its ENTIRE silhouette "
f"(wings, tail, halo, horns, cape, every appendage) fully INSIDE its own "
f"cell. Leave at least {gap_px}px of empty chroma-key background between "
f"neighboring silhouettes at their closest point (wingtip to wingtip), and "
f"the same empty margin before the first pose and after the last. If a wing, "
f"cape, or tail would reach into a neighbor, FOLD or angle it inward rather "
f"than letting it cross the gap. Silhouettes must NEVER touch, overlap, "
f"share a shadow, share a ground line, share motion trails, or merge into "
f"one connected shape. "
# Registration: a clean sprite sheet keeps the character locked in place
# so only the action moves — this is what stops the loop sliding/pulsing.
"REGISTRATION (critical): the character is the SAME height and SAME width "
"in every frame, drawn at the SAME scale, centered over the SAME point, "
"with all feet aligned to the SAME invisible horizontal baseline across the "
"whole strip — this baseline is conceptual ONLY: draw NO ground line, floor, "
"platform, horizon, or contact shadow beneath the feet. Keep the body's center, size, and stance fixed frame to "
"frame — ONLY the limbs/features the action needs may move. Capes, cloaks, "
"bags, and scarves stay in the SAME place and shape every frame (no "
"swinging, flowing, or drifting) unless the action itself requires it. No "
"pose is cropped at the strip edges. "
f"{_BACKGROUND}{style_hint(style)}"
)
-165
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@@ -1,165 +0,0 @@
"""Fetch the public petdex manifest.
``https://petdex.dev/api/manifest`` 307-redirects to a JSON document on R2:
{
"generatedAt": "...",
"total": 2926,
"pets": [
{"slug": "boba", "displayName": "Boba", "kind": "creature",
"submittedBy": "railly",
"spritesheetUrl": "https://assets.petdex.dev/.../spritesheet.webp",
"petJsonUrl": "https://assets.petdex.dev/.../pet.json",
"zipUrl": "https://assets.petdex.dev/.../boba.zip"},
...
]
}
Read-only and unauthenticated; no credentials involved.
"""
from __future__ import annotations
import logging
import threading
import time
from dataclasses import dataclass
logger = logging.getLogger(__name__)
MANIFEST_URL = "https://petdex.dev/api/manifest"
_DEFAULT_TIMEOUT = 10.0
# In-process cache for the (large, slow, identical-per-call) manifest. The list
# is a static CDN object that barely changes, yet a single session can ask for
# it many times — every gallery open, plus a full re-fetch per install/select
# (``find_entry``). A short TTL collapses those into one network hit without
# going stale for long. Cleared by :func:`clear_cache` (tests).
_MANIFEST_TTL = 300.0
_cache: tuple[float, list[ManifestEntry]] | None = None
_prefetch_lock = threading.Lock()
_prefetching = False
def clear_cache() -> None:
"""Drop the cached manifest (forces the next fetch to hit the network)."""
global _cache
_cache = None
def _cache_is_warm() -> bool:
return _cache is not None and time.monotonic() - _cache[0] < _MANIFEST_TTL
def prefetch(*, timeout: float = _DEFAULT_TIMEOUT) -> None:
"""Warm the manifest cache in a daemon thread — idempotent, never blocks.
The desktop picker calls this when it loads the (instant) local-only gallery
so the full petdex catalog is usually cached by the time it's requested,
without ever holding up the user's own pets on a network round-trip.
"""
global _prefetching
if _cache_is_warm():
return
with _prefetch_lock:
if _prefetching:
return
_prefetching = True
def _run() -> None:
global _prefetching
try:
fetch_manifest(timeout=timeout)
except Exception as exc: # noqa: BLE001 - best-effort warm
logger.debug("petdex manifest prefetch failed: %s", exc)
finally:
_prefetching = False
threading.Thread(target=_run, name="petdex-prefetch", daemon=True).start()
@dataclass(frozen=True)
class ManifestEntry:
"""A single pet's row in the manifest."""
slug: str
display_name: str
kind: str
submitted_by: str
spritesheet_url: str
pet_json_url: str
zip_url: str
@classmethod
def from_dict(cls, data: dict) -> "ManifestEntry":
return cls(
slug=str(data.get("slug", "")).strip(),
display_name=str(data.get("displayName", "") or data.get("slug", "")),
kind=str(data.get("kind", "") or "pet"),
submitted_by=str(data.get("submittedBy", "") or ""),
spritesheet_url=str(data.get("spritesheetUrl", "") or ""),
pet_json_url=str(data.get("petJsonUrl", "") or ""),
zip_url=str(data.get("zipUrl", "") or ""),
)
class ManifestError(RuntimeError):
"""Raised when the manifest can't be fetched or parsed."""
def fetch_manifest(*, timeout: float = _DEFAULT_TIMEOUT, force: bool = False) -> list[ManifestEntry]:
"""Return every approved pet from the public manifest.
Cached in-process for ``_MANIFEST_TTL`` seconds (pass ``force=True`` to
bypass). Follows the 307 redirect to R2. Raises :class:`ManifestError` on
any network/parse failure so callers can surface a clean message.
"""
global _cache
if not force and _cache is not None and time.monotonic() - _cache[0] < _MANIFEST_TTL:
return _cache[1]
try:
import httpx
except ImportError as exc: # pragma: no cover - httpx is a core dep
raise ManifestError("httpx is required to fetch the petdex manifest") from exc
try:
resp = httpx.get(
MANIFEST_URL,
timeout=timeout,
follow_redirects=True,
headers={"User-Agent": "hermes-agent-petdex"},
)
resp.raise_for_status()
payload = resp.json()
except Exception as exc: # noqa: BLE001 - normalize to one error type
raise ManifestError(f"could not fetch petdex manifest: {exc}") from exc
pets = payload.get("pets") if isinstance(payload, dict) else None
if not isinstance(pets, list):
raise ManifestError("petdex manifest had no 'pets' array")
entries: list[ManifestEntry] = []
for raw in pets:
if not isinstance(raw, dict):
continue
entry = ManifestEntry.from_dict(raw)
if entry.slug and entry.spritesheet_url:
entries.append(entry)
_cache = (time.monotonic(), entries)
return entries
def find_entry(slug: str, *, timeout: float = _DEFAULT_TIMEOUT) -> ManifestEntry | None:
"""Return the manifest entry for *slug*, or ``None`` if not listed."""
slug = slug.strip().lower()
for entry in fetch_manifest(timeout=timeout):
if entry.slug.lower() == slug:
return entry
return None
-618
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@@ -1,618 +0,0 @@
"""Decode a pet spritesheet and encode frames for a terminal.
Shared by the base CLI (writes the escape bytes to its own stdout) and the
TUI (``tui_gateway`` ships the encoded bytes to Ink, which writes them) so the
decode + capability-detection + protocol-encoding logic exists exactly once.
Supported output modes, in fidelity order:
- ``kitty`` — the kitty graphics protocol (kitty, Ghostty, WezTerm).
- ``iterm`` — iTerm2 inline images (iTerm2, WezTerm).
- ``sixel`` — DEC sixel (xterm -ti vt340, foot, mlterm, WezTerm, …).
- ``unicode`` — 24-bit half-block downscale; works in any truecolor terminal.
Frame decoding requires Pillow (a core Hermes dependency). If Pillow or the
spritesheet is unavailable the renderer degrades to ``unicode`` text or an
empty string rather than raising.
"""
from __future__ import annotations
import base64
import io
import logging
import os
import sys
from functools import lru_cache
from pathlib import Path
from agent.pet.constants import (
DEFAULT_SCALE,
FRAME_H,
FRAME_W,
FRAMES_PER_STATE,
PetState,
state_row_index,
)
logger = logging.getLogger(__name__)
# Public render-mode names accepted by ``display.pet.render_mode``.
RENDER_MODES = ("auto", "kitty", "iterm", "sixel", "unicode", "off")
# ─────────────────────────────────────────────────────────────────────────
# Terminal capability detection
# ─────────────────────────────────────────────────────────────────────────
def detect_terminal_graphics() -> str:
"""Best-effort detection of the richest graphics protocol available.
Env-based (non-blocking — we never issue a DA1/terminal query that could
hang a pipe). Returns one of ``kitty`` / ``iterm`` / ``sixel`` /
``unicode``. Conservative: unknown terminals get ``unicode``, which works
anywhere with truecolor.
"""
term = os.environ.get("TERM", "").lower()
term_program = os.environ.get("TERM_PROGRAM", "").lower()
# The VS Code / Cursor integrated terminal sets TERM_PROGRAM=vscode
# authoritatively but does NOT scrub the terminal env vars it inherits when
# launched from another emulator (ITERM_SESSION_ID, KITTY_WINDOW_ID, …).
# Trusting those leaks emits an image protocol the embedded xterm.js can't
# display — you get a blank frame. Inline images there are opt-in
# (terminal.integrated.enableImages), so default to half-blocks, which
# always render in its truecolor grid. Users who enabled images can pin
# display.pet.render_mode explicitly.
if term_program == "vscode":
return "unicode"
# kitty graphics protocol
if os.environ.get("KITTY_WINDOW_ID") or "kitty" in term or "ghostty" in term:
return "kitty"
if term_program in {"ghostty"}:
return "kitty"
# WezTerm speaks both kitty and iterm; prefer kitty (richer placement).
if term_program == "wezterm" or os.environ.get("WEZTERM_PANE"):
return "kitty"
# iTerm2 inline images
if term_program == "iterm.app" or os.environ.get("ITERM_SESSION_ID"):
return "iterm"
# sixel-capable terminals (env heuristics only)
if term_program in {"mintty"} or "foot" in term or "mlterm" in term:
return "sixel"
if "sixel" in term:
return "sixel"
return "unicode"
def resolve_mode(configured: str | None, *, stream=None) -> str:
"""Resolve the effective render mode from config + the environment.
``configured`` is ``display.pet.render_mode`` (``auto`` → detect). Returns
``off`` when not attached to a TTY (no point emitting graphics into a pipe
or logfile).
"""
mode = (configured or "auto").strip().lower()
if mode not in RENDER_MODES:
mode = "auto"
if mode == "off":
return "off"
stream = stream or sys.stdout
try:
if not (hasattr(stream, "isatty") and stream.isatty()):
return "off"
except (ValueError, OSError):
return "off"
if mode == "auto":
return detect_terminal_graphics()
return mode
# ─────────────────────────────────────────────────────────────────────────
# Frame decoding
# ─────────────────────────────────────────────────────────────────────────
def _open_sheet(path: Path):
from PIL import Image
img = Image.open(path)
return img.convert("RGBA")
# Max alpha at/below which a frame counts as blank padding. petdex sheets are
# left-packed: a state with fewer real frames than ``FRAMES_PER_STATE`` fills
# the trailing columns with fully transparent cells. Animating into one flashes
# the pet blank, so we stop the row at the first such gap.
_BLANK_ALPHA = 8
def _frame_is_blank(frame) -> bool:
"""True if *frame* has no meaningfully opaque pixel (transparent padding)."""
return frame.getchannel("A").getextrema()[1] <= _BLANK_ALPHA
@lru_cache(maxsize=16)
def _raw_frames(
sheet_path: str,
state_value: str,
frame_w: int,
frame_h: int,
frames_per_state: int,
) -> tuple:
"""Cropped, padding-trimmed RGBA frames for one state row (unscaled).
Steps across the row until the first blank column so pets with ragged
per-state frame counts never animate into empty padding. Cached; returns
``()`` on any decode failure.
"""
try:
sheet = _open_sheet(Path(sheet_path))
cols = max(1, sheet.width // frame_w)
rows = max(1, sheet.height // frame_h)
row = state_row_index(state_value, rows)
top = row * frame_h
# Clamp the row to the sheet (some pets ship fewer rows than the 8 the
# taxonomy reserves).
if top + frame_h > sheet.height:
top = max(0, sheet.height - frame_h)
frames = []
for i in range(min(frames_per_state, cols)):
left = i * frame_w
frame = sheet.crop((left, top, left + frame_w, top + frame_h))
if _frame_is_blank(frame):
break # trailing transparent padding — real frames end here
frames.append(frame)
return tuple(frames)
except Exception as exc: # noqa: BLE001 - cosmetic feature, never fatal
logger.debug("pet frame decode failed (%s, %s): %s", sheet_path, state_value, exc)
return ()
@lru_cache(maxsize=8)
def _frames_for(
sheet_path: str,
state_value: str,
frame_w: int,
frame_h: int,
frames_per_state: int,
scale_w: int,
scale_h: int,
):
"""Return padding-trimmed RGBA frames for one state row, scaled.
Thin scaling layer over :func:`_raw_frames`; both are cached so repeated
frame requests during animation are free.
"""
raw = _raw_frames(sheet_path, state_value, frame_w, frame_h, frames_per_state)
if not raw or (scale_w, scale_h) == (frame_w, frame_h):
return list(raw)
from PIL import Image
return [f.resize((scale_w, scale_h), Image.LANCZOS) for f in raw]
def state_frame_counts(
sheet_path: str | Path,
*,
frame_w: int = FRAME_W,
frame_h: int = FRAME_H,
frames_per_state: int = FRAMES_PER_STATE,
) -> dict[str, int]:
"""Map each driven :class:`PetState` → its real (padding-trimmed) frame count.
The single source of truth for "how many frames does this state actually
have?". The CLI/TUI consume the trimmed frame lists directly; the gateway
ships this map to the desktop canvas, which steps its own loop.
"""
return {
state.value: len(
_raw_frames(str(sheet_path), state.value, frame_w, frame_h, frames_per_state)
)
for state in PetState
}
# ─────────────────────────────────────────────────────────────────────────
# Encoders
# ─────────────────────────────────────────────────────────────────────────
def _png_bytes(frame) -> bytes:
buf = io.BytesIO()
frame.save(buf, format="PNG")
return buf.getvalue()
def _kitty_apc(ctrl: str, data: str) -> str:
"""Emit a kitty APC escape for *data*, chunked into ≤4096-byte ``m`` pieces."""
chunk = 4096
if len(data) <= chunk:
return f"\x1b_G{ctrl},m=0;{data}\x1b\\"
out = [f"\x1b_G{ctrl},m=1;{data[:chunk]}\x1b\\"]
rest = data[chunk:]
while rest:
piece, rest = rest[:chunk], rest[chunk:]
out.append(f"\x1b_Gm={1 if rest else 0};{piece}\x1b\\")
return "".join(out)
def _encode_kitty(frame, *, cell_cols: int | None = None, cell_rows: int | None = None) -> str:
"""Encode one frame via the kitty graphics protocol (transmit + display).
``a=T`` transmits & displays at the cursor; ``c``/``r`` request a display
box in terminal cells so successive frames overwrite the same area.
"""
ctrl = "f=100,a=T,q=2"
if cell_cols:
ctrl += f",c={cell_cols}"
if cell_rows:
ctrl += f",r={cell_rows}"
return _kitty_apc(ctrl, base64.standard_b64encode(_png_bytes(frame)).decode("ascii"))
# ─────────────────────────────────────────────────────────────────────────
# kitty Unicode placeholders
#
# Ink (the TUI's React-for-terminal layer) owns the screen and measures every
# cell's width, so it can't host raw kitty image escapes (no width to count,
# clobbered on the next repaint). kitty's *Unicode placeholder* protocol is the
# grid-safe path: transmit the image once (q=2, virtual placement U=1), then the
# host app prints ordinary-width placeholder cells (U+10EEEE + diacritics) whose
# foreground color encodes the image id. Ink counts those as width-1 text, so
# layout stays correct and the terminal paints the image underneath.
# https://sw.kovidgoyal.net/kitty/graphics-protocol/#unicode-placeholders
# ─────────────────────────────────────────────────────────────────────────
_KITTY_PLACEHOLDER = "\U0010eeee"
# Row/column diacritics, in order (index → diacritic). Verbatim from kitty's
# gen/rowcolumn-diacritics.txt (Unicode 6.0.0, combining class 230). Index i is
# the diacritic that encodes the number i; we only ever need the row index.
_ROWCOL_DIACRITICS: tuple[int, ...] = (
0x0305, 0x030D, 0x030E, 0x0310, 0x0312, 0x033D, 0x033E, 0x033F, 0x0346, 0x034A,
0x034B, 0x034C, 0x0350, 0x0351, 0x0352, 0x0357, 0x035B, 0x0363, 0x0364, 0x0365,
0x0366, 0x0367, 0x0368, 0x0369, 0x036A, 0x036B, 0x036C, 0x036D, 0x036E, 0x036F,
0x0483, 0x0484, 0x0485, 0x0486, 0x0487, 0x0592, 0x0593, 0x0594, 0x0595, 0x0597,
0x0598, 0x0599, 0x059C, 0x059D, 0x059E, 0x059F, 0x05A0, 0x05A1, 0x05A8, 0x05A9,
0x05AB, 0x05AC, 0x05AF, 0x05C4, 0x0610, 0x0611, 0x0612, 0x0613, 0x0614, 0x0615,
0x0616, 0x0617, 0x0657, 0x0658, 0x0659, 0x065A, 0x065B, 0x065D, 0x065E, 0x06D6,
0x06D7, 0x06D8, 0x06D9, 0x06DA, 0x06DB, 0x06DC, 0x06DF, 0x06E0, 0x06E1, 0x06E2,
0x06E4, 0x06E7, 0x06E8, 0x06EB, 0x06EC, 0x0730, 0x0732, 0x0733, 0x0735, 0x0736,
0x073A, 0x073D, 0x073F, 0x0740, 0x0741, 0x0743, 0x0745, 0x0747, 0x0749, 0x074A,
0x07EB, 0x07EC, 0x07ED, 0x07EE, 0x07EF, 0x07F0, 0x07F1, 0x07F3, 0x0816, 0x0817,
0x0818, 0x0819, 0x081B, 0x081C, 0x081D, 0x081E, 0x081F, 0x0820, 0x0821, 0x0822,
0x0823, 0x0825, 0x0826, 0x0827, 0x0829, 0x082A, 0x082B, 0x082C, 0x082D, 0x0951,
0x0953, 0x0954, 0x0F82, 0x0F83, 0x0F86, 0x0F87, 0x135D, 0x135E, 0x135F, 0x17DD,
0x193A, 0x1A17, 0x1A75, 0x1A76, 0x1A77, 0x1A78, 0x1A79, 0x1A7A, 0x1A7B, 0x1A7C,
0x1B6B, 0x1B6D, 0x1B6E, 0x1B6F, 0x1B70, 0x1B71, 0x1B72, 0x1B73, 0x1CD0, 0x1CD1,
0x1CD2, 0x1CDA, 0x1CDB, 0x1CE0, 0x1DC0, 0x1DC1, 0x1DC3, 0x1DC4, 0x1DC5, 0x1DC6,
0x1DC7, 0x1DC8, 0x1DC9, 0x1DCB, 0x1DCC, 0x1DD1, 0x1DD2, 0x1DD3, 0x1DD4, 0x1DD5,
0x1DD6, 0x1DD7, 0x1DD8, 0x1DD9, 0x1DDA, 0x1DDB, 0x1DDC, 0x1DDD, 0x1DDE, 0x1DDF,
0x1DE0, 0x1DE1, 0x1DE2, 0x1DE3, 0x1DE4, 0x1DE5, 0x1DE6, 0x1DFE, 0x20D0, 0x20D1,
0x20D4, 0x20D5, 0x20D6, 0x20D7, 0x20DB, 0x20DC, 0x20E1, 0x20E7, 0x20E9, 0x20F0,
0x2CEF, 0x2CF0, 0x2CF1, 0x2DE0, 0x2DE1, 0x2DE2, 0x2DE3, 0x2DE4, 0x2DE5, 0x2DE6,
0x2DE7, 0x2DE8, 0x2DE9, 0x2DEA, 0x2DEB, 0x2DEC, 0x2DED, 0x2DEE, 0x2DEF, 0x2DF0,
0x2DF1, 0x2DF2, 0x2DF3, 0x2DF4, 0x2DF5, 0x2DF6, 0x2DF7, 0x2DF8, 0x2DF9, 0x2DFA,
0x2DFB, 0x2DFC, 0x2DFD, 0x2DFE, 0x2DFF, 0xA66F, 0xA67C, 0xA67D, 0xA6F0, 0xA6F1,
0xA8E0, 0xA8E1, 0xA8E2, 0xA8E3, 0xA8E4, 0xA8E5, 0xA8E6, 0xA8E7, 0xA8E8, 0xA8E9,
0xA8EA, 0xA8EB, 0xA8EC, 0xA8ED, 0xA8EE, 0xA8EF, 0xA8F0, 0xA8F1, 0xAAB0, 0xAAB2,
0xAAB3, 0xAAB7, 0xAAB8, 0xAABE, 0xAABF, 0xAAC1, 0xFE20, 0xFE21, 0xFE22, 0xFE23,
0xFE24, 0xFE25, 0xFE26, 0x10A0F, 0x10A38, 0x1D185, 0x1D186, 0x1D187, 0x1D188,
0x1D189, 0x1D1AA, 0x1D1AB, 0x1D1AC, 0x1D1AD, 0x1D242, 0x1D243, 0x1D244,
)
def kitty_image_id(slug: str) -> int:
"""Stable per-pet image id in ``[1, 0x7FFF]``.
The id is encoded in the placeholder's 24-bit foreground color, so it must
be non-zero and fit comfortably under ``0xFFFFFF``. A small CRC keeps it
deterministic per slug (so re-renders reuse the same terminal-side image)
while making collisions between two different pets unlikely.
"""
import zlib
return (zlib.crc32(slug.encode("utf-8")) % 0x7FFE) + 1
def kitty_color_hex(image_id: int) -> str:
"""Hex foreground color (``#rrggbb``) that encodes *image_id* for kitty."""
return "#%06x" % (image_id & 0xFFFFFF)
def kitty_placeholder_rows(cols: int, rows: int) -> list[str]:
"""Build the placeholder text grid for an *rows*×*cols* image.
Each line is one row of the grid: the first cell carries the row diacritic
(column defaults to 0), and the remaining ``cols-1`` bare placeholders let
the terminal auto-increment the column. The foreground color (the image id)
is applied by the caller / Ink, not embedded here.
"""
cols = max(1, cols)
out: list[str] = []
for r in range(max(1, rows)):
idx = min(r, len(_ROWCOL_DIACRITICS) - 1)
first = _KITTY_PLACEHOLDER + chr(_ROWCOL_DIACRITICS[idx])
out.append(first + _KITTY_PLACEHOLDER * (cols - 1))
return out
def _encode_kitty_virtual(frame, *, image_id: int, cols: int, rows: int) -> str:
"""Transmit a frame as a kitty *virtual* placement for Unicode placeholders.
``a=T`` transmits and creates the placement in one shot; ``U=1`` marks it
virtual (no on-screen output, cursor untouched); ``q=2`` suppresses the
terminal's OK/error replies that would otherwise corrupt the host app's
output. Re-sending with the same ``i`` replaces the image, so the static
placeholder cells animate underneath.
"""
ctrl = f"a=T,U=1,i={image_id},c={cols},r={rows},f=100,q=2"
return _kitty_apc(ctrl, base64.standard_b64encode(_png_bytes(frame)).decode("ascii"))
def _encode_iterm(frame, *, cell_cols: int | None = None, cell_rows: int | None = None) -> str:
"""Encode one frame as an iTerm2 inline image (OSC 1337 File)."""
payload = base64.standard_b64encode(_png_bytes(frame)).decode("ascii")
size = len(payload)
args = [f"inline=1", f"size={size}", "preserveAspectRatio=1"]
if cell_cols:
args.append(f"width={cell_cols}")
if cell_rows:
args.append(f"height={cell_rows}")
return f"\x1b]1337;File={';'.join(args)}:{payload}\x07"
def _encode_sixel(frame) -> str:
"""Encode one frame as DEC sixel.
Quantizes to an adaptive palette (≤255 colors) and emits the sixel band
stream. Pillow has no sixel writer, so this is a compact hand-rolled
encoder. Transparent pixels render as background (color register skipped).
"""
from PIL import Image
rgba = frame
# Composite onto transparent-as-skip: track alpha to decide background.
pal = rgba.convert("RGB").quantize(colors=255, method=Image.MEDIANCUT)
palette = pal.getpalette() or []
px = pal.load()
alpha = rgba.getchannel("A").load()
w, h = pal.size
out = ["\x1bP0;1;0q", '"1;1;%d;%d' % (w, h)]
# Color register definitions (sixel uses 0..100 scale).
used = sorted({px[x, y] for y in range(h) for x in range(w)})
for idx in used:
r = palette[idx * 3] if idx * 3 < len(palette) else 0
g = palette[idx * 3 + 1] if idx * 3 + 1 < len(palette) else 0
b = palette[idx * 3 + 2] if idx * 3 + 2 < len(palette) else 0
out.append("#%d;2;%d;%d;%d" % (idx, r * 100 // 255, g * 100 // 255, b * 100 // 255))
# Emit in 6-row bands.
for band in range(0, h, 6):
for color_idx in used:
line = ["#%d" % color_idx]
run_char = None
run_len = 0
def flush():
nonlocal run_char, run_len
if run_char is None:
return
if run_len > 3:
line.append("!%d%s" % (run_len, run_char))
else:
line.append(run_char * run_len)
run_char, run_len = None, 0
for x in range(w):
bits = 0
for bit in range(6):
y = band + bit
if y < h and alpha[x, y] > 32 and px[x, y] == color_idx:
bits |= 1 << bit
ch = chr(63 + bits)
if ch == run_char:
run_len += 1
else:
flush()
run_char, run_len = ch, 1
flush()
out.append("".join(line) + "$") # carriage return within band
out.append("-") # next band
out.append("\x1b\\")
return "".join(out)
_HALF_BLOCK = "▀"
# A single half-block cell: top pixel + bottom pixel as (r, g, b, a) tuples.
Cell = tuple[tuple[int, int, int, int], tuple[int, int, int, int]]
def _downscale_cells(frame, *, target_cols: int) -> list[list[Cell]]:
"""Downscale a frame to a grid of half-block cells.
Each cell pairs a top and bottom pixel so one terminal row encodes two
pixel rows. Returns rows of ``((tr,tg,tb,ta),(br,bg,bb,ba))`` — the
framework-neutral representation shared by the ANSI encoder (CLI) and the
structured ``cells`` API (Ink).
"""
from PIL import Image
target_cols = max(4, target_cols)
aspect = frame.height / max(1, frame.width)
target_rows = max(2, int(round(target_cols * aspect * 0.5)) * 2)
small = frame.resize((target_cols, target_rows), Image.LANCZOS).convert("RGBA")
px = small.load()
grid: list[list[Cell]] = []
for y in range(0, target_rows, 2):
row: list[Cell] = []
for x in range(target_cols):
top = px[x, y]
bottom = px[x, y + 1] if y + 1 < target_rows else (0, 0, 0, 0)
row.append((top, bottom))
grid.append(row)
return grid
def _encode_unicode(frame, *, target_cols: int) -> str:
"""Downscale to truecolor ANSI half-blocks (one char = 2 vertical pixels)."""
lines: list[str] = []
for row in _downscale_cells(frame, target_cols=target_cols):
cells: list[str] = []
for (tr, tg, tb, ta), (br, bg, bb, ba) in row:
if ta < 32 and ba < 32:
cells.append("\x1b[0m ") # fully transparent → blank
continue
cells.append(f"\x1b[38;2;{tr};{tg};{tb}m\x1b[48;2;{br};{bg};{bb}m{_HALF_BLOCK}")
lines.append("".join(cells) + "\x1b[0m")
return "\n".join(lines)
# ─────────────────────────────────────────────────────────────────────────
# Public renderer
# ─────────────────────────────────────────────────────────────────────────
class PetRenderer:
"""Holds a pet's spritesheet and yields encoded frames per (state, index).
Construct once per pet, then call :meth:`frame` on an animation timer.
Cheap to call repeatedly — decoded frames are cached.
"""
def __init__(
self,
spritesheet: str | Path,
*,
mode: str = "unicode",
scale: float = DEFAULT_SCALE,
unicode_cols: int = 20,
frame_w: int = FRAME_W,
frame_h: int = FRAME_H,
frames_per_state: int = FRAMES_PER_STATE,
) -> None:
self.spritesheet = str(spritesheet)
self.mode = mode if mode in RENDER_MODES else "unicode"
self.scale = scale
self.unicode_cols = unicode_cols
self.frame_w = frame_w
self.frame_h = frame_h
self.frames_per_state = frames_per_state
@property
def available(self) -> bool:
return self.mode != "off" and Path(self.spritesheet).is_file()
def frame_count(self, state: PetState | str) -> int:
return len(self._frames(state))
def _frames(self, state: PetState | str):
value = state.value if isinstance(state, PetState) else str(state)
scale_w = max(1, int(self.frame_w * self.scale))
scale_h = max(1, int(self.frame_h * self.scale))
return _frames_for(
self.spritesheet,
value,
self.frame_w,
self.frame_h,
self.frames_per_state,
scale_w,
scale_h,
)
def cells(self, state: PetState | str, index: int, *, cols: int | None = None) -> list[list[Cell]]:
"""Return one frame as a half-block cell grid (framework-neutral).
Used by the TUI, which renders the grid with native Ink color props
instead of raw ANSI. Returns ``[]`` when no frame is available.
"""
frames = self._frames(state)
if not frames:
return []
frame = frames[index % len(frames)]
return _downscale_cells(frame, target_cols=cols or self.unicode_cols)
def _cell_box(self, frame) -> tuple[int, int]:
"""Terminal cell box for a scaled frame (~8×16 px per cell).
Must match :meth:`frame` graphics sizing — kitty stretches the image to
fill ``c``×``r`` cells, so these must reflect the scaled pixel
dimensions, not a native-aspect column count (that upscales small pets).
"""
return max(1, frame.width // 8), max(1, frame.height // 16)
def kitty_payload(self, state: PetState | str, *, image_id: int) -> dict | None:
"""Build the kitty Unicode-placeholder payload for one state.
Returns ``{cols, rows, placeholder, frames}`` where ``frames`` is a
list of transmit escapes (one per animation frame, all reusing
``image_id``) and ``placeholder`` is the static text grid Ink paints.
Placement geometry is derived from the scaled frame pixels (via
:meth:`_cell_box`), not ``unicode_cols`` — kitty upscales to fill
``c``×``r`` cells. ``None`` when no frame is available.
"""
frames = self._frames(state)
if not frames:
return None
cols, rows = self._cell_box(frames[0])
return {
"cols": cols,
"rows": rows,
"placeholder": kitty_placeholder_rows(cols, rows),
"frames": [
_encode_kitty_virtual(f, image_id=image_id, cols=cols, rows=rows) for f in frames
],
}
def frame(self, state: PetState | str, index: int) -> str:
"""Return the encoded escape string for one frame, or ``""``.
``index`` is taken modulo the available frame count so callers can pass
a free-running counter.
"""
if self.mode == "off":
return ""
frames = self._frames(state)
if not frames:
return ""
frame = frames[index % len(frames)]
cell_cols, cell_rows = self._cell_box(frame)
try:
if self.mode == "kitty":
return _encode_kitty(frame, cell_cols=cell_cols, cell_rows=cell_rows)
if self.mode == "iterm":
return _encode_iterm(frame, cell_cols=cell_cols, cell_rows=cell_rows)
if self.mode == "sixel":
return _encode_sixel(frame)
return _encode_unicode(frame, target_cols=self.unicode_cols)
except Exception as exc: # noqa: BLE001 - degrade silently
logger.debug("pet frame encode failed (mode=%s): %s", self.mode, exc)
return ""
def build_renderer(
spritesheet: str | Path,
*,
configured_mode: str | None = None,
scale: float = DEFAULT_SCALE,
unicode_cols: int = 20,
stream=None,
) -> PetRenderer:
"""Convenience factory: resolve the mode from config+env, then construct."""
mode = resolve_mode(configured_mode, stream=stream)
return PetRenderer(
spritesheet,
mode=mode,
scale=scale,
unicode_cols=unicode_cols,
)

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