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Author SHA1 Message Date
Brooklyn Nicholson 9d19cfbb78 refactor(desktop): extract app-version IPC from main.cjs into version-ipc.cjs
Ninth main.cjs cluster peel. The hermes:version handler moves verbatim into
electron/version-ipc.cjs behind a registerVersionIpc({ ipcMain,
resolveHermesVersion, resolveUpdateRoot }) registrar. The version + root resolvers
stay in the main process (shared with the About menu) and are injected.

Channel name unchanged → preload + renderer untouched. Adds
electron/version-ipc.test.cjs (surface + payload behavior).
2026-06-30 14:12:39 -05:00
Brooklyn Nicholson 150e023fe2 refactor(desktop): extract uninstall IPC from main.cjs into uninstall-ipc.cjs
Eighth main.cjs cluster peel. The two hermes:uninstall:* handlers (summary, run)
move verbatim into electron/uninstall-ipc.cjs behind a registerUninstallIpc({
ipcMain, getUninstallSummary, runDesktopUninstall }) registrar. The uninstall
engine stays in the main process and is injected.

Channel names unchanged → preload + renderer untouched. Adds
electron/uninstall-ipc.test.cjs (surface invariant + run mode normalization).
2026-06-30 14:11:50 -05:00
Brooklyn Nicholson 8a48d54193 refactor(desktop): extract VS Code Marketplace theme IPC from main.cjs into vscode-theme-ipc.cjs
Seventh main.cjs cluster peel. The two hermes:vscode-theme:* handlers (fetch,
search) move verbatim into electron/vscode-theme-ipc.cjs behind a
registerVscodeThemeIpc({ ipcMain }) registrar. Both delegate to the
vscode-marketplace sibling module, which the new module requires directly — so
the now-dead require in main.cjs is removed.

Channel names unchanged → preload + renderer untouched. Adds
electron/vscode-theme-ipc.test.cjs (surface invariant).
2026-06-30 13:34:12 -05:00
Brooklyn Nicholson e167ed7bb1 refactor(desktop): extract project-dir + workspace settings IPC from main.cjs into project-dir-ipc.cjs
Sixth main.cjs cluster peel. The hermes:setting:defaultProjectDir:get/set/pick
handlers + hermes:workspace:sanitize move verbatim into
electron/project-dir-ipc.cjs behind a registerProjectDirIpc({ ipcMain,
readDefaultProjectDir, writeDefaultProjectDir, resolveHermesCwd,
sanitizeWorkspaceCwd }) registrar. The config readers/writers + cwd resolvers stay
in the main process and are injected.

Channel names unchanged → preload + renderer untouched. Adds
electron/project-dir-ipc.test.cjs (surface + set/sanitize behavior; get/pick touch
Electron app/dialog and are exercised in-app only).
2026-06-30 13:32:22 -05:00
Brooklyn Nicholson 0ed0c2d39f refactor(desktop): extract desktop-log IPC handlers from main.cjs into logs-ipc.cjs
Fifth main.cjs cluster peel. The two hermes:logs:* handlers (reveal, recent) move
verbatim into electron/logs-ipc.cjs behind a registerLogsIpc({ ipcMain,
DESKTOP_LOG_PATH, hermesLog, fileExists }) registrar. The log path and the
in-memory ring buffer live in the main process and are injected.

Channel names unchanged → preload + renderer untouched. Adds
electron/logs-ipc.test.cjs (surface invariant + recent-tail behavior).
2026-06-30 13:30:48 -05:00
Brooklyn Nicholson c147270a1c refactor(desktop): extract auto-update IPC handlers from main.cjs into updates-ipc.cjs
Fourth main.cjs cluster peel. The four hermes:updates:* handlers (check, apply,
branch:get, branch:set) move verbatim into electron/updates-ipc.cjs behind a
registerUpdatesIpc({ ipcMain, checkUpdates, applyUpdates, readDesktopUpdateConfig,
writeDesktopUpdateConfig, DEFAULT_UPDATE_BRANCH }) registrar. The update engine
and on-disk update config stay in the main process and are injected.

Channel names unchanged → preload + renderer untouched. The interleaved
resolveHermesVersion/showAboutPanelFresh helpers + hermes:version handler are
shared with the menu and intentionally left in place. Adds
electron/updates-ipc.test.cjs (surface invariant + branch default fallback +
check-failure payload).
2026-06-30 13:29:38 -05:00
Brooklyn Nicholson 880f5837a1 refactor(desktop): extract terminal (PTY) IPC handlers from main.cjs into terminal-ipc.cjs
Third main.cjs cluster peel. The four hermes:terminal:* handlers (start, write,
resize, dispose) move verbatim into electron/terminal-ipc.cjs behind a
registerTerminalIpc({ ipcMain, nodePty, terminalSessions, ... }) registrar. The
PTY runtime, the shared session registry (also used by app-quit cleanup), and the
shell-spec/env/cwd helpers (deep Windows-PATH + app-path coupling) stay in the
main process and are injected, so the module owns only the request wiring.

Channel names unchanged → preload + renderer untouched. Adds
electron/terminal-ipc.test.cjs (surface invariant + unknown-session no-throw +
PTY-unavailable error).
2026-06-30 13:28:44 -05:00
Brooklyn Nicholson f3ce17bf9e refactor(desktop): extract filesystem IPC handlers from main.cjs into fs-ipc.cjs
Second main.cjs cluster peel (after git-ipc). The six hermes:fs:* handlers
(readDir, gitRoot, reveal, rename, writeText, trash) move verbatim into
electron/fs-ipc.cjs behind a registerFsIpc({ ipcMain, directoryExists,
expandUserPath }) registrar — same injection pattern as registerGitIpc. Path
hardening / read-dir / git-root come from their sibling modules directly; the
two main-process path helpers are injected so the module stays side-effect free.

Channel names are unchanged, so preload + renderer are untouched. main.cjs drops
~85 lines; the now-dead fs-read-dir / git-root requires in main.cjs are removed.
Adds electron/fs-ipc.test.cjs asserting the hermes:fs:* surface by invariant.
2026-06-30 13:26:53 -05:00
Brooklyn Nicholson b29bb6ef9d refactor(desktop): assert git-ipc surface by invariant, drop channel snapshot 2026-06-30 02:05:07 -05:00
Brooklyn Nicholson 025c8f0604 refactor(desktop): extract git IPC handlers from main.cjs into git-ipc.cjs
electron/main.cjs is the worst god file in the desktop app (~7.6k lines, 93 IPC
handlers across unrelated domains). Begin peeling cohesive handler clusters into
sibling modules — the established main.cjs pattern.

First cluster: the 19 git/worktree/review IPC handlers (all thin delegators to
the existing git-*-ops modules) move into a new electron/git-ipc.cjs exposing
registerGitIpc({ ipcMain, resolveGitBinary, resolveGhBinary }). The git/gh
binary resolvers stay in main.cjs (Windows PATH discovery) and are injected, so
the new module is pure. Channel names are unchanged, so preload/renderer are
unaffected.

Adds electron/git-ipc.test.cjs (wired into test:desktop:platforms) asserting
the full channel surface and resolver delegation. main.cjs: 7,617 -> 7,530.
2026-06-30 01:42:33 -05:00
2817 changed files with 48283 additions and 375296 deletions
-20
View File
@@ -1,13 +1,6 @@
# Hermes Agent Environment Configuration
# Copy this file to .env and fill in your API keys
# =============================================================================
# LLM PROVIDER (Fireworks AI)
# =============================================================================
# Get your key at: https://app.fireworks.ai/settings/users/api-keys
# Address models directly by catalog ID, e.g.
# accounts/fireworks/models/kimi-k2p6, accounts/fireworks/models/glm-5p2
# FIREWORKS_API_KEY=
# =============================================================================
# LLM PROVIDER (OpenRouter)
# =============================================================================
@@ -115,10 +108,6 @@
# 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
# DeepInfra — 100+ top open models, pay-per-use.
# Get your key at: https://deepinfra.com/dash/api_keys
# DEEPINFRA_API_KEY=
# =============================================================================
# LLM PROVIDER (Qwen OAuth)
# =============================================================================
@@ -136,15 +125,6 @@
# Optional base URL override:
# XIAOMI_BASE_URL=https://api.xiaomimimo.com/v1
# =============================================================================
# LLM PROVIDER (Upstage Solar)
# =============================================================================
# Upstage provides access to Upstage Solar models.
# Get your key at: https://console.upstage.ai/api-keys
# UPSTAGE_API_KEY=your_key_here
# Optional base URL override:
# UPSTAGE_BASE_URL=https://api.upstage.ai/v1
# =============================================================================
# TOOL API KEYS
# =============================================================================
+1 -1
View File
@@ -1,4 +1,4 @@
watch_file pyproject.toml uv.lock hermes
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
+6 -46
View File
@@ -5,18 +5,12 @@ description: >-
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.
inputs:
github-token:
description: Token for the GitHub API (gh CLI). Pass steps.app-token.outputs.token from the calling workflow.
required: false
default: ${{ github.token }}
outputs:
python:
description: Run Python tests / ruff / ty / windows-footguns.
value: ${{ steps.classify.outputs.python }}
frontend:
description: Run the TypeScript testing matrix + desktop build.
description: Run the TypeScript typecheck matrix + desktop build.
value: ${{ steps.classify.outputs.frontend }}
docker_meta:
description: Docker setup and meta files have changed.
@@ -30,15 +24,9 @@ outputs:
deps:
description: Check pyproject.toml dependency upper bounds.
value: ${{ steps.classify.outputs.deps }}
npm_lock:
description: Post/update the semantic package-lock.json diff PR comment.
value: ${{ steps.classify.outputs.npm_lock }}
mcp_catalog:
description: Require MCP catalog security review label.
value: ${{ steps.classify.outputs.mcp_catalog }}
ci_review:
description: Require CI-sensitive file review label.
value: ${{ steps.classify.outputs.ci_review }}
runs:
using: composite
@@ -47,12 +35,7 @@ runs:
id: classify
shell: bash
env:
# Fall back to the built-in read-only token when the caller passes an
# empty value. Fork PRs get no repo secrets, so AUTOFIX_BOT_PAT is ""
# there, and an input `default:` only applies when the input is omitted,
# not when it's passed empty. Without this fallback the compare API
# fails on forks and the classifier fails open (every lane forced on).
GH_TOKEN: ${{ inputs.github-token || github.token }}
GH_TOKEN: ${{ github.token }}
REPO: ${{ github.repository }}
EVENT_NAME: ${{ github.event_name }}
BASE_SHA: ${{ github.event.pull_request.base.sha }}
@@ -68,33 +51,10 @@ runs:
# event payload instead of the "current PR files" endpoint. The SHAs
# are frozen at trigger time, so the file list is deterministic even
# if the PR receives a new push between trigger and detect.
#
# Retried: a rate-limit blip or eventual-consistency 404 on a
# freshly-pushed HEAD would otherwise silently fall open (all lanes
# run — safe, but wasteful and it masks the API failure).
#
# `.files[]?` (null-safe): with --paginate, a PR more than 100
# commits ahead of its merge-base paginates the compare, and pages
# after the first carry `files: null` — bare `.files[]` makes jq
# die with "cannot iterate over: null", which fails every retry
# and forces the fail-open path (seen on stacked PRs). The full
# file list (up to the API's 300-file cap) is on page one.
CHANGED=""
for i in 1 2 3; do
if CHANGED="$(gh api \
--paginate \
"repos/${REPO}/compare/${BASE_SHA}...${HEAD_SHA}" \
--jq '.files[]?.filename')"; then
break
fi
if [ "$i" = 3 ]; then
echo "::warning::compare API failed after 3 attempts — failing open (all lanes run)"
CHANGED=""
break
fi
echo "::warning::compare API failed (attempt $i); retrying in 10s"
sleep 10
done
CHANGED="$(gh api \
--paginate \
"repos/${REPO}/compare/${BASE_SHA}...${HEAD_SHA}" \
--jq '.files[].filename' || true)"
fi
echo "Changed files:"
-59
View File
@@ -1,59 +0,0 @@
name: Get GitHub App Token
description: >-
Mint a short-lived (1-hour) installation access token from the repo's
GitHub App, replacing the long-lived AUTOFIX_BOT_PAT. App tokens get
5,000 req/hr per installation (vs 1,000 for the default GITHUB_TOKEN)
and are scoped to the App's installation permissions, not a user account.
Falls back to the built-in GITHUB_TOKEN when APP_CLIENT_ID is not set —
this happens on fork PRs where repo secrets are unavailable. The fallback
ensures classification, timings, and review comments still work on
forks (with the lower GITHUB_TOKEN rate limit).
Composite actions cannot access the secrets context directly, so the
calling workflow must pass secrets.APP_CLIENT_ID and secrets.APP_PRIVATE_KEY
as inputs. When both are empty (fork PRs), the fallback fires.
inputs:
client-id:
description: GitHub App Client ID. Pass secrets.APP_CLIENT_ID from the calling workflow.
required: false
default: ''
private-key:
description: GitHub App private key PEM. Pass secrets.APP_PRIVATE_KEY from the calling workflow.
required: false
default: ''
outputs:
token:
description: A GitHub App installation access token (1-hour TTL), or GITHUB_TOKEN on forks.
value: ${{ steps.app-token.outputs.token || steps.fallback.outputs.token }}
runs:
using: composite
steps:
- name: Check if App credentials exist
id: check
shell: bash
env:
CLIENT_ID: ${{ inputs.client-id }}
run: |
if [ -n "$CLIENT_ID" ]; then
echo "has_app=true" >> "$GITHUB_OUTPUT"
else
echo "has_app=false" >> "$GITHUB_OUTPUT"
fi
- name: Create GitHub App token
id: app-token
if: steps.check.outputs.has_app == 'true'
uses: actions/create-github-app-token@bcd2ba49218906704ab6c1aa796996da409d3eb1 # v3.2.0
with:
client-id: ${{ inputs.client-id }}
private-key: ${{ inputs.private-key }}
- name: Fall back to GITHUB_TOKEN
id: fallback
if: steps.check.outputs.has_app != 'true'
shell: bash
run: echo "token=${{ github.token }}" >> "$GITHUB_OUTPUT"
+3 -23
View File
@@ -3,8 +3,7 @@ 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. Can also capture stdout as a step
output for commands whose result must be consumed by later steps.
instead of needing a manual re-run.
inputs:
command:
@@ -20,16 +19,10 @@ inputs:
description: Directory to run in.
default: "."
outputs:
stdout:
description: Captured stdout from the successful attempt (empty if not needed).
value: ${{ steps.retry.outputs.stdout }}
runs:
using: composite
steps:
- id: retry
shell: bash
- 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.
@@ -39,25 +32,12 @@ runs:
_DELAY: ${{ inputs.delay }}
run: |
set -uo pipefail
_OUTFILE="$(mktemp)"
trap 'rm -f "$_OUTFILE"' EXIT
n=0
while :; do
n=$((n + 1))
echo "::group::attempt $n/$_ATTEMPTS: $_CMD"
# Run the command, capturing stdout to a temp file while still
# streaming to the log. We redirect first, then tee the file to
# stdout — this avoids pipefail + tee exit-code interactions that
# can cause the if-branch to be skipped under set -e.
if bash -c "$_CMD" > "$_OUTFILE"; then
cat "$_OUTFILE"
if bash -c "$_CMD"; then
echo "::endgroup::"
# Preserve newlines in the output via heredoc delimiter.
{
echo 'stdout<<__RETRY_STDOUT_EOF__'
cat "$_OUTFILE"
echo '__RETRY_STDOUT_EOF__'
} >> "$GITHUB_OUTPUT"
exit 0
fi
echo "::endgroup::"
+18 -185
View File
@@ -17,7 +17,7 @@ on:
permissions:
contents: read
pull-requests: write # needed by lint (PR comment) + supply-chain review_status
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
@@ -35,33 +35,20 @@ jobs:
detect:
name: Detect affected areas
runs-on: ubuntu-latest
timeout-minutes: 10
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 }}
npm_lock: ${{ steps.classify.outputs.npm_lock }}
docker_meta: ${{ steps.classify.outputs.docker_meta }}
mcp_catalog: ${{ steps.classify.outputs.mcp_catalog }}
ci_review: ${{ steps.classify.outputs.ci_review }}
event_name: ${{ github.event_name }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Detect affected areas
id: classify
uses: ./.github/actions/detect-changes
with:
# The get-app-token composite action falls back to GITHUB_TOKEN
# on fork PRs where APP_ID is unavailable.
github-token: ${{ steps.app-token.outputs.token }}
# ─────────────────────────────────────────────────────────────────────
# Lane-gated sub-workflows. Each runs in parallel after detect finishes.
@@ -74,7 +61,6 @@ jobs:
uses: ./.github/workflows/tests.yml
with:
slice_count: 8
secrets: inherit
lint:
name: Python lints
@@ -83,61 +69,41 @@ jobs:
uses: ./.github/workflows/lint.yml
with:
event_name: ${{ needs.detect.outputs.event_name }}
secrets: inherit
js-tests:
name: JS & TS checks
typecheck:
name: TypeScript
needs: detect
if: needs.detect.outputs.frontend == 'true'
uses: ./.github/workflows/js-tests.yml
secrets: inherit
e2e-desktop:
name: Desktop E2E
needs: detect
if: needs.detect.outputs.python == 'true' || needs.detect.outputs.frontend == 'true'
uses: ./.github/workflows/e2e-desktop.yml
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
secrets: inherit
history-check:
name: Deny unrelated histories
needs: detect
if: needs.detect.outputs.event_name == 'pull_request'
uses: ./.github/workflows/history-check.yml
secrets: inherit
contributor-check:
name: Check contributors
needs: detect
if: needs.detect.outputs.python == 'true'
uses: ./.github/workflows/contributor-check.yml
secrets: inherit
uv-lockfile:
name: Check uv.lock
needs: detect
uses: ./.github/workflows/uv-lockfile-check.yml
secrets: inherit
lockfile-diff:
name: package-lock.json diff
needs: detect
if: needs.detect.outputs.event_name == 'pull_request' && needs.detect.outputs.npm_lock == 'true'
uses: ./.github/workflows/lockfile-diff.yml
secrets: inherit
docker-lint:
name: Lint Docker scripts
needs: detect
if: needs.detect.outputs.docker_meta == 'true'
uses: ./.github/workflows/docker-lint.yml
secrets: inherit
docker:
name: Build&Test Docker image
@@ -149,102 +115,17 @@ jobs:
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')
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' }}
review-labels:
name: Review label gate
needs: detect
if: needs.detect.outputs.event_name == 'pull_request' && (needs.detect.outputs.ci_review == 'true' || needs.detect.outputs.mcp_catalog == 'true')
uses: ./.github/workflows/review-labels.yml
with:
ci_review: ${{ needs.detect.outputs.ci_review == 'true' }}
mcp_catalog: ${{ needs.detect.outputs.mcp_catalog == 'true' }}
secrets: inherit
osv-scanner:
name: OSV scan
uses: ./.github/workflows/osv-scanner.yml
secrets: inherit
# ─────────────────────────────────────────────────────────────────────
# Live-updating PR review comment.
#
# A single ``comment-live`` job polls the GitHub Actions API every 15s
# for job statuses in this run, re-assembles the review comment from
# whatever results are available, and upserts it via the
# ``<!-- hermes-ci-review-bot -->`` marker.
#
# The poller exits when all non-infra jobs are completed (or on
# timeout). ci-timings' review_status is picked up automatically when
# its artifact becomes available — the poller downloads and merges it.
# ─────────────────────────────────────────────────────────────────────
comment-live:
name: CI review comment (live)
needs: [detect, review-labels, lockfile-diff, supply-chain, osv-scanner, uv-lockfile, history-check, contributor-check]
if: always() && github.event_name == 'pull_request' && github.event.pull_request.head.repo.fork != true
runs-on: ubuntu-latest
timeout-minutes: 40
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Run live comment poller
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GITHUB_REPOSITORY: ${{ github.repository }}
GITHUB_RUN_ID: ${{ github.run_id }}
PR_NUMBER: ${{ github.event.pull_request.number }}
RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
# Commit info for the review comment header.
COMMIT_SHA: ${{ github.event.pull_request.head.sha }}
COMMIT_MESSAGE: ${{ github.event.pull_request.head.commit.message }}
COMMIT_URL: ${{ github.server_url }}/${{ github.repository }}/pull/${{ github.event.pull_request.number }}/commits/${{ github.event.pull_request.head.sha }}
# Structured review statuses from workflow_call jobs.
# Each job outputs a JSON array of {source, results: [...]} objects
# that the assembler renders directly — no hardcoded job-name
# matching. We merge all available outputs into one array.
REVIEW_STATUSES: ${{ toJSON(needs.*.outputs.review_status) }}
run: |
set -uo pipefail
# REVIEW_STATUSES is a JSON array of strings (some may be empty
# when a job was skipped). Parse each string and merge into one
# flat array for the assembler.
python3 - <<'PYEOF'
import json, os, sys
raw = os.environ.get("REVIEW_STATUSES", "")
merged = []
if raw:
try:
arr = json.loads(raw)
except (json.JSONDecodeError, TypeError):
arr = []
for item in arr:
if not item:
continue
try:
statuses = json.loads(item)
except (json.JSONDecodeError, TypeError):
continue
if isinstance(statuses, list):
merged.extend(statuses)
# Write merged array to a temp file the poller reads.
with open("/tmp/review_statuses.json", "w") as f:
json.dump(merged, f)
print(f"Merged {len(merged)} review status entries")
PYEOF
python3 scripts/ci/live_comment.py \
--interval 15 \
--timeout 2100 \
--review-statuses-file /tmp/review_statuses.json
# ─────────────────────────────────────────────────────────────────────
# Gate: runs after everything. ``if: always()`` ensures it reports a
@@ -252,56 +133,35 @@ jobs:
# results cause it to fail; ``skipped`` is treated as success.
#
# Branch protection should require ONLY this check.
#
# Outputs ``needs-json`` — a compact ``{job_name: result}`` dict — so
# the live comment poller can list failed jobs in the PR comment.
# ─────────────────────────────────────────────────────────────────────
all-checks-pass:
name: All required checks pass
needs:
- detect
- tests
- lint
- js-tests
- e2e-desktop
- typecheck
- docs-site
- history-check
- contributor-check
- uv-lockfile
- lockfile-diff
- docker-lint
- supply-chain
- review-labels
- osv-scanner
# comment-live is a polling job — it doesn't block the gate.
# we don't require docker to pass rn because it's so slow lol
# - docker
if: always()
runs-on: ubuntu-latest
timeout-minutes: 10
outputs:
needs-json: ${{ steps.evaluate.outputs.needs-json }}
steps:
- name: Evaluate job results
id: evaluate
env:
NEEDS: ${{ toJSON(needs) }}
RESULTS: ${{ toJSON(needs.*.result) }}
run: |
echo "$NEEDS" | python3 -c "
echo "$RESULTS" | python3 -c "
import json, sys
needs = json.load(sys.stdin)
# Emit compact {job_name: result} for the comment assembler.
compact = {name: info['result'] for name, info in needs.items()}
print(f'needs-json={json.dumps(compact)}')
with open('$GITHUB_OUTPUT', 'a') as f:
f.write(f'needs-json={json.dumps(compact)}\n')
failed = [name for name, info in needs.items() if info['result'] == 'failure']
for name, info in sorted(needs.items()):
result = info['result']
icon = '✅' if result in ('success', 'skipped') else '❌'
print(f'{icon} {name}: {result}')
results = json.load(sys.stdin)
failed = [r for r in results if r == 'failure']
if failed:
print(f'::error::{len(failed)} job(s) failed: {\", \".join(failed)}')
print(f'::error::{len(failed)} job(s) failed')
sys.exit(1)
print('All checks passed (or were skipped)')
"
@@ -311,27 +171,16 @@ jobs:
# 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.
#
# The live comment poller picks up ci-timings' completion automatically —
# it reads review-status.json from the artifact when the job finishes.
# ─────────────────────────────────────────────────────────────────────
ci-timings:
name: CI timing report
needs: [all-checks-pass, docker]
if: always()
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Restore baseline cache (PR only)
if: github.event_name == 'pull_request'
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
@@ -345,31 +194,22 @@ jobs:
- name: Collect timings and generate report
env:
# Forks get no repo secrets (AUTOFIX_BOT_PAT is empty); fall back to
# the built-in read-only token so the timings API read still works
# there instead of hard-failing this advisory job on every fork PR.
# The get-app-token composite action falls back to GITHUB_TOKEN
# on fork PRs where APP_ID is unavailable.
GITHUB_TOKEN: ${{ steps.app-token.outputs.token }}
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 \
--review-status-out review-status.json
--summary-out ci-timings-summary.md
- name: Upload HTML report + review status
# Advisory report — artifact-service blips must not fail the job.
continue-on-error: true
- name: Upload HTML report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
id: ci-timings-artifact
with:
name: ci-timings-report
path: |
ci-timings-report.html
review-status.json
path: ci-timings-report.html
retention-days: 14
archive: false
- name: Output summary
env:
@@ -381,17 +221,10 @@ jobs:
- name: Save baseline cache (main only)
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
run: |
# Degraded runs (API rate-limited) produce no ci-timings.json —
# skip rather than fail, and never cache an empty baseline.
if [ -f ci-timings.json ]; then
cp ci-timings.json ci-timings-baseline.json
else
echo "No timings JSON this run — skipping baseline update"
fi
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' && hashFiles('ci-timings-baseline.json') != ''
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: ci-timings-baseline.json
+7 -31
View File
@@ -2,10 +2,6 @@ name: Contributor Attribution Check
on:
workflow_call:
outputs:
review_status:
description: "JSON array of review status objects"
value: ${{ jobs.check-attribution.outputs.review_status }}
permissions:
contents: read
@@ -13,16 +9,12 @@ permissions:
jobs:
check-attribution:
runs-on: ubuntu-latest
timeout-minutes: 10
outputs:
review_status: ${{ steps.check-emails.outputs.review_status }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0 # Full history needed for git log
- name: Check for unmapped contributor emails
id: check-emails
run: |
# Get the merge base between this PR and main
MERGE_BASE=$(git merge-base origin/main HEAD)
@@ -32,13 +24,10 @@ jobs:
if [ -z "$NEW_EMAILS" ]; then
echo "No new commits to check."
echo "review_status=[]" >> "$GITHUB_OUTPUT"
exit 0
fi
# An email is mapped if it has a file in contributors/emails/
# (one file per email — conflict-free) or an entry in the frozen
# legacy AUTHOR_MAP in scripts/release.py.
# Check each email against AUTHOR_MAP in release.py
MISSING=""
while IFS= read -r email; do
# Skip teknium and bot emails
@@ -47,12 +36,9 @@ jobs:
continue ;;
esac
# Check if email is in AUTHOR_MAP (either as a key or matches noreply pattern)
if echo "$email" | grep -qP '\+.*@users\.noreply\.github\.com'; then
continue # GitHub id+login noreply emails auto-resolve
fi
if [ -f "contributors/emails/${email}" ]; then
continue # mapped via the contributors directory
continue # GitHub noreply emails auto-resolve
fi
if ! grep -qF "\"${email}\"" scripts/release.py 2>/dev/null; then
@@ -63,29 +49,19 @@ jobs:
if [ -n "$MISSING" ]; then
echo ""
echo "⚠️ New contributor email(s) without a mapping:"
echo "⚠️ New contributor email(s) not in AUTHOR_MAP:"
echo -e "$MISSING"
echo ""
echo "Add a mapping file (do NOT edit AUTHOR_MAP in release.py):"
echo "Please add mappings to scripts/release.py AUTHOR_MAP:"
echo -e "$MISSING" | while read -r line; do
email=$(echo "$line" | sed 's/^ *//' | cut -d' ' -f1)
[ -z "$email" ] && continue
echo " python3 scripts/add_contributor.py ${email} <github-username>"
echo " \"${email}\": \"<github-username>\","
done
echo ""
echo "To find the GitHub username for an email:"
echo " gh api 'search/users?q=EMAIL+in:email' --jq '.items[0].login'"
# Emit review_status for unmapped emails
DETAIL=$(echo -e "$MISSING" | sed '/^$/d; s/^ //')
HOW_TO_FIX=$'Add mappings to scripts/release.py AUTHOR_MAP:\n```\n"<email>": "<github-username>",\n```\nTo find the GitHub username for an email:\n```\ngh api \'search/users?q=EMAIL+in:email\' --jq \'.items[0].login\'\n```\n'
REVIEW_STATUS=$(jq -nc \
--arg detail "$DETAIL" \
--arg how_to_fix "$HOW_TO_FIX" \
'[{"source":"contributor attribution","results":[{"kind":"action_required","title":"Unmapped contributor email(s)","summary":"New contributor email(s) are not in AUTHOR_MAP.","detail":$detail,"how_to_fix":$how_to_fix}]}]')
echo "review_status=$REVIEW_STATUS" >> "$GITHUB_OUTPUT"
exit 1
else
echo "✅ All contributor emails are mapped."
echo "✅ All contributor emails are mapped in AUTHOR_MAP."
fi
+6 -19
View File
@@ -41,28 +41,19 @@ jobs:
# doesn't auto-deploy via the deploy-docs path.
if: github.event_name == 'release' || github.event_name == 'workflow_dispatch'
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- name: Trigger Vercel Deploy
run: curl -fsS --retry 3 --retry-delay 10 -X POST "${{ secrets.VERCEL_DEPLOY_HOOK }}"
run: curl -X POST "${{ secrets.VERCEL_DEPLOY_HOOK }}"
deploy-docs:
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-latest
timeout-minutes: 30
environment:
name: github-pages
url: ${{ steps.deploy.outputs.page_url }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
@@ -74,14 +65,12 @@ jobs:
python-version: '3.11'
- name: Install PyYAML for skill extraction
uses: ./.github/actions/retry
with:
command: pip install pyyaml==6.0.2 httpx==0.28.1
run: pip install pyyaml==6.0.2 httpx==0.28.1
- name: Prepare skills index (unified multi-source catalog)
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
GITHUB_TOKEN: ${{ steps.app-token.outputs.token }}
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' }}
run: |
@@ -161,10 +150,8 @@ jobs:
run: python3 website/scripts/generate-skill-docs.py
- name: Install dependencies
uses: ./.github/actions/retry
with:
command: npm ci
working-directory: website
run: npm ci
working-directory: website
- name: Build Docusaurus
run: npm run build
+19 -29
View File
@@ -127,13 +127,12 @@ jobs:
run: uv python install 3.11
- name: Install Python dependencies (for docker tests)
# ``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.
uses: ./.github/actions/retry
with:
command: uv sync --locked --python 3.11 --extra dev
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:
@@ -179,9 +178,6 @@ jobs:
- name: Create manifest list and push
working-directory: /tmp/digests
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
RELEASE_TAG: ${{ github.event.release.tag_name }}
run: |
set -euo pipefail
args=()
@@ -189,31 +185,25 @@ jobs:
args+=("${IMAGE_NAME}@sha256:${digest_file}")
done
if [ "${{ github.event_name }}" = "release" ]; then
tags=(-t "${IMAGE_NAME}:${RELEASE_TAG}")
TAG="${{ github.event.release.tag_name }}"
docker buildx imagetools create \
-t "${IMAGE_NAME}:${TAG}" \
"${args[@]}"
else
tags=(-t "${IMAGE_NAME}:main" -t "${IMAGE_NAME}:latest")
docker buildx imagetools create \
-t "${IMAGE_NAME}:main" \
-t "${IMAGE_NAME}:latest" \
"${args[@]}"
fi
# Retry: Docker Hub API + just-pushed digest eventual consistency
# can transiently fail the create; the operation is idempotent.
for i in 1 2 3; do
if docker buildx imagetools create "${tags[@]}" "${args[@]}"; then
break
fi
if [ "$i" = 3 ]; then
echo "::error::imagetools create failed after 3 attempts"
exit 1
fi
echo "::warning::imagetools create failed (attempt $i); retrying in 20s"
sleep 20
done
- name: Inspect image
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
RELEASE_TAG: ${{ github.event.release.tag_name }}
- name: Inspect image
run: |
if [ "${{ github.event_name }}" = "release" ]; then
docker buildx imagetools inspect "${IMAGE_NAME}:${RELEASE_TAG}"
docker buildx imagetools inspect "${IMAGE_NAME}:${{ github.event.release.tag_name }}"
else
docker buildx imagetools inspect "${IMAGE_NAME}:main"
fi
env:
IMAGE_NAME: ${{ env.IMAGE_NAME }}
-1
View File
@@ -9,7 +9,6 @@ permissions:
jobs:
docs-site-checks:
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
-212
View File
@@ -1,212 +0,0 @@
name: E2E Desktop
on:
workflow_call:
permissions:
contents: read
concurrency:
group: e2e-desktop-${{ github.ref }}
cancel-in-progress: true
jobs:
e2e:
name: Playwright E2E (Linux)
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
# ── System deps for Electron on headless Ubuntu ───────────────────
# Electron needs GTK, NSS,atk, etc. even under xvfb. Playwright's
# install-deps covers browsers; for Electron we install the apt
# packages directly.
- name: Install system dependencies for Electron
run: |
sudo apt-get update -qq
sudo apt-get install -y -qq \
xvfb \
libgtk-3-0 libnotify4 libnss3 libxss1 libxtst6 \
xdg-utils libatspi2.0-0 libdrm2 libgbm1 libasound2t64
# ── Node ───────────────────────────────────────────────────────────
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
# Full npm ci (not --ignore-scripts): electron's postinstall
# downloads the binary we launch, and node-pty's native build is
# needed for the terminal pane.
- uses: ./.github/actions/retry
with:
command: npm ci
# ── Python (for the hermes serve backend) ──────────────────────────
- name: Install uv
uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # 8.2.0
with:
enable-cache: true
cache-dependency-glob: |
pyproject.toml
uv.lock
- name: Set up Python 3.11
run: uv python install 3.11
- name: Install Python dependencies
uses: ./.github/actions/retry
with:
command: uv sync --locked --python 3.11 --extra all --extra dev
# ── Build desktop app ─────────────────────────────────────────────
- run: npm run --prefix apps/desktop build
# ── Restore visual baseline screenshots from main ──────────────────
# Baselines are generated on main (via --update-snapshots) and cached.
# On PRs, we restore them so toHaveScreenshot has something to compare
# against. The cache key is keyed on the desktop source files so a
# UI change naturally invalidates it — but we fall back to the main
# cache to avoid cold starts on unrelated PRs.
- name: Restore visual baseline screenshots
id: restore-baselines
uses: actions/cache@0400d5f644dc74513175e3cd8d07132dd4860809 # v4.2.4
with:
path: apps/desktop/e2e/*-snapshots
key: visual-baselines-${{ github.ref_name }}
restore-keys: |
visual-baselines-main
# ── Run Playwright E2E under xvfb ─────────────────────────────────
# xvfb runs at a fixed 1280x1024 screen so the 1220x800 Electron
# window always has a consistent viewport for screenshot comparison.
# On main, we run with --update-snapshots to generate baselines.
- name: Run Playwright E2E tests
working-directory: apps/desktop
run: |
if [ "${{ github.ref_name }}" = "main" ]; then
echo "On main — generating/updating baseline screenshots"
xvfb-run -a --server-args="-screen 0 1280x1024x24" \
npx playwright test --reporter=list --update-snapshots
else
echo "On PR — comparing against cached baselines"
xvfb-run -a --server-args="-screen 0 1280x1024x24" \
npx playwright test --reporter=list
fi
env:
CI: "true"
# Ensure no real API keys leak into the test env.
OPENROUTER_API_KEY: ""
OPENAI_API_KEY: ""
NOUS_API_KEY: ""
# ── Save updated baselines to cache (main only) ───────────────────
- name: Save updated baselines to cache
if: github.ref_name == 'main' && always()
uses: actions/cache/save@0400d5f644dc74513175e3cd8d07132dd4860809 # v4.2.4
with:
path: apps/desktop/e2e/*-snapshots
key: visual-baselines-main
# ── Upload Playwright report (HTML + traces) ──────────────────────
- name: Upload Playwright report
id: upload-report
if: always()
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: playwright-report-${{ github.sha }}
path: apps/desktop/playwright-report
retention-days: 14
overwrite: true
# ── Upload test results (screenshots, traces, diffs) ───────────────
- name: Upload test results
id: upload-results
if: always()
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: playwright-test-results-${{ github.sha }}
path: apps/desktop/test-results
retention-days: 14
overwrite: true
# ── Upload just the visual diffs (small, fast to review) ──────────
- name: Upload visual diffs
id: upload-diffs
if: always() && github.ref_name != 'main'
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: visual-diffs-${{ github.sha }}
path: |
apps/desktop/test-results/**/*-diff.png
apps/desktop/test-results/**/*-actual.png
apps/desktop/test-results/**/*-expected.png
retention-days: 14
overwrite: true
if-no-files-found: ignore
# ── Generate step summary with visual diff info ───────────────────
# Parse the JSON report + scan for diff images, then post a summary
# to the GitHub Actions step output so reviewers can see what changed
# without downloading artifacts. Runs AFTER uploads so it can link
# the artifact download URLs from their step outputs.
- name: Generate visual diff summary
if: always()
working-directory: apps/desktop
env:
REPORT_URL: ${{ steps.upload-report.outputs.artifact-url }}
RESULTS_URL: ${{ steps.upload-results.outputs.artifact-url }}
DIFFS_URL: ${{ steps.upload-diffs.outputs.artifact-url }}
run: |
echo "## Desktop E2E — Visual Diff Report" >> "$GITHUB_STEP_SUMMARY"
echo "" >> "$GITHUB_STEP_SUMMARY"
# Count diff images (playwright writes *-diff.png on mismatch)
DIFF_COUNT=$(find test-results -name '*-diff.png' 2>/dev/null | wc -l)
ACTUAL_COUNT=$(find test-results -name '*-actual.png' 2>/dev/null | wc -l)
if [ "$DIFF_COUNT" -eq 0 ]; then
echo "✅ All $ACTUAL_COUNT screenshot(s) matched their baselines (or no baselines existed yet)." >> "$GITHUB_STEP_SUMMARY"
else
echo "📸 **$DIFF_COUNT of $ACTUAL_COUNT screenshot(s) differ from baseline:**" >> "$GITHUB_STEP_SUMMARY"
echo "" >> "$GITHUB_STEP_SUMMARY"
echo "| Test | Diff | Actual | Expected |" >> "$GITHUB_STEP_SUMMARY"
echo "|------|------|--------|----------|" >> "$GITHUB_STEP_SUMMARY"
# List each diff image with a link to the artifact
for diff in $(find test-results -name '*-diff.png' 2>/dev/null | sort); do
base=$(echo "$diff" | sed 's/-diff\.png$//')
test_name=$(basename "$base")
echo "| $test_name | [diff]($diff) | [actual](${base}-actual.png) | [expected](${base}-expected.png) |" >> "$GITHUB_STEP_SUMMARY"
done
fi
echo "" >> "$GITHUB_STEP_SUMMARY"
echo "📥 **Artifacts:**" >> "$GITHUB_STEP_SUMMARY"
echo "" >> "$GITHUB_STEP_SUMMARY"
if [ -n "$RESULTS_URL" ]; then
echo "- [playwright-test-results]($RESULTS_URL) — all screenshots (actual + expected + diff) + traces" >> "$GITHUB_STEP_SUMMARY"
fi
if [ -n "$REPORT_URL" ]; then
echo "- [playwright-report]($REPORT_URL) — interactive HTML report" >> "$GITHUB_STEP_SUMMARY"
fi
if [ -n "$DIFFS_URL" ]; then
echo "- [visual-diffs]($DIFFS_URL) — just the diffed screenshots (small, fast to review)" >> "$GITHUB_STEP_SUMMARY"
fi
echo "" >> "$GITHUB_STEP_SUMMARY"
echo "**To update baselines:** merge to main (baselines auto-update on main runs) or run \`npx playwright test --update-snapshots\` locally." >> "$GITHUB_STEP_SUMMARY"
# Also parse the JSON report for pass/fail counts
if [ -f playwright-report/results.json ]; then
echo "" >> "$GITHUB_STEP_SUMMARY"
echo "### Test Results" >> "$GITHUB_STEP_SUMMARY"
echo "" >> "$GITHUB_STEP_SUMMARY"
node -e "
const r = require('./playwright-report/results.json');
const stats = r.stats || {};
console.log('| Status | Count |');
console.log('|--------|-------|');
console.log('| ✅ Passed | ' + (stats.expected || 0) + ' |');
console.log('| ❌ Failed | ' + (stats.unexpected || 0) + ' |');
console.log('| ⏭️ Skipped | ' + (stats.skipped || 0) + ' |');
console.log('| 🔄 Flaky | ' + (stats.flaky || 0) + ' |');
" >> "$GITHUB_STEP_SUMMARY" 2>/dev/null || true
fi
+1 -12
View File
@@ -15,10 +15,6 @@ name: History Check
on:
workflow_call:
outputs:
review_status:
description: "JSON array of review_status objects for the synthesizer."
value: ${{ jobs.check-common-ancestor.outputs.review_status }}
permissions:
contents: read
@@ -26,24 +22,18 @@ permissions:
jobs:
check-common-ancestor:
runs-on: ubuntu-latest
timeout-minutes: 10
outputs:
review_status: ${{ steps.merge-base-check.outputs.review_status }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0 # full history both sides for merge-base
- id: merge-base-check
name: Reject PRs with no common ancestor on main
- name: Reject PRs with no common ancestor on main
run: |
# `git merge-base` exits non-zero AND prints nothing when the two
# commits share no ancestor. We check both conditions explicitly
# so the failure message is clear regardless of which signal fires
# first.
if ! BASE=$(git merge-base origin/main HEAD 2>/dev/null) || [ -z "$BASE" ]; then
STATUS='[{"source":"unrelated histories","results":[{"kind":"action_required","title":"Unrelated histories","summary":"This PR has no common ancestor with main.","detail":"","how_to_fix":"Rebase your changes onto current main:\n```\ngit fetch origin main\ngit checkout -b fix-branch origin/main\n# re-apply your changes (cherry-pick, copy files, etc.)\ngit push -f origin fix-branch\n```\n"}]}]'
echo "review_status=${STATUS}" >> "$GITHUB_OUTPUT"
echo ""
echo "::error::This PR has no common ancestor with main."
echo ""
@@ -65,4 +55,3 @@ jobs:
exit 1
fi
echo "::notice::Common ancestor with main: $BASE"
echo "review_status=[]" >> "$GITHUB_OUTPUT"
-258
View File
@@ -1,258 +0,0 @@
name: auto-fix lint issues & formatting
# On push to main (or manual trigger), run `npm run fix` on each workspace
# package and apply any changes via a PR.
#
# Fixable lint issues (import sorting, unused imports, curly braces, etc.) are
# auto-corrected on merge so PRs aren't blocked by them. The PR-time eslint
# check in typecheck.yml fails only when un-fixable errors remain.
#
# NOTE: App token pushes DO trigger further workflow runs (unlike
# secrets.GITHUB_TOKEN). The concurrency group (ts-autofix-${{ github.ref }})
# with cancel-in-progress: true prevents an infinite loop — a re-triggered
# run cancels the in-flight one, and since the second run finds no new fixes
# (the first run already applied them), it exits with an empty patch.
#
# ── Security model: two-job split ───────────────────────────────────────────
#
# The eslint process executes repo code (eslint.config.mjs, package.json
# scripts, installed plugins). To prevent a malicious PR from getting arbitrary
# code execution on a runner with push access, the work is split:
#
# 1. generate-patch (unprivileged, contents: read only)
# Checks out, installs deps, runs eslint --fix, produces a .patch artifact.
# Worst case: malicious code runs here on an ephemeral runner with zero
# push permissions.
#
# 2. apply-patch (privileged, contents: write + pull-requests: write)
# Checks out, downloads the patch artifact, applies it, pushes to the
# bot/js-autofix branch, creates/updates a PR, and enables auto-merge.
# This job never runs npm, never installs anything, never executes any
# repo code. The only input it trusts is the patch artifact.
# Skipped entirely when generate-patch reports no fixes (has-fixes != true).
# The PR auto-merges (squash) once CI passes. If CI fails or main moves,
# the PR is auto-closed and the branch deleted — the next run re-applies
# on the current state.
on:
push:
branches: [main]
paths:
- '**/*.js'
- '**/*.cjs'
- '**/*.mjs'
- '**/*.ts'
- '**/*.tsx'
- 'package.json'
- 'package-lock.json'
workflow_dispatch:
permissions:
contents: read # default; apply-patch job overrides to write
concurrency:
group: ts-autofix-${{ github.ref }}
cancel-in-progress: true
jobs:
generate-patch:
name: Generate eslint --fix patch
runs-on: ubuntu-latest
timeout-minutes: 5
outputs:
has-fixes: ${{ steps.produce-patch.outputs.has-fixes }}
# No permissions override → inherits workflow-level contents: read.
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
# --ignore-scripts: eslint only needs TS sources + eslint packages.
- uses: ./.github/actions/retry
with:
command: npm ci --ignore-scripts
- name: npm run fix in all workspaces
# continue-on-error: if un-fixable errors exist on main, we still want
# to commit whatever fixes were applied. The PR-time check in
# typecheck.yml is what blocks un-fixable errors from landing.
continue-on-error: true
run: npm run fix
- name: Produce patch
id: produce-patch
run: |
if git diff --quiet; then
echo "No fixes needed."
echo "has-fixes=false" >> "$GITHUB_OUTPUT"
# Empty patch signals "nothing to do" to apply-patch.
: > js-fix.patch
else
git diff > js-fix.patch
echo "has-fixes=true" >> "$GITHUB_OUTPUT"
echo "Patch size: $(wc -c < js-fix.patch) bytes"
# Reject patches that touch anything outside JS/TS/JSON sources.
# `npm run fix` should only ever modify those; anything else means
# eslint/prettier or a plugin went rogue and we refuse to ship it.
BAD=$(git diff --name-only | grep -vE '\.(js|cjs|mjs|ts|tsx|json)$' || true)
if [ -n "$BAD" ]; then
echo "::error::Refusing to upload patch — touches disallowed files:"
echo "$BAD"
exit 1
fi
fi
- name: Upload patch artifact
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
with:
name: js-fix-patch
path: js-fix.patch
retention-days: 1
include-hidden-files: true
apply-patch:
name: Apply patch
needs: generate-patch
# Skip entirely when generate-patch found no fixes — saves a runner,
# avoids a redundant checkout/download, and keeps the job graph honest.
if: needs.generate-patch.outputs.has-fixes == 'true'
runs-on: ubuntu-latest
timeout-minutes: 15
permissions:
contents: write # needed to push to bot/js-autofix
pull-requests: write # needed for PR creation + auto-merge
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Download patch
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
name: js-fix-patch
# ${{ runner.temp }} expands in with: params (shell-style $VAR does not).
# download-artifact's path is a *directory* — the artifact's js-fix.patch
# file lands inside it, so $RUNNER_TEMP/js-fix.patch resolves correctly
# in the run step below.
path: ${{ runner.temp }}
- name: Apply patch and push to bot branch
env:
BOT_BRANCH: bot/js-autofix
run: |
set -euo pipefail
# Empty patch = nothing to do.
if [ ! -s "$RUNNER_TEMP/js-fix.patch" ]; then
echo "Patch is empty. No fixes to apply."
exit 0
fi
# Apply the patch produced by the unprivileged job.
git apply --check "$RUNNER_TEMP/js-fix.patch" || {
echo "::error::Patch does not apply cleanly. Branch may have moved."
exit 1
}
git apply "$RUNNER_TEMP/js-fix.patch"
git config user.name "github-actions[bot]"
git config user.email "github-actions[bot]@users.noreply.github.com"
git add -A
git commit -m "fmt(js): \`npm run fix\` on merge"
# Push to the dedicated bot branch. Force-push is safe here:
# bot/js-autofix is a bot-only branch that gets rewritten each run.
# If the branch was deleted after a previous PR merge, this
# recreates it.
git push --force origin HEAD:"$BOT_BRANCH"
- name: Create/update PR and enable auto-merge
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
BOT_BRANCH: bot/js-autofix
run: |
set -euo pipefail
# Create PR if one doesn't exist. If it already exists, the
# force-push above already updated it with the latest fixes.
PR_NUM=$(gh pr list --head "$BOT_BRANCH" --state open --json number --jq '.[0].number' 2>/dev/null || true)
if [ -z "$PR_NUM" ]; then
# gh pr create prints the PR URL. Extract the number from it
# (https://github.com/<org>/<repo>/pull/<number>).
PR_URL=$(gh pr create \
--head "$BOT_BRANCH" --base main \
--title 'fmt(js): `npm run fix` auto-fix' \
--body 'Auto-generated by the `auto-fix lint issues & formatting` workflow. Auto-merges (squash) once CI passes. If CI fails or `main` moves, the PR is auto-closed and the branch deleted — the next run re-applies on the current state.')
PR_NUM=$(echo "$PR_URL" | grep -oE '[0-9]+$')
fi
# Enable auto-merge (squash). If already enabled, this is a no-op.
gh pr merge "$PR_NUM" --auto --squash || true
- name: Wait for merge, auto-close on failure or stale
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
START_SHA: ${{ github.sha }}
run: |
set -euo pipefail
PR_NUM=$(gh pr list --head bot/js-autofix --state open --json number --jq '.[0].number' 2>/dev/null || true)
if [ -z "$PR_NUM" ]; then
echo "No open PR. Nothing to wait for."
exit 0
fi
echo "Waiting for PR #$PR_NUM to merge..."
# Poll every 15s for up to ~10 minutes. Auto-merge will handle the
# PR even if this job times out — the polling is for cleanup only
# (auto-close on CI failure, conflicts, or main moving).
for i in $(seq 1 40); do
sleep 15
STATE=$(gh pr view "$PR_NUM" --json state --jq '.state')
if [ "$STATE" = "MERGED" ] || [ "$STATE" = "CLOSED" ]; then
echo "PR #$PR_NUM is $STATE."
exit 0
fi
# If main moved, the PR may have already merged (which moves
# main) or another commit landed. Re-check state first.
CURRENT_SHA=$(gh api "repos/${{ github.repository }}/branches/main" --jq '.commit.sha')
if [ "$CURRENT_SHA" != "$START_SHA" ]; then
STATE=$(gh pr view "$PR_NUM" --json state --jq '.state')
if [ "$STATE" = "MERGED" ]; then
echo "PR #$PR_NUM merged (main moved to $CURRENT_SHA)."
exit 0
fi
echo "Main moved ($START_SHA → $CURRENT_SHA). Closing stale PR."
gh pr close "$PR_NUM" --delete-branch || true
exit 0
fi
# If CI checks failed, close + delete the branch.
if gh pr checks "$PR_NUM" 2>/dev/null | grep -qi "fail"; then
echo "CI failed on PR #$PR_NUM. Closing + deleting branch."
gh pr close "$PR_NUM" --delete-branch
exit 0
fi
# If PR is conflicted, close + delete the branch.
MERGEABLE=$(gh pr view "$PR_NUM" --json mergeable --jq '.mergeable')
if [ "$MERGEABLE" = "CONFLICTING" ]; then
echo "PR #$PR_NUM is conflicted. Closing + deleting branch."
gh pr close "$PR_NUM" --delete-branch
exit 0
fi
done
echo "Timeout reached. Auto-merge will handle PR #$PR_NUM if CI passes."
-51
View File
@@ -1,51 +0,0 @@
# .github/workflows/js-tests.yml
name: JS Tests
on:
workflow_call:
jobs:
workspaces:
name: List npm workspaces
runs-on: ubuntu-latest
timeout-minutes: 20
outputs:
packages: ${{ steps.set-matrix.outputs.packages }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4
with:
node-version: 22
cache: npm
- uses: ./.github/actions/retry
with:
command: npm ci --ignore-scripts
- id: set-matrix
run: |
PACKAGES=$(npm query .workspace | jq -c '[.[].location]')
if [ "$PACKAGES" = "[]" ] || [ -z "$PACKAGES" ]; then
echo "::error::Workspace discovery produced an empty package list — refusing to emit a zero-length matrix (would skip all JS/TS checks silently)."
exit 1
fi
echo "packages=$PACKAGES" >> "$GITHUB_OUTPUT"
check:
name: Typecheck & Test
needs: workspaces
runs-on: ubuntu-latest
timeout-minutes: 20
strategy:
matrix:
package: ${{ fromJson(needs.workspaces.outputs.packages) }}
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
- uses: ./.github/actions/retry
with:
command: npm ci
- run: npm run --prefix ${{ matrix.package }} check
- run: npm run --prefix ${{ matrix.package }} fix
-81
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@@ -1,81 +0,0 @@
name: Label rerun
# When the ``ci-reviewed`` label is added to a PR, rerun all failed jobs in
# the latest CI run. This re-evaluates ``review-labels`` (which now sees the
# label) and GitHub automatically reruns dependent jobs (``comment-live``,
# ``all-checks-pass``) — so the review comment gets updated too.
#
# If the CI run is still in progress when the label is added, we wait for it
# to finish before rerunning (``gh run rerun`` only works on completed runs).
# The wait can be long (20+ min for a full CI run), but it's better than
# silently failing and leaving the reviewer stuck.
on:
pull_request:
types: [labeled]
permissions:
actions: write
pull-requests: read
concurrency:
group: label-rerun-${{ github.event.pull_request.number }}
cancel-in-progress: true
jobs:
rerun-review-labels:
name: Rerun review-labels job
if: github.event.label.name == 'ci-reviewed'
runs-on: ubuntu-latest
timeout-minutes: 40
steps:
- name: Wait for CI run to finish, then rerun failed jobs
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
REPO: ${{ github.repository }}
HEAD_SHA: ${{ github.event.pull_request.head.sha }}
run: |
set -uo pipefail
# Find the latest CI run for this PR's head SHA.
RUN_ID=$(gh run list \
--repo "$REPO" \
--commit "$HEAD_SHA" \
--workflow ci.yml \
--limit 1 \
--json databaseId,status \
--jq '.[0] | "\(.databaseId) \(.status)"' 2>/dev/null || true)
if [ -z "$RUN_ID" ]; then
echo "No CI run found for this PR — nothing to rerun."
exit 0
fi
# Split "RUN_ID STATUS" into two vars.
RUN_ID="${RUN_ID%% *}"
STATUS="${RUN_ID##* }"
echo "Latest CI run: $RUN_ID (status: $STATUS)"
# If the run is still in progress, wait for it to finish.
# gh run rerun only works on completed runs — if we try while it's
# running, GitHub rejects with "cannot be rerun; This workflow is
# already running".
if [ "$STATUS" != "completed" ]; then
echo "Run is $STATUS — waiting for completion (this may take a while)..."
# gh run watch --exit-status exits non-zero if the run fails,
# which is expected (the label gate fails). Don't let that kill
# the workflow — we WANT to rerun failed jobs.
timeout 2100 gh run watch "$RUN_ID" --repo "$REPO" --interval 15 || true
# Verify it's actually completed now.
STATUS=$(gh run view "$RUN_ID" --repo "$REPO" --json status --jq '.status' 2>/dev/null || echo "unknown")
if [ "$STATUS" != "completed" ]; then
echo "Run is still $STATUS after wait — giving up."
exit 0
fi
fi
echo "Run completed. Rerunning all failed jobs..."
gh run rerun "$RUN_ID" --repo "$REPO" --failed || true
echo "Done. GitHub will rerun review-labels and all dependent jobs."
+5 -9
View File
@@ -2,14 +2,11 @@ name: Lint (ruff + ty)
# Two things here:
# 1. Advisory diff — ruff + ty diagnostics as a diff vs the target branch.
# Writes a Markdown summary to the run page. Exit zero always.
# Posts a Markdown summary and a PR comment. Exit zero always.
# 2. Blocking ``ruff check .`` — enforces the explicit rules in
# ``[tool.ruff.lint.select]`` (currently PLW1514). Failure blocks merge.
# Separate job so the advisory diff still runs even when enforcement
# fails.
#
# CI-sensitive file review was previously here as a ``ci-review`` job but
# has moved to ``review-labels.yml`` so it can be rerun independently.
# Separate job so the advisory diff still runs and posts even when
# enforcement fails.
on:
workflow_call:
@@ -21,6 +18,7 @@ on:
permissions:
contents: read
pull-requests: write # needed to post/update PR comments
concurrency:
group: lint-${{ github.ref }}
@@ -100,8 +98,6 @@ jobs:
echo "base ty: $(wc -c < .lint-reports/base/ty.json) bytes"
- name: Generate diff summary
env:
HEAD_REF: ${{ inputs.event_name == 'pull_request' && github.head_ref || github.ref_name }}
run: |
python scripts/lint_diff.py \
--base-ruff .lint-reports/base/ruff.json \
@@ -109,7 +105,7 @@ jobs:
--base-ty .lint-reports/base/ty.json \
--head-ty .lint-reports/head/ty.json \
--base-ref "${{ steps.base.outputs.ref }}" \
--head-ref "$HEAD_REF" \
--head-ref "${{ inputs.event_name == 'pull_request' && github.head_ref || github.ref_name }}" \
--output .lint-reports/summary.md
cat .lint-reports/summary.md >> "$GITHUB_STEP_SUMMARY"
-95
View File
@@ -1,95 +0,0 @@
name: Lockfile diff
# Advisory PR comment showing the *semantic* diff of package-lock.json
# changes — which packages were added/removed/updated and their versions.
# The raw textual diff of a lockfile is unreadable (npm reorders entries
# and rewrites integrity hashes), so scripts/ci/lockfile_diff.py parses
# the ``packages`` map at the merge base and at HEAD and set-diffs the
# {install path: version} maps instead.
#
# The semantic diff is exposed as a workflow_call output ``review_status``
# (a JSON array in the unified status format) and an artifact
# (``lockfile-diff`` containing the markdown fragment) for the step
# summary.
#
# Never blocking — this is review signal, not enforcement.
on:
workflow_call:
outputs:
changed:
description: Whether package-lock.json changed relative to the target branch.
value: ${{ jobs.diff.outputs.changed }}
review_status:
description: JSON array of review status objects for the unified PR comment.
value: ${{ jobs.diff.outputs.review_status }}
permissions:
contents: read
concurrency:
group: lockfile-diff-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
diff:
name: package-lock.json semantic diff
runs-on: ubuntu-latest
timeout-minutes: 5
outputs:
changed: ${{ steps.diff.outputs.changed }}
review_status: ${{ steps.emit-status.outputs.review_status }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0 # need history for the merge base
- name: Generate semantic lockfile diff
id: diff
run: |
set -euo pipefail
# Three-dot semantics by hand: diff from the merge base with the
# target branch to the PR head, so changes that landed on main
# after the branch point don't show up as this PR's doing.
BASE_SHA=$(git merge-base "origin/${{ github.base_ref }}" HEAD)
echo "Merge base: ${BASE_SHA}"
python3 scripts/ci/lockfile_diff.py \
--base "$BASE_SHA" \
--head HEAD \
--output /tmp/lockfile-diff.md
if [ -s /tmp/lockfile-diff.md ]; then
echo "changed=true" >> "$GITHUB_OUTPUT"
{
echo "## package-lock.json semantic diff"
echo ""
cat /tmp/lockfile-diff.md
} >> "$GITHUB_STEP_SUMMARY"
else
echo "changed=false" >> "$GITHUB_OUTPUT"
: > /tmp/lockfile-diff.md
fi
- name: Emit review_status
id: emit-status
run: |
set -euo pipefail
CHANGED="${{ steps.diff.outputs.changed }}"
STATUS="[]"
if [ "$CHANGED" = "true" ]; then
CONTENT=$(cat /tmp/lockfile-diff.md | python3 -c "import sys,json; print(json.dumps(sys.stdin.read()))")
STATUS="[{\"source\":\"lockfile-diff\",\"results\":[{\"kind\":\"action_required\",\"title\":\"package-lock.json\",\"summary\":\"Locked npm dependency versions changed.\",\"detail\":${CONTENT},\"how_to_fix\":\"Add the \`ci-reviewed\` label after verifying the version changes are expected.\"}]}"
else
STATUS="[]"
fi
echo "review_status=${STATUS}" >> "$GITHUB_OUTPUT"
- name: Upload diff artifact
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
with:
name: lockfile-diff
path: /tmp/lockfile-diff.md
retention-days: 1
overwrite: true
+2 -79
View File
@@ -18,21 +18,17 @@ name: OSV-Scanner
# Findings land in the repo's Security tab (Code Scanning > OSV-Scanner).
# fail-on-vuln is disabled so the job does not block merges on pre-existing
# vulnerabilities in pinned deps that we may need to patch deliberately.
#
# The reusable workflow can't emit custom outputs, so a wrapper job
# downloads the SARIF result and summarizes the vulnerability count into
# a review_status for the unified PR comment.
on:
workflow_call:
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:
# Required to upload SARIF file to CodeQL. See: https://github.com/github/codeql-action/issues/2117
# Required by the reusable workflow to upload SARIF to the Security tab.
actions: read
contents: read
security-events: write
@@ -49,76 +45,3 @@ jobs:
--lockfile=package-lock.json
--lockfile=website/package-lock.json
fail-on-vuln: false
emit-status:
name: Emit review status
runs-on: ubuntu-latest
needs: scan
if: always()
outputs:
review_status: ${{ steps.emit.outputs.review_status }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Download SARIF result
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4
with:
name: osv-results
path: /tmp/osv-results
continue-on-error: true
- name: Emit review_status
id: emit
run: |
set -euo pipefail
STATUS="[]"
if [ -f /tmp/osv-results/osv-results.sarif ]; then
# Count vulnerabilities from the SARIF file
VULN_COUNT=$(python3 -c "
import json, sys
try:
with open('/tmp/osv-results/osv-results.sarif') as f:
data = json.load(f)
count = 0
vulns = []
for run in data.get('runs', []):
for result in run.get('results', []):
count += 1
rule_id = result.get('ruleId', 'unknown')
message = result.get('message', {}).get('text', '')
loc = result.get('locations', [{}])[0].get('physicalLocation', {}).get('artifactLocation', {}).get('uri', '')
vulns.append(f'- {rule_id} in {loc}: {message}')
print(count)
if vulns:
print('\n'.join(vulns[:20]), file=sys.stderr)
except Exception:
print(0)
")
VULN_DETAIL=""
if [ "$VULN_COUNT" -gt 0 ] 2>/dev/null; then
VULN_PLURAL=$([ "$VULN_COUNT" -eq 1 ] && echo "y" || echo "ies")
VULN_DETAIL=$(python3 -c "
import json, sys
try:
with open('/tmp/osv-results/osv-results.sarif') as f:
data = json.load(f)
vulns = []
for run in data.get('runs', []):
for result in run.get('results', []):
rule_id = result.get('ruleId', 'unknown')
loc = result.get('locations', [{}])[0].get('physicalLocation', {}).get('artifactLocation', {}).get('uri', '')
vulns.append(f'- {rule_id} in {loc}')
print(json.dumps('\n'.join(vulns[:20])))
except Exception:
print(json.dumps(''))
")
STATUS="[{\"source\":\"osv scan\",\"results\":[{\"kind\":\"warning\",\"title\":\"OSV vulnerability scan\",\"summary\":\"${VULN_COUNT} known vulnerabilit${VULN_PLURAL} found in pinned dependencies.\",\"detail\":${VULN_DETAIL},\"how_to_fix\":\"Review the findings in the [Security tab](../../security/code-scanning). Update the affected dependencies if a patched version is available.\"}]}]"
else
STATUS="[]"
fi
fi
echo "review_status=${STATUS}" >> "$GITHUB_OUTPUT"
-92
View File
@@ -1,92 +0,0 @@
name: Review labels
# Require explicit maintainer review when CI-sensitive files or the MCP
# catalog change. Previously this was split across two jobs in two
# workflows: ``ci-review`` in lint.yml (gated on ``ci_review``) and
# ``mcp-catalog-review`` in supply-chain-audit.yml (gated on
# ``mcp_catalog``). Both checked for their own label.
#
# Now consolidated: a single ``ci-reviewed`` label covers both. The
# comment sections tell the reviewer exactly what to verify per area,
# so one label is enough — the human reads the comment, not the label
# name.
#
# Outputs:
# ci_reviewed — "true" / "false" / "" (empty when neither lane ran)
# review_status — JSON array of status objects consumed by the review
# comment assembler. See scripts/ci/emit_review_status.py.
on:
workflow_call:
inputs:
ci_review:
description: Whether CI-sensitive files (eslint config, workflows, actions) changed.
type: boolean
default: false
mcp_catalog:
description: Whether the MCP catalog / installer changed.
type: boolean
default: false
outputs:
ci_reviewed:
description: Whether the ci-reviewed label is present. Empty when neither input was true.
value: ${{ jobs.check.outputs.ci_reviewed }}
review_status:
description: JSON array of status objects for the review comment assembler.
value: ${{ jobs.check.outputs.review_status }}
permissions:
contents: read
pull-requests: read # read PR labels
jobs:
check:
name: Review label gate
if: inputs.ci_review || inputs.mcp_catalog
runs-on: ubuntu-latest
timeout-minutes: 2
outputs:
ci_reviewed: ${{ steps.label-check.outputs.ci_reviewed }}
review_status: ${{ steps.build-status.outputs.review_status }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Check ci-reviewed label
id: label-check
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
REPO: ${{ github.repository }}
run: |
set -euo pipefail
PR="${{ github.event.pull_request.number }}"
LABELS=$(gh pr view "$PR" --repo "$REPO" --json labels --jq '.labels[].name' || true)
if echo "$LABELS" | grep -Fxq 'ci-reviewed'; then
echo "ci-reviewed label present."
echo "ci_reviewed=true" >> "$GITHUB_OUTPUT"
else
echo "ci-reviewed label missing."
echo "ci_reviewed=false" >> "$GITHUB_OUTPUT"
fi
- name: Build review_status JSON
id: build-status
env:
CI_REVIEW: ${{ inputs.ci_review }}
MCP_CATALOG: ${{ inputs.mcp_catalog }}
LABEL_PRESENT: ${{ steps.label-check.outputs.ci_reviewed }}
run: |
set -euo pipefail
ARGS=""
if [ "$CI_REVIEW" = "true" ]; then ARGS="$ARGS --ci-review"; fi
if [ "$MCP_CATALOG" = "true" ]; then ARGS="$ARGS --mcp-catalog"; fi
if [ "$LABEL_PRESENT" = "true" ]; then ARGS="$ARGS --label-present"; fi
python3 scripts/ci/emit_review_status.py $ARGS --output "$GITHUB_OUTPUT"
- name: Fail on missing label
if: steps.label-check.outputs.ci_reviewed != 'true'
run: |
echo "::error::CI-sensitive changes require the ci-reviewed label. Add the label and re-run this check."
exit 1
+2 -11
View File
@@ -20,7 +20,6 @@ jobs:
check-freshness:
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Probe live index
id: probe
@@ -29,7 +28,7 @@ jobs:
URL="https://hermes-agent.nousresearch.com/docs/api/skills-index.json"
echo "Probing $URL"
# -L follows redirects; -f fails on HTTP errors; -s suppresses progress
if ! curl -fsSL --retry 3 --retry-delay 10 -o /tmp/skills-index.json "$URL"; then
if ! curl -fsSL -o /tmp/skills-index.json "$URL"; then
echo "status=fetch-failed" >> "$GITHUB_OUTPUT"
echo "detail=Could not download $URL" >> "$GITHUB_OUTPUT"
exit 0
@@ -108,18 +107,10 @@ jobs:
echo "Summary: ${{ steps.probe.outputs.summary }}"
fi
- name: Get GitHub App token
if: steps.probe.outputs.status != 'ok'
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Open issue on degraded / failed probe
if: steps.probe.outputs.status != 'ok'
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
STATUS: ${{ steps.probe.outputs.status }}
DETAIL: ${{ steps.probe.outputs.detail }}
run: |
+3 -20
View File
@@ -20,29 +20,19 @@ jobs:
# Only run on the upstream repository, not on forks
if: github.repository == 'NousResearch/hermes-agent'
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: "3.11"
- name: Install dependencies
uses: ./.github/actions/retry
with:
command: pip install httpx==0.28.1 pyyaml==6.0.2
run: pip install httpx==0.28.1 pyyaml==6.0.2
- name: Build skills index
env:
GITHUB_TOKEN: ${{ steps.app-token.outputs.token }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: python scripts/build_skills_index.py
- name: Upload index artifact
@@ -59,15 +49,8 @@ jobs:
needs: build-index
if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch'
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Trigger Deploy Site workflow
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: gh workflow run deploy-site.yml --repo ${{ github.repository }} -f skills_index_run_id=${{ github.run_id }}
+67 -109
View File
@@ -10,17 +10,9 @@ name: Supply Chain Audit
# advisory-only workflow instead.
#
# Path-gating is handled centrally by the ``ci.yml`` orchestrator's
# ``detect`` job. The orchestrator passes ``scan`` / ``deps`` booleans as
# inputs; this workflow's jobs gate on those inputs instead of re-computing
# the diff. MCP catalog review was previously here but has moved to
# ``review-labels.yml`` so it can be rerun independently.
#
# Outputs:
# review_status — JSON array of status objects consumed by the review
# comment assembler (scripts/ci/assemble_review_comment.py).
# Each job (``scan``, ``dep-bounds``) emits its own
# array; an ``aggregate`` job merges them into the
# workflow-level output.
# ``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:
@@ -37,10 +29,10 @@ on:
description: Whether pyproject.toml changed.
type: boolean
required: true
outputs:
review_status:
description: JSON array of review status objects for the review comment assembler.
value: ${{ jobs.aggregate.outputs.review_status }}
mcp_catalog:
description: Whether the MCP catalog / installer changed.
type: boolean
required: true
permissions:
pull-requests: write
@@ -51,26 +43,16 @@ jobs:
name: Scan PR for critical supply chain risks
if: inputs.scan
runs-on: ubuntu-latest
timeout-minutes: 15
outputs:
review_status: ${{ steps.emit-status.outputs.review_status }}
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Scan diff for critical patterns
id: scan
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
set -euo pipefail
@@ -78,7 +60,7 @@ jobs:
HEAD="${{ github.event.pull_request.head.sha }}"
# Added lines only, excluding lockfiles.
# Three-point diff (base...head) diffs from the merge base to HEAD,
# Three-dot diff (base...head) diffs from the merge base to HEAD,
# so only changes introduced by this PR are included — not changes
# that landed on main after the PR branched off.
DIFF=$(git diff "$BASE"..."$HEAD" -- . ':!uv.lock' ':!*.lock' ':!package-lock.json' ':!yarn.lock' || true)
@@ -156,50 +138,32 @@ jobs:
echo "found=false" >> "$GITHUB_OUTPUT"
fi
- name: Emit review_status
id: emit-status
if: always()
- name: Post critical finding comment
if: steps.scan.outputs.found == 'true'
env:
FOUND: ${{ steps.scan.outputs.found }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
python3 - <<'PYEOF'
import json, os
BODY="## 🚨 CRITICAL Supply Chain Risk Detected
found = os.environ.get("FOUND", "") == "true"
This PR contains a pattern that has been used in real supply chain attacks. A maintainer must review the flagged code carefully before merging.
if found:
with open("/tmp/findings.md", encoding="utf-8") as f:
detail = f.read()
status = [{
"source": "supply chain",
"results": [{
"kind": "error",
"title": "Critical supply chain risk",
"summary": "Critical supply chain risk patterns detected in this PR.",
"detail": detail,
"how_to_fix": "Review the flagged code carefully. If intentional, add the `ci-reviewed` label."
}]
}]
else:
status = []
$(cat /tmp/findings.md)
with open(os.environ["GITHUB_OUTPUT"], "a", encoding="utf-8") as f:
f.write(f"review_status={json.dumps(status)}\n")
PYEOF
---
*Scanner only fires on high-signal indicators: .pth files, base64+exec/eval combos, subprocess with encoded commands, or install-hook files. Low-signal warnings were removed intentionally — if you're seeing this comment, the finding is worth inspecting.*"
gh pr comment "${{ github.event.pull_request.number }}" --body "$BODY" || echo "::warning::Could not post PR comment (expected for fork PRs — GITHUB_TOKEN is read-only)"
- name: Fail on critical findings
if: steps.scan.outputs.found == 'true'
run: |
echo "::error::CRITICAL supply chain risk patterns detected in this PR. See the review comment for details."
echo "::error::CRITICAL supply chain risk patterns detected in this PR. See the PR comment for details."
exit 1
dep-bounds:
name: Check PyPI dependency upper bounds
if: inputs.deps
runs-on: ubuntu-latest
timeout-minutes: 15
outputs:
review_status: ${{ steps.emit-status.outputs.review_status }}
steps:
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
@@ -222,7 +186,7 @@ jobs:
exit 0
fi
# Match PyPI dep specs that have >= and no < ceiling.
# Match PyPI dep specs that have >= but no < ceiling.
# Pattern: "package>=version" without a following ",<" bound.
# Excludes git+ URLs (which use commit SHAs) and comments.
UNBOUNDED=$(echo "$ADDED" | grep -oE '"[a-zA-Z0-9_-]+(\[[^\]]*\])?>=[ 0-9.]+"' | grep -v ',<' || true)
@@ -234,36 +198,26 @@ jobs:
echo "found=false" >> "$GITHUB_OUTPUT"
fi
- name: Emit review_status
id: emit-status
if: always()
- name: Post unbounded dep warning
if: steps.bounds.outputs.found == 'true'
env:
FOUND: ${{ steps.bounds.outputs.found }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
python3 - <<'PYEOF'
import json, os
BODY="## ⚠️ Unbounded PyPI Dependency Detected
found = os.environ.get("FOUND", "") == "true"
This PR adds PyPI dependencies without a \`<next_major\` upper bound. Per our [supply chain policy](../blob/main/CONTRIBUTING.md#dependency-pinning-policy-supply-chain-hardening), all PyPI deps must be pinned as \`>=floor,<next_major\`.
if found:
with open("/tmp/unbounded.txt", encoding="utf-8") as f:
detail = f.read()
status = [{
"source": "supply chain",
"results": [{
"kind": "action_required",
"title": "Unbounded PyPI dependencies",
"summary": "This PR adds PyPI dependencies without upper bounds.",
"detail": detail,
"how_to_fix": 'Add a `<next_major` upper bound, e.g. `"package>=1.2.0,<2"`. See CONTRIBUTING.md dependency pinning policy.'
}]
}]
else:
status = []
**Unbounded specs found:**
\`\`\`
$(cat /tmp/unbounded.txt)
\`\`\`
with open(os.environ["GITHUB_OUTPUT"], "a", encoding="utf-8") as f:
f.write(f"review_status={json.dumps(status)}\n")
PYEOF
**Fix:** Add an upper bound, e.g. \`"package>=1.2.0,<2"\`
---
*See PR #2810 and CONTRIBUTING.md for the full policy rationale.*"
gh pr comment "${{ github.event.pull_request.number }}" --body "$BODY" || echo "::warning::Could not post PR comment (expected for fork PRs)"
- name: Fail on unbounded deps
if: steps.bounds.outputs.found == 'true'
@@ -271,36 +225,40 @@ jobs:
echo "::error::PyPI dependencies without upper bounds detected. Add <next_major ceiling per CONTRIBUTING.md policy."
exit 1
aggregate:
name: Aggregate review statuses
needs: [scan, dep-bounds]
if: always()
mcp-catalog-review:
name: MCP catalog security review
if: inputs.mcp_catalog
runs-on: ubuntu-latest
timeout-minutes: 15
outputs:
review_status: ${{ steps.merge.outputs.review_status }}
steps:
- name: Merge review statuses
id: merge
- name: Checkout
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
fetch-depth: 0
- name: Require explicit MCP catalog review label
env:
SCAN_STATUS: ${{ needs.scan.outputs.review_status }}
DEP_STATUS: ${{ needs.dep-bounds.outputs.review_status }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
python3 - <<'PYEOF'
import json, os
set -euo pipefail
PR="${{ github.event.pull_request.number }}"
LABELS=$(gh pr view "$PR" --json labels --jq '.labels[].name' || true)
if echo "$LABELS" | grep -Fxq 'mcp-catalog-reviewed'; then
echo "MCP catalog review label present."
exit 0
fi
merged = []
for key in ("SCAN_STATUS", "DEP_STATUS"):
raw = os.environ.get(key, "")
if not raw:
continue
try:
data = json.loads(raw)
except (json.JSONDecodeError, TypeError):
continue
if isinstance(data, list):
merged.extend(data)
BODY="## ⚠️ MCP catalog security review required
with open(os.environ["GITHUB_OUTPUT"], "a", encoding="utf-8") as f:
f.write(f"review_status={json.dumps(merged)}\n")
PYEOF
This PR changes the bundled MCP catalog or MCP catalog installer code. MCP entries can define local commands that users later install into \`mcp_servers\`, so this needs explicit maintainer review before merge.
A maintainer should verify:
- any new/changed \`optional-mcps/**/manifest.yaml\` command and args are expected,
- stdio transports do not use shell+egress/exfiltration payloads,
- git install refs are pinned and bootstrap commands are minimal,
- requested env vars/secrets match the upstream MCP's documented needs.
After review, add the \`mcp-catalog-reviewed\` label and re-run this check."
gh pr comment "$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
-11
View File
@@ -20,7 +20,6 @@ jobs:
generate:
name: "Generate slices"
runs-on: ubuntu-latest
timeout-minutes: 10
outputs:
matrix: ${{ steps.matrix.outputs.matrix }}
steps:
@@ -32,12 +31,6 @@ jobs:
with:
path: test_durations.json
key: test-durations
# Saves use test-durations-${run_id}, so the exact key above never
# matches — without this prefix fallback the cache ALWAYS missed,
# LPT slicing ran on no data, and unbalanced slices pushed heavy
# files toward the per-file timeout under load.
restore-keys: |
test-durations-
- name: Generate test slices
id: matrix
@@ -121,9 +114,6 @@ jobs:
NOUS_API_KEY: ""
- name: Upload per-slice durations
# Advisory artifact (feeds slice balancing) — a transient artifact-
# service blip must not fail an otherwise-green test slice.
continue-on-error: true
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: test-durations-slice-${{ matrix.slice.index }}
@@ -136,7 +126,6 @@ jobs:
needs: test
if: needs.test.result == 'success' && github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Download all slice durations
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
+51
View File
@@ -0,0 +1,51 @@
# .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
+4 -28
View File
@@ -26,7 +26,6 @@ jobs:
build:
name: Build distribution 📦
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
@@ -57,24 +56,10 @@ jobs:
node-version: "22"
- name: Build web dashboard
uses: ./.github/actions/retry
with:
command: npm ci
working-directory: web
- name: Compile web dashboard
run: npm run build
working-directory: web
run: cd web && npm ci && npm run build
- name: Build TUI bundle
uses: ./.github/actions/retry
with:
command: npm ci
working-directory: ui-tui
- name: Compile TUI bundle
run: npm run build
working-directory: ui-tui
run: cd ui-tui && npm ci && npm run build
- name: Bundle TUI into hermes_cli
run: |
@@ -105,7 +90,6 @@ jobs:
name: Publish to PyPI
needs: build
runs-on: ubuntu-latest
timeout-minutes: 30
environment:
name: pypi
url: https://pypi.org/p/hermes-agent
@@ -131,7 +115,6 @@ jobs:
if: startsWith(github.ref, 'refs/tags/')
needs: publish
runs-on: ubuntu-latest
timeout-minutes: 30
permissions:
contents: write # attach assets to the existing release
id-token: write # sigstore signing
@@ -143,16 +126,9 @@ jobs:
name: python-package-distributions
path: dist/
- name: Get GitHub App token
id: app-token
uses: ./.github/actions/get-app-token
with:
client-id: ${{ secrets.APP_CLIENT_ID }}
private-key: ${{ secrets.APP_PRIVATE_KEY }}
- name: Wait for GitHub Release to exist
env:
GITHUB_TOKEN: ${{ steps.app-token.outputs.token }}
GITHUB_TOKEN: ${{ github.token }}
# release.py creates the GitHub Release after pushing the tag,
# but this workflow starts from the tag push — wait for it.
run: |
@@ -178,7 +154,7 @@ jobs:
- name: Attach signed artifacts to GitHub Release
if: env.skip_sign != 'true'
env:
GITHUB_TOKEN: ${{ steps.app-token.outputs.token }}
GITHUB_TOKEN: ${{ github.token }}
# release.py already created the GitHub Release — just upload
# the Sigstore signatures alongside the existing assets.
run: >-
+1 -25
View File
@@ -45,10 +45,6 @@ name: uv.lock check
on:
workflow_call:
outputs:
review_status:
description: "JSON review status for the review-status aggregator"
value: ${{ jobs.check.outputs.review_status }}
permissions:
contents: read
@@ -62,8 +58,6 @@ jobs:
name: uv lock --check
runs-on: ubuntu-latest
timeout-minutes: 5
outputs:
review_status: ${{ steps.verify.outputs.review_status }}
steps:
- name: Checkout code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
@@ -79,22 +73,8 @@ jobs:
# of this file) — failures often mean "your branch is behind main,
# rebase and regenerate uv.lock."
- name: Verify uv.lock is up-to-date
id: verify
run: |
# uv lock --check re-resolves against PyPI (network). Retry so a
# registry blip doesn't read as "lockfile stale". A genuinely stale
# lockfile fails all attempts (deterministic), costing only seconds.
ok=false
for i in 1 2 3; do
if uv lock --check; then
ok=true
break
fi
[ "$i" = 3 ] && break
echo "::warning::uv lock --check failed (attempt $i); retrying in 10s"
sleep 10
done
if [ "$ok" != true ]; then
if ! uv lock --check; then
cat <<'EOF' >> "$GITHUB_STEP_SUMMARY"
## ❌ uv.lock is out of sync with pyproject.toml
@@ -124,9 +104,5 @@ jobs:
on `main` post-merge.
EOF
echo "::error title=uv.lock out of sync::Run \`uv lock\` locally and commit the result. If on a PR, sync with main first."
review_status='[{"source":"uv.lock check","results":[{"kind":"action_required","title":"uv.lock out of sync","summary":"uv.lock is out of sync with pyproject.toml.","how_to_fix":"Run `uv lock` locally and commit the result. If on a PR, sync with main first:\n```\ngit fetch origin main\ngit rebase origin/main\nuv lock\ngit add uv.lock\ngit commit -m \"chore: refresh uv.lock\"\n```\n"}]}]'
echo "review_status=${review_status}" >> "$GITHUB_OUTPUT"
exit 1
fi
review_status='[]'
echo "review_status=${review_status}" >> "$GITHUB_OUTPUT"
+1 -22
View File
@@ -4,13 +4,10 @@
/_pycache/
*.pyc*
__pycache__/
act/
.act-sandbox-agent.*
.venv/
.venv
.vscode/
.env
.op.env
.env.local
.env.development.local
.env.test.local
@@ -56,10 +53,6 @@ __pycache__/
hermes_agent.egg-info/
wandb/
testlogs
playwright-report/
test-results/
# Playwright visual regression baselines — cached from main in CI, not committed
*-snapshots/
# CLI config (may contain sensitive SSH paths)
cli-config.yaml
@@ -74,19 +67,8 @@ environments/benchmarks/evals/
hermes_cli/web_dist/
apps/desktop/build/
apps/desktop/dist/
# tsc-emitted artifacts (a stray `tsc -b` compiles into src/, and vite then
# resolves the stale .js OVER the .tsx — never track these)
apps/desktop/src/**/*.js
apps/desktop/src/**/*.js.map
apps/desktop/src/**/*.d.ts
!apps/desktop/src/global.d.ts
!apps/desktop/src/vite-env.d.ts
apps/shared/src/**/*.js
apps/shared/src/**/*.js.map
apps/shared/src/**/*.d.ts
apps/desktop/release/
*.tsbuildinfo
apps/desktop/*.tsbuildinfo
# Web UI assets — synced from @nous-research/ui at build time via
# `npm run sync-assets` (see web/package.json).
@@ -136,9 +118,6 @@ docs/superpowers/*
# treat it as a local edit and autostash it on every run (#38529).
.hermes-bootstrap-complete
# Persistent dev sandbox dir (scripts/dev-sandbox.sh --persistent)
.hermes-sandbox/
# 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.
-4
View File
@@ -1,4 +0,0 @@
# Lockfiles must never be reformatted — main has a repo rule requiring
# team approval when lockfiles change, so an autofix PR touching one
# would hang waiting for review.
package-lock.json
+12 -89
View File
@@ -491,18 +491,18 @@ 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 entirely: `serve` sets `headless_backend=True`, so `cmd_dashboard` skips `_build_web_ui` AND exports `HERMES_SERVE_HEADLESS=1` so `mount_spa()` disables the SPA even if a stray `web_dist/` exists — only the JSON-RPC/WS/API surface is reachable). `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.ts` + `backendSupportsServe()` in `electron/main.ts`) 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. For scoped Desktop architecture, state, resolver, transport, and testing rules, read `apps/desktop/AGENTS.md`.
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`.
**Slash commands in the desktop app are curated client-side, then dispatched to the backend.** The pipeline:
- **Backend already provides everything.** `tui_gateway/server.py` `commands.catalog` (empty-query list) and `complete.slash` (typed-query completions) both include built-in commands, user `quick_commands`, AND skill-derived commands (`scan_skill_commands()` / `get_skill_commands()`). The desktop app does not need a new RPC to see skills.
- **The renderer curates via `apps/desktop/src/lib/desktop-slash-commands.ts`.** This is the load-bearing file. It holds `DESKTOP_COMMAND_SPECS` (the built-ins and their Desktop surfaces) plus `NO_DESKTOP_SURFACE` block-lists for terminal-only / messaging-only / picker-owned / settings-owned / advanced commands that should NOT clutter the desktop popover.
- **The renderer curates via `apps/desktop/src/lib/desktop-slash-commands.ts`.** This is the load-bearing file. It holds `DESKTOP_COMMANDS` (the ~19 built-ins shown in the palette) plus block-lists for terminal-only / messaging-only / picker-owned / settings-owned / advanced commands that should NOT clutter the desktop popover.
- `isDesktopSlashCommand(name)` — gates **execution**. Returns true for built-ins AND for any non-built-in (skill / quick command), so typed extension commands run.
- `isDesktopSlashSuggestion(name)` — gates **discovery/completion**. Used by BOTH completion paths in `app/chat/composer/hooks/use-slash-completions.ts` (empty-query catalog filter + typed-query `complete.slash` filter) and by `filterDesktopCommandsCatalog`.
- `isDesktopSlashExtensionCommand(name)` — true when the command is NOT a known Hermes built-in (i.e. a skill or user quick command). Both suggestion and catalog-filter paths allow extensions through so skill commands surface in the palette. (Added when fixing "skill commands missing from the desktop slash palette" — the curated allow-list was silently dropping every skill/quick command from completions even though they executed fine when typed.)
- **Dispatch** lives in `app/session/hooks/use-prompt-actions/slash.ts` (`runSlash`): built-ins that the desktop owns (`/skin`, `/help`, `/new`, …) are handled locally or via `commands.catalog`; everything else goes to `slash.exec`, falling back to `command.dispatch` (which the gateway resolves into skill / alias / exec directives). A skill command resolves to `{type: "skill", message}` and is submitted as a normal prompt.
- **Dispatch** lives in `app/session/hooks/use-prompt-actions.ts` (`runSlash`): built-ins that the desktop owns (`/skin`, `/help`, `/new`, …) are handled locally or via `commands.catalog`; everything else goes to `slash.exec`, falling back to `command.dispatch` (which the gateway resolves into skill / alias / exec directives). A skill command resolves to `{type: "skill", message}` and is submitted as a normal prompt.
**Rule:** the desktop slash palette's curation is about hiding noise (terminal-only / messaging-only built-ins), NOT about hiding user-activated extensions. Skill commands and `quick_commands` are extensions the backend surfaces — they belong in completions. If you tighten `desktop-slash-commands.ts`, keep `isDesktopSlashExtensionCommand` flowing into both the suggestion and catalog-filter paths. Tests: from `apps/desktop`, run `npx vitest run src/lib/desktop-slash-commands.test.ts` (workspace dependencies are installed at the repo root).
**Rule:** the desktop slash palette's curation is about hiding noise (terminal-only / messaging-only built-ins), NOT about hiding user-activated extensions. Skill commands and `quick_commands` are extensions the backend surfaces — they belong in completions. If you tighten `desktop-slash-commands.ts`, keep `isDesktopSlashExtensionCommand` flowing into both the suggestion and catalog-filter paths. Tests: `apps/desktop/src/lib/desktop-slash-commands.test.ts` (run via the repo-root `vitest`, since `apps/desktop` resolves deps from the root workspace install).
---
@@ -1094,16 +1094,14 @@ kanban task.
- **CLI:** `hermes_cli/kanban.py` wires `hermes kanban` with verbs
`init`, `create`, `list` (alias `ls`), `show`, `assign`, `link`,
`unlink`, `comment`, `attach`, `attachments`, `attach-rm`, `complete`,
`block`, `unblock`, `archive`, `tail`, plus less-commonly-used `watch`,
`stats`, `runs`, `log`, `assignees`, `heartbeat`, `notify-*`,
`dispatch`, `daemon`, `gc`.
`unlink`, `comment`, `complete`, `block`, `unblock`, `archive`,
`tail`, plus less-commonly-used `watch`, `stats`, `runs`, `log`,
`assignees`, `heartbeat`, `notify-*`, `dispatch`, `daemon`, `gc`.
- **Worker/orchestrator toolset:** `tools/kanban_tools.py` exposes
`kanban_show`, `kanban_complete`, `kanban_block`, `kanban_heartbeat`,
`kanban_comment`, `kanban_create`, `kanban_link`, `kanban_attach`,
`kanban_attach_url`, `kanban_attachments`; profiles that explicitly
enable the `kanban` toolset outside a dispatcher-spawned task also get
`kanban_list` and `kanban_unblock` for board routing.
`kanban_comment`, `kanban_create`, `kanban_link`; profiles that
explicitly enable the `kanban` toolset outside a dispatcher-spawned
task also get `kanban_list` and `kanban_unblock` for board routing.
- **Dispatcher:** long-lived loop that (default every 60s) reclaims
stale claims, promotes ready tasks, atomically claims, and spawns
assigned profiles. Runs **inside the gateway** by default via
@@ -1280,7 +1278,6 @@ def profile_env(tmp_path, monkeypatch):
## Testing
### Python
**ALWAYS use `scripts/run_tests.sh`** — do not call `pytest` directly. The script enforces
hermetic environment parity with CI (unset credential vars, TZ=UTC, LANG=C.UTF-8,
`-n auto` xdist workers, in-tree subprocess-isolation plugin). Direct `pytest`
@@ -1294,20 +1291,12 @@ scripts/run_tests.sh tests/agent/test_foo.py::test_x # one test
scripts/run_tests.sh -v --tb=long # pass-through pytest flags
```
**Flake policy:** the runner auto-retries a failing test FILE once in a fresh
subprocess (`--file-retries`, default 1; `HERMES_TEST_FILE_RETRIES=0` to
disable). Pass-on-retry counts as green but is printed in a `⚠ FLAKY` summary
section with both attempts' output. A FLAKY report is a bug to fix, not noise
to ignore — timing-sensitive tests must not assume a quiet runner (loose
wall-clock bounds ≥ 2s, event-based sync, no `assert not _wait_until(...)`
negative-timing races).
#### Subprocess-per-test-file isolation
### Subprocess-per-test-file 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.
#### Why the wrapper
### Why the wrapper
| | Without wrapper | With wrapper |
| ------------------- | ------------------------------------------- | ----------------------------------------- |
@@ -1316,17 +1305,6 @@ ContextVars from one test file cannot leak into the next.
| Timezone | Local TZ (PDT etc.) | UTC |
| Locale | Whatever is set | C.UTF-8 |
### Where to place what tests
The CI change classifier (`scripts/ci/classify_changes.py`) runs specific jobs based on what files changed. A Python test that asserts
about the contents of `package.json`, `package-lock.json`, `.ts`/`.tsx`
source, or any other JS-side artifact will not run on a PR that only touches
those files. This means a regression can go green on a PR and red on `main` (where the
classifier fails open and runs everything).
Any test that reads or asserts about `package.json`,
`package-lock.json`, `tsconfig.json`, `.ts`/`.tsx`/`.js`/`.mjs`/`.cjs`
source files configuration belongs in the JS (vitest) test suite, not in `tests/*.py`.
### Don't write change-detector tests
@@ -1376,58 +1354,3 @@ not the specific names.
Reviewers should reject new change-detector tests; authors should convert
them into invariants before re-requesting review.
### Never read source code in tests
A test that reads a source file's text is testing *the shape of the
source code*, not its behavior. This is a hard antipattern, banned outright.
Any test that reads a .py, .ts, .tsx, etc., file is suspect.
**Why it's actively harmful, not just weak:**
- It passes when the implementation is subtly broken (the regex matches a
call site that exists but is wired wrong) and fails when a correct
refactor changes formatting, variable names, or control flow with
identical runtime behavior. Both directions of failure are wrong.
- It can't be run against a built/bundled/minified artifact, so it silently
stops testing anything the moment code moves, gets renamed, or a
dependency reformats it.
- It actively blocks refactors: reviewers see "keeps a pattern intact" tests
fail during pure structural cleanup with no behavior change, and either
hand-wave the failure (dangerous) or waste time updating regexes that add
nothing (waste).
- It gives false confidence. a green suite full of source-regex tests
looks like coverage but has never once executed the code path it claims
to guard.
**Do not write:**
```ts
const source = fs.readFileSync(path.join(__dirname, 'main.ts'), 'utf8')
test('backend spawn hides the Windows console', () => {
assert.match(source, /spawn\(\s*backend\.command,\s*backend\.args[\s\S]{0,300}hiddenWindowsChildOptions/)
})
```
**Do write — extract the logic into a small pure/DI-testable function and
call it for real:**
```ts
// backend-spawn.ts
export function hiddenWindowsChildOptions(options: SpawnOptionsLike = {}, isWindows = process.platform === 'win32') {
if (!isWindows || 'windowsHide' in options) return options
return { ...options, windowsHide: true }
}
// backend-spawn.test.ts
test('windowsHide defaults to true on Windows, is left alone elsewhere', () => {
assert.equal(hiddenWindowsChildOptions({}, true).windowsHide, true)
assert.equal(hiddenWindowsChildOptions({}, false).windowsHide, undefined)
assert.equal(hiddenWindowsChildOptions({ windowsHide: false }, true).windowsHide, false)
})
```
If the logic lives inline in a god-file (`main.ts`, `cli.py`,
`gateway/run.py`) and extracting it feels disruptive: that's the actual
signal to do the extraction, not to regex around it.
+1 -1
View File
@@ -74,7 +74,7 @@ Esto no es una barra de calidad — es una decisión de acoplamiento y mantenimi
| Requisito | Notas |
|-----------|-------|
| **Git** | Con la extensión `git-lfs` instalada |
| **Python 3.11–3.13** | uv lo instalará si falta |
| **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) |
+1 -1
View File
@@ -109,7 +109,7 @@ A well-built third-party-product plugin can clear automated review and still be
| Requirement | Notes |
|-------------|-------|
| **Git** | With the `git-lfs` extension installed |
| **Python 3.11–3.13** | uv will install it if missing |
| **Python 3.11+** | uv will install it if missing |
| **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) |
+29 -31
View File
@@ -26,8 +26,8 @@ ENV PLAYWRIGHT_BROWSERS_PATH=/opt/hermes/.playwright
# replaces tini with s6-overlay's /init (PID 1 = s6-svscan), which reaps
# zombies non-blockingly on SIGCHLD and additionally supervises the main
# hermes process, the dashboard, and per-profile gateways.
RUN apt-get -o Acquire::Retries=3 update && \
apt-get -o Acquire::Retries=3 install -y --no-install-recommends \
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 && \
rm -rf /var/lib/apt/lists/*
@@ -40,30 +40,33 @@ RUN apt-get -o Acquire::Retries=3 update && \
# we map between them inline. The noarch + symlinks tarballs are
# architecture-independent and reused as-is.
#
# We use `curl` instead of `ADD` for ALL three tarballs: `ADD` evaluates its
# URL at parse time (no ARG / TARGETARCH substitution) and — critically for
# CI reliability — cannot retry, so a single GitHub-release CDN blip fails
# the whole 15-45 min build. curl -fsSL --retry 3 self-heals those blips,
# and every tarball is still checksum-verified below before extraction.
# We use `curl` instead of `ADD` for the per-arch tarball because `ADD`
# evaluates its URL at parse time, before any ARG / TARGETARCH substitution
# — splitting one URL per arch into two ADDs would download both on every
# build and leave dead bytes in the cache. A single curl + arch-keyed URL
# is simpler and cache-friendlier.
#
# Supply-chain integrity: every tarball is checksum-verified against the
# upstream-published SHA256. To bump S6_OVERLAY_VERSION, fetch the four
# `.sha256` files from the corresponding release and update the ARGs. The
# checksum lookup happens during build, so a compromised release artifact
# fails the build loudly instead of silently producing a tampered image.
ARG TARGETARCH
ARG S6_OVERLAY_VERSION=3.2.3.0
ARG S6_OVERLAY_NOARCH_SHA256=b720f9d9340efc8bb07528b9743813c836e4b02f8693d90241f047998b4c53cf
ARG S6_OVERLAY_X86_64_SHA256=a93f02882c6ed46b21e7adb5c0add86154f01236c93cd82c7d682722e8840563
ARG S6_OVERLAY_AARCH64_SHA256=0952056ff913482163cc30e35b2e944b507ba1025d78f5becbb89367bf344581
ARG S6_OVERLAY_SYMLINKS_SHA256=a60dc5235de3ecbcf874b9c1f18d73263ab99b289b9329aa950e8729c4789f0e
ADD https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-noarch.tar.xz /tmp/
ADD https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-symlinks-noarch.tar.xz /tmp/
RUN set -eu; \
case "${TARGETARCH:-amd64}" in \
amd64) s6_arch="x86_64"; s6_arch_sha="${S6_OVERLAY_X86_64_SHA256}" ;; \
arm64) s6_arch="aarch64"; s6_arch_sha="${S6_OVERLAY_AARCH64_SHA256}" ;; \
*) echo "Unsupported TARGETARCH=${TARGETARCH} for s6-overlay" >&2; exit 1 ;; \
esac; \
base="https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}"; \
curl -fsSL --retry 3 -o /tmp/s6-overlay-noarch.tar.xz \
"${base}/s6-overlay-noarch.tar.xz"; \
curl -fsSL --retry 3 -o /tmp/s6-overlay-symlinks-noarch.tar.xz \
"${base}/s6-overlay-symlinks-noarch.tar.xz"; \
curl -fsSL --retry 3 -o /tmp/s6-overlay-arch.tar.xz \
"${base}/s6-overlay-${s6_arch}.tar.xz"; \
"https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-${s6_arch}.tar.xz"; \
{ \
printf '%s %s\n' "${S6_OVERLAY_NOARCH_SHA256}" /tmp/s6-overlay-noarch.tar.xz; \
printf '%s %s\n' "${s6_arch_sha}" /tmp/s6-overlay-arch.tar.xz; \
@@ -73,19 +76,17 @@ RUN set -eu; \
tar -C / -Jxpf /tmp/s6-overlay-noarch.tar.xz; \
tar -C / -Jxpf /tmp/s6-overlay-arch.tar.xz; \
tar -C / -Jxpf /tmp/s6-overlay-symlinks-noarch.tar.xz; \
rm /tmp/s6-overlay-*.tar.xz /tmp/s6-overlay.sha256
# #34192 / #66679: backward-compat shim for orchestration templates that
# still reference the legacy /usr/bin/tini entrypoint (Hostinger's
# 'Hermes WebUI' catalog, NAS compose projects that preserve an old
# entrypoint on image update, etc.). A plain symlink to /init made the
# path exist, but forwarded tini flags like `-g` into s6-overlay's
# rc.init as the container CMD (`rc.init: 91: -g: not found`) and
# boot-looped any `restart: unless-stopped` deploy. The shim strips the
# tini CLI surface, then exec's /init + main-wrapper — see
# docker/tini-shim.sh. Safe to drop once the affected catalogs are
# updated.
COPY --chmod=0755 docker/tini-shim.sh /usr/bin/tini
rm /tmp/s6-overlay-*.tar.xz /tmp/s6-overlay.sha256; \
# #34192: backward-compat shim for orchestration templates that still\
# reference the legacy /usr/bin/tini entrypoint (e.g. Hostinger's\
# 'Hermes WebUI' catalog). The image has moved to s6-overlay /init\
# as PID 1 (see ENTRYPOINT below + the migration comment at the top\
# of this file), but external wrappers pinned to /usr/bin/tini will\
# crash with 'tini: No such file or directory' on startup. The shim\
# symlinks /usr/bin/tini -> /init so legacy wrappers exec the right\
# PID-1 reaper without behavior change for users on the current\
# ENTRYPOINT. Safe to drop once the affected catalogs are updated.\
ln -sf /init /usr/bin/tini
# Non-root user for runtime; UID can be overridden via HERMES_UID at runtime
RUN useradd -u 10000 -m -d /opt/data hermes
@@ -134,11 +135,8 @@ COPY apps/shared/ apps/shared/
# guards against a future regression if the source npm version changes.
ENV npm_config_install_links=false
RUN npm install --prefer-offline --no-audit --fetch-retries=5 && \
for i in 1 2 3; do \
npx playwright install --with-deps chromium --only-shell && break || \
{ [ "$i" = 3 ] && exit 1; echo "playwright install failed (attempt $i); retrying in 10s"; sleep 10; }; \
done && \
RUN npm install --prefer-offline --no-audit && \
npx playwright install --with-deps chromium --only-shell && \
npm cache clean --force
# ---------- Layer-cached Python dependency install ----------
-2
View File
@@ -7,7 +7,5 @@ graft locales
# built from the sdist (e.g. Homebrew, downstream packagers). package-data
# below covers the wheel; this covers the sdist. See #34034 / #28149.
recursive-include plugins plugin.yaml plugin.yml
# Gateway assets include images plus YAML catalogs such as status_phrases.yaml.
recursive-include gateway/assets *
global-exclude __pycache__
global-exclude *.py[cod]
-1
View File
@@ -109,7 +109,6 @@ hermes # Interactive CLI — start a conversation
hermes model # Choose your LLM provider and model
hermes tools # Configure which tools are enabled
hermes config set # Set individual config values
hermes config get # Print individual config values
hermes gateway # Start the messaging gateway (Telegram, Discord, etc.)
hermes setup # Run the full setup wizard (configures everything at once)
hermes claw migrate # Migrate from OpenClaw (if coming from OpenClaw)
+2 -54
View File
@@ -10,7 +10,6 @@ from __future__ import annotations
import asyncio
import json
import logging
import re
import tempfile
from concurrent.futures import TimeoutError as FutureTimeout
from contextvars import ContextVar, Token
@@ -128,64 +127,13 @@ def _proposal_for_patch_replace(arguments: dict[str, Any]) -> EditProposal:
)
def _extract_v4a_patch_paths(patch_body: str) -> list[str]:
paths: list[str] = []
for match in re.finditer(
r'^\*\*\*\s+(?:Update|Add|Delete)\s+File:\s*(.+)$',
patch_body,
re.MULTILINE,
):
path = match.group(1).strip()
if path:
paths.append(path)
for match in re.finditer(
r'^\*\*\*\s+Move\s+File:\s*(.+?)\s*->\s*(.+)$',
patch_body,
re.MULTILINE,
):
src = match.group(1).strip()
dst = match.group(2).strip()
if src:
paths.append(src)
if dst:
paths.append(dst)
return paths
def _proposal_for_patch_v4a(arguments: dict[str, Any]) -> EditProposal:
patch_body = arguments.get("patch")
if not isinstance(patch_body, str) or not patch_body:
raise ValueError("patch content required")
paths = _extract_v4a_patch_paths(patch_body)
if not paths:
raise ValueError("no file paths found in V4A patch")
proposal_path = paths[0] if len(paths) == 1 else ", ".join(paths)
old_text = _read_text_if_exists(paths[0]) if len(paths) == 1 else None
return EditProposal(
tool_name="patch",
path=proposal_path,
old_text=old_text,
# ACP only supports a single diff payload here. Surface the exact V4A
# patch content before execution so patch-mode calls are permissioned
# and denied patches cannot mutate.
new_text=patch_body,
arguments=dict(arguments),
)
def build_edit_proposal(tool_name: str, arguments: dict[str, Any]) -> EditProposal | None:
"""Return an edit proposal for supported file mutation calls."""
if tool_name == "write_file":
return _proposal_for_write_file(arguments)
if tool_name == "patch":
mode = arguments.get("mode", "replace")
if mode == "replace":
return _proposal_for_patch_replace(arguments)
if mode == "patch":
return _proposal_for_patch_v4a(arguments)
if tool_name == "patch" and arguments.get("mode", "replace") == "replace":
return _proposal_for_patch_replace(arguments)
return None
+9 -16
View File
@@ -38,22 +38,19 @@ def _permission_option_supports_kind(kind: str) -> bool:
return True
def _build_permission_options(
*, allow_permanent: bool, smart_denied: bool = False,
) -> list[PermissionOption]:
def _build_permission_options(*, allow_permanent: bool) -> list[PermissionOption]:
"""Return ACP options that match Hermes approval semantics."""
options = [PermissionOption(
option_id="allow_once", kind="allow_once", name="Allow once",
)]
if not smart_denied:
options.append(PermissionOption(
options = [
PermissionOption(option_id="allow_once", kind="allow_once", name="Allow once"),
PermissionOption(
option_id="allow_session",
# ACP has no session-scoped kind, so use the closest persistent
# hint while keeping Hermes semantics in the option id.
kind="allow_always",
name="Allow for session",
))
if allow_permanent and not smart_denied:
),
]
if allow_permanent:
options.append(
PermissionOption(
option_id="allow_always",
@@ -62,7 +59,7 @@ def _build_permission_options(
),
)
options.append(PermissionOption(option_id="deny", kind="reject_once", name="Deny"))
if not smart_denied and _permission_option_supports_kind("reject_always"):
if _permission_option_supports_kind("reject_always"):
options.append(
PermissionOption(
option_id="deny_always",
@@ -132,15 +129,11 @@ def make_approval_callback(
description: str,
*,
allow_permanent: bool = True,
smart_denied: bool = False,
**_: object,
) -> str:
from agent.async_utils import safe_schedule_threadsafe
options = _build_permission_options(
allow_permanent=allow_permanent,
smart_denied=smart_denied,
)
options = _build_permission_options(allow_permanent=allow_permanent)
tool_call = _build_permission_tool_call(command, description)
coro = request_permission_fn(
+15 -48
View File
@@ -74,10 +74,6 @@ 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
from tools.approval import (
reset_hermes_interactive_context,
set_hermes_interactive_context,
)
logger = logging.getLogger(__name__)
@@ -1450,23 +1446,20 @@ class HermesACPAgent(acp.Agent):
# Approval callback is per-thread (thread-local, GHSA-qg5c-hvr5-hjgr).
# Set it INSIDE _run_agent so the TLS write happens in the executor
# thread — setting it here would write to the event-loop thread's TLS,
# not the executor's. Interactive routing uses a contextvar in
# tools.approval (set_hermes_interactive_context) rather than
# os.environ["HERMES_INTERACTIVE"], so concurrent executor workers can't
# race on a process-global flag — one session's restore can't drop
# another onto the non-interactive auto-approve path mid-run
# (GHSA-96vc-wcxf-jjff). The contextvar write is isolated by the
# contextvars.copy_context() wrapper around the executor call below.
# not the executor's. Also set HERMES_INTERACTIVE so approval.py
# takes the CLI-interactive path (which calls the registered
# callback via prompt_dangerous_approval) instead of the
# non-interactive auto-approve branch (GHSA-96vc-wcxf-jjff).
# ACP's conn.request_permission maps cleanly to the interactive
# callback shape — not the gateway-queue HERMES_EXEC_ASK path,
# which requires a notify_cb registered in _gateway_notify_cbs.
previous_approval_cb = None
interactive_token = None
previous_interactive = None
edit_approval_token = None
previous_session_id = None
def _run_agent() -> dict:
nonlocal previous_approval_cb, interactive_token, edit_approval_token, previous_session_id
nonlocal previous_approval_cb, previous_interactive, edit_approval_token, previous_session_id
# Bind HERMES_SESSION_KEY for this session so per-session caches
# (e.g. the interactive sudo password cache in tools.terminal_tool)
# scope to the ACP session rather than leaking across sessions
@@ -1498,10 +1491,9 @@ class HermesACPAgent(acp.Agent):
except Exception:
logger.debug("Could not set ACP edit approval requester", exc_info=True)
# Signal to tools.approval that we have an interactive callback
# and the non-interactive auto-approve path must not fire. Uses a
# contextvar (not os.environ) so concurrent executor workers don't
# race on the flag (GHSA-96vc-wcxf-jjff).
interactive_token = set_hermes_interactive_context(True)
# and the non-interactive auto-approve path must not fire.
previous_interactive = os.environ.get("HERMES_INTERACTIVE")
os.environ["HERMES_INTERACTIVE"] = "1"
# Propagate the originating ACP session id to tools that want to
# tag side-effects with it (e.g. ``kanban_create`` stamps it on
# the new task so clients can render a per-session board). Save
@@ -1521,9 +1513,11 @@ class HermesACPAgent(acp.Agent):
logger.exception("Agent error in session %s", session_id)
return {"final_response": f"Error: {e}", "messages": state.history}
finally:
# Restore the interactive contextvar for this context.
if interactive_token is not None:
reset_hermes_interactive_context(interactive_token)
# Restore HERMES_INTERACTIVE.
if previous_interactive is None:
os.environ.pop("HERMES_INTERACTIVE", None)
else:
os.environ["HERMES_INTERACTIVE"] = previous_interactive
# Restore HERMES_SESSION_ID symmetrically.
if previous_session_id is None:
os.environ.pop("HERMES_SESSION_ID", None)
@@ -1617,28 +1611,12 @@ class HermesACPAgent(acp.Agent):
self._send_session_info_update(session_id),
)
# Snapshot the runtime identity; the validator lets the
# background titler skip its LLM call if the session's model
# changed before it fires (#19027).
_title_model = getattr(state.agent, "model", None)
_title_provider = getattr(state.agent, "provider", None)
maybe_auto_title(
self.session_manager._get_db(),
session_id,
user_text,
final_response,
state.history,
main_runtime={
"model": getattr(state.agent, "model", None),
"provider": getattr(state.agent, "provider", None),
"base_url": getattr(state.agent, "base_url", None),
"api_key": getattr(state.agent, "api_key", None),
"api_mode": getattr(state.agent, "api_mode", None),
},
runtime_validator=lambda: (
getattr(state.agent, "model", None) == _title_model
and getattr(state.agent, "provider", None) == _title_provider
),
title_callback=_notify_title_update,
)
except Exception:
@@ -1919,18 +1897,7 @@ class HermesACPAgent(acp.Agent):
def _cmd_reset(self, args: str, state: SessionState) -> str:
state.history.clear()
reset_failed = False
try:
reset_session_state = getattr(state.agent, "reset_session_state", None)
if callable(reset_session_state):
reset_session_state()
except Exception:
reset_failed = True
logger.warning("ACP session state reset failed for %s", state.session_id, exc_info=True)
finally:
self.session_manager.save_session(state.session_id)
if reset_failed:
return "Conversation history cleared. Agent session state reset failed; see logs."
self.session_manager.save_session(state.session_id)
return "Conversation history cleared."
def _cmd_compact(self, args: str, state: SessionState) -> str:
+26 -58
View File
@@ -26,18 +26,31 @@ from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
def _win_path_to_wsl(path: str) -> str | None:
"""Convert a Windows drive path to its WSL /mnt/<drive>/... equivalent."""
match = re.match(r"^([A-Za-z]):[\\/](.*)$", path)
if not match:
return None
drive = match.group(1).lower()
tail = match.group(2).replace("\\", "/")
return f"/mnt/{drive}/{tail}"
def _translate_acp_cwd(cwd: str) -> str:
"""Translate Windows ACP cwd values when Hermes itself is running in WSL.
Windows ACP clients can launch ``hermes acp`` inside WSL while still sending
editor workspaces as Windows drive paths (``E:\\Projects``) or
``\\\\wsl.localhost\\`` UNC paths. Store and execute against the POSIX form so
agents, tools, and persisted ACP sessions all agree on the usable workspace.
Native Linux/macOS keeps the original cwd unchanged.
editor workspaces as Windows drive paths such as ``E:\\Projects``. Store
and execute against the WSL mount path so agents, tools, and persisted ACP
sessions all agree on the usable workspace. Native Linux/macOS keeps the
original cwd unchanged.
"""
from hermes_constants import translate_cwd_for_wsl_backend
from hermes_constants import is_wsl
return translate_cwd_for_wsl_backend(str(cwd))
if not is_wsl():
return cwd
translated = _win_path_to_wsl(str(cwd))
return translated if translated is not None else cwd
def _normalize_cwd_for_compare(cwd: str | None) -> str:
@@ -48,9 +61,7 @@ def _normalize_cwd_for_compare(cwd: str | None) -> str:
# Normalize Windows drive paths into the equivalent WSL mount form so
# ACP history filters match the same workspace across Windows and WSL.
from hermes_constants import windows_path_to_wsl
translated = windows_path_to_wsl(expanded)
translated = _win_path_to_wsl(expanded)
if translated is not None:
expanded = translated
elif re.match(r"^/mnt/[A-Za-z]/", expanded):
@@ -450,47 +461,10 @@ class SessionManager:
except Exception:
logger.debug("Failed to update ACP session metadata", exc_info=True)
# When the agent owns persistence to this same SessionDB it has
# already flushed the live transcript incrementally during
# run_conversation (append_message), and it preserves pre-compaction
# turns non-destructively via archive_and_compact() — keeping them on
# disk as searchable active=0/compacted=1 rows. Calling
# replace_messages() here would then be a redundant double-write that
# DELETEs exactly those archived rows (and, after a compression-driven
# id rotation where agent.session_id no longer equals
# state.session_id, clobbers the ended parent transcript) — silent
# data loss for any ACP conversation long enough to compress.
#
# Only fall back to the destructive atomic replace when the agent is
# NOT persisting itself to this DB (e.g. a test agent factory, or a
# fresh create/fork whose copied history the agent has not flushed
# yet). That path still rolls back on a mid-rewrite failure so the
# previously persisted conversation survives (salvaged from #13675).
agent = state.agent
agent_db = getattr(agent, "_session_db", None)
agent_owns_persistence = (
agent_db is not None
and agent_db is db
and bool(getattr(agent, "_session_db_created", False))
)
if not agent_owns_persistence:
# Even when the current agent doesn't "own" persistence, the
# session on disk may already carry compaction-archived rows —
# e.g. after a model switch or a /restore, both of which mint a
# fresh agent with _session_db_created=False (so the check above
# is False) yet leave the durable archived transcript in place.
# A full-history replace would DELETE those archived rows just
# like the owned-agent case. Guard against it: when archived
# rows exist, replace ONLY the live (active=1) set and leave the
# archived turns untouched; otherwise the destructive replace is
# safe (fresh create/fork with no archived history to lose).
try:
has_archived = db.has_archived_messages(state.session_id)
except Exception:
has_archived = False
db.replace_messages(
state.session_id, state.history, active_only=has_archived
)
# Replace stored messages with current history atomically so a
# mid-rewrite failure rolls back and the previously persisted
# conversation is preserved (salvaged from #13675).
db.replace_messages(state.session_id, state.history)
except Exception:
logger.warning("Failed to persist ACP session %s", state.session_id, exc_info=True)
@@ -534,15 +508,9 @@ class SessionManager:
model = row.get("model") or None
# Load conversation history. repair_alternation: this restore feeds
# LIVE REPLAY — the loaded list becomes the resumed agent's working
# conversation. A durable ``user;user`` violation left in state.db would
# otherwise re-fire the pre-request defensive repair on every request
# for the rest of the session (see hermes_state.get_messages_as_conversation).
# Load conversation history.
try:
history = db.get_messages_as_conversation(
session_id, repair_alternation=True
)
history = db.get_messages_as_conversation(session_id)
except Exception:
logger.warning("Failed to load messages for ACP session %s", session_id, exc_info=True)
history = []
+3 -59
View File
@@ -3,7 +3,6 @@
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Dict, List, Optional
@@ -15,8 +14,6 @@ from acp.schema import (
ToolKind,
)
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Map hermes tool names -> ACP ToolKind
# ---------------------------------------------------------------------------
@@ -113,12 +110,7 @@ def build_tool_title(tool_name: str, args: Dict[str, Any]) -> str:
if tool_name == "web_extract":
urls = args.get("urls", [])
if urls:
first = urls[0]
if isinstance(first, dict):
first = first.get("url") or first.get("href") or "?"
elif not isinstance(first, str):
first = "?"
return f"extract: {first}" + (f" (+{len(urls)-1})" if len(urls) > 1 else "")
return f"extract: {urls[0]}" + (f" (+{len(urls)-1})" if len(urls) > 1 else "")
return "web extract"
if tool_name == "process":
action = str(args.get("action") or "").strip() or "manage"
@@ -387,24 +379,6 @@ def _format_execute_code_result(result: Optional[str]) -> Optional[str]:
error = str(data.get("error") or "")
exit_code = data.get("exit_code")
parts = [f"Exit code: {exit_code}" if exit_code is not None else "Execution complete"]
if data.get("stdout_truncated"):
total = data.get("stdout_bytes_total")
captured = data.get("stdout_bytes_captured")
omitted = data.get("stdout_bytes_omitted")
if all(isinstance(v, int) for v in (captured, total, omitted)):
parts.extend([
"",
(
"Output truncated: "
f"captured {captured:,} of {total:,} bytes "
f"({omitted:,} omitted)."
),
])
else:
parts.extend(["", "Output truncated."])
warning = str(data.get("warning") or "").strip()
if warning:
parts.extend(["", "Warning:", warning])
if output:
parts.extend(["", "Output:", output])
if error:
@@ -643,7 +617,7 @@ def _format_session_search_result(result: Optional[str]) -> Optional[str]:
return None
mode = data.get("mode") or "search"
query = data.get("query")
lines = ["Recent sessions" if mode == "recent" else "Session search results" + (f" for `{query}`" if query else "")]
lines = ["Recent sessions" if mode == "recent" else f"Session search results" + (f" for `{query}`" if query else "")]
if not results:
lines.append(str(data.get("message") or "No matching sessions found."))
return "\n".join(lines)
@@ -1047,37 +1021,7 @@ def build_tool_start(
*,
edit_diff: Any = None,
) -> ToolCallStart:
"""Create a ToolCallStart event for the given hermes tool invocation.
A malformed tool argument (e.g. a non-string ``command``/``path`` from a
model that ignores the schema) must never abort the ACP tool-call render —
``build_tool_start`` runs on the live tool-progress callback and during
session history replay. On any failure in the title/content/location
builders, fall back to a minimal, valid start event. Mirrors
``get_cute_tool_message`` in ``agent/display.py``, wrapped for the same
reason on the CLI side.
"""
try:
return _build_tool_start(
tool_call_id, tool_name, arguments, edit_diff=edit_diff
)
except Exception as exc: # noqa: BLE001 — a tool-call render must never abort the turn
logger.debug("ACP tool-start render failed for %r: %s", tool_name, exc)
safe_name = tool_name if isinstance(tool_name, str) and tool_name else "tool"
return acp.start_tool_call(
tool_call_id, safe_name, kind=get_tool_kind(safe_name),
content=None, locations=[], raw_input=None,
)
def _build_tool_start(
tool_call_id: str,
tool_name: str,
arguments: Dict[str, Any],
*,
edit_diff: Any = None,
) -> ToolCallStart:
"""Build the ToolCallStart event (unguarded; see ``build_tool_start``)."""
"""Create a ToolCallStart event for the given hermes tool invocation."""
kind = get_tool_kind(tool_name)
title = build_tool_title(tool_name, arguments)
locations = extract_locations(arguments)
+2 -2
View File
@@ -1,7 +1,7 @@
{
"id": "hermes-agent",
"name": "Hermes Agent",
"version": "0.19.0",
"version": "0.17.0",
"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]==0.19.0",
"package": "hermes-agent[acp]==0.17.0",
"args": ["hermes-acp"]
}
}
+20 -272
View File
@@ -9,7 +9,7 @@ from typing import TYPE_CHECKING, Any, Optional
import httpx
from agent.anthropic_adapter import _is_oauth_token, resolve_anthropic_token
from hermes_cli.auth import AuthError, _read_codex_tokens, resolve_codex_runtime_credentials
from hermes_cli.auth import _read_codex_tokens, resolve_codex_runtime_credentials
from hermes_cli.runtime_provider import resolve_runtime_provider
if TYPE_CHECKING:
@@ -214,7 +214,7 @@ def build_nous_credits_snapshot(account_info) -> Optional[AccountUsageSnapshot]:
return None
details.append(f"Top up: {nous_portal_topup_url(account_info)}")
details.append("(or run /topup)")
details.append("(or run /credits)")
plan = getattr(sub, "plan", None) if sub is not None else None
return AccountUsageSnapshot(
@@ -340,7 +340,7 @@ def _snapshot_from_credits_state(state) -> Optional[AccountUsageSnapshot]:
@dataclass(frozen=True)
class CreditsView:
"""Surface-agnostic data for the ``/topup`` balance view.
"""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
@@ -356,11 +356,11 @@ class CreditsView:
def build_credits_view(*, markdown: bool = False, timeout: float = 10.0) -> CreditsView:
"""Build the /topup balance view: balance block + identity line + top-up URL.
"""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 /topup surface
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)
@@ -386,7 +386,7 @@ def build_credits_view(*, markdown: bool = False, timeout: float = 10.0) -> Cred
timeout=timeout
)
except Exception:
logger.debug("credits ▸ /topup portal fetch failed (fail-open)", exc_info=True)
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):
@@ -394,8 +394,8 @@ def build_credits_view(*, markdown: bool = False, timeout: float = 10.0) -> Cred
snapshot = build_nous_credits_snapshot(account)
# Balance lines = the snapshot block minus the two trailing affordance lines
# ("Top up: <url>" + "(or run /topup)") that build_nous_credits_snapshot
# appends for the /usage surface. /topup renders its own button/panel.
# ("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)
@@ -425,102 +425,31 @@ def build_credits_view(*, markdown: bool = False, timeout: float = 10.0) -> Cred
)
def _codex_backend_urls(base_url: str) -> tuple[str, str, str]:
"""Resolve the Codex backend endpoints (usage, reset-credits list, consume).
Mirrors the Codex CLI's PathStyle split (codex-rs backend-client): base URLs
containing ``/backend-api`` use the ChatGPT ``/wham/...`` paths; everything
else uses ``/api/codex/...``.
"""
def _resolve_codex_usage_url(base_url: str) -> str:
normalized = (base_url or "").strip().rstrip("/")
if not normalized:
normalized = "https://chatgpt.com/backend-api/codex"
if normalized.endswith("/codex"):
normalized = normalized[: -len("/codex")]
prefix = normalized + ("/wham" if "/backend-api" in normalized else "/api/codex")
return (
prefix + "/usage",
prefix + "/rate-limit-reset-credits",
prefix + "/rate-limit-reset-credits/consume",
)
if "/backend-api" in normalized:
return normalized + "/wham/usage"
return normalized + "/api/codex/usage"
def _resolve_codex_usage_url(base_url: str) -> str:
return _codex_backend_urls(base_url)[0]
def _resolve_codex_usage_credentials(
base_url: Optional[str],
api_key: Optional[str],
) -> tuple[str, str, Optional[str]]:
"""Resolve Codex quota credentials from the native runtime path.
Prefer explicit live-agent credentials, then the legacy singleton OAuth
state, then the credential pool. Hermes's native OAuth setup now stores
device-code logins in the pool, so quota diagnostics must not depend only
on the older singleton store.
"""
explicit_key = str(api_key or "").strip()
if explicit_key:
return explicit_key, str(base_url or "").strip(), None
# Tier 2: the native runtime resolver. It ALREADY falls back to the
# credential pool when the singleton is empty (see
# ``resolve_codex_runtime_credentials`` — issue #32992), so in a pool-only
# setup this returns a usable ``source="credential_pool"`` token.
#
# Only ``AuthError`` ("no creds" / rate-limited) is caught so tier 3 can
# run: a broad ``except Exception`` would (a) mask a transient refresh /
# network failure and silently hand back a DIFFERENT pool account's usage,
# and (b) hide genuine programming errors. A refresh/network error must
# propagate — the outer ``fetch_account_usage`` guard fails open (shows
# nothing this turn) rather than reporting the wrong account.
#
# The ``account_id`` (for the ``ChatGPT-Account-Id`` header) is read
# best-effort: a partial/missing singleton token store must not sink an
# otherwise-usable resolver credential and force a header-less pool fallback.
try:
creds = resolve_codex_runtime_credentials(refresh_if_expiring=True)
account_id: Optional[str] = None
try:
token_data = _read_codex_tokens()
tokens = token_data.get("tokens") or {}
account_id = str(tokens.get("account_id", "") or "").strip() or None
except AuthError:
# Pool-only creds carry no singleton account_id; header is optional.
logger.debug("codex ▸ /usage account_id read failed (best-effort)", exc_info=True)
return creds["api_key"], str(creds.get("base_url", "") or "").strip(), account_id
except AuthError:
logger.debug("codex ▸ /usage runtime resolver returned no creds; trying pool", exc_info=True)
# Tier 3: direct pool select. Reached only when the resolver itself raises
# AuthError (e.g. singleton missing AND its own pool read found nothing at
# resolve time, but a pool entry is usable now). Pool credentials have no
# account_id concept, so the ChatGPT-Account-Id header is intentionally
# omitted here.
from agent.credential_pool import load_pool
pool = load_pool("openai-codex")
entry = pool.select()
if entry is None:
raise RuntimeError("No available openai-codex credential in credential pool")
return entry.runtime_api_key, str(entry.runtime_base_url or base_url or "").strip(), None
def _fetch_codex_account_usage(
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> Optional[AccountUsageSnapshot]:
token, resolved_base_url, account_id = _resolve_codex_usage_credentials(base_url, api_key)
def _fetch_codex_account_usage() -> Optional[AccountUsageSnapshot]:
creds = resolve_codex_runtime_credentials(refresh_if_expiring=True)
token_data = _read_codex_tokens()
tokens = token_data.get("tokens") or {}
account_id = str(tokens.get("account_id", "") or "").strip() or None
headers = {
"Authorization": f"Bearer {token}",
"Authorization": f"Bearer {creds['api_key']}",
"Accept": "application/json",
"User-Agent": "codex-cli",
}
if account_id:
headers["ChatGPT-Account-Id"] = account_id
with httpx.Client(timeout=15.0) as client:
response = client.get(_resolve_codex_usage_url(resolved_base_url), headers=headers)
response = client.get(_resolve_codex_usage_url(creds.get("base_url", "")), headers=headers)
response.raise_for_status()
payload = response.json() or {}
rate_limit = payload.get("rate_limit") or {}
@@ -538,14 +467,6 @@ def _fetch_codex_account_usage(
)
)
details: list[str] = []
reset_credits = payload.get("rate_limit_reset_credits") or {}
banked = reset_credits.get("available_count")
if isinstance(banked, (int, float)) and int(banked) > 0:
count = int(banked)
plural = "s" if count != 1 else ""
details.append(
f"You have {count} reset{plural} banked - use /usage reset to activate"
)
credits = payload.get("credits") or {}
if credits.get("has_credits"):
balance = credits.get("balance")
@@ -563,179 +484,6 @@ def _fetch_codex_account_usage(
)
@dataclass(frozen=True)
class CodexResetRedeemResult:
"""Outcome of a `/usage reset` attempt against the Codex backend."""
status: str # reset | nothing_to_reset | no_credit | already_redeemed |
# not_exhausted | no_credits_banked | unavailable
message: str
available_count: int = 0
windows_reset: int = 0
@property
def redeemed(self) -> bool:
return self.status == "reset"
# Client-side guard threshold: a rate-limit window only counts as exhausted
# when it is fully used. Below this, redeeming a banked reset wastes most of
# its value, so we block and point at --force instead.
_CODEX_WINDOW_EXHAUSTED_PERCENT = 100.0
def redeem_codex_reset_credit(
*,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
force: bool = False,
) -> CodexResetRedeemResult:
"""Redeem one banked Codex rate-limit reset credit (`/usage reset`).
Flow (mirrors the Codex CLI's reset-credits picker, codex-rs
``backend-client``):
1. ``GET .../usage`` — read the current windows + banked credit count.
2. Guard: zero banked credits → refuse. No window fully used and not
``force`` → refuse with a warning (a banked reset restores the WHOLE
5h + weekly allowance; burning it early wastes it). The backend has
the same protection (``nothing_to_reset`` doesn't consume the
credit), but failing fast client-side gives a clearer message.
3. ``POST .../rate-limit-reset-credits/consume`` with a fresh UUID
idempotency key (``redeem_request_id``). No ``credit_id`` — the
backend picks the next available credit, exactly like the CLI's
default "Full reset" option.
Never raises: every failure mode returns a ``CodexResetRedeemResult``
with a user-renderable message.
"""
import uuid
try:
token, resolved_base_url, account_id = _resolve_codex_usage_credentials(base_url, api_key)
except Exception:
return CodexResetRedeemResult(
status="unavailable",
message="No Codex credentials available. Run `hermes auth` to sign in with your ChatGPT account.",
)
usage_url, _credits_url, consume_url = _codex_backend_urls(resolved_base_url)
headers = {
"Authorization": f"Bearer {token}",
"Accept": "application/json",
"User-Agent": "codex-cli",
}
if account_id:
headers["ChatGPT-Account-Id"] = account_id
try:
with httpx.Client(timeout=15.0) as client:
usage_resp = client.get(usage_url, headers=headers)
usage_resp.raise_for_status()
payload = usage_resp.json() or {}
reset_credits = payload.get("rate_limit_reset_credits") or {}
raw_count = reset_credits.get("available_count")
available = int(raw_count) if isinstance(raw_count, (int, float)) else 0
if available <= 0:
return CodexResetRedeemResult(
status="no_credits_banked",
message="No banked reset credits on this account — nothing to redeem.",
)
rate_limit = payload.get("rate_limit") or {}
worst_used: Optional[float] = None
for key in ("primary_window", "secondary_window"):
used = (rate_limit.get(key) or {}).get("used_percent")
if isinstance(used, (int, float)):
worst_used = max(worst_used or 0.0, float(used))
exhausted = worst_used is not None and worst_used >= _CODEX_WINDOW_EXHAUSTED_PERCENT
if not exhausted and not force:
usage_note = (
f"your busiest window is only {worst_used:.0f}% used"
if worst_used is not None
else "your current usage could not be confirmed as exhausted"
)
plural = "s" if available != 1 else ""
return CodexResetRedeemResult(
status="not_exhausted",
message=(
f"⚠️ Not redeeming: {usage_note}. A banked reset restores your FULL "
f"5h + weekly limits, so spending it now would waste most of it. "
f"You have {available} reset{plural} banked. "
f"Use `/usage reset --force` to redeem anyway."
),
available_count=available,
)
consume_resp = client.post(
consume_url,
headers={**headers, "Content-Type": "application/json"},
json={"redeem_request_id": str(uuid.uuid4())},
)
consume_resp.raise_for_status()
body = consume_resp.json() or {}
except httpx.HTTPStatusError as exc:
code = exc.response.status_code
if code in (401, 403):
return CodexResetRedeemResult(
status="unavailable",
message=(
"Codex backend rejected the request (HTTP "
f"{code}). Reset credits require ChatGPT-account (OAuth) auth — "
"run `hermes auth` and sign in with your ChatGPT account."
),
)
return CodexResetRedeemResult(
status="unavailable",
message=f"Codex backend error (HTTP {code}) — try again shortly.",
)
except Exception as exc:
return CodexResetRedeemResult(
status="unavailable",
message=f"Could not reach the Codex backend: {exc}",
)
code = str(body.get("code", "") or "").strip().lower()
windows_reset = body.get("windows_reset")
windows_reset = int(windows_reset) if isinstance(windows_reset, (int, float)) else 0
remaining = max(0, available - 1)
plural = "s" if remaining != 1 else ""
if code == "reset":
return CodexResetRedeemResult(
status="reset",
message=(
f"✅ Reset redeemed — your usage limits have been reset. "
f"{remaining} banked reset{plural} remaining."
),
available_count=remaining,
windows_reset=windows_reset,
)
if code == "nothing_to_reset":
return CodexResetRedeemResult(
status="nothing_to_reset",
message=(
"Backend reports nothing to reset — your limits aren't exhausted. "
"The credit was NOT spent."
),
available_count=available,
)
if code == "no_credit":
return CodexResetRedeemResult(
status="no_credit",
message="Backend reports no available reset credit on this account.",
)
if code == "already_redeemed":
return CodexResetRedeemResult(
status="already_redeemed",
message="This redemption was already processed — no additional credit was spent.",
available_count=remaining,
)
return CodexResetRedeemResult(
status="unavailable",
message=f"Unexpected response from the Codex backend: {body!r}",
)
def _fetch_anthropic_account_usage() -> Optional[AccountUsageSnapshot]:
token = (resolve_anthropic_token() or "").strip()
if not token:
@@ -880,7 +628,7 @@ def fetch_account_usage(
return None
try:
if normalized == "openai-codex":
return _fetch_codex_account_usage(base_url=base_url, api_key=api_key)
return _fetch_codex_account_usage()
if normalized == "anthropic":
return _fetch_anthropic_account_usage()
if normalized == "openrouter":
+45 -363
View File
@@ -68,118 +68,24 @@ def _ra():
return run_agent
def _build_codex_gpt5_autoraise_notice(autoraise: Dict[str, Any]) -> str:
"""Build the one-time notice shown when Codex gpt-5.x raises compaction.
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 ``{"model": <slug>, "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.
``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.
"""
model = str(autoraise.get("model") or "gpt-5.4/5.5").strip().lower().rsplit("/", 1)[-1]
# gpt-5.3-codex-spark has a native 128K window; the gpt-5.4/5.5/5.6 family
# is capped at 272K by the Codex OAuth backend.
cap = "128K" if model.startswith("gpt-5.3-codex-spark") else "272K"
from_pct = int(round(autoraise["from"] * 100))
to_pct = int(round(autoraise["to"] * 100))
return (
f"ℹ Codex {model} caps context at {cap}, so auto-compaction was raised "
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 _resolve_compression_threshold(
global_threshold: float,
model_cthresh: Optional[float],
*,
model: Optional[str] = None,
is_codex_autoraise: bool,
) -> tuple[float, Optional[Dict[str, Any]]]:
"""Combine the user's global compaction threshold with a per-model override.
Returns ``(effective_threshold, autoraise_notice)``. ``autoraise_notice`` is
``{"model": <slug>, "from": <old>, "to": <new>}`` only when a Codex
autoraise (gpt-5.4/5.5 272K family or gpt-5.3-codex-spark) actually raises
the threshold, otherwise ``None``.
The Codex overrides are *autoraises*: they must never LOWER a higher
user-configured threshold. A user who already set ``compression.threshold``
above the raised value deliberately keeps more raw context, and silently
dropping them would both waste usable window and contradict the feature's
purpose (use more of the window). Other overrides (e.g. Arcee Trinity)
keep their existing unconditional behaviour.
"""
if model_cthresh is None:
return global_threshold, None
if is_codex_autoraise:
if model_cthresh <= global_threshold + 1e-9:
# Autoraise never lowers; keep the user's higher/equal threshold.
return global_threshold, None
return model_cthresh, {
"model": model,
"from": global_threshold,
"to": model_cthresh,
}
return model_cthresh, None
def _codex_gpt55_autoraise_notice_marker():
"""Path to the per-profile marker recording that the autoraise notice ran.
Lives under ``$HERMES_HOME`` (which is profile-scoped) alongside the other
internal markers like ``.container-mode`` — so it is not a user-facing config
key, and every profile tracks its own notice state independently.
"""
return get_hermes_home() / ".codex_gpt55_autoraise_notice"
def _codex_gpt55_autoraise_notice_state(autoraise: Dict[str, Any]) -> str:
"""Stable identity for one autoraise notice, keyed on what it displays.
Uses the model slug plus the same from→to percentages the notice text
shows, so an unchanged threshold stays silent across restarts while a
later change (the user edits their global ``threshold``, or switches to a
different autoraised Codex model) re-notifies once.
"""
model = str(autoraise.get("model") or "").strip().lower().rsplit("/", 1)[-1]
from_pct = int(round(float(autoraise["from"]) * 100))
to_pct = int(round(float(autoraise["to"]) * 100))
return f"{model}:{from_pct}:{to_pct}"
def _codex_gpt55_autoraise_notice_seen(autoraise: Dict[str, Any]) -> bool:
"""True if this exact autoraise notice was already shown for this profile.
A missing/unreadable marker (or one recording a different threshold) reads
as unseen, so the notice shows.
"""
try:
current = _codex_gpt55_autoraise_notice_state(autoraise)
return _codex_gpt55_autoraise_notice_marker().read_text(
encoding="utf-8"
).strip() == current
except (OSError, KeyError, TypeError, ValueError):
return False
def _record_codex_gpt55_autoraise_notice(autoraise: Dict[str, Any]) -> None:
"""Persist that the autoraise notice was shown for this profile/config state.
Best-effort: a read-only or missing ``$HERMES_HOME`` just means the notice
may show again next init, which is preferable to breaking agent init.
"""
try:
marker = _codex_gpt55_autoraise_notice_marker()
marker.parent.mkdir(parents=True, exist_ok=True)
marker.write_text(
_codex_gpt55_autoraise_notice_state(autoraise), encoding="utf-8"
)
except (OSError, KeyError, TypeError, ValueError):
pass
def _normalized_custom_base_url(value: Any) -> str:
if not isinstance(value, str):
return ""
@@ -187,26 +93,10 @@ def _normalized_custom_base_url(value: Any) -> str:
def _custom_provider_model_matches(agent_model: str, entry: Dict[str, Any]) -> bool:
agent_model_norm = str(agent_model or "").strip().lower()
# Multi-model entries (v12+ `providers.<name>.models` mapping / legacy
# `models:` list): the agent's model matching ANY catalog entry counts.
# Without this, a provider whose `model`/`default_model` differs from the
# session model silently fails to match and per-provider request settings
# (extra_body, e.g. OpenAI service_tier) are dropped — billing the whole
# session at the wrong tier (July 2026 sweeper incident: flex config
# ignored, ~2.3x overbilling).
models = entry.get("models")
catalog: List[str] = []
if isinstance(models, dict):
catalog = [str(k).strip().lower() for k in models.keys()]
elif isinstance(models, (list, tuple)):
catalog = [str(m).strip().lower() for m in models]
if catalog and agent_model_norm in catalog:
return True
provider_model = str(entry.get("model", "") or "").strip().lower()
if not provider_model and not catalog:
if not provider_model:
return True
return provider_model == agent_model_norm
return provider_model == str(agent_model or "").strip().lower()
def _custom_provider_extra_body_for_agent(
@@ -318,7 +208,6 @@ def init_agent(
notice_callback: callable = None,
notice_clear_callback: callable = None,
event_callback: Optional[Callable[[str, dict], None]] = None,
reaction_callback: Optional[Callable[[str], None]] = None,
max_tokens: int = None,
reasoning_config: Dict[str, Any] = None,
service_tier: str = None,
@@ -428,13 +317,13 @@ def init_agent(
agent.skip_context_files = skip_context_files
agent.load_soul_identity = load_soul_identity
agent.pass_session_id = pass_session_id
agent._credential_pool = credential_pool
agent.log_prefix_chars = log_prefix_chars
agent.log_prefix = f"{log_prefix} " if log_prefix else ""
# Store effective base URL for feature detection (prompt caching, reasoning, etc.)
agent.base_url = base_url or ""
provider_name = provider.strip().lower() if isinstance(provider, str) and provider.strip() else None
agent.provider = provider_name or ""
agent._credential_pool = credential_pool
agent.acp_command = acp_command or command
agent.acp_args = list(acp_args or args or [])
if api_mode in {"chat_completions", "codex_responses", "anthropic_messages", "bedrock_converse", "codex_app_server"}:
@@ -470,24 +359,6 @@ def init_agent(
else:
agent.api_mode = "chat_completions"
# Credential-pool validation runs AFTER provider auto-detection so
# a pool scoped to e.g. "anthropic" is not rejected when the agent
# was constructed with provider=None and an anthropic.com URL.
# Regression from #63048 which placed this check before the
# URL-based auto-detection block above (fixed #63425).
if credential_pool is not None:
try:
from agent.credential_pool import credential_pool_matches_provider
if not credential_pool_matches_provider(
credential_pool,
agent.provider,
base_url=agent.base_url,
):
agent._credential_pool = None
except Exception:
agent._credential_pool = None
# Eagerly warm the transport cache so import errors surface at init,
# not mid-conversation. Also validates the api_mode is registered.
try:
@@ -570,7 +441,6 @@ def init_agent(
agent.notice_callback = notice_callback
agent.notice_clear_callback = notice_clear_callback
agent.event_callback = event_callback
agent.reaction_callback = reaction_callback
agent.tool_gen_callback = tool_gen_callback
@@ -743,25 +613,6 @@ def init_agent(
# commentary when the provider later returns it as a completed interim
# assistant message.
agent._current_streamed_assistant_text = ""
# Completed interim messages delivered during the current user turn.
# Unlike token-stream tracking, this spans Codex continuation/tool calls so
# repeated commentary is not re-sent before normalization can deduplicate it.
agent._delivered_interim_texts: set[str] = set()
# Single-writer guard for the streaming delta sink (#65991). A stale/
# superseded stream (e.g. one the stale-stream detector reconnected past,
# whose socket abort raced and never actually stopped the old worker) must
# NOT keep writing tokens into the turn alongside the retry's stream —
# otherwise two coherent responses interleave token-by-token into one
# transcript. Every streaming attempt claims a monotonic writer token; the
# delta sink drops chunks whose calling thread holds a stale token. The
# threading.local means threads that never claimed (non-streaming callers)
# are never fenced, so the guard can only ever drop a superseded stream,
# never the single legitimate writer.
agent._stream_writer_lock = threading.Lock()
agent._stream_writer_token = 0
agent._stream_writer_tls = threading.local()
agent._stream_writer_dropped = 0
# Optional current-turn user-message override used when the API-facing
# user message intentionally differs from the persisted transcript
@@ -977,7 +828,7 @@ def init_agent(
client_kwargs["default_headers"] = build_nvidia_nim_headers(effective_base)
elif base_url_host_matches(effective_base, "api.routermint.com"):
client_kwargs["default_headers"] = _ra()._routermint_headers()
elif base_url_host_matches(effective_base, "githubcopilot.com"):
elif base_url_host_matches(effective_base, "api.githubcopilot.com"):
from hermes_cli.models import copilot_default_headers
client_kwargs["default_headers"] = copilot_default_headers()
@@ -1123,34 +974,6 @@ def init_agent(
# this mutation is reflected in the client built just below.
agent._apply_user_default_headers()
try:
from hermes_cli.config import (
apply_custom_provider_extra_headers_to_client_kwargs,
apply_custom_provider_tls_to_client_kwargs,
get_compatible_custom_providers,
load_config,
)
_cp_config = load_config()
_cp_entries = get_compatible_custom_providers(_cp_config)
_cp_base_url = str(client_kwargs.get("base_url") or agent.base_url or "")
apply_custom_provider_tls_to_client_kwargs(
client_kwargs,
_cp_base_url,
_cp_entries,
)
# Per-provider extra HTTP headers (providers.<name>.extra_headers /
# custom_providers[].extra_headers) — proxies, gateways, custom
# auth. Applied last so the most specific config level wins.
# SECURITY: values may carry credentials — never log them.
apply_custom_provider_extra_headers_to_client_kwargs(
client_kwargs,
_cp_base_url,
_cp_entries,
)
except Exception:
logger.debug("custom-provider TLS resolution skipped", exc_info=True)
agent.api_key = client_kwargs.get("api_key", "")
agent.base_url = client_kwargs.get("base_url", agent.base_url)
try:
@@ -1336,14 +1159,6 @@ def init_agent(
# SQLite session store (optional -- provided by CLI or gateway)
agent._session_db = session_db
agent._parent_session_id = parent_session_id
# A close flush and the worker's turn-start flush can overlap. The durable
# marker is attached to each in-memory message dict, so its test-and-append
# sequence must be serialized per agent rather than relying on SQLite alone.
agent._session_persist_lock = threading.RLock()
# CLI retains its just-accepted user dict until turn setup can reuse it.
# This preserves the message-local durable marker if close persistence wins
# the race before the agent's normal early turn flush.
agent._pending_cli_user_message = None
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().
@@ -1352,11 +1167,6 @@ def init_agent(
# 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
# When True, this agent NEVER persists to the canonical session store
# (state.db) or the JSON snapshot, regardless of session_id. Set on the
# background skill/memory review fork so its harness turn can't leak into
# the user's real session and hijack the next live turn. Default False.
agent._persist_disabled = False
agent._session_init_model_config = {
"max_iterations": agent.max_iterations,
"reasoning_config": reasoning_config,
@@ -1373,40 +1183,6 @@ def init_agent(
_agent_cfg = _load_agent_config()
except Exception:
_agent_cfg = {}
# Codex commentary visibility (display.show_commentary, default true).
# When true, completed Codex phase=commentary messages are delivered as
# visible mid-turn updates through the interim message path. When false,
# commentary falls back to the reasoning channel (visible only with
# show_reasoning enabled).
agent.show_commentary = True
try:
_display_section = _agent_cfg.get("display", {})
if isinstance(_display_section, dict):
agent.show_commentary = bool(_display_section.get("show_commentary", True))
except Exception:
agent.show_commentary = True
# LM Studio can either be explicitly preloaded through LM Studio's
# management API (the historical Hermes behavior) or left to LM Studio's
# just-in-time / Auto-Evict chat-completions path. Keep the default
# explicit for backward compatibility; users with LM Studio Auto-Evict can
# opt into JIT via ``model.lmstudio_load_mode: jit``.
agent.lmstudio_load_mode = "explicit"
try:
_model_section = _agent_cfg.get("model", {})
if isinstance(_model_section, dict):
_load_mode = str(_model_section.get("lmstudio_load_mode", "explicit") or "explicit").strip().lower()
if _load_mode in {"explicit", "jit"}:
agent.lmstudio_load_mode = _load_mode
else:
logger.warning(
"Invalid model.lmstudio_load_mode=%r; expected 'explicit' or 'jit'. Using explicit.",
_model_section.get("lmstudio_load_mode"),
)
except Exception:
agent.lmstudio_load_mode = "explicit"
try:
agent._tool_guardrails = ToolCallGuardrailController(
ToolCallGuardrailConfig.from_mapping(
@@ -1554,17 +1330,6 @@ def init_agent(
# line). Useful for users on exotic setups where the probe heuristics
# are noisy.
agent._environment_probe = bool(_agent_section.get("environment_probe", True))
# Warm the probe off-thread: it shells out to python3/pip (~0.5s of
# subprocess round-trips) and its result lands in the FIRST system
# prompt build, which sits on the time-to-first-token critical path.
# The warm runs during agent init (network/credential setup dominates),
# so by the time the first prompt is built the line is already cached.
if agent._environment_probe:
try:
from tools.env_probe import warm_environment_probe_async
warm_environment_probe_async()
except Exception:
pass
# Per-platform prompt-hint overrides (config.yaml → platform_hints).
# Lets an enterprise admin append to or replace Hermes' built-in
@@ -1600,48 +1365,41 @@ 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-5.4 / gpt-5.5
# raise to 85% (the Codex backend caps both families 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. The
# notice has its own display gate so users can keep the threshold
# autoraise without getting the banner on gateway turns.
# 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"}
_codex_gpt55_autoraise_notice = str(
_compression_cfg.get("codex_gpt55_autoraise_notice", 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_gpt54_or_gpt55 as _is_codex_gpt54_or_gpt55_fn,
_is_codex_spark as _is_codex_spark_fn,
_is_codex_gpt55 as _is_codex_gpt55_fn,
)
_model_cthresh = _cthresh_fn(
agent.model,
agent.provider,
allow_codex_gpt55_autoraise=_codex_gpt55_autoraise,
)
# The Codex autoraises (gpt-5.4/5.5 272K family and gpt-5.3-codex-spark)
# apply only when they RAISE (never lower a user's higher global
# threshold). The notice is populated only when it actually fires, and
# carries the model slug so the banner names the right family. Arcee
# Trinity keeps its long-standing unconditional behaviour.
compression_threshold, agent._compression_threshold_autoraised = (
_resolve_compression_threshold(
compression_threshold,
_model_cthresh,
model=agent.model,
is_codex_autoraise=(
_is_codex_gpt54_or_gpt55_fn(agent.model, agent.provider)
or _is_codex_spark_fn(agent.model, agent.provider)
),
)
)
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"}
@@ -1667,16 +1425,6 @@ def init_agent(
compression_in_place = is_truthy_value(
_compression_cfg.get("in_place"), default=False
)
codex_app_server_auto_compaction = str(
_compression_cfg.get("codex_app_server_auto", "native") or "native"
).lower()
if codex_app_server_auto_compaction not in {"native", "hermes", "off"}:
_ra().logger.warning(
"Invalid compression.codex_app_server_auto=%r; using 'native'. "
"Valid values are: native, hermes, off.",
codex_app_server_auto_compaction,
)
codex_app_server_auto_compaction = "native"
# Read optional explicit context_length override for the auxiliary
# compression model. Custom endpoints often cannot report this via
@@ -1880,12 +1628,6 @@ def init_agent(
if _selected_engine is not None:
agent.context_compressor = _selected_engine
# External engines own compaction policy: the host compression
# threshold (including the Codex gpt-5.5 autoraise above) only
# configures the built-in ContextCompressor and never reaches the
# plugin, so the autoraise notice would announce a change that does
# not apply. Drop it. (#44439)
agent._compression_threshold_autoraised = None
# Resolve context_length for plugin engines — mirrors switch_model() path
from agent.model_metadata import get_model_context_length
_plugin_ctx_len = get_model_context_length(
@@ -1923,15 +1665,8 @@ def init_agent(
abort_on_summary_failure=compression_abort_on_summary_failure,
max_tokens=agent.max_tokens,
)
_bind_session_state = getattr(agent.context_compressor, "bind_session_state", None)
if callable(_bind_session_state):
try:
_bind_session_state(session_db=session_db, session_id=agent.session_id)
except Exception:
pass
agent.compression_enabled = compression_enabled
agent.compression_in_place = compression_in_place
agent.codex_app_server_auto_compaction = codex_app_server_auto_compaction
# Reject models whose context window is below the minimum required
# for reliable tool-calling workflows (64K tokens).
@@ -1947,33 +1682,6 @@ def init_agent(
f"(this must be at least {MINIMUM_CONTEXT_LENGTH // 1000}K)."
)
# Nous Hermes 3/4 are chat models, not tool-call-tuned. The interactive
# CLI already warns via cli.py show_banner() (richer output + /model hint),
# so skip platform=="cli" here to avoid emitting the warning twice per
# startup. (Gateway/TUI/cron construct with quiet_mode=True and are already
# gated off by the `not agent.quiet_mode` check above; this guard's active
# job is the CLI dedup, and it leaves the door open for any non-quiet
# non-CLI surface to still surface the warning.)
if not agent.quiet_mode and (agent.platform or "cli") != "cli":
try:
from hermes_cli.model_switch import _check_hermes_model_warning
_hermes_warn = _check_hermes_model_warning(agent.model or "")
if _hermes_warn:
_user_msg = (
"⚠ Nous Research Hermes 3 & 4 models are NOT agentic — they "
"lack reliable tool-calling for agent workflows (delegation, "
"cron, proactive tools). Consider an agentic model instead "
"(Claude, GPT, Gemini, Qwen-Coder, etc.)."
)
if hasattr(agent, "_emit_warning"):
agent._emit_warning(_user_msg)
else:
print(f"\n{_user_msg}\n", file=sys.stderr)
_ra().logger.warning(_hermes_warn)
except Exception:
pass
# Inject context engine tool schemas (e.g. lcm_grep, lcm_describe, lcm_expand).
# Skip names that are already present — the _ra().get_tool_definitions()
# quiet_mode cache returned a shared list pre-#17335, so a stray
@@ -2043,8 +1751,6 @@ def init_agent(
working_dir=os.getenv("TERMINAL_CWD") or None,
)
agent._user_turn_count = 0
# Copilot x-initiator flag: first API call of a user turn sends "user" (#3040).
agent._is_user_initiated_turn = False
# Cumulative token usage for the session
agent.session_prompt_tokens = 0
@@ -2109,53 +1815,29 @@ def init_agent(
agent._ollama_num_ctx,
)
# Codex gpt-5.x autoraise notice: show at most once per profile/config
# state. Without the persisted marker the notice re-fires on every agent
# init — and the gateway rebuilds the agent per inbound message, so Discord
# etc. saw it repeatedly (#54432). A change in the raised threshold (or the
# autoraised model) updates the marker state and re-notifies once. The
# config display gate (compression.codex_gpt55_autoraise_notice) still
# suppresses the banner entirely without disabling the threshold autoraise.
_autoraise = getattr(agent, "_compression_threshold_autoraised", None)
_show_autoraise_notice = (
bool(_autoraise)
and compression_enabled
and _codex_gpt55_autoraise_notice
and not _codex_gpt55_autoraise_notice_seen(_autoraise)
)
if not agent.quiet_mode:
if compression_enabled:
# Report the active engine's own threshold — for a plugin engine
# the host compression_threshold is not in effect, and mixing the
# two printed a percent that contradicted the token count. (#44439)
_active_threshold_pct = getattr(
agent.context_compressor, "threshold_percent", compression_threshold
)
print(f"📊 Context limit: {agent.context_compressor.context_length:,} tokens (compress at {int(_active_threshold_pct*100)}% = {agent.context_compressor.threshold_tokens:,})")
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)")
# Notice 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).
if _show_autoraise_notice:
print(_build_codex_gpt5_autoraise_notice(_autoraise))
# 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.x autoraise notice: the startup print
# 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.).
if _show_autoraise_notice:
agent._compression_warning = _build_codex_gpt5_autoraise_notice(_autoraise)
# Mark shown so repeated inits in this profile (e.g. every gateway message)
# stay silent. Recorded once, whether the notice went to the CLI print or
# the gateway replay slot.
if _show_autoraise_notice:
_record_codex_gpt55_autoraise_notice(_autoraise)
_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
+29 -684
View File
@@ -26,11 +26,10 @@ import copy
import json
import logging
import re
import threading
import time
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional
from hermes_cli.timeouts import get_provider_request_timeout
from agent.prompt_builder import format_steer_marker
@@ -38,7 +37,6 @@ from agent.tool_dispatch_helpers import _trajectory_normalize_msg, make_tool_res
from agent.trajectory import convert_scratchpad_to_think
from agent.credential_pool import STATUS_EXHAUSTED
from agent.error_classifier import FailoverReason
from agent.turn_context import drop_stale_api_content
from utils import base_url_host_matches, base_url_hostname, env_var_enabled, atomic_json_write
logger = logging.getLogger(__name__)
@@ -308,13 +306,7 @@ def sanitize_tool_call_arguments(
try:
json.loads(arguments)
except json.JSONDecodeError:
# Use the canonical ``call_id || id`` precedence so both the
# scan for an existing tool result and any inserted stub key
# on the same id the rest of the pipeline uses. Keying on bare
# ``id`` here would fail to find a result built with ``call_id``
# (Codex Responses format) and insert a duplicate stub that
# itself becomes an orphan (#58168).
tool_call_id = _ra().AIAgent._get_tool_call_id_static(tool_call) or None
tool_call_id = tool_call.get("id")
function_name = function.get("name", "?")
preview = arguments[:80]
log.warning(
@@ -359,108 +351,6 @@ def sanitize_tool_call_arguments(
return repaired
# Session-scoped in-flight registry backing note_turn_start's cross-agent
# check. The per-agent marker catches a second turn on the SAME AIAgent
# object, but the gateway caches agents per *routing key* (``_agent_cache``
# in gateway/run.py) while the durable transcript is keyed by *session_id* —
# and the key→id mapping is many-to-one (``switch_session``: /resume from a
# second chat/topic, CLI-continuity rebinding, async-delegation pinning,
# topic-binding tip-walks). Two routing keys mapped to one session_id run
# concurrent turns on two different agent objects, which per-agent state can
# never see (#64934). Keyed by session_id so that route produces the same
# named warning. Process-local by design — same visibility scope as the
# per-agent marker it extends.
_INFLIGHT_TURNS_BY_SESSION: Dict[str, Tuple[str, float]] = {}
_INFLIGHT_TURNS_LOCK = threading.Lock()
def note_turn_start(agent, turn_id: str):
"""Tripwire: detect a turn starting while a previous turn of the same
agent — or of the same underlying *session* on a different agent object —
has not completed its turn-end persist.
Two turns interleaving on one session corrupt the durable transcript:
their flushes race (user rows can persist out of arrival order), a row
can be swallowed by the identity-marker dedup over shared history dicts,
and the second turn runs on a history base that never saw the first
turn's exchange. This helper does NOT prevent any of that — it names the
occurrence, with both turn ids, so the dispatch route that let the
second turn through the busy guard can be identified from logs.
Returns the previous in-flight turn_id when an overlap is detected,
else None. Takes ownership of the in-flight slot either way, so a turn
that crashed before its persist produces at most one warning."""
prev = getattr(agent, "_inflight_turn_id", None)
prev_started = getattr(agent, "_inflight_turn_started", 0.0)
agent._inflight_turn_id = turn_id
agent._inflight_turn_started = time.time()
overlap = None
if prev and prev != turn_id:
logger.warning(
"turn %s starting while turn %s (started %.0fs ago) has not "
"completed its turn-end persist (session=%s) — concurrent turns "
"on one session; transcript writes may interleave",
turn_id,
prev,
time.time() - prev_started if prev_started else -1.0,
getattr(agent, "session_id", None) or "-",
)
overlap = prev
# Cross-agent leg: same session_id in flight under a different agent
# object means two routing keys resolve to one durable session — the
# busy guard (keyed by routing key) cannot see this overlap at all.
# Persist-disabled agents (background-review forks) deliberately share
# the live parent's session_id for prompt-cache warmth but can never
# write to the transcript — they must not register here (would warn a
# false overlap against the parent's real turn) nor pop the parent's
# slot at their persist (note_turn_persisted skips them symmetrically).
session_id = getattr(agent, "session_id", None)
if session_id and not getattr(agent, "_persist_disabled", False):
now = time.time()
with _INFLIGHT_TURNS_LOCK:
entry = _INFLIGHT_TURNS_BY_SESSION.get(session_id)
_INFLIGHT_TURNS_BY_SESSION[session_id] = (turn_id, now)
# Stamp the session id this turn registered under: compression can
# rotate agent.session_id mid-turn, and the persist-time clear must
# pop the slot the turn actually holds, not the rotated id.
agent._inflight_turn_session_id = session_id
if entry and entry[0] not in (turn_id, prev):
logger.warning(
"turn %s starting while turn %s (started %.0fs ago) is still "
"in flight on session %s under a different agent object — "
"two routing keys are mapped to one session_id; concurrent "
"turns on one session; transcript writes may interleave",
turn_id,
entry[0],
now - entry[1] if entry[1] else -1.0,
session_id,
)
overlap = overlap or entry[0]
return overlap
def note_turn_persisted(agent):
"""Clear the in-flight marker at turn-end persist (see note_turn_start).
Called from the single persist funnel; unconditional by design — when two
turns genuinely overlap, the first persist clears the second turn's slot
and the tripwire under-reports instead of double-reporting. A diagnostic
must never be noisier than the defect it hunts."""
agent._inflight_turn_id = None
# Symmetric with note_turn_start's cross-agent leg: persist-disabled
# forks never registered a session slot, and their persist funnel still
# runs — popping here would steal the live parent turn's slot and make
# the tripwire under-report the real overlap it exists to catch.
if not getattr(agent, "_persist_disabled", False):
session_id = getattr(agent, "_inflight_turn_session_id", None) or getattr(
agent, "session_id", None
)
if session_id:
with _INFLIGHT_TURNS_LOCK:
_INFLIGHT_TURNS_BY_SESSION.pop(session_id, None)
agent._inflight_turn_session_id = None
def repair_message_sequence(agent, messages: List[Dict]) -> int:
"""Collapse malformed role-alternation left in the live history.
@@ -478,18 +368,6 @@ def repair_message_sequence(agent, messages: List[Dict]) -> int:
host code) can feed in already-broken histories.
Repairs applied:
0. Consecutive ``assistant`` messages with no intervening
``tool``/``user`` turn — merged into a single assistant turn
(union of ``tool_calls``, concatenated ``content``). Strict
OpenAI-compatible providers (DeepSeek v4, Moonshot/Kimi) reject
a history where an ``assistant`` message carrying ``tool_calls``
is immediately followed by another ``assistant`` message instead
of its ``tool`` results — HTTP 400 "An assistant message with
'tool_calls' must be followed by tool messages…". The split
shape is produced by recovery/continuation paths that append an
interim assistant turn (thinking-prefill, codex
incomplete-continuation) or by host-fed / legacy-persisted /
resumed histories. Refs #29148, #49147.
1. Stray ``tool`` messages whose ``tool_call_id`` doesn't match
any preceding assistant tool_call — dropped.
2. Consecutive ``user`` messages — merged with newline separator
@@ -509,105 +387,12 @@ def repair_message_sequence(agent, messages: List[Dict]) -> int:
repairs = 0
# Pass 0: merge consecutive assistant messages. Runs BEFORE Pass 1 so
# the merged turn's union of tool_call ids is known when Pass 1
# validates which tool-result messages are orphans. Two assistant
# messages are only adjacent here when nothing (no tool result, no
# user turn) separates them — an intervening ``tool`` message means
# two distinct, valid tool-call rounds that must NOT be merged.
#
# Codex Responses interim turns are exempt: the codex_responses
# api_mode legitimately keeps multiple consecutive incomplete
# assistant turns in history, each carrying its own encrypted
# continuation state (codex_reasoning_items / codex_message_items)
# that must be replayed verbatim. Collapsing them corrupts the
# Responses replay chain (the duplicate-detection logic at
# conversation_loop.py already de-dups identical codex interims).
def _is_codex_interim(m: Dict) -> bool:
return bool(
m.get("codex_reasoning_items")
or m.get("codex_message_items")
or m.get("finish_reason") == "incomplete"
)
def _is_verification_candidate(m: Dict) -> bool:
return m.get("finish_reason") in {
"verification_required",
"verify_hook_continue",
}
collapsed: List[Dict] = []
for msg in messages:
if (
collapsed
and isinstance(msg, dict)
and msg.get("role") == "assistant"
and isinstance(collapsed[-1], dict)
and collapsed[-1].get("role") == "assistant"
and not _is_codex_interim(msg)
and not _is_codex_interim(collapsed[-1])
):
prev = collapsed[-1]
# Verification candidate collapsing: when the earlier assistant
# message is a provisional candidate (finish_reason =
# verification_required / verify_hook_continue), the later
# response supersedes it for model replay — replace rather than
# union. Both remain durable in state.db; this only affects the
# in-memory sequence sent to the model. (#65919 §7)
if _is_verification_candidate(prev):
collapsed[-1] = msg
repairs += 1
continue
# Union tool_calls (preserve order, both may carry them).
prev_calls = list(prev.get("tool_calls") or [])
new_calls = list(msg.get("tool_calls") or [])
if new_calls:
prev["tool_calls"] = prev_calls + new_calls
elif prev_calls:
prev["tool_calls"] = prev_calls
# Concatenate plain-text content; leave multimodal (list)
# content on either side alone to avoid mangling attachment
# blocks — fall back to keeping the existing content.
prev_content = prev.get("content")
new_content = msg.get("content")
if isinstance(prev_content, str) and isinstance(new_content, str):
joined = "\n".join(
p for p in (prev_content.strip(), new_content.strip()) if p
)
prev["content"] = joined
elif not prev_content and new_content is not None:
prev["content"] = new_content
# Carry reasoning_content from the later turn only if the
# earlier turn lacks it (strict thinking providers require a
# reasoning_content on the merged tool-call turn; the first
# non-empty one suffices).
if not prev.get("reasoning_content") and msg.get("reasoning_content"):
prev["reasoning_content"] = msg["reasoning_content"]
repairs += 1
continue
collapsed.append(msg)
# Pass 1: drop stray tool messages that don't follow a known
# assistant tool_call_id. Uses a rolling set of known ids refreshed
# on each assistant message.
#
# Both ``id`` AND ``call_id`` are registered for every assistant
# tool_call. In the Codex Responses API format the two differ
# (``id`` = ``fc_...`` response-item id, ``call_id`` = ``call_...``
# the function-call id), and a tool result's ``tool_call_id`` may be
# matched against *either* depending on which code path built it
# (the OpenAI-compatible path stores ``tc.id``; codex paths store
# ``call_id``). Registering only ``id`` — as this pass did before —
# made a valid tool result look orphaned whenever the assistant
# tool_call carried a distinct ``call_id`` (or only ``call_id``); the
# pass then dropped it, leaving the assistant tool_call unanswered and
# producing an HTTP 400 on strict providers (DeepSeek, Kimi). Matching
# on the *superset* of both keys achieves the same tolerance as
# ``_get_tool_call_id_static``'s ``call_id || id`` — a match set must
# accept every legitimate reference, not just the canonical one (#58168).
known_tool_ids: set = set()
filtered: List[Dict] = []
for msg in collapsed:
for msg in messages:
if not isinstance(msg, dict):
filtered.append(msg)
continue
@@ -615,23 +400,14 @@ def repair_message_sequence(agent, messages: List[Dict]) -> int:
if role == "assistant":
known_tool_ids = set()
for tc in (msg.get("tool_calls") or []):
if not isinstance(tc, dict):
continue
for key in ("id", "call_id"):
tc_id = tc.get(key)
if tc_id:
known_tool_ids.add(tc_id)
tc_id = tc.get("id") if isinstance(tc, dict) else None
if tc_id:
known_tool_ids.add(tc_id)
filtered.append(msg)
elif role == "tool":
tc_id = msg.get("tool_call_id")
if tc_id and tc_id in known_tool_ids:
filtered.append(msg)
# Consume the id so a SECOND tool result carrying the same
# tool_call_id (duplicate from a retry/crash/session-resume
# glitch) falls into the drop branch below instead of being
# replayed — strict providers (DeepSeek) reject a duplicate
# tool_call_id with HTTP 400 (#58327). Credit: #55436.
known_tool_ids.discard(tc_id)
else:
repairs += 1
else:
@@ -665,10 +441,6 @@ def repair_message_sequence(agent, messages: List[Dict]) -> int:
if prev_content and new_content
else (prev_content or new_content)
)
# Merged content invalidates the api_content sidecar (exact
# bytes previously sent for the pre-merge message) — drop it
# so replay can't substitute stale bytes.
drop_stale_api_content(prev)
repairs += 1
continue
merged.append(msg)
@@ -725,16 +497,16 @@ def strip_think_blocks(agent, content: str) -> str:
"""Remove reasoning/thinking blocks from content, returning only visible text.
Handles four cases:
1. Closed tag pairs (`` <think>… ``) — the common path when
1. Closed tag pairs (``<think>…</think>``) — the common path when
the provider emits complete reasoning blocks.
2. Unterminated open tag at a block boundary (start of text or
after a newline) — e.g. MiniMax M2.7 / NIM endpoints where the
closing tag is dropped. Everything from the open tag to end
of string is stripped. The block-boundary check mirrors
``gateway/stream_consumer.py``'s filter so models that mention
`` <think>`` in prose aren't over-stripped.
``<think>`` in prose aren't over-stripped.
3. Stray orphan open/close tags that slip through.
4. Tag variants: `` <think>``, ``<thinking>``, ``<reasoning>``,
4. Tag variants: ``<think>``, ``<thinking>``, ``<reasoning>``,
``<REASONING_SCRATCHPAD>``, ``<thought>`` (Gemma 4), all
case-insensitive.
@@ -754,39 +526,6 @@ def strip_think_blocks(agent, content: str) -> str:
"""
if not content:
return ""
# Coerce non-string content to text before any regex runs. Providers
# that return assistant ``content`` as a list of blocks (Anthropic via
# OpenRouter emits ``[{"type":"text",...}, {"type":"thinking",...}]``) or
# as a dict flow into this shared helper from several callers — most
# notably ``_interim_assistant_visible_text`` reading a *stored* history
# message whose content was persisted as a list. A raw list/dict reaching
# ``re.sub`` below raises ``TypeError: expected string or bytes-like
# object, got 'list'``, which the outer conversation loop swallows and
# retries forever (observed as an infinite "preparing terminal…" loop on
# Anthropic models via OpenRouter). Flatten here so every caller is safe.
if not isinstance(content, str):
if isinstance(content, list):
_parts: list[str] = []
for _part in content:
if isinstance(_part, str):
_parts.append(_part)
elif isinstance(_part, dict):
_ptype = str(_part.get("type") or "").strip().lower()
# Drop reasoning/thinking blocks outright — this function's
# whole job is to strip them, and their text lives under
# different keys ("thinking", "reasoning") per provider.
if _ptype in {"thinking", "reasoning", "redacted_thinking"}:
continue
_text = _part.get("text")
if isinstance(_text, str) and _text:
_parts.append(_text)
content = "".join(_parts)
elif isinstance(content, dict):
content = str(content.get("text") or content.get("content") or "")
else:
content = str(content)
if not content:
return ""
# 1. Closed tag pairs — case-insensitive for all variants so
# mixed-case tags (<THINK>, <Thinking>) don't slip through to
# the unterminated-tag pass and take trailing content with them.
@@ -886,14 +625,7 @@ def recover_with_credential_pool(
# that seeded the pool.
current_provider = (getattr(agent, "provider", "") or "").strip().lower()
pool_provider = (getattr(pool, "provider", "") or "").strip().lower()
# Guard: skip credential pool recovery when the pool is scoped to a
# different provider than the agent. Only guard when the pool has a
# known provider — an empty pool provider means "unscoped" (applies to
# any provider). An empty agent provider is treated as a mismatch
# because swapping the pool's credentials would set base_url/api_key
# without fixing the empty provider field, leaving the agent in a
# corrupted state (provider="" model="").
if pool_provider and current_provider != pool_provider:
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
@@ -931,35 +663,9 @@ def recover_with_credential_pool(
elif status_code in {401, 403}:
effective_reason = FailoverReason.auth
if effective_reason == FailoverReason.upstream_rate_limit:
# An upstream provider (e.g. DeepSeek behind OpenRouter) is
# rate-limiting the aggregator's traffic — the user's credential is
# healthy. Do NOT rotate or mark exhausted; let the caller's fallback
# path switch to a different model entirely.
upstream = (error_context or {}).get("upstream_provider") if error_context else None
if upstream:
_ra().logger.info(
"Upstream provider %s rate-limited via aggregator — skipping "
"credential rotation, deferring to fallback chain",
upstream,
)
else:
_ra().logger.info(
"Upstream aggregator 429 (provider unknown) — skipping "
"credential rotation, deferring to fallback chain"
)
return False, has_retried_429
if effective_reason == FailoverReason.billing:
rotate_status = status_code if status_code is not None else 402
next_entry = pool.mark_exhausted_and_rotate(
status_code=rotate_status,
error_context=error_context,
# Runtime credentials can be resolved by a separate pool instance,
# leaving this recovery pool without ``current_id``. Match the key
# that actually failed instead of quarantining a different account.
api_key_hint=getattr(agent, "api_key", None),
)
next_entry = pool.mark_exhausted_and_rotate(status_code=rotate_status, error_context=error_context)
if next_entry is not None:
_ra().logger.info(
"Credential %s (billing) — rotated to pool entry %s",
@@ -1372,42 +1078,7 @@ def restore_primary_runtime(agent) -> bool:
api_mode=rt.get("compressor_api_mode", ""),
)
# ── Rebind and re-select the primary credential pool ──
# A cross-provider fallback attaches the fallback provider's pool. The
# runtime fields above restore the primary, but leaving that pool in
# place makes the next primary 401/429 hit the provider-mismatch guard
# and disables credential rotation. Reload the primary pool first; if
# auth storage is temporarily unreadable, clear the mismatched pool.
primary_provider = str(rt.get("provider") or "").strip().lower()
pool = getattr(agent, "_credential_pool", None)
pool_provider = str(getattr(pool, "provider", "") or "").strip().lower()
pool_matches_primary = pool_provider == primary_provider
if (
primary_provider == "custom"
and pool_provider.startswith("custom:")
):
try:
from agent.credential_pool import get_custom_provider_pool_key
primary_key = (
get_custom_provider_pool_key(str(rt.get("base_url") or "")) or ""
).strip().lower()
pool_matches_primary = bool(primary_key) and primary_key == pool_provider
except Exception:
pool_matches_primary = False
if pool is not None and pool_provider and not pool_matches_primary:
agent._credential_pool = None
try:
from agent.credential_pool import load_pool
agent._credential_pool = load_pool(primary_provider)
except Exception as exc:
logger.warning(
"Restore could not reload primary credential pool for %s: %s",
primary_provider,
exc,
)
# ── 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
@@ -1420,37 +1091,11 @@ def restore_primary_runtime(agent) -> bool:
if pool is not None and pool.has_available():
entry = pool.select()
if entry is not None:
entry_provider = str(getattr(entry, "provider", "") or "").strip().lower()
entry_matches_primary = entry_provider == primary_provider
# Custom endpoints all carry the generic ``custom`` provider on
# the agent while the pool entry is keyed ``custom:<name>`` (see
# CUSTOM_POOL_PREFIX). Resolve the primary's base_url to its
# ``custom:<name>`` key via the canonical helper and compare
# against the entry's key — this mirrors the sibling guard in
# ``recover_with_credential_pool`` (see above) and correctly
# disambiguates multiple custom providers that share one gateway
# base_url. Fixes #56885.
from agent.credential_pool import CUSTOM_POOL_PREFIX
if (
primary_provider == "custom"
and entry_provider.startswith(CUSTOM_POOL_PREFIX)
):
entry_matches_primary = False
try:
from agent.credential_pool import get_custom_provider_pool_key
primary_base_url = str(rt.get("base_url") or "").strip()
primary_key = (
get_custom_provider_pool_key(primary_base_url) or ""
).strip().lower()
entry_matches_primary = bool(primary_key) and primary_key == entry_provider
except Exception:
entry_matches_primary = False
entry_key = (
getattr(entry, "runtime_api_key", None)
or getattr(entry, "access_token", "")
)
if entry_key and entry_matches_primary:
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).
@@ -1460,32 +1105,11 @@ def restore_primary_runtime(agent) -> bool:
getattr(entry, "id", "?"),
getattr(entry, "label", "?"),
)
elif entry_key:
logger.info(
"Restore skipped pool entry %s (%s): provider %s does not match primary provider %s",
getattr(entry, "id", "?"),
getattr(entry, "label", "?"),
entry_provider or "?",
primary_provider or "?",
)
# ── Restore reasoning_config if it was saved ──
# switch_model saves reasoning_config in _primary_runtime. If the
# snapshot predates that (older sessions), keep the current value.
saved_reasoning = rt.get("reasoning_config")
if saved_reasoning is not None:
agent.reasoning_config = dict(saved_reasoning)
# ── Reset fallback chain for the new turn ──
agent._fallback_activated = False
agent._fallback_index = 0
# Reset the stale-call circuit breaker (#58962): the streak measured
# the FALLBACK provider we're leaving; the restored primary deserves
# a fresh stream attempt before the breaker can trip again.
from agent.chat_completion_helpers import _reset_stale_streak
_reset_stale_streak(agent)
# 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
@@ -1726,60 +1350,9 @@ def anthropic_prompt_cache_policy(
eff_api_mode = api_mode if api_mode is not None else (agent.api_mode or "")
eff_model = (model if model is not None else agent.model) or ""
# MoA virtual provider: the agent's model/provider are the preset name and
# "moa" — neither matches any caching branch, so the ACTING AGGREGATOR
# (often Claude on OpenRouter) silently lost prompt caching entirely
# (measured: 85% cache share solo vs 2% on the identical model via MoA —
# tens of millions of re-billed input tokens per benchmark run). Resolve
# the policy from the preset's real aggregator slot instead.
if eff_provider.strip().lower() == "moa":
try:
from hermes_cli.config import load_config as _load_moa_cfg
from hermes_cli.moa_config import resolve_moa_preset
from hermes_cli.runtime_provider import resolve_runtime_provider
_preset = resolve_moa_preset(
_load_moa_cfg().get("moa") or {}, eff_model or None
)
_agg = _preset.get("aggregator") or {}
_agg_provider = str(_agg.get("provider") or "").strip()
_agg_model = str(_agg.get("model") or "").strip()
if _agg_provider and _agg_model:
_agg_base_url = ""
_agg_api_mode = ""
try:
_rt = resolve_runtime_provider(
requested=_agg_provider, target_model=_agg_model
)
_agg_base_url = _rt.get("base_url") or ""
_agg_api_mode = _rt.get("api_mode") or ""
except Exception:
pass
return anthropic_prompt_cache_policy(
agent,
provider=_agg_provider,
base_url=_agg_base_url,
api_mode=_agg_api_mode,
model=_agg_model,
)
except Exception as _moa_exc: # pragma: no cover - defensive
logger.debug("MoA aggregator cache-policy resolution failed: %s", _moa_exc)
return False, False
model_lower = eff_model.lower()
provider_lower = eff_provider.lower()
is_claude = "claude" in model_lower
# Kimi / Moonshot family via OpenRouter: same cache_control wire format
# as Claude on OpenRouter (envelope layout). Without this branch
# moonshotai/kimi-k2.6 falls through to (False, False), serving ~1%
# cache hits on 64K-token prompts and re-billing the full prompt on
# every turn. Observed within-turn progression with cache enabled:
# 1% → 67% → 84% → 97% (#25970). Reuses the canonical family matcher
# (covers bare k1./k2./k25 release slugs the substring check missed).
from agent.anthropic_adapter import _model_name_is_kimi_family
is_kimi = (
_model_name_is_kimi_family(eff_model) or "moonshot" in model_lower
)
is_openrouter = base_url_host_matches(eff_base_url, "openrouter.ai")
# Nous Portal proxies to OpenRouter behind the scenes — identical
# OpenAI-wire envelope cache_control semantics. Treat it as an
@@ -1793,7 +1366,7 @@ def anthropic_prompt_cache_policy(
if is_native_anthropic:
return True, True
if (is_openrouter or is_nous_portal) and (is_claude or is_kimi):
if (is_openrouter or is_nous_portal) and is_claude:
return True, False
# Nous Portal Qwen (e.g. qwen3.6-plus) takes the same envelope-layout
# cache_control path as Portal Claude. Portal proxies to OpenRouter
@@ -1847,7 +1420,6 @@ def anthropic_prompt_cache_policy(
def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: bool) -> Any:
from agent.auxiliary_client import _validate_base_url, _validate_proxy_env_urls
from agent.ssl_verify import resolve_httpx_verify
# Treat client_kwargs as read-only. Callers pass agent._client_kwargs (or shallow
# copies of it) in; any in-place mutation leaks back into the stored dict and is
# reused on subsequent requests. #10933 hit this by injecting an httpx.Client
@@ -1857,9 +1429,6 @@ def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: boo
# copy locks the contract so future transport/keepalive work can't reintroduce
# the same class of bug.
client_kwargs = dict(client_kwargs)
ssl_ca_cert = client_kwargs.pop("ssl_ca_cert", None)
ssl_verify_cfg = client_kwargs.pop("ssl_verify", None)
httpx_verify = resolve_httpx_verify(ca_bundle=ssl_ca_cert, ssl_verify=ssl_verify_cfg)
_validate_proxy_env_urls()
_validate_base_url(client_kwargs.get("base_url"))
if agent.provider == "copilot-acp" or str(client_kwargs.get("base_url", "")).startswith("acp://copilot"):
@@ -1883,9 +1452,7 @@ def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: boo
if k in {"api_key", "base_url", "default_headers", "timeout", "http_client"}
}
if "http_client" not in safe_kwargs:
keepalive_http = agent._build_keepalive_http_client(
base_url, verify=httpx_verify,
)
keepalive_http = agent._build_keepalive_http_client(base_url)
if keepalive_http is not None:
safe_kwargs["http_client"] = keepalive_http
client = GeminiNativeClient(**safe_kwargs)
@@ -1914,9 +1481,7 @@ def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: boo
# Tests in ``tests/run_agent/test_create_openai_client_reuse.py`` and
# ``tests/run_agent/test_sequential_chats_live.py`` pin this invariant.
if "http_client" not in client_kwargs:
keepalive_http = agent._build_keepalive_http_client(
client_kwargs.get("base_url", ""), verify=httpx_verify,
)
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,
@@ -2021,30 +1586,13 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
# ── Swap core runtime fields ──
agent.model = new_model
agent.provider = new_provider
# Use the new base_url when provided. When it's empty AND the
# provider is actually changing, do NOT fall back to the current
# (old provider's) URL — that silently pairs the new provider label
# with the previous provider's endpoint (e.g. new_provider=minimax
# paired with the leftover api.githubcopilot.com URL), and every
# request after the switch 400s at the wrong host. This mismatched
# pair also gets snapshotted into _primary_runtime below, so it
# keeps re-applying on every subsequent turn until a full restart.
# Fail loud instead: the caller (model_switch.switch_model())
# already resolves base_url for every real provider, so an empty
# value here means resolution failed upstream, not that the
# provider genuinely has none. Re-selecting the SAME provider with
# an empty base_url (e.g. a credential-only refresh) is still fine
# to keep the current URL. See #47828.
old_norm_provider = (old_provider or "").strip().lower()
new_norm_provider = (new_provider or "").strip().lower()
# Use new base_url when provided; only fall back to current when the
# new provider genuinely has no endpoint (e.g. native SDK providers).
# Without this guard the old provider's URL (e.g. Ollama's localhost
# address) would persist silently after switching to a cloud provider
# that returns an empty base_url string.
if base_url:
agent.base_url = base_url
elif old_norm_provider != new_norm_provider:
raise ValueError(
f"switch_model: no base_url resolved for provider "
f"'{new_provider}' (switching from '{old_provider}'); "
"refusing to keep the previous provider's endpoint"
)
agent.api_mode = api_mode
# Invalidate transport cache — new api_mode may need a different transport
if hasattr(agent, "_transport_cache"):
@@ -2063,9 +1611,6 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
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:
# A pool bound to the old provider is worse than no pool: the
# recovery guard rejects it and every later 401/429 skips rotation.
agent._credential_pool = None
try:
from agent.credential_pool import load_pool
agent._credential_pool = load_pool(new_provider)
@@ -2080,18 +1625,6 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
if (new_provider or "").strip().lower() == "moa":
from agent.moa_loop import MoAClient
# The MoA virtual provider speaks only chat.completions via the
# MoAClient facade — the aggregator's real transport
# (codex_responses / anthropic_messages) is resolved and applied
# *inside* the reference/aggregator fan-out, never on the outer
# primary call. determine_api_mode("moa", ...) above may have left
# api_mode set to the aggregator's transport; if the conversation
# loop sees that, it dispatches client.responses.create (which the
# facade has no .responses for) and the call falls through to the
# moa://local placeholder → HTTP 404 → fallback to a reference
# model. Pin chat_completions here so the primary call always goes
# through MoAClient.chat.completions, matching agent_init.py.
agent.api_mode = "chat_completions"
agent.api_key = api_key or "moa-virtual-provider"
agent.base_url = "moa://local"
agent._client_kwargs = {}
@@ -2140,32 +1673,9 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
"api_key": effective_key,
"base_url": effective_base,
}
try:
from hermes_cli.config import (
apply_custom_provider_tls_to_client_kwargs,
get_compatible_custom_providers,
load_config_readonly,
)
# Read custom_providers from live config (not the init-time
# snapshot on ``agent._custom_providers``) so ssl_ca_cert /
# ssl_verify edits are honored when switching mid-session,
# matching the context-length reload below (#15779).
apply_custom_provider_tls_to_client_kwargs(
agent._client_kwargs,
str(effective_base or ""),
get_compatible_custom_providers(load_config_readonly()),
)
except Exception:
logger.debug("custom-provider TLS resolution skipped on switch_model", exc_info=True)
_sm_timeout = get_provider_request_timeout(agent.provider, agent.model)
if _sm_timeout is not None:
agent._client_kwargs["timeout"] = _sm_timeout
# Reapply provider-specific headers (e.g. OpenRouter HTTP-Referer,
# X-Title) that were lost when _client_kwargs was rebuilt from
# scratch. Without this, model switches clear attribution headers
# and OpenRouter logs show "Unknown" for subsequent requests.
agent._apply_client_headers_for_base_url(effective_base)
agent.client = agent._create_openai_client(
dict(agent._client_kwargs),
reason="switch_model",
@@ -2236,35 +1746,9 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
api_mode=agent.api_mode,
)
# ── Re-resolve reasoning_config from per-model override ──
# The new model may have a different reasoning_effort override. Re-read
# config so the override takes effect immediately on /model switch —
# resolved through the shared chokepoint (per-model > global; YAML
# boolean False = disabled).
try:
from hermes_constants import resolve_reasoning_config
from hermes_cli.config import load_config as _sm_load_config
_reasoning_cfg = _sm_load_config() or {}
agent.reasoning_config = resolve_reasoning_config(_reasoning_cfg, agent.model)
logger.info(
"switch_model: reasoning_config resolved for %s: %s",
agent.model, agent.reasoning_config,
)
except Exception as _reasoning_err:
logger.debug("switch_model: could not re-resolve reasoning_config: %s", _reasoning_err)
# ── Invalidate cached system prompt so it rebuilds next turn ──
agent._cached_system_prompt = None
# ── Reset the cross-turn stale-call circuit breaker (#58962) ──
# The breaker's error text tells the user to "switch models ... then
# retry"; without this reset the streak stays latched and the freshly
# selected (healthy) provider would keep short-circuiting before any
# stream is even attempted.
from agent.chat_completion_helpers import _reset_stale_streak
_reset_stale_streak(agent)
# ── Update _primary_runtime so the change persists across turns ──
_cc = agent.context_compressor if hasattr(agent, "context_compressor") and agent.context_compressor else None
agent._primary_runtime = {
@@ -2276,7 +1760,6 @@ def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mo
"client_kwargs": dict(agent._client_kwargs),
"use_prompt_caching": agent._use_prompt_caching,
"use_native_cache_layout": agent._use_native_cache_layout,
"reasoning_config": dict(agent.reasoning_config) if getattr(agent, "reasoning_config", None) else None,
"compressor_model": getattr(_cc, "model", agent.model) if _cc else agent.model,
"compressor_base_url": getattr(_cc, "base_url", agent.base_url) if _cc else agent.base_url,
"compressor_api_key": getattr(_cc, "api_key", "") if _cc else "",
@@ -2375,12 +1858,12 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
except Exception as _mw_err:
logger.debug("tool_request middleware error: %s", _mw_err)
# Check plugin hooks for a block or approval directive before executing.
# Check plugin hooks for a block directive before executing anything.
block_message: Optional[str] = None
if not pre_tool_block_checked:
try:
from hermes_cli.plugins import resolve_pre_tool_block
block_message = resolve_pre_tool_block(
from hermes_cli.plugins import get_pre_tool_call_block_message
block_message = get_pre_tool_call_block_message(
function_name,
function_args,
task_id=effective_task_id or "",
@@ -2391,7 +1874,7 @@ def invoke_tool(agent, function_name: str, function_args: dict, effective_task_i
middleware_trace=list(_tool_middleware_trace),
)
except Exception:
block_message = None
pass
if block_message is not None:
result = json.dumps({"error": block_message}, ensure_ascii=False)
try:
@@ -2669,89 +2152,6 @@ def sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]
filtered.append(msg)
messages = filtered
# --- Drop empty / malformed tool_calls arrays on assistant messages ---
# An assistant message carrying ``tool_calls: []`` (an empty array) — or a
# non-list value under the key — is semantically identical to an assistant
# message with no tool calls, but strict OpenAI-compatible providers reject
# the empty array outright: DeepSeek v4 returns HTTP 400 "Invalid
# 'messages[N].tool_calls': empty array. Expected an array with minimum
# length 1, but got an empty array instead." (#58755, follow-up to #56980).
# Empty arrays reach here from session resume, host-fed histories, or the
# consecutive-assistant merge in ``repair_message_sequence`` (which
# preserves a pre-existing ``[]`` on the surviving turn). This is the final
# pre-API chokepoint, so normalize defensively — and, per the #56980
# review, do it HERE on the per-call copy rather than in
# ``repair_message_sequence``, which would destructively rewrite the
# persisted trajectory. Shallow-copy the message before dropping the key so
# stored history (and prompt caching) stays byte-stable.
normalized: List[Dict[str, Any]] = []
dropped_empty_tool_calls = 0
for msg in messages:
if (
isinstance(msg, dict)
and msg.get("role") == "assistant"
and "tool_calls" in msg
and not (isinstance(msg["tool_calls"], list) and msg["tool_calls"])
):
msg = {k: v for k, v in msg.items() if k != "tool_calls"}
dropped_empty_tool_calls += 1
normalized.append(msg)
if dropped_empty_tool_calls:
messages = normalized
_ra().logger.debug(
"Pre-call sanitizer: dropped empty/invalid tool_calls on %d "
"assistant message(s)",
dropped_empty_tool_calls,
)
# --- Repair tool_calls whose function.name is empty/missing ---
# Some providers (and partially-streamed responses) emit a tool_call with
# id="call_xxx" but function.name="". Downstream Responses-API adapters
# silently DROP such function_call items while still emitting the matching
# function_call_output, producing the gateway's HTTP 400
# "No tool call found for function call output with call_id ...".
#
# We do NOT drop the call: hermes' own dispatch loop intentionally keeps an
# empty-name call paired with a synthesized anti-priming tool result
# ("tool name was empty", see #47967) so weak models self-correct instead of
# being fed the full tool catalog. Dropping the call here would (a) orphan
# that result and strip the anti-priming signal, and (b) still leave any
# provider-side orphan. Instead, rename the blank name to a non-empty
# sentinel so the call and its result stay PAIRED — the adapter no longer
# drops the function_call, so there is no orphaned output and no 400, while
# the result content the model needs is preserved.
_EMPTY_NAME_SENTINEL = "invalid_tool_call"
for msg in messages:
if msg.get("role") != "assistant":
continue
tcs = msg.get("tool_calls") or []
if not tcs:
continue
for tc in tcs:
if isinstance(tc, dict):
fn = tc.get("function")
name = fn.get("name") if isinstance(fn, dict) else getattr(fn, "name", None)
else:
fn = getattr(tc, "function", None)
name = getattr(fn, "name", None) if fn else None
if isinstance(name, str) and name.strip():
continue
_ra().logger.warning(
"Pre-call sanitizer: repairing tool_call with empty "
"function.name -> %r (id=%s)",
_EMPTY_NAME_SENTINEL,
_ra().AIAgent._get_tool_call_id_static(tc),
)
if isinstance(fn, dict):
fn["name"] = _EMPTY_NAME_SENTINEL
elif fn is not None and hasattr(fn, "name"):
try:
fn.name = _EMPTY_NAME_SENTINEL
except Exception:
pass
elif isinstance(tc, dict):
tc["function"] = {"name": _EMPTY_NAME_SENTINEL, "arguments": "{}"}
surviving_call_ids: set = set()
for msg in messages:
if msg.get("role") == "assistant":
@@ -2763,7 +2163,7 @@ def sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]
result_call_ids: set = set()
for msg in messages:
if msg.get("role") == "tool":
cid = (msg.get("tool_call_id") or "").strip()
cid = msg.get("tool_call_id")
if cid:
result_call_ids.add(cid)
@@ -2772,7 +2172,7 @@ def sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]
if orphaned_results:
messages = [
m for m in messages
if not (m.get("role") == "tool" and (m.get("tool_call_id") or "").strip() in orphaned_results)
if not (m.get("role") == "tool" and m.get("tool_call_id") in orphaned_results)
]
_ra().logger.debug(
"Pre-call sanitizer: removed %d orphaned tool result(s)",
@@ -2800,57 +2200,13 @@ def sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]
"Pre-call sanitizer: added %d stub tool result(s)",
len(missing_results),
)
# 3. Deduplicate tool_call_ids. Strict providers (DeepSeek) reject a
# payload where the same tool_call_id appears more than once with HTTP 400
# "Duplicate value for 'tool_call_id'" (#58327). Duplicates can arise from
# retries, crash/resume glitches, or a compression window that re-emits a
# tool result. This is the final pre-API chokepoint, so dedup defensively
# here even though repair_message_sequence also consumes matched ids.
# (a) collapse duplicate tool_calls WITHIN an assistant message
# (b) drop later tool result messages reusing an already-seen id
seen_assistant_call_ids: set = set()
seen_result_call_ids: set = set()
deduped: List[Dict[str, Any]] = []
removed_dupes = 0
for msg in messages:
role = msg.get("role")
if role == "assistant" and msg.get("tool_calls"):
kept_tcs = []
for tc in msg.get("tool_calls") or []:
cid = _ra().AIAgent._get_tool_call_id_static(tc)
if cid and cid in seen_assistant_call_ids:
removed_dupes += 1
continue
if cid:
seen_assistant_call_ids.add(cid)
kept_tcs.append(tc)
if len(kept_tcs) != len(msg.get("tool_calls") or []):
msg = {**msg, "tool_calls": kept_tcs}
deduped.append(msg)
elif role == "tool":
cid = (msg.get("tool_call_id") or "").strip()
if cid and cid in seen_result_call_ids:
removed_dupes += 1
continue
if cid:
seen_result_call_ids.add(cid)
deduped.append(msg)
else:
deduped.append(msg)
if removed_dupes:
messages = deduped
_ra().logger.debug(
"Pre-call sanitizer: removed %d duplicate tool_call_id reference(s)",
removed_dupes,
)
return messages
def looks_like_codex_intermediate_ack(
agent,
user_message: Any,
user_message: str,
assistant_content: str,
messages: List[Dict[str, Any]],
require_workspace: bool = True,
@@ -2930,14 +2286,7 @@ def looks_like_codex_intermediate_ack(
if not require_workspace:
return True
# ``user_message`` is typed ``str`` but can arrive as an OpenAI-style
# multi-part content list (``[{type:"text",...}, {type:"image_url",...}]``)
# for vision requests routed through the OpenAI-compat API server. A
# truthy list survives ``(user_message or "")`` and then ``.strip()``
# raises ``AttributeError`` — flatten to text first.
from agent.codex_responses_adapter import _summarize_user_message_for_log
user_text = _summarize_user_message_for_log(user_message).strip().lower()
user_text = (user_message or "").strip().lower()
user_targets_workspace = (
any(marker in user_text for marker in workspace_markers)
or "~/" in user_text
@@ -3256,10 +2605,6 @@ def extract_api_error_context(error: Exception) -> Dict[str, Any]:
if isinstance(reason, str) and reason.strip():
context["reason"] = reason.strip()
message = payload.get("message") or payload.get("error_description")
if not message and isinstance(payload.get("error"), str):
# xAI uses a top-level string ``error`` beside a structured
# ``code`` (for example personal-team-blocked:spending-limit).
message = payload.get("error")
if isinstance(message, str) and message.strip():
context["message"] = message.strip()
for key in ("resets_at", "reset_at"):
+80 -155
View File
@@ -65,7 +65,6 @@ THINKING_BUDGET = {"xhigh": 32000, "high": 16000, "medium": 8000, "low": 4000}
# maps to low on every model. See:
# https://platform.claude.com/docs/en/about-claude/models/migration-guide
ADAPTIVE_EFFORT_MAP = {
"ultra": "max",
"max": "max",
"xhigh": "xhigh",
"high": "high",
@@ -127,8 +126,6 @@ _FAST_MODE_SUPPORTED_SUBSTRINGS = ("opus-4-6", "opus-4.6")
_ANTHROPIC_OUTPUT_LIMITS = {
# Mythos-class named models (claude-fable-5, …) — 1M context, reasoning
"claude-fable": 128_000,
# Claude Sonnet 5
"claude-sonnet-5": 128_000,
# Claude 4.8
"claude-opus-4-8": 128_000,
# Claude 4.7
@@ -249,13 +246,7 @@ def _supports_adaptive_thinking(model: str) -> bool:
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.
Kimi / Moonshot models are the exception: their Anthropic-compatible
endpoints implement the adaptive contract (``thinking.type="adaptive"``
+ ``output_config.effort``, including ``xhigh`` and ``display``).
"""
if _model_name_is_kimi_family(model):
return True
if not _is_claude_model(model):
return False
m = model.lower()
@@ -457,8 +448,7 @@ def _is_kimi_coding_endpoint(base_url: str | None) -> bool:
# Model-name prefixes that identify the Kimi / Moonshot family. Covers
# - official slugs: ``kimi-k2.5``, ``kimi_thinking``, ``moonshot-v1-8k``
# - common release lines: ``k1.5-...``, ``k2-thinking``, ``k25-...``, ``k2.5-...``,
# and the bare Coding Plan slug ``k3`` (plus ``k3.x``/``k3-...`` variants)
# - common release lines: ``k1.5-...``, ``k2-thinking``, ``k25-...``, ``k2.5-...``
# Matched case-insensitively against the post-``normalize_model_name`` form,
# so a caller's ``provider/vendor/model`` slug is handled the same as a
# bare name.
@@ -468,14 +458,8 @@ _KIMI_FAMILY_MODEL_PREFIXES = (
"k1.", "k1-",
"k2.", "k2-",
"k25", "k2.5",
"k3.", "k3-",
)
# Bare release slugs with no separator suffix (Kimi Coding Plan serves K3
# as the exact slug ``k3``). Kept exact-match so unrelated model names that
# merely start with the same characters don't get misclassified.
_KIMI_FAMILY_EXACT_SLUGS = frozenset({"k3"})
def _model_name_is_kimi_family(model: str | None) -> bool:
if not isinstance(model, str):
@@ -486,8 +470,6 @@ def _model_name_is_kimi_family(model: str | None) -> bool:
# Strip vendor prefix (e.g. ``moonshotai/kimi-k2.5`` → ``kimi-k2.5``)
if "/" in m:
m = m.rsplit("/", 1)[-1]
if m in _KIMI_FAMILY_EXACT_SLUGS:
return True
return m.startswith(_KIMI_FAMILY_MODEL_PREFIXES)
@@ -551,9 +533,8 @@ def _requires_bearer_auth(base_url: str | None) -> bool:
Some third-party /anthropic endpoints implement Anthropic's Messages API but
require Authorization: Bearer instead of Anthropic's native x-api-key header.
MiniMax's global and China Anthropic-compatible endpoints, Azure AI
Foundry's Anthropic-style endpoint, and Palantir Foundry's LLM proxy
follow this pattern.
MiniMax's global and China Anthropic-compatible endpoints, and Azure AI
Foundry's Anthropic-style endpoint follow this pattern.
"""
normalized = _normalize_base_url_text(base_url)
if not normalized:
@@ -562,11 +543,6 @@ def _requires_bearer_auth(base_url: str | None) -> bool:
return (
normalized.startswith(("https://api.minimax.io/anthropic", "https://api.minimaxi.com/anthropic"))
or "azure.com" in normalized
# Palantir Foundry LLM proxy (<org>.palantirfoundry.com/api/v2/llm/proxy/anthropic)
# rejects x-api-key with 401 and requires Authorization: Bearer.
# Hostname match (not substring) so e.g. evil.com/palantirfoundry
# paths don't trigger Bearer auth.
or base_url_host_matches(normalized, "palantirfoundry.com")
)
@@ -841,7 +817,7 @@ def build_anthropic_client(
kwargs["auth_token"] = api_key
kwargs["default_headers"] = {
"anthropic-beta": ",".join(all_betas),
"user-agent": f"claude-code/{_get_claude_code_version()} (external, cli)",
"user-agent": f"claude-cli/{_get_claude_code_version()} (external, cli)",
"x-app": "cli",
}
else:
@@ -1069,7 +1045,7 @@ def refresh_anthropic_oauth_pure(refresh_token: str, *, use_json: bool = False)
data=data,
headers={
"Content-Type": content_type,
"User-Agent": _OAUTH_TOKEN_USER_AGENT,
"User-Agent": f"claude-cli/{_get_claude_code_version()} (external, cli)",
},
method="POST",
)
@@ -1402,20 +1378,9 @@ _OAUTH_TOKEN_URLS = [
"https://console.anthropic.com/v1/oauth/token",
]
_OAUTH_TOKEN_URL = _OAUTH_TOKEN_URLS[0]
# User-Agent sent on the OAuth *token endpoint* (login exchange + refresh).
# Anthropic rate-limits (HTTP 429) any token-endpoint request whose UA starts
# with ``claude-code/`` — verified empirically against platform.claude.com:
# ``claude-code/2.1.200`` and ``Mozilla/5.0`` -> 429; ``axios/*``, ``node``,
# and SDK-style UAs -> 400 (reached code validation). The real Claude Code CLI
# exchanges the auth code with a bare axios client (``axios/<ver>``), NOT its
# ``claude-code/`` inference UA. We mirror that here. NOTE: the *inference* path
# (build_anthropic_kwargs) still uses the ``claude-code/`` UA + ``x-app: cli`` —
# that fingerprint is required there and is NOT throttled on the messages API.
_OAUTH_TOKEN_USER_AGENT = "axios/1.7.9"
_OAUTH_REDIRECT_URI = "https://console.anthropic.com/oauth/code/callback"
_OAUTH_SCOPES = "org:create_api_key user:profile user:inference"
def _get_hermes_oauth_file() -> Path:
return get_hermes_home() / ".anthropic_oauth.json"
_HERMES_OAUTH_FILE = get_hermes_home() / ".anthropic_oauth.json"
def _generate_pkce() -> tuple:
@@ -1513,9 +1478,6 @@ def run_hermes_oauth_login_pure() -> Optional[Dict[str, Any]]:
# 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).
# UA is _OAUTH_TOKEN_USER_AGENT (a non-claude-code UA) — see the
# constant's definition for why the token endpoint must not send
# claude-code/ (429 UA-prefix block).
result = None
last_error = None
for endpoint in _OAUTH_TOKEN_URLS:
@@ -1524,7 +1486,7 @@ def run_hermes_oauth_login_pure() -> Optional[Dict[str, Any]]:
data=exchange_data,
headers={
"Content-Type": "application/json",
"User-Agent": _OAUTH_TOKEN_USER_AGENT,
"User-Agent": f"claude-cli/{_get_claude_code_version()} (external, cli)",
},
method="POST",
)
@@ -1563,10 +1525,9 @@ def run_hermes_oauth_login_pure() -> Optional[Dict[str, Any]]:
def read_hermes_oauth_credentials() -> Optional[Dict[str, Any]]:
"""Read Hermes-managed OAuth credentials from ~/.hermes/.anthropic_oauth.json."""
oauth_file = _get_hermes_oauth_file()
if oauth_file.exists():
if _HERMES_OAUTH_FILE.exists():
try:
data = json.loads(oauth_file.read_text(encoding="utf-8"))
data = json.loads(_HERMES_OAUTH_FILE.read_text(encoding="utf-8"))
if data.get("accessToken"):
return data
except (json.JSONDecodeError, OSError, IOError) as e:
@@ -1591,10 +1552,7 @@ def _is_bedrock_model_id(model: str) -> bool:
"""
lower = model.lower()
# Regional inference-profile prefixes
if any(lower.startswith(p) for p in (
"global.", "us.", "eu.", "apac.", "ap.", "au.", "jp.",
"ca.", "sa.", "me.", "af.",
)):
if any(lower.startswith(p) for p in ("global.", "us.", "eu.", "ap.", "jp.")):
return True
# Bare Bedrock model IDs: provider.model-family
if lower.startswith("anthropic."):
@@ -1933,18 +1891,6 @@ def _sanitize_replay_block(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
return None
def _apply_assistant_cache_control_to_last_cacheable_block(
blocks: List[Dict[str, Any]],
cache_control: Any,
) -> None:
if not isinstance(cache_control, dict):
return
for block in reversed(blocks):
if isinstance(block, dict) and block.get("type") in {"text", "tool_use"}:
block.setdefault("cache_control", dict(cache_control))
break
def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
"""Convert an assistant message to Anthropic content blocks.
@@ -1999,9 +1945,6 @@ def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
clean["input"] = redacted
replayed.append(clean)
if replayed:
_apply_assistant_cache_control_to_last_cacheable_block(
replayed, m.get("cache_control")
)
return {"role": "assistant", "content": replayed}
blocks = _extract_preserved_thinking_blocks(m)
@@ -2027,9 +1970,6 @@ def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
"name": fn.get("name", ""),
"input": parsed_args,
})
_apply_assistant_cache_control_to_last_cacheable_block(
blocks, m.get("cache_control")
)
# Kimi's /coding endpoint (Anthropic protocol) requires assistant
# tool-call messages to carry reasoning_content when thinking is
# enabled server-side. Preserve it as a thinking block so Kimi
@@ -2129,7 +2069,7 @@ def _convert_user_message(content: Any) -> Dict[str, Any]:
if isinstance(content, list):
converted_blocks = _convert_content_to_anthropic(content)
if not converted_blocks or all(
(b.get("text") or "").strip() == ""
b.get("text", "").strip() == ""
for b in converted_blocks
if isinstance(b, dict) and b.get("type") == "text"
):
@@ -2145,81 +2085,57 @@ def _strip_orphaned_tool_blocks(result: List[Dict[str, Any]]) -> None:
"""Strip tool_use blocks with no matching tool_result, and vice versa.
Context compression or session truncation can remove either side of a
tool-call pair, or insert messages between a tool_use and its result.
Anthropic requires each tool_use to have a matching tool_result in the
IMMEDIATELY FOLLOWING user message — a global ID match is not enough.
tool-call pair. Anthropic rejects both orphans with HTTP 400.
Mutates ``result`` in place.
"""
# Pass 1: For each assistant message with tool_use blocks, check that
# EACH tool_use ID has a matching tool_result in the immediately following
# user message. Strip tool_use blocks that lack an adjacent result —
# Anthropic rejects non-adjacent pairs with HTTP 400 even when the IDs
# match somewhere later in the conversation.
for i, m in enumerate(result):
if m.get("role") != "assistant" or not isinstance(m.get("content"), list):
continue
tool_use_ids_in_turn = {
b.get("id")
for b in m["content"]
if isinstance(b, dict) and b.get("type") == "tool_use"
}
if not tool_use_ids_in_turn:
continue
# Collect result IDs from the immediately following user message only.
adjacent_result_ids: set = set()
if i + 1 < len(result):
nxt = result[i + 1]
if nxt.get("role") == "user" and isinstance(nxt.get("content"), list):
for block in nxt["content"]:
if isinstance(block, dict) and block.get("type") == "tool_result":
adjacent_result_ids.add(block.get("tool_use_id"))
orphaned = tool_use_ids_in_turn - adjacent_result_ids
if not orphaned:
continue
kept = [
b
for b in m["content"]
if not (isinstance(b, dict) and b.get("type") == "tool_use" and b.get("id") in orphaned)
]
# If stripping an orphaned tool_use mutated a turn that also carries a
# signed thinking block, that block's Anthropic signature was computed
# against the ORIGINAL (un-stripped) turn content and is now invalid.
# Anthropic rejects the replayed turn with HTTP 400 "thinking blocks in
# the latest assistant message cannot be modified". Flag the turn so
# _manage_thinking_signatures can demote the dead signature instead of
# replaying it verbatim. See hermes-agent: extended-thinking + parallel
# tool batch interrupted mid-flight → non-retryable 400 crash-loop.
if len(kept) != len(m["content"]) and any(
isinstance(b, dict) and b.get("type") in {"thinking", "redacted_thinking"}
for b in m["content"]
):
m["_thinking_signature_invalidated"] = True
m["content"] = kept if kept else [{"type": "text", "text": "(tool call removed)"}]
# Pass 2: Rebuild the set of tool_use IDs that survived pass 1, then
# strip tool_result blocks that no longer have any matching tool_use
# anywhere in the conversation.
surviving_tool_use_ids: set = set()
# Strip orphaned tool_use blocks (no matching tool_result follows)
tool_result_ids = set()
for m in result:
if m.get("role") == "assistant" and isinstance(m.get("content"), list):
if m["role"] == "user" and isinstance(m["content"], list):
for block in m["content"]:
if isinstance(block, dict) and block.get("type") == "tool_use":
surviving_tool_use_ids.add(block.get("id"))
if block.get("type") == "tool_result":
tool_result_ids.add(block.get("tool_use_id"))
for m in result:
if m.get("role") != "user" or not isinstance(m.get("content"), list):
continue
new_content = [
b
for b in m["content"]
if not (isinstance(b, dict) and b.get("type") == "tool_result")
or b.get("tool_use_id") in surviving_tool_use_ids
]
if len(new_content) != len(m["content"]):
m["content"] = new_content if new_content else [{"type": "text", "text": "(tool result removed)"}]
if m["role"] == "assistant" and isinstance(m["content"], list):
kept = [
b
for b in m["content"]
if b.get("type") != "tool_use" or b.get("id") in tool_result_ids
]
# If stripping an orphaned tool_use mutated a turn that also carries a
# signed thinking block, that block's Anthropic signature was computed
# against the ORIGINAL (un-stripped) turn content and is now invalid.
# Anthropic rejects the replayed turn with HTTP 400 "thinking blocks in
# the latest assistant message cannot be modified". Flag the turn so
# _manage_thinking_signatures can demote the dead signature instead of
# replaying it verbatim. See hermes-agent: extended-thinking + parallel
# tool batch interrupted mid-flight → non-retryable 400 crash-loop.
if len(kept) != len(m["content"]) and any(
isinstance(b, dict) and b.get("type") in {"thinking", "redacted_thinking"}
for b in m["content"]
):
m["_thinking_signature_invalidated"] = True
m["content"] = kept
if not m["content"]:
m["content"] = [{"type": "text", "text": "(tool call removed)"}]
# Strip orphaned tool_result blocks (no matching tool_use precedes them)
tool_use_ids = set()
for m in result:
if m["role"] == "assistant" and isinstance(m["content"], list):
for block in m["content"]:
if block.get("type") == "tool_use":
tool_use_ids.add(block.get("id"))
for m in result:
if m["role"] == "user" and isinstance(m["content"], list):
m["content"] = [
b
for b in m["content"]
if b.get("type") != "tool_result" or b.get("tool_use_id") in tool_use_ids
]
if not m["content"]:
m["content"] = [{"type": "text", "text": "(tool result removed)"}]
def _merge_consecutive_roles(result: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
@@ -2296,6 +2212,13 @@ def _manage_thinking_signatures(
"""
_THINKING_TYPES = frozenset(("thinking", "redacted_thinking"))
_is_third_party = _is_third_party_anthropic_endpoint(base_url)
# Kimi / DeepSeek share a contract: strip signed Anthropic blocks
# (neither upstream can validate Anthropic signatures), preserve unsigned
# ones synthesised from reasoning_content. See #13848, #16748.
_preserve_unsigned_thinking = (
_is_kimi_family_endpoint(base_url, model)
or _is_deepseek_anthropic_endpoint(base_url)
)
last_assistant_idx = None
for i in range(len(result) - 1, -1, -1):
@@ -2307,12 +2230,8 @@ def _manage_thinking_signatures(
if m.get("role") != "assistant" or not isinstance(m.get("content"), list):
continue
if _is_kimi_family_endpoint(base_url, model):
# Kimi does not enforce thinking signatures — replay as-is
# (shared cleanup below still strips cache markers + the internal flag).
pass
elif _is_deepseek_anthropic_endpoint(base_url):
# DeepSeek: strip signed, preserve unsigned.
if _preserve_unsigned_thinking:
# Kimi / DeepSeek: strip signed, preserve unsigned.
new_content = []
for b in m["content"]:
if not isinstance(b, dict) or b.get("type") not in _THINKING_TYPES:
@@ -2644,19 +2563,25 @@ def build_anthropic_kwargs(
# MiniMax Anthropic-compat endpoints support thinking (manual mode only,
# not adaptive). Haiku does NOT support extended thinking — skip entirely.
#
# Kimi / Moonshot models also use adaptive thinking: their
# Anthropic-compatible endpoints (api.moonshot.cn/anthropic,
# api.kimi.com/coding) accept ``thinking.type="adaptive"`` +
# ``output_config.effort``, and the replay-validation 400s that
# originally motivated dropping the parameter (#13848) no longer
# occur. (Kimi on chat_completions enables thinking via extra_body
# in the ChatCompletionsTransport — see #13503.)
# Kimi's /coding endpoint speaks the Anthropic Messages protocol but has
# its own thinking semantics: when ``thinking.enabled`` is sent, Kimi
# validates the message history and requires every prior assistant
# tool-call message to carry OpenAI-style ``reasoning_content``. The
# Anthropic path never populates that field, and
# ``convert_messages_to_anthropic`` strips all Anthropic thinking blocks
# on third-party endpoints — so the request fails with HTTP 400
# "thinking is enabled but reasoning_content is missing in assistant
# tool call message at index N". Kimi's reasoning is driven server-side
# on the /coding route, so skip Anthropic's thinking parameter entirely
# for that host. (Kimi on chat_completions enables thinking via
# extra_body in the ChatCompletionsTransport — see #13503.)
#
# On 4.7+ the `thinking.display` field defaults to "omitted", which
# silently hides reasoning text that Hermes surfaces in its CLI. We
# request "summarized" so the reasoning blocks stay populated — matching
# 4.6 behavior and preserving the activity-feed UX during long tool runs.
if reasoning_config and isinstance(reasoning_config, dict):
_is_kimi_coding = _is_kimi_family_endpoint(base_url, model)
if reasoning_config and isinstance(reasoning_config, dict) and not _is_kimi_coding:
if reasoning_config.get("enabled") is not False and "haiku" not in model.lower():
effort = str(reasoning_config.get("effort", "medium")).lower()
budget = THINKING_BUDGET.get(effort, 8000)
-16
View File
@@ -66,19 +66,3 @@ def safe_schedule_threadsafe(
coro.close()
log.log(log_level, "%s: %s", log_message, exc)
return None
def consume_detached_task_result(task: "asyncio.Future[Any]") -> None:
"""Retrieve a detached task's result without surfacing cancellation.
Used as an ``add_done_callback`` on tasks that were cancelled and
detached (e.g. an adapter close path that swallows ``CancelledError``
past its teardown deadline). Observing ``task.exception()`` prevents
"exception was never retrieved" noise on the event loop; cancellation
and any terminal error are deliberately swallowed — the task's owner
already gave up on it.
"""
try:
task.exception()
except (asyncio.CancelledError, Exception):
pass
-138
View File
@@ -1,138 +0,0 @@
"""Ambient session-accounting context for auxiliary LLM calls.
Auxiliary calls (vision, compression, title generation, web_extract,
session_search, ...) funnel through ``agent.auxiliary_client`` which has no
session handle — so their token usage was historically discarded, leaving
dashboard analytics blind to aux model spend (issue #23270).
Instead of threading ``session_db``/``session_id`` parameters through every
aux call site, the agent loop publishes them here (mirroring the Nous Portal
conversation context in ``agent.portal_tags``) and the auxiliary client
records usage at its single response-validation chokepoint.
ContextVar semantics give us the right isolation for free:
* concurrent agents in one process (gateway sessions, delegate subagents)
never see each other's accounting context;
* worker threads spawned via ``tools.thread_context.propagate_context_to_thread``
(MoA fan-out, background review) inherit the parent turn's context;
* asyncio tasks inherit the context of the code that created them.
MoA reference/aggregator slots are explicitly EXCLUDED from recording:
``agent/conversation_loop.py`` already folds MoA advisor usage and cost into
the main loop's ``update_token_counts`` delta, so recording them here would
double-count (see ``_EXCLUDED_TASKS``).
"""
from __future__ import annotations
import logging
from contextvars import ContextVar
from typing import Any, Optional
logger = logging.getLogger(__name__)
# (session_db, session_id) for the active agent turn, or None outside one.
_accounting: ContextVar[Optional[tuple]] = ContextVar(
"aux_accounting_context", default=None
)
# Aux tasks whose usage is already accounted by the main loop — recording
# them here would double-count. MoA advisor/aggregator usage is folded into
# conversation_loop's update_token_counts delta (tokens AND cost).
_EXCLUDED_TASKS = frozenset({"moa_reference", "moa_aggregator"})
def set_accounting_context(session_db: Any, session_id: Optional[str]):
"""Publish the active session's accounting handles for aux usage recording.
Called by the agent loop at turn entry. Returns the ContextVar token so
callers can ``reset_accounting_context(token)`` on turn exit. Publishing
``None`` handles (no DB / no session id) clears the context.
"""
if session_db is None or not session_id:
return _accounting.set(None)
return _accounting.set((session_db, session_id))
def reset_accounting_context(token) -> None:
"""Restore the previous accounting context (pair with ``set_...``)."""
try:
_accounting.reset(token)
except Exception:
_accounting.set(None)
def get_accounting_context() -> Optional[tuple]:
"""Return ``(session_db, session_id)`` for the active turn, or ``None``."""
return _accounting.get()
def record_aux_usage(
response: Any,
task: Optional[str],
*,
provider: Optional[str] = None,
base_url: Optional[str] = None,
) -> None:
"""Record an auxiliary response's token usage against the ambient session.
Called from the auxiliary client's response-validation chokepoint. Strictly
best-effort: any failure is swallowed (accounting must never break an aux
call). No-ops when:
* no accounting context is published (call is outside any agent turn),
* the task is main-loop-accounted (MoA slots — see ``_EXCLUDED_TASKS``),
* the response carries no usage object.
The model is read from ``response.model`` (accurate even after the aux
client's provider-fallback chains); *provider*/*base_url* reflect the
originally-resolved route and are best-effort.
"""
try:
if not task or task in _EXCLUDED_TASKS:
return
ctx = _accounting.get()
if ctx is None:
return
session_db, session_id = ctx
raw_usage = getattr(response, "usage", None)
if raw_usage is None:
return
from agent.usage_pricing import estimate_usage_cost, normalize_usage
usage = normalize_usage(raw_usage, provider=provider)
if not (
usage.input_tokens or usage.output_tokens
or usage.cache_read_tokens or usage.cache_write_tokens
or usage.reasoning_tokens
):
return
model = str(getattr(response, "model", "") or "") or "unknown"
estimated_cost = None
try:
cost = estimate_usage_cost(
model, usage, provider=provider, base_url=base_url
)
if cost.amount_usd is not None:
estimated_cost = float(cost.amount_usd)
except Exception:
logger.debug("Aux usage cost estimation failed", exc_info=True)
session_db.record_auxiliary_usage(
session_id,
task,
model=model,
billing_provider=provider,
billing_base_url=base_url,
input_tokens=usage.input_tokens,
output_tokens=usage.output_tokens,
cache_read_tokens=usage.cache_read_tokens,
cache_write_tokens=usage.cache_write_tokens,
reasoning_tokens=usage.reasoning_tokens,
estimated_cost_usd=estimated_cost,
)
except Exception:
logger.debug("Aux usage recording failed (non-fatal)", exc_info=True)
+210 -1625
View File
File diff suppressed because it is too large Load Diff
+26 -143
View File
@@ -18,13 +18,12 @@ for invariants and PR review criteria.
from __future__ import annotations
import contextlib
import json
import logging
import os
from typing import Any, Dict, List, Optional
from agent.thread_scoped_output import thread_scoped_silence
logger = logging.getLogger(__name__)
@@ -62,11 +61,6 @@ def _resolve_review_runtime(agent: Any) -> Dict[str, Any]:
"api_key": parent_runtime.get("api_key") or None,
"base_url": parent_runtime.get("base_url") or None,
"api_mode": parent_api_mode,
"credential_pool": getattr(agent, "_credential_pool", None),
"request_overrides": dict(getattr(agent, "request_overrides", {}) or {}),
"max_tokens": getattr(agent, "max_tokens", None),
"command": getattr(agent, "acp_command", None),
"args": list(getattr(agent, "acp_args", []) or []),
"routed": False,
}
try:
@@ -94,15 +88,10 @@ def _resolve_review_runtime(agent: Any) -> Dict[str, Any]:
)
return {
"provider": rp.get("provider") or task_provider,
"model": rp.get("model") or task_model,
"model": task_model,
"api_key": rp.get("api_key"),
"base_url": rp.get("base_url"),
"api_mode": rp.get("api_mode"),
"credential_pool": rp.get("credential_pool"),
"request_overrides": dict(rp.get("request_overrides") or {}),
"max_tokens": rp.get("max_output_tokens"),
"command": rp.get("command"),
"args": list(rp.get("args") or []),
"routed": True,
}
except Exception as e:
@@ -459,21 +448,10 @@ def summarize_background_review_actions(
data = json.loads(msg.get("content", "{}"))
except (json.JSONDecodeError, TypeError):
continue
# ``data`` may not be a dict — some memory/skill tool responses in
# older codepaths or wrapper MCP servers return a top-level JSON
# list (e.g. ``[{"success": true, ...}]``) or a scalar. The original
# isinstance check below silently skips non-dict payloads, which
# is correct, but ``data.get("_change")`` further down can still
# hand back a list and break ``change.get("description", "")``.
# Defensively normalize everything through a dict-typed alias so
# the rest of the function can stay terse without per-call
# ``isinstance`` guards (#59437).
if not isinstance(data, dict) or not data.get("success"):
continue
message = data.get("message", "")
detail = call_details.get(tcid) or {}
if not isinstance(detail, dict):
detail = {}
detail = call_details.get(tcid, {})
target = data.get("target", "") or detail.get("target", "")
is_skill = detail.get("tool") == "skill_manage"
@@ -501,30 +479,12 @@ def summarize_background_review_actions(
content = detail.get("content", "")
old_text = detail.get("old_text", "")
skill_name = detail.get("name", "")
# ``operations`` may be anything callable put into the JSON
# arguments. Anything non-iterable that isn't a list[str]
# of dicts becomes unusable here, so coerce defensively.
ops_raw = detail.get("operations")
operations: list = (
ops_raw if isinstance(ops_raw, list) else []
)
operations = detail.get("operations") or []
max_preview = 120
if is_skill:
# ``_change`` is a free-form dict the skill tool leaves in
# the response. Older / wrapper MCP backends return it
# as a list, an int, or a JSON-shaped scalar — normalize
# to a dict so the .get() calls downstream don't
# AttributeError (#59437).
change_raw = data.get("_change")
change: dict = (
change_raw if isinstance(change_raw, dict) else {}
)
old_string = (
change.get("old", "") or detail.get("old_string", "")
)
new_string = (
change.get("new", "") or detail.get("new_string", "")
)
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", " ") + (
@@ -545,13 +505,7 @@ def summarize_background_review_actions(
actions.append(f"📝 {message}" if message else f"Skill {action}")
elif operations:
for op in operations:
# Each element must be a dict-of-fields; some
# legacy codepaths serialize the entry as a bare
# string and the message dict doesn't exist. Skip
# non-dict items defensively — they have no
# actionable fields anyway (#59437).
if not isinstance(op, dict):
continue
op = op or {}
op_act = op.get("action", "")
op_content = (op.get("content") or "")
op_old = (op.get("old_text") or "")
@@ -648,15 +602,9 @@ def _run_review_in_thread(
review_agent = None
review_messages: List[Dict] = []
try:
# Silence stdout/stderr for THIS worker thread only. A process-global
# ``contextlib.redirect_stdout(devnull)`` here would also blank
# ``sys.stdout``/``sys.stderr`` for every other thread — including a
# gateway event-loop thread driving a Telegram long-poll — for the full
# duration of the review (tens of seconds), swallowing their console
# output (#55769 / #55925). ``thread_scoped_silence`` routes only this
# thread's writes to devnull and leaves all other threads on the real
# streams.
with thread_scoped_silence():
with open(os.devnull, "w", encoding="utf-8") as _devnull, \
contextlib.redirect_stdout(_devnull), \
contextlib.redirect_stderr(_devnull):
# Inherit the parent agent's live runtime (provider, model,
# base_url, api_key, api_mode) so the fork uses the exact
# same credentials the main turn is using. Without this,
@@ -690,25 +638,6 @@ def _run_review_in_thread(
# Match parent's toolset config so ``tools[]`` is byte-identical
# in the request body — Anthropic's cache key includes it.
# (The runtime whitelist below still restricts dispatch.)
_fork_kwargs: Dict[str, Any] = {}
if isinstance(_rt.get("max_tokens"), int):
_fork_kwargs["max_tokens"] = _rt["max_tokens"]
if isinstance(_rt.get("command"), str) and _rt["command"]:
_fork_kwargs["acp_command"] = _rt["command"]
_fork_kwargs["acp_args"] = _rt.get("args") or []
# Match parent's reasoning config so the fork's ``thinking`` /
# ``output_config`` are byte-identical in the request body —
# Anthropic's cache key is namespaced by ``thinking`` presence.
# Same-model path only: when routed to a different aux model the
# cache is cold regardless (parity buys nothing) and the parent's
# effort vocabulary may not be valid for the routed model/provider
# (e.g. OpenRouter ``extra_body.reasoning.effort`` is forwarded
# unclamped; codex_responses passes ``max``/``ultra`` through
# unmapped except on gpt-5.6/xAI). Let the routed fork use
# provider defaults — matching the ``not _routed`` gate on
# _cached_system_prompt below.
if not _routed:
_fork_kwargs["reasoning_config"] = getattr(agent, "reasoning_config", None)
review_agent = AIAgent(
model=_rt.get("model") or agent.model,
max_iterations=16,
@@ -718,13 +647,11 @@ def _run_review_in_thread(
api_mode=_rt.get("api_mode"),
base_url=_rt.get("base_url") or None,
api_key=_rt.get("api_key") or None,
credential_pool=_rt.get("credential_pool"),
request_overrides=_rt.get("request_overrides") or {},
credential_pool=getattr(agent, "_credential_pool", None),
parent_session_id=agent.session_id,
enabled_toolsets=getattr(agent, "enabled_toolsets", None),
disabled_toolsets=getattr(agent, "disabled_toolsets", None),
skip_memory=True,
**_fork_kwargs,
)
review_agent._memory_write_origin = "background_review"
review_agent._memory_write_context = "background_review"
@@ -740,20 +667,6 @@ def _run_review_in_thread(
review_agent._user_profile_enabled = agent._user_profile_enabled
review_agent._memory_nudge_interval = 0
review_agent._skill_nudge_interval = 0
# PERSISTENCE ISOLATION (the curator-takeover root cause): the fork
# shares the parent's session_id (set below, for prompt-cache
# warmth), so without this it would write its harness turn ("Review
# the conversation above and update the skill library…") + its own
# response straight into the user's REAL session in state.db. On the
# user's next live turn the agent re-reads that injected user message
# as a standing instruction and "becomes" the curator, refusing the
# actual task. _persist_disabled hard-stops every DB write/lazy-open
# path (_flush_messages_to_session_db, _ensure_db_session,
# _get_session_db_for_recall); the review writes only to the skill
# and memory stores via its tools, which is all it needs.
review_agent._persist_disabled = True
review_agent._session_db = None
review_agent._session_json_enabled = False
# Suppress all status/warning emits from the fork so the
# user only sees the final successful-action summary.
# Without this, mid-review "Iteration budget exhausted",
@@ -812,17 +725,10 @@ def _run_review_in_thread(
clear_thread_tool_whitelist,
)
# Gate the built-in memory tool on the profile's memory_enabled flag.
# Hardcoding ["memory", "skills"] granted the review LLM the MEMORY.md
# read/write tool even when a profile set memory_enabled: false,
# contaminating a memory-disabled profile (#54937 layer 2).
review_toolsets = ["skills"]
if review_agent._memory_enabled or review_agent._user_profile_enabled:
review_toolsets.insert(0, "memory")
review_whitelist = {
t["function"]["name"]
for t in get_tool_definitions(
enabled_toolsets=review_toolsets,
enabled_toolsets=["memory", "skills"],
quiet_mode=True,
)
}
@@ -833,13 +739,6 @@ def _run_review_in_thread(
"{tool_name}. Only memory/skill tools are allowed."
),
)
try:
from tools.skill_manager_tool import _reset_background_review_read_marks
_reset_background_review_read_marks()
except Exception:
pass
try:
# Routed to a different model -> replay a digest (cache is cold
# on that model anyway, so minimise cold-written tokens). Same
@@ -885,29 +784,11 @@ def _run_review_in_thread(
# the review agent inherits that history and would otherwise
# re-surface stale "created"/"updated" messages from the prior
# conversation as if they just happened (issue #14944).
#
# Wrapped in try/except: a buggy/legacy tool response shape
# (e.g. ``_change`` returned as a list instead of a dict, #59437)
# must NOT take down the whole review with an AttributeError,
# since the caller's outer except logs only "Background
# memory/skill review failed" and discards every successful
# action the fork DID complete before the crash. Coerce an
# exception into an empty actions list so the partial valid
# actions from earlier in the messages are returned instead.
try:
actions = summarize_background_review_actions(
review_messages,
messages_snapshot,
notification_mode=getattr(agent, "memory_notifications", "on"),
)
except Exception as e:
logger.warning(
"summarize_background_review_actions returned partial results "
"after exception (treating as empty); suppressing AttributeError "
"that previously aborted the entire review (#59437): %s",
e,
)
actions = []
actions = summarize_background_review_actions(
review_messages,
messages_snapshot,
notification_mode=getattr(agent, "memory_notifications", "on"),
)
if actions:
summary = " · ".join(dict.fromkeys(actions))
@@ -927,14 +808,16 @@ def _run_review_in_thread(
logger.warning("Background memory/skill review failed: %s", e)
agent._emit_auxiliary_failure("background review", e)
finally:
# Safety-net cleanup for the exception path. Normal completion already
# shut down inside the thread-scoped silence above. Re-enter the
# thread-scoped silence here so teardown output (Honcho flush, Hindsight
# sync, background thread joins) stays quiet even on the exception path,
# without blanking other threads' streams.
# Safety-net cleanup for the exception path. Normal
# completion already shut down inside redirect_stdout above.
# Re-open devnull here so any teardown output (Honcho flush,
# Hindsight sync, background thread joins) stays silent even
# on the exception path where redirect_stdout already exited.
if review_agent is not None:
try:
with thread_scoped_silence():
with open(os.devnull, "w", encoding="utf-8") as _fn, \
contextlib.redirect_stdout(_fn), \
contextlib.redirect_stderr(_fn):
try:
review_agent.shutdown_memory_provider()
except Exception:
+23 -204
View File
@@ -448,10 +448,7 @@ def is_anthropic_bedrock_model(model_id: str) -> bool:
"""
model_lower = model_id.lower()
# Strip regional prefix if present
for prefix in (
"global.", "us.", "eu.", "apac.", "ap.", "au.", "jp.",
"ca.", "sa.", "me.", "af.",
):
for prefix in ("us.", "global.", "eu.", "ap.", "jp."):
if model_lower.startswith(prefix):
model_lower = model_lower[len(prefix):]
break
@@ -493,26 +490,6 @@ def convert_tools_to_converse(tools: List[Dict]) -> List[Dict]:
return result
# Bedrock's Converse API rejects any text content block whose text is empty
# OR whitespace-only (ValidationException: "text content blocks must contain
# non-whitespace text"). A lone space is whitespace and is rejected too — the
# placeholder MUST itself be non-whitespace. Ref: issue #9486.
_EMPTY_TEXT_PLACEHOLDER = "(empty)"
def _safe_text(text) -> str:
"""Return ``text`` if it's non-whitespace, else a non-whitespace placeholder.
Handles None, empty string, and whitespace-only string (spaces, tabs,
newlines) — all of which Bedrock's Converse API rejects as text content.
"""
if text is None:
return _EMPTY_TEXT_PLACEHOLDER
if not isinstance(text, str):
text = str(text)
return text if text.strip() else _EMPTY_TEXT_PLACEHOLDER
def _convert_content_to_converse(content) -> List[Dict]:
"""Convert OpenAI message content (string or list) to Converse content blocks.
@@ -520,27 +497,26 @@ def _convert_content_to_converse(content) -> List[Dict]:
- Plain text strings → [{"text": "..."}]
- Content arrays with text/image_url parts → mixed text/image blocks
Replaces empty/whitespace-only text blocks with a non-whitespace
placeholder — Bedrock's Converse API rejects messages where a text
content block is empty or whitespace-only (ValidationException:
"text content blocks must contain non-whitespace text"). Ref: issue #9486.
Filters out empty text blocks — Bedrock's Converse API rejects messages
where a text content block has an empty ``text`` field (ValidationException:
"text content blocks must be non-empty"). Ref: issue #9486.
"""
if content is None:
return [{"text": _safe_text(content)}]
return [{"text": " "}]
if isinstance(content, str):
return [{"text": _safe_text(content)}]
return [{"text": content}] if content.strip() else [{"text": " "}]
if isinstance(content, list):
blocks = []
for part in content:
if isinstance(part, str):
blocks.append({"text": _safe_text(part)})
blocks.append({"text": part})
continue
if not isinstance(part, dict):
continue
part_type = part.get("type", "")
if part_type == "text":
text = part.get("text", "")
blocks.append({"text": _safe_text(text)})
blocks.append({"text": text if text else " "})
elif part_type == "image_url":
image_url = part.get("image_url", {})
url = image_url.get("url", "") if isinstance(image_url, dict) else ""
@@ -552,27 +528,18 @@ def _convert_content_to_converse(content) -> List[Dict]:
mime_part = header[5:].split(";")[0]
if mime_part:
media_type = mime_part
# Decode base64 to raw bytes — boto3 re-encodes at the
# wire layer, so passing the base64 string directly
# results in double-encoding and Bedrock rejects it with
# "Failed to sanitize image". Ref: #33317.
import base64
try:
raw_bytes = base64.b64decode(data)
except Exception:
raw_bytes = data.encode("utf-8")
blocks.append({
"image": {
"format": media_type.split("/")[-1] if "/" in media_type else "jpeg",
"source": {"bytes": raw_bytes},
"source": {"bytes": data},
}
})
else:
# Remote URL — Converse doesn't support URLs directly,
# include as text reference for the model.
blocks.append({"text": f"[Image: {url}]"})
return blocks if blocks else [{"text": _EMPTY_TEXT_PLACEHOLDER}]
return [{"text": _safe_text(content)}]
return blocks if blocks else [{"text": " "}]
return [{"text": str(content)}]
def convert_messages_to_converse(
@@ -602,18 +569,14 @@ def convert_messages_to_converse(
content = msg.get("content")
if role == "system":
# System messages become the system prompt. Blank/whitespace-only
# parts are dropped entirely (not placeholder-filled) since a
# system prompt made up of only placeholder text is meaningless.
# System messages become the system prompt
if isinstance(content, str) and content.strip():
system_blocks.append({"text": content})
elif isinstance(content, list):
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text = part.get("text", "")
if isinstance(text, str) and text.strip():
system_blocks.append({"text": text})
elif isinstance(part, str) and part.strip():
system_blocks.append({"text": part.get("text", "")})
elif isinstance(part, str):
system_blocks.append({"text": part})
continue
@@ -624,7 +587,7 @@ def convert_messages_to_converse(
tool_result_block = {
"toolResult": {
"toolUseId": tool_call_id,
"content": [{"text": _safe_text(result_content)}],
"content": [{"text": result_content}],
}
}
# In Converse, tool results go in a "user" role message
@@ -663,7 +626,7 @@ def convert_messages_to_converse(
})
if not content_blocks:
content_blocks = [{"text": _EMPTY_TEXT_PLACEHOLDER}]
content_blocks = [{"text": " "}]
# Merge with previous assistant message if needed (strict alternation)
if converse_msgs and converse_msgs[-1]["role"] == "assistant":
@@ -689,11 +652,11 @@ def convert_messages_to_converse(
# Converse requires the first message to be from the user
if converse_msgs and converse_msgs[0]["role"] != "user":
converse_msgs.insert(0, {"role": "user", "content": [{"text": _EMPTY_TEXT_PLACEHOLDER}]})
converse_msgs.insert(0, {"role": "user", "content": [{"text": " "}]})
# Converse requires the last message to be from the user
if converse_msgs and converse_msgs[-1]["role"] != "user":
converse_msgs.append({"role": "user", "content": [{"text": _EMPTY_TEXT_PLACEHOLDER}]})
converse_msgs.append({"role": "user", "content": [{"text": " "}]})
return (system_blocks if system_blocks else None, converse_msgs)
@@ -817,7 +780,6 @@ def stream_converse_with_callbacks(
on_tool_start=None,
on_reasoning_delta=None,
on_interrupt_check=None,
on_event=None,
) -> SimpleNamespace:
"""Process a Bedrock ConverseStream event stream with real-time callbacks.
@@ -837,12 +799,6 @@ def stream_converse_with_callbacks(
on supported models (Claude 4.6+).
on_interrupt_check: Called on each event. Should return True if the
agent has been interrupted and streaming should stop.
on_event: Called once at the top of the loop body for EVERY yielded
Bedrock event (text/tool-input/reasoning/metadata deltas alike),
before any branching. Provides a wire-level liveness signal so an
external watchdog can distinguish "still receiving events" from
"stream wedged with no data". Errors raised by the callback are
swallowed so a liveness hook can never abort the stream.
Returns:
An OpenAI-compatible SimpleNamespace response, identical in shape to
@@ -858,15 +814,6 @@ def stream_converse_with_callbacks(
usage_data: Dict[str, int] = {}
for event in event_stream.get("stream", []):
# Wire-level liveness signal: fire on EVERY yielded event (text, tool
# input, reasoning, metadata) before branching so an external watchdog
# can tell a still-flowing stream from a wedged one. Best-effort — a
# liveness callback must never be able to abort the stream.
if on_event is not None:
try:
on_event()
except Exception:
pass
# Check for interrupt
if on_interrupt_check and on_interrupt_check():
break
@@ -1349,24 +1296,9 @@ def classify_bedrock_error(error_message: str) -> str:
# detection is unavailable.
BEDROCK_CONTEXT_LENGTHS: Dict[str, int] = {
# Anthropic Claude models on Bedrock.
# Context windows per Anthropic's official models comparison
# (https://platform.claude.com/docs/en/about-claude/models/overview).
# Fable / Sonnet 5 / Opus 4.8 / 4.7 / 4.6 / Sonnet 4.6 have 1M generally
# available (no beta header required as of April 2026). Sonnet 4.5 and
# Sonnet 4 had their `context-1m-2025-08-07` beta retired on
# April 30, 2026, so they are standard 200K; Haiku 4.5 is 200K.
# These 1M entries must match agent/model_metadata.py
# DEFAULT_CONTEXT_LENGTHS or the agent compresses context prematurely.
# Keys are matched by longest-substring, so the versioned 4-6/4-7/4-8
# entries win over the generic "anthropic.claude-opus-4" fallback.
"anthropic.claude-fable-5": 1_000_000,
"anthropic.claude-fable": 1_000_000,
"anthropic.claude-sonnet-5": 1_000_000,
"anthropic.claude-opus-4-8": 1_000_000,
"anthropic.claude-opus-4-7": 1_000_000,
"anthropic.claude-opus-4-6": 1_000_000,
"anthropic.claude-sonnet-4-6": 1_000_000,
# Anthropic Claude models on Bedrock
"anthropic.claude-opus-4-6": 200_000,
"anthropic.claude-sonnet-4-6": 200_000,
"anthropic.claude-sonnet-4-5": 200_000,
"anthropic.claude-haiku-4-5": 200_000,
"anthropic.claude-opus-4": 200_000,
@@ -1393,22 +1325,9 @@ BEDROCK_CONTEXT_LENGTHS: Dict[str, int] = {
# Default for unknown Bedrock models
BEDROCK_DEFAULT_CONTEXT_LENGTH = 128_000
# Probe tiers (in tokens). We send a request padded just past each tier and
# read the real window from Bedrock's length-validation error. Two reasons
# this is tiered rather than one giant request:
# 1. A wildly oversized payload (e.g. 5M tokens) makes Bedrock return an
# opaque InternalServerException after retries instead of a clean
# ValidationException — so we must stay within a sane overage.
# 2. Stepping up lets us discover larger windows (2M+) without over-padding
# smaller ones.
# Each tier value is the *padding target*; the error reports the true maximum,
# which is what we actually return.
_BEDROCK_PROBE_TIERS = (1_300_000, 2_200_000)
_WORDS_PER_TOKEN = 0.9 # conservative: ensures the padded prompt clears the tier
def _static_bedrock_context_length(model_id: str) -> int:
"""Longest-substring-match lookup against the static fallback table.
def get_bedrock_context_length(model_id: str) -> int:
"""Look up the context window size for a Bedrock model.
Uses substring matching so versioned IDs like
``anthropic.claude-sonnet-4-6-20250514-v1:0`` resolve correctly.
@@ -1421,103 +1340,3 @@ def _static_bedrock_context_length(model_id: str) -> int:
best_key = key
best_val = val
return best_val
def probe_bedrock_context_length(model_id: str, region: str) -> Optional[int]:
"""Discover a Bedrock model's real context window by provoking a length error.
Bedrock does not expose the context window via any metadata API
(``get-foundation-model`` omits it, ``Converse`` metrics omit it,
``CountTokens`` is unsupported on several models). The only authoritative
source is the ``ValidationException`` raised when a prompt exceeds the
window:
"The model returned the following errors: prompt is too long:
1300032 tokens > 1000000 maximum"
Length validation happens *before* inference, so an oversized request is
rejected immediately and cheaply — no tokens are generated and no input is
actually processed. We pad a request just past each tier in
``_BEDROCK_PROBE_TIERS`` and parse the reported ``maximum``. Tiers exist
because (a) a *wildly* oversized payload makes Bedrock fail with an opaque
InternalServerException instead of a clean length error, and (b) stepping
up discovers larger windows without over-padding smaller ones.
Returns the detected window, or ``None`` if the probe could not run
(missing credentials, network error, or no parseable limit) so the caller
can fall back to the static table.
"""
try:
from agent.model_metadata import parse_context_limit_from_error
except ImportError: # pragma: no cover — same package
return None
try:
client = _get_bedrock_runtime_client(region)
except Exception as exc: # boto3 missing / credential resolution failure
logger.debug("Bedrock context probe skipped for %s: %s", model_id, exc)
return None
last_error = ""
for tier_tokens in _BEDROCK_PROBE_TIERS:
pad_words = int(tier_tokens / _WORDS_PER_TOKEN)
oversized = "data " * pad_words
try:
client.converse(
modelId=model_id,
messages=[{"role": "user", "content": [{"text": oversized}]}],
inferenceConfig={"maxTokens": 8},
)
# Accepted a prompt this large → the window is at least this tier.
# Returning the tier as a lower bound is safe and avoids inventing
# a number we can't confirm.
logger.debug(
"Bedrock context probe for %s accepted ~%s-token prompt; "
"window is at least that", model_id, f"{tier_tokens:,}",
)
return tier_tokens
except Exception as exc:
msg = str(exc)
last_error = msg
limit = parse_context_limit_from_error(msg)
if limit and limit >= 1024:
logger.info(
"Probed Bedrock context window for %s: %s tokens",
model_id, f"{limit:,}",
)
return limit
# No parseable limit at this tier (opaque server error, auth,
# throttle). Try the next, smaller-overage strategy is N/A here —
# tiers ascend — so just continue; if all fail we return None.
continue
logger.debug(
"Bedrock context probe for %s returned no parseable limit: %s",
model_id, last_error[:200],
)
return None
def get_bedrock_context_length(model_id: str, region: str = "", probe: bool = True) -> int:
"""Resolve the context window for a Bedrock model.
Resolution order:
1. Live probe against Bedrock (authoritative; cached by the caller).
2. Static fallback table (longest-substring match).
3. Conservative default.
The static table is intentionally a *fallback*, not the primary source:
AWS ships new model versions (opus-4-7, opus-4-8, ...) faster than the
table can track, and a stale entry silently caps the window (e.g. a
1M-token Opus pinned to 200K via an ``opus-4`` substring match). The
probe asks Bedrock directly so every model — current or future — gets its
real window with no table maintenance.
``probe=False`` (or an empty ``region``) skips the network call and uses
the static table only — used by pure-offline/display code paths.
"""
if probe and region:
probed = probe_bedrock_context_length(model_id, region)
if probed:
return probed
return _static_bedrock_context_length(model_id)
-323
View File
@@ -1,323 +0,0 @@
"""Shared dollar-denominated usage model for the billing/subscription surfaces.
The single source of truth behind the ``/usage`` and ``/subscription`` usage
bars (TUI + CLI). User feedback (Jun 2026): the terminal surfaces show
**dollars**, never "credits", and every usage bar must make the monthly
subscription allowance and separately-purchased top-up dollars distinctly
visible.
Data source: the NAS account-info fetch (``NousPortalAccountInfo``), whose
``paid_service_access_info`` carries the three dollar magnitudes we render
(despite the legacy ``*_credits`` field names, these are USD floats):
- ``subscription_credits_remaining`` -> plan dollars left this month
- ``purchased_credits_remaining`` -> top-up dollars left (rolls over)
- ``total_usable_credits`` -> total spendable
plus ``subscription.monthly_credits`` (the plan's monthly $ allowance, the
denominator for the "% used" plan bar) and ``current_period_end`` (renewal).
Design: two SEPARATE bars (decided with the user) rather than one crammed
three-segment bar — at terminal widths three same-glyph density segments are
unreadable. The plan bar is "spent vs allowance this month" (carries % used);
the top-up bar is "money you bought, doesn't expire". Each gets full
resolution and a single fill glyph, so the bar is never ambiguous and never
relies on color.
Fail-open everywhere: any missing/non-finite field degrades to fewer bars or a
magnitudes-only view; a logged-out / unreachable portal yields
``available=False`` and the surface shows nothing.
"""
from __future__ import annotations
import logging
import math
import os
from dataclasses import dataclass, field
from typing import Any, Optional
logger = logging.getLogger(__name__)
# Below this TOTAL spendable ($), a paid account is flagged "low" — the alert
# state that nudges top-up/upgrade before a mid-run cutoff. Product threshold
# (user feedback): "any amount below $5 should be an alert status."
LOW_BALANCE_THRESHOLD_USD = 5.0
def _finite(value: Any) -> Optional[float]:
"""Return value as a float iff it's a real finite number (not bool/NaN/Inf)."""
if isinstance(value, bool) or not isinstance(value, (int, float)):
return None
f = float(value)
return f if math.isfinite(f) else None
def _fmt_usd(value: Optional[float]) -> str:
"""``$X.YY`` for display. ``None`` -> ``$0.00`` (callers gate on presence)."""
return f"${(value or 0.0):,.2f}"
def format_renews(value: Optional[str]) -> Optional[str]:
"""Format an ISO date/timestamp as a human date, e.g. ``Jul 24, 2026``.
Accepts ``2026-07-24``, ``2026-07-24T11:05:01.000Z``, etc. Returns the raw
string unchanged if it can't be parsed (never raises), and ``None`` for
empty input.
"""
if not value:
return None
from datetime import datetime
text = str(value).strip()
if not text:
return None
iso = text[:-1] + "+00:00" if text.endswith("Z") else text
try:
dt = datetime.fromisoformat(iso)
except ValueError:
# Fall back to a bare date prefix (YYYY-MM-DD) if present.
try:
dt = datetime.strptime(text[:10], "%Y-%m-%d")
except ValueError:
return text
# %-d isn't portable to Windows; build the day without a leading zero.
return f"{dt.strftime('%b')} {dt.day}, {dt.year}"
@dataclass(frozen=True)
class UsageBar:
"""One full-resolution bar: ``spent`` of ``total``, plus a remaining figure.
``kind`` is ``"plan"`` (monthly allowance, shows % used) or ``"topup"``
(purchased dollars, no denominator — ``spent`` is 0 and ``total`` ==
``remaining`` so it renders as a full bar of available balance).
"""
kind: str # "plan" | "topup"
remaining_usd: float
total_usd: float
spent_usd: float = 0.0
@property
def pct_used(self) -> Optional[int]:
if self.kind != "plan" or self.total_usd <= 0:
return None
return max(0, min(100, round(self.spent_usd / self.total_usd * 100)))
@property
def fill_fraction(self) -> float:
"""Fraction of the bar that should read as 'remaining' (filled)."""
if self.total_usd <= 0:
return 0.0
return max(0.0, min(1.0, self.remaining_usd / self.total_usd))
@dataclass(frozen=True)
class UsageModel:
"""Surface-agnostic dollar usage model shared by /usage and /subscription.
``status`` classifies the account for copy selection:
- ``"free"`` : no paid access / no subscription (free models only)
- ``"low"`` : paid, but total spendable < $5 (ALERT)
- ``"healthy"`` : paid, total spendable >= $5
- ``"depleted"`` : paid access lost (balance exhausted)
"""
available: bool
status: str = "free"
plan_name: Optional[str] = None
renews_at: Optional[str] = None
renews_display: Optional[str] = None
subscription_remaining_usd: Optional[float] = None
topup_remaining_usd: Optional[float] = None
total_spendable_usd: Optional[float] = None
plan_bar: Optional[UsageBar] = None
topup_bar: Optional[UsageBar] = None
@property
def has_topup(self) -> bool:
return bool(self.topup_remaining_usd and self.topup_remaining_usd > 0)
def usage_model_from_account(account_info: Any) -> UsageModel:
"""Build a :class:`UsageModel` from a ``NousPortalAccountInfo``. Fail-open.
Returns ``UsageModel(available=False)`` when there's no usable account info
(logged out, no entitlement block). Never raises.
"""
try:
if account_info is None or not getattr(account_info, "logged_in", False):
return UsageModel(available=False)
access = getattr(account_info, "paid_service_access_info", None)
sub = getattr(account_info, "subscription", None)
paid = getattr(account_info, "paid_service_access", None)
sub_remaining = _finite(getattr(access, "subscription_credits_remaining", None)) if access else None
topup_remaining = _finite(getattr(access, "purchased_credits_remaining", None)) if access else None
total_usable = _finite(getattr(access, "total_usable_credits", None)) if access else None
plan_name = getattr(sub, "plan", None) if sub is not None else None
renews_at = getattr(sub, "current_period_end", None) if sub is not None else None
monthly = _finite(getattr(sub, "monthly_credits", None)) if sub is not None else None
has_subscription = bool(plan_name) or (monthly is not None and monthly > 0)
# Total spendable: prefer the server's total; else sum the parts we have.
if total_usable is not None:
total_spendable = total_usable
else:
parts = [v for v in (sub_remaining, topup_remaining) if v is not None]
total_spendable = sum(parts) if parts else None
# Status classification.
if paid is False:
status = "depleted"
elif not has_subscription and not (topup_remaining and topup_remaining > 0):
# No plan and no purchased balance -> free-models-only.
status = "free"
elif total_spendable is not None and total_spendable < LOW_BALANCE_THRESHOLD_USD:
status = "low"
else:
status = "healthy"
# Plan bar — only with a positive monthly allowance AND a remaining we
# can place on it. spent = cap - remaining, clamped (a debt/over-cap
# balance reads as fully spent rather than a nonsensical negative).
plan_bar: Optional[UsageBar] = None
if monthly is not None and monthly > 0 and sub_remaining is not None:
remaining = max(0.0, min(monthly, sub_remaining))
plan_bar = UsageBar(
kind="plan",
remaining_usd=remaining,
total_usd=monthly,
spent_usd=max(0.0, monthly - sub_remaining),
)
# Top-up bar — only when there are purchased dollars to show. No
# denominator (top-up has no monthly cap), so it renders full = balance.
topup_bar: Optional[UsageBar] = None
if topup_remaining is not None and topup_remaining > 0:
topup_bar = UsageBar(
kind="topup",
remaining_usd=topup_remaining,
total_usd=topup_remaining,
spent_usd=0.0,
)
return UsageModel(
available=True,
status=status,
plan_name=plan_name,
renews_at=renews_at,
renews_display=format_renews(renews_at),
subscription_remaining_usd=sub_remaining,
topup_remaining_usd=topup_remaining,
total_spendable_usd=total_spendable,
plan_bar=plan_bar,
topup_bar=topup_bar,
)
except Exception:
logger.debug("usage ▸ model build failed (fail-open)", exc_info=True)
return UsageModel(available=False)
def build_usage_model(*, timeout: float = 10.0) -> UsageModel:
"""Fetch account-info and build the shared usage model. Fail-open.
Dev override: ``HERMES_DEV_CREDITS_FIXTURE`` short-circuits to a fixture so
every usage state is testable without a live account (mirrors the existing
``/usage`` credits-block fixture path).
"""
fixture = _dev_fixture_usage_model()
if fixture is not None:
return fixture
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 UsageModel(available=False)
except Exception:
return UsageModel(available=False)
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)
return usage_model_from_account(account)
except Exception:
logger.debug("usage ▸ portal fetch failed (fail-open)", exc_info=True)
return UsageModel(available=False)
# =============================================================================
# Dev fixtures (throwaway scaffolding — env-var driven, no live portal)
# =============================================================================
def _dev_fixture_usage_model() -> Optional[UsageModel]:
"""Map ``HERMES_DEV_CREDITS_FIXTURE`` to a usage model for offline UX work.
Recognized names: ``free | healthy | low | topup | depleted``. Returns
``None`` when the env var is unset (real portal path runs).
"""
name = (os.getenv("HERMES_DEV_CREDITS_FIXTURE") or "").strip().lower()
if not name:
return None
if name == "free":
return UsageModel(available=True, status="free", plan_name=None)
if name in ("healthy", "mid"):
return UsageModel(
available=True,
status="healthy",
plan_name="Plus",
renews_at="2026-07-01",
subscription_remaining_usd=14.0,
total_spendable_usd=14.0,
plan_bar=UsageBar(kind="plan", remaining_usd=14.0, total_usd=20.0, spent_usd=6.0),
)
if name in ("topup", "top-up"):
return UsageModel(
available=True,
status="healthy",
plan_name="Plus",
renews_at="2026-07-01",
subscription_remaining_usd=14.0,
topup_remaining_usd=12.0,
total_spendable_usd=26.0,
plan_bar=UsageBar(kind="plan", remaining_usd=14.0, total_usd=20.0, spent_usd=6.0),
topup_bar=UsageBar(kind="topup", remaining_usd=12.0, total_usd=12.0, spent_usd=0.0),
)
if name == "low":
return UsageModel(
available=True,
status="low",
plan_name="Plus",
renews_at="2026-07-01",
subscription_remaining_usd=3.4,
total_spendable_usd=3.4,
plan_bar=UsageBar(kind="plan", remaining_usd=3.4, total_usd=20.0, spent_usd=16.6),
)
if name == "depleted":
return UsageModel(
available=True,
status="depleted",
plan_name="Plus",
renews_at="2026-07-01",
subscription_remaining_usd=0.0,
total_spendable_usd=0.0,
plan_bar=UsageBar(kind="plan", remaining_usd=0.0, total_usd=20.0, spent_usd=20.0),
)
return None
+9 -172
View File
@@ -1,4 +1,4 @@
"""Surface-agnostic core for the Phase 2b Remote Spending screens.
"""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
@@ -15,7 +15,6 @@ We keep them as :class:`decimal.Decimal` end-to-end and only format for display.
from __future__ import annotations
import logging
import os
import uuid
from dataclasses import dataclass, field
from decimal import Decimal, InvalidOperation
@@ -65,47 +64,15 @@ def format_money(value: Optional[Decimal]) -> str:
# =============================================================================
# resolvedVia → the human answer to "why THIS card?". Keys are the server's card
# resolution rungs (NAS card-on-file ladder); absent/unknown rungs render no label
# so the display degrades cleanly on servers that don't send resolvedVia yet.
_CARD_PROVENANCE_LABELS = {
"subPin": "the card on your subscription",
"customerDefault": "your default card saved on the portal",
"autoRefill": "your auto-reload card",
}
@dataclass(frozen=True)
class CardInfo:
brand: str
last4: str
# NAS card-on-file field (post card-resolver): which ladder rung found the
# card. Defaults off so pre-resolver payloads parse unchanged.
resolved_via: Optional[str] = None
@property
def masked(self) -> str:
# A Link payment method has no card number (last4 = "") — render the
# brand alone, not "Link ····".
if not self.last4:
return self.brand
return f"{self.brand} ····{self.last4}"
@property
def provenance(self) -> Optional[str]:
"""Human label for why this card was picked, or None (unknown rung /
server too old to say)."""
if self.resolved_via is None:
return None
return _CARD_PROVENANCE_LABELS.get(self.resolved_via)
@property
def display(self) -> str:
"""The one-line card display: ``Visa ····4242 — the card on your
subscription`` (or just the masked card when provenance is unknown)."""
label = self.provenance
return f"{self.masked} — {label}" if label else self.masked
@dataclass(frozen=True)
class MonthlyCap:
@@ -114,20 +81,11 @@ class MonthlyCap:
is_default_ceiling: bool = False
@dataclass(frozen=True)
class AutoReloadCard:
kind: str # "canonical" | "distinct" | "none"
payment_method_id: Optional[str] = None
brand: Optional[str] = None
last4: Optional[str] = None
@dataclass(frozen=True)
class AutoReload:
enabled: bool = False
threshold_usd: Optional[Decimal] = None
reload_to_usd: Optional[Decimal] = None
card: Optional[AutoReloadCard] = None
@dataclass(frozen=True)
@@ -142,8 +100,7 @@ class BillingState:
org_id: Optional[str] = None
org_slug: Optional[str] = None
org_name: Optional[str] = None
role: Optional[str] = None # "OWNER" | "ADMIN" | "FINANCE_ADMIN" | "SECURITY_ADMIN" | "MEMBER"
can_change_plan_raw: Optional[bool] = None
role: Optional[str] = None # "OWNER" | "ADMIN" | "MEMBER"
balance_usd: Optional[Decimal] = None
cli_billing_enabled: bool = False
charge_presets: tuple[Decimal, ...] = ()
@@ -158,33 +115,17 @@ class BillingState:
@property
def is_admin(self) -> bool:
"""Deprecated/display only — a legacy OWNER/ADMIN check.
NOT a capability check; use :attr:`can_change_plan` for gating billing
plan-change actions.
"""
"""True for OWNER/ADMIN — the roles that can manage billing."""
return (self.role or "").upper() in ("OWNER", "ADMIN")
@property
def can_change_plan(self) -> bool:
"""Server capability when supplied; otherwise the legacy role fallback."""
if self.can_change_plan_raw is not None:
return self.can_change_plan_raw
return self.is_admin
@property
def can_charge(self) -> bool:
"""True when the UI should offer charge/auto-reload actions.
Uses the server-granted plan-change capability (``can_change_plan``,
which itself falls back to the legacy OWNER/ADMIN role check when the
server omits ``canChangePlan``) AND the per-org kill-switch. This lets
the server grant charge capability to non-OWNER/ADMIN roles (e.g.
FINANCE_ADMIN) via ``canChangePlan``, instead of hard-coding the
deprecated 3-role admin check. (The server still enforces; this is
just for graying out actions the user can't take.)
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.can_change_plan and self.cli_billing_enabled
return self.is_admin and self.cli_billing_enabled
def _parse_card(raw: Any) -> Optional[CardInfo]:
@@ -192,13 +133,9 @@ def _parse_card(raw: Any) -> Optional[CardInfo]:
return None
brand = raw.get("brand")
last4 = raw.get("last4")
if not (isinstance(brand, str) and isinstance(last4, str)):
return None
# Post-resolver fields — all optional so both payload generations parse.
resolved_via = raw.get("resolvedVia")
if not isinstance(resolved_via, str):
resolved_via = None
return CardInfo(brand=brand, last4=last4, resolved_via=resolved_via)
if isinstance(brand, str) and isinstance(last4, str):
return CardInfo(brand=brand, last4=last4)
return None
def _parse_monthly_cap(raw: Any) -> Optional[MonthlyCap]:
@@ -218,27 +155,6 @@ def _parse_auto_reload(raw: Any) -> Optional[AutoReload]:
enabled=bool(raw.get("enabled")),
threshold_usd=parse_money(raw.get("thresholdUsd")),
reload_to_usd=parse_money(raw.get("reloadToUsd")),
card=_parse_auto_reload_card(raw.get("card")),
)
def _parse_auto_reload_card(raw: Any) -> Optional[AutoReloadCard]:
if not isinstance(raw, dict):
return None
kind = raw.get("kind")
if kind not in ("canonical", "distinct", "none"):
return None
if kind in ("canonical", "none"):
return AutoReloadCard(kind=kind)
payment_method_id = raw.get("paymentMethodId")
brand = raw.get("brand")
last4 = raw.get("last4")
return AutoReloadCard(
kind=kind,
payment_method_id=payment_method_id if isinstance(payment_method_id, str) else None,
brand=brand if isinstance(brand, str) else None,
last4=last4 if isinstance(last4, str) else None,
)
@@ -263,11 +179,6 @@ def billing_state_from_payload(
org_slug=org.get("slug"),
org_name=org.get("name"),
role=org.get("role"),
can_change_plan_raw=(
payload.get("canChangePlan")
if isinstance(payload.get("canChangePlan"), bool)
else None
),
balance_usd=parse_money(payload.get("balanceUsd")),
cli_billing_enabled=bool(payload.get("cliBillingEnabled")),
charge_presets=tuple(presets),
@@ -291,15 +202,7 @@ def build_billing_state(*, timeout: float = 15.0) -> BillingState:
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.
Dev override: ``HERMES_DEV_BILLING_FIXTURE`` short-circuits to a fixture so the
card-on-file / admin / scope states are testable offline (mirrors
``HERMES_DEV_CREDITS_FIXTURE`` for the usage model).
"""
fixture = _dev_fixture_billing_state()
if fixture is not None:
return fixture
try:
from hermes_cli.nous_billing import (
BillingAuthError,
@@ -340,72 +243,6 @@ def _fallback_portal_url(base: str) -> str:
return f"{base.rstrip('/')}/billing?topup=open"
# =============================================================================
# Dev fixtures (throwaway scaffolding — env-var driven, no live portal)
# =============================================================================
def _dev_fixture_billing_state() -> Optional[BillingState]:
"""Map ``HERMES_DEV_BILLING_FIXTURE`` to a :class:`BillingState` for offline UX.
Recognized names::
nocard logged in · billing on · admin · NO card on file
card card on file · auto-reload off
card-autoreload card on file · auto-reload on
notadmin logged in · MEMBER role (billing actions disabled)
billing-off logged in · admin · per-org kill-switch OFF
logged-out not logged in
Returns ``None`` when the env var is unset (the real portal path runs).
Mirrors ``HERMES_DEV_CREDITS_FIXTURE``; the usage *bar* still comes from
``HERMES_DEV_CREDITS_FIXTURE`` (set both to pair a bar with a billing state).
"""
name = (os.getenv("HERMES_DEV_BILLING_FIXTURE") or "").strip().lower()
if not name:
return None
# Shared fixture portal host (matches subscription_view._DEV_FIXTURE_PORTAL —
# prod host, not staging; the ?topup=open suffix is the /topup deep-link).
portal = "https://portal.nousresearch.com/billing?topup=open"
common: dict[str, Any] = dict(
org_id="org_acme",
org_slug="acme",
org_name="Acme Inc",
role="OWNER",
balance_usd=Decimal("3.40"),
cli_billing_enabled=True,
charge_presets=(Decimal("10"), Decimal("25"), Decimal("50")),
min_usd=Decimal("5"),
max_usd=Decimal("500"),
portal_url=portal,
)
card = CardInfo(brand="Visa", last4="4242")
autoreload_on = AutoReload(enabled=True, threshold_usd=Decimal("5"), reload_to_usd=Decimal("25"))
if name in ("logged-out", "logged_out", "loggedout"):
return BillingState(logged_in=False)
if name == "nocard":
return BillingState(logged_in=True, card=None, **common)
if name == "card":
return BillingState(logged_in=True, card=card, **common)
if name in ("card-sub", "card_sub"):
# Post-resolver: the card came from the subscription (provenance label).
_sub_card = CardInfo(brand="Visa", last4="4242", resolved_via="subPin")
return BillingState(logged_in=True, card=_sub_card, **common)
if name in ("card-autoreload", "card_autoreload", "autoreload"):
return BillingState(logged_in=True, card=card, auto_reload=autoreload_on, **common)
if name in ("notadmin", "not-admin", "member"):
opts = {**common, "role": "MEMBER"}
return BillingState(logged_in=True, card=card, **opts)
if name in ("billing-off", "billing_off", "off"):
opts = {**common, "cli_billing_enabled": False}
return BillingState(logged_in=True, card=None, **opts)
# Unknown name → logged-out so the misconfiguration is visible.
return BillingState(logged_in=False, error=f"unknown HERMES_DEV_BILLING_FIXTURE: {name}")
# =============================================================================
# Idempotency
# =============================================================================
-148
View File
@@ -1,148 +0,0 @@
"""Bounded reads of HTTP error response bodies.
When a provider returns a non-OK status on a *streaming* request, Hermes reads
the response body to build a useful diagnostic error. A bare ``response.read()``
on a streaming httpx response is unbounded in two dangerous ways:
1. A server can declare (or stream) an arbitrarily large body, so the read can
balloon memory.
2. A server can open the body and then stall forever (no ``Content-Length``,
no further bytes), so the read hangs the agent indefinitely.
Both are realistic against a misbehaving proxy, a hijacked endpoint, or a
provider having a bad day. The diagnostic body is only ever shown to the user
truncated to a few hundred characters, so reading megabytes — or blocking
forever — buys nothing.
``read_streaming_error_body`` bounds the read to a byte cap and enforces a
hard wall-clock deadline, returning the decoded text snippet. Callers pass the
returned text into their existing error builders instead of touching
``response.text`` (which would be unbounded / would raise after a partial
stream read).
A subtlety the implementation must respect: ``httpx``'s ``iter_bytes()`` blocks
*inside* the C/socket read while waiting for the next chunk. A wall-clock check
placed only between yielded chunks cannot interrupt a server that opens the
body and then stalls mid-chunk — control never returns to Python until httpx's
own (often 30s+) read timeout fires. To guarantee a bounded stop regardless of
socket behavior, the read runs on a daemon worker thread and the caller waits
on it with a hard deadline; on timeout we close the response (which unblocks /
cancels the read) and return whatever partial bytes were collected.
Ported and adapted from openclaw/openclaw#95108 ("bound Anthropic error
streams"), generalized to cover Hermes's three streaming error-body sites
(native Gemini, Gemini Cloud Code, Antigravity Cloud Code).
"""
from __future__ import annotations
import logging
import threading
from typing import List, Optional
import httpx
logger = logging.getLogger(__name__)
# Defaults chosen to comfortably hold any real provider error envelope (Google
# RPC error JSON, Anthropic error JSON) while rejecting pathological bodies.
DEFAULT_ERROR_BODY_MAX_BYTES = 64 * 1024
# Hard wall-clock deadline for the whole bounded read. A streaming error body
# that does not finish within this window is abandoned and the connection is
# closed; we keep whatever partial bytes arrived.
DEFAULT_ERROR_BODY_TIMEOUT_S = 10.0
def read_streaming_error_body(
response: httpx.Response,
*,
max_bytes: int = DEFAULT_ERROR_BODY_MAX_BYTES,
timeout_s: float = DEFAULT_ERROR_BODY_TIMEOUT_S,
) -> str:
"""Read a non-OK streaming response body with a byte cap and a hard deadline.
Returns the decoded body text (UTF-8, errors replaced), truncated to
``max_bytes``. Never raises: any transport error, stall, or oversize
condition is swallowed and the best-effort partial text (or an empty
string) is returned, because this runs on the error path and must not
mask the original HTTP failure with a read error.
The byte cap protects against huge bodies; the wall-clock deadline (enforced
via a worker thread so it can interrupt a socket read that stalls mid-chunk)
protects against bodies that open and then hang.
"""
chunks: List[bytes] = []
state = {"truncated": False}
done = threading.Event()
def _drain() -> None:
total = 0
try:
for chunk in response.iter_bytes():
if not chunk:
continue
remaining = max_bytes - total
if remaining <= 0:
state["truncated"] = True
break
if len(chunk) > remaining:
chunks.append(chunk[:remaining])
total += remaining
state["truncated"] = True
break
chunks.append(chunk)
total += len(chunk)
except Exception as exc: # noqa: BLE001 - error path must not raise
logger.debug("bounded error-body read failed: %s", exc)
finally:
done.set()
worker = threading.Thread(
target=_drain, name="bounded-error-body-read", daemon=True
)
worker.start()
finished = done.wait(timeout=timeout_s)
if not finished:
logger.debug(
"bounded error-body read: hard timeout after %.1fs (%d bytes so far)",
timeout_s,
sum(len(c) for c in chunks),
)
# Closing the response cancels the in-flight socket read, letting the
# worker thread unwind. We do not join (it is a daemon and may be
# blocked in C); the partial `chunks` collected so far are returned.
_safe_close(response)
else:
_safe_close(response)
if state["truncated"]:
logger.debug(
"bounded error-body read: capped at %d bytes (max=%d)",
sum(len(c) for c in chunks),
max_bytes,
)
return b"".join(chunks).decode("utf-8", errors="replace")
def _safe_close(response: httpx.Response) -> None:
try:
response.close()
except Exception: # noqa: BLE001
pass
def read_error_body_or_default(
response: httpx.Response,
*,
max_bytes: int = DEFAULT_ERROR_BODY_MAX_BYTES,
timeout_s: float = DEFAULT_ERROR_BODY_TIMEOUT_S,
) -> Optional[str]:
"""Like ``read_streaming_error_body`` but returns ``None`` on empty body.
Convenience for callers that distinguish "no body" from "empty string".
"""
text = read_streaming_error_body(
response, max_bytes=max_bytes, timeout_s=timeout_s
)
return text or None
File diff suppressed because it is too large Load Diff
+20 -151
View File
@@ -288,13 +288,6 @@ _RESPONSES_BUILTIN_TOOL_TYPES = {
_RESPONSE_MESSAGE_STATUSES = {"completed", "incomplete", "in_progress"}
# The Responses API rejects input[].id longer than this with a non-retryable
# HTTP 400 ("string too long"). Codex-issued assistant message ids are
# server-assigned base64 blobs that can run 400+ chars, while Hermes-minted
# ids (msg_...) stay well under this cap and are worth keeping for
# prefix-cache hits. Drop only the oversized ones on replay.
_MAX_RESPONSES_ITEM_ID_LENGTH = 64
def _normalize_responses_message_status(value: Any, *, default: str = "completed") -> str:
"""Normalize a Responses assistant message status for replay.
@@ -314,7 +307,6 @@ def _chat_messages_to_responses_input(
messages: List[Dict[str, Any]],
*,
is_xai_responses: bool = False,
is_github_responses: bool = False,
replay_encrypted_reasoning: bool = True,
current_issuer_kind: Optional[str] = None,
) -> List[Dict[str, Any]]:
@@ -339,16 +331,6 @@ def _chat_messages_to_responses_input(
items from the conversation history and threads ``replay_enabled=False``
through this converter so subsequent turns send no reasoning items.
``is_github_responses`` drops the ``id`` field from replayed
``codex_message_items`` regardless of length. The Copilot backend
(api.githubcopilot.com/responses) binds these ids to a specific
backend "connection" — credential-pool rotation, a gateway restart,
or routine load-balancer churn between turns all invalidate it — and
rejects a stale id with HTTP 401 "input item ID does not belong to
this connection" even for short ids (see #32716). ``phase``/
``status``/``content`` are still replayed; only ``id`` is unsafe to
reuse across a Copilot connection.
``current_issuer_kind`` enables a per-item cross-issuer guard. The
Responses API's ``encrypted_content`` blob is decryptable only by the
endpoint that minted it — replaying a Codex-issued blob against xAI
@@ -481,14 +463,8 @@ def _chat_messages_to_responses_input(
"content": normalized_content_parts,
}
item_id = raw_item.get("id")
if (
not is_github_responses
and isinstance(item_id, str)
and item_id.strip()
):
stripped_id = item_id.strip()
if len(stripped_id) <= _MAX_RESPONSES_ITEM_ID_LENGTH:
replay_item["id"] = stripped_id
if isinstance(item_id, str) and item_id.strip():
replay_item["id"] = item_id.strip()
phase = raw_item.get("phase")
if isinstance(phase, str) and phase.strip():
replay_item["phase"] = phase.strip()
@@ -600,11 +576,7 @@ def _chat_messages_to_responses_input(
# Input preflight / validation
# ---------------------------------------------------------------------------
def _preflight_codex_input_items(
raw_items: Any,
*,
is_github_responses: bool = False,
) -> List[Dict[str, Any]]:
def _preflight_codex_input_items(raw_items: Any) -> List[Dict[str, Any]]:
if not isinstance(raw_items, list):
raise ValueError("Codex Responses input must be a list of input items.")
@@ -745,14 +717,8 @@ def _preflight_codex_input_items(
"content": normalized_content,
}
item_id = item.get("id")
if (
not is_github_responses
and isinstance(item_id, str)
and item_id.strip()
):
stripped_id = item_id.strip()
if len(stripped_id) <= _MAX_RESPONSES_ITEM_ID_LENGTH:
normalized_item["id"] = stripped_id
if isinstance(item_id, str) and item_id.strip():
normalized_item["id"] = item_id.strip()
phase = item.get("phase")
if isinstance(phase, str) and phase.strip():
normalized_item["phase"] = phase.strip()
@@ -824,7 +790,6 @@ def _preflight_codex_api_kwargs(
api_kwargs: Any,
*,
allow_stream: bool = False,
is_github_responses: bool = False,
) -> Dict[str, Any]:
if not isinstance(api_kwargs, dict):
raise ValueError("Codex Responses request must be a dict.")
@@ -846,10 +811,7 @@ def _preflight_codex_api_kwargs(
instructions = str(instructions)
instructions = instructions.strip() or DEFAULT_AGENT_IDENTITY
normalized_input = _preflight_codex_input_items(
api_kwargs.get("input"),
is_github_responses=is_github_responses,
)
normalized_input = _preflight_codex_input_items(api_kwargs.get("input"))
tools = api_kwargs.get("tools")
normalized_tools = None
@@ -1118,22 +1080,6 @@ def _normalize_codex_response(
differs from the one that minted the encrypted_content blob and drop
the item instead of triggering HTTP 400 invalid_encrypted_content.
"""
response_status = getattr(response, "status", None)
if isinstance(response_status, str):
response_status = response_status.strip().lower()
else:
response_status = None
incomplete_details = getattr(response, "incomplete_details", None)
incomplete_reason = ""
if isinstance(incomplete_details, dict):
incomplete_reason = str(incomplete_details.get("reason") or "").strip().lower()
elif incomplete_details is not None:
incomplete_reason = str(getattr(incomplete_details, "reason", "") or "").strip().lower()
response_incomplete_content_filter = (
response_status == "incomplete" and incomplete_reason == "content_filter"
)
output = getattr(response, "output", None)
if not isinstance(output, list) or not output:
# The Codex backend can return empty output when the answer was
@@ -1150,18 +1096,15 @@ def _normalize_codex_response(
content=[SimpleNamespace(type="output_text", text=out_text.strip())],
)]
response.output = output
elif response_incomplete_content_filter:
# This is a deterministic provider safety block, not a partial
# answer. Synthesize an empty message so finish_reason below becomes
# content_filter and the conversation loop can fallback/surface it
# instead of burning three continuation attempts.
output = [SimpleNamespace(
type="message", role="assistant", status="completed", content=[]
)]
response.output = output
else:
raise RuntimeError("Responses API returned no output items")
response_status = getattr(response, "status", None)
if isinstance(response_status, str):
response_status = response_status.strip().lower()
else:
response_status = None
if response_status in {"failed", "cancelled"}:
error_obj = getattr(response, "error", None)
error_msg = _format_responses_error(error_obj, response_status)
@@ -1223,28 +1166,15 @@ def _normalize_codex_response(
if item_type == "message":
item_phase = getattr(item, "phase", None)
normalized_phase = None
is_commentary_phase = False
if isinstance(item_phase, str):
normalized_phase = item_phase.strip().lower()
if normalized_phase in {"commentary", "analysis"}:
saw_commentary_phase = True
is_commentary_phase = True
elif normalized_phase in {"final_answer", "final"}:
saw_final_answer_phase = True
message_text = _extract_responses_message_text(item)
if message_text:
# Responses ``commentary``/``analysis`` phase text is mid-turn
# preamble/progress narration, never the turn's final answer
# (Codex CLI excludes it from last-message extraction; issues
# #24933 / #41293). Keep it out of assistant content so it
# can't be concatenated into — or leak as — the final response,
# but surface it through the reasoning channel so the CLI/
# gateway display it like thinking text. The exact message
# item is still preserved below for replay/cache continuity.
if is_commentary_phase:
reasoning_parts.append(message_text)
else:
content_parts.append(message_text)
content_parts.append(message_text)
raw_message_item: Dict[str, Any] = {
"type": "message",
"role": "assistant",
@@ -1339,11 +1269,7 @@ def _normalize_codex_response(
))
final_text = "\n".join([p for p in content_parts if p]).strip()
if (
not final_text
and hasattr(response, "output_text")
and not (saw_commentary_phase and not saw_final_answer_phase)
):
if not final_text and hasattr(response, "output_text"):
out_text = getattr(response, "output_text", "")
if isinstance(out_text, str):
final_text = out_text.strip()
@@ -1379,45 +1305,6 @@ def _normalize_codex_response(
# so the model keeps its chain-of-thought on the retry.
final_text = ""
# ── Reasoning-channel answer salvage (xAI grok) ──────────────
# grok-4.x on the xAI /v1/responses surface sometimes emits its final
# answer inside the reasoning item instead of as a ``message`` output
# item, marking where the answer starts with grok's internal
# ``<response>`` delimiter. Without salvage, the reasoning-only rule
# below classifies the turn ``incomplete`` — and because reasoning
# items on this surface carry no ``encrypted_content``, the interim
# message replays as nothing, so every continuation request is
# byte-identical to the one that just failed. The turn burns its 3
# retries and dies with "Codex response remained incomplete after 3
# continuation attempts" even though the answer was produced on the
# first attempt. Observed live with grok-4.20 on xai-oauth
# (2026-07-13). Promote the delimited tail to assistant content and
# keep the untagged prefix as thinking text.
if (
issuer_kind == "xai_responses"
and not final_text
and not tool_calls
and reasoning_parts
):
joined_reasoning = "\n\n".join(reasoning_parts)
marker = joined_reasoning.rfind("<response>")
if marker != -1:
salvaged = joined_reasoning[marker + len("<response>"):]
closing = salvaged.find("</response>")
if closing != -1:
salvaged = salvaged[:closing]
salvaged = salvaged.strip()
if salvaged:
logger.warning(
"xAI response delivered its final answer inside the "
"reasoning channel (<response> delimiter); promoting "
"%d chars to assistant content.",
len(salvaged),
)
final_text = salvaged
reasoning_prefix = joined_reasoning[:marker].strip()
reasoning_parts = [reasoning_prefix] if reasoning_prefix else []
assistant_message = SimpleNamespace(
content=final_text,
tool_calls=tool_calls,
@@ -1430,8 +1317,6 @@ def _normalize_codex_response(
if tool_calls:
finish_reason = "tool_calls"
elif response_incomplete_content_filter:
finish_reason = "content_filter"
elif leaked_tool_call_text:
finish_reason = "incomplete"
elif saw_streaming_or_item_incomplete:
@@ -1440,28 +1325,12 @@ def _normalize_codex_response(
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
# human-readable summary) with no visible content or tool calls.
#
# For the specially-handled backends (Codex, xAI, GitHub/Copilot),
# reasoning-only with status="completed" means "the model is still
# thinking and needs another turn" — treat it as incomplete so the
# Codex continuation path retries instead of falling into the
# empty-content retry loop.
#
# For all other backends (other:<base_url>, etc.), trust the provider's
# own response.status signal. When status == "completed" and no items
# are queued/in_progress/incomplete, reasoning alone is a valid final
# state — forcing "incomplete" causes multi-minute stalls as the
# continuation path re-issues calls (3 retries × up to 240s each).
# See https://github.com/NousResearch/hermes-agent/issues/64434
if response_status == "completed" and issuer_kind not in (
"codex_backend",
"xai_responses",
"github_responses",
):
finish_reason = "stop"
else:
finish_reason = "incomplete"
# human-readable summary) with no visible content or tool calls. The
# model is still thinking and needs another turn to produce the actual
# answer. Marking this as "stop" would send it into the empty-content
# retry loop which burns retries then fails — treat it as incomplete so
# the Codex continuation path handles it correctly.
finish_reason = "incomplete"
else:
finish_reason = "stop"
return assistant_message, finish_reason
+82 -642
View File
@@ -16,18 +16,70 @@ compatibility.
from __future__ import annotations
import json
import logging
import os
import time
from types import SimpleNamespace
from typing import Any, Callable, Dict, List
from agent.stream_single_writer import claim_stream_writer, stream_writer_is_current
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
@@ -61,15 +113,6 @@ def _record_codex_app_server_usage(agent, turn) -> dict[str, Any]:
usage = getattr(turn, "token_usage_last", None)
if not isinstance(usage, dict) or not usage:
compressor = getattr(agent, "context_compressor", None)
if (
compressor is not None
and getattr(compressor, "awaiting_real_usage_after_compression", False)
):
# No usage means this turn cannot adjudicate the pending compaction.
# Consume the marker so a later unrelated reading is not charged to
# it and preflight deferral cannot stay latched indefinitely.
compressor.update_from_response({})
if agent._session_db and agent.session_id:
try:
if not agent._session_db_created:
@@ -77,9 +120,6 @@ def _record_codex_app_server_usage(agent, turn) -> dict[str, Any]:
agent._session_db.update_token_counts(
agent.session_id,
model=agent.model,
billing_provider=agent.provider,
billing_base_url=agent.base_url,
billing_mode="subscription_included",
api_call_count=1,
)
except Exception as exc:
@@ -188,430 +228,6 @@ def _record_codex_app_server_usage(agent, turn) -> dict[str, Any]:
}
def _record_codex_app_server_compaction(
agent,
turn,
*,
approx_tokens: int | None = None,
force: bool = False,
) -> bool:
"""Record a Codex-native context compaction boundary in Hermes state.
The app-server owns the compacted thread context, so Hermes should not
rewrite local transcript rows here; state.db records the boundary via the
session event/usage counters while preserving the visible transcript.
"""
if not force and not getattr(turn, "compacted", False):
return False
thread_id = getattr(turn, "thread_id", None) or ""
turn_id = getattr(turn, "turn_id", None) or ""
logger.info(
"codex app-server compaction observed: session=%s thread=%s turn=%s force=%s",
getattr(agent, "session_id", None) or "none",
thread_id,
turn_id,
force,
)
if not force:
try:
from agent.conversation_compression import COMPACTION_STATUS
agent._emit_status(COMPACTION_STATUS)
except Exception:
pass
compressor = getattr(agent, "context_compressor", None)
if compressor is not None:
compressor.compression_count = getattr(
compressor, "compression_count", 0
) + 1
compressor.last_compression_rough_tokens = approx_tokens or 0
# The app server has already completed a real compaction boundary. Its
# usage update (when supplied) is therefore the same real-vs-real
# effectiveness verdict used by the normal compression path.
record_boundary = getattr(
type(compressor), "record_completed_compaction", None
)
if callable(record_boundary):
# Codex owns this summary. A prior Hermes deterministic-fallback
# flag must not leak into the native boundary's quality verdict.
record_boundary(compressor, used_fallback=False)
elif hasattr(compressor, "_verify_compaction_cleared_threshold"):
compressor._verify_compaction_cleared_threshold = True
if not getattr(turn, "token_usage_last", None):
compressor.last_prompt_tokens = -1
compressor.last_completion_tokens = 0
compressor.awaiting_real_usage_after_compression = True
agent._last_compaction_in_place = False
try:
if getattr(agent, "event_callback", None):
agent.event_callback(
"session:compress",
{
"platform": getattr(agent, "platform", None) or "",
"session_id": getattr(agent, "session_id", None) or "",
"old_session_id": "",
"in_place": False,
"compression_count": getattr(
compressor, "compression_count", 0
)
if compressor is not None
else 0,
"runtime": "codex_app_server",
"thread_id": thread_id,
"turn_id": turn_id,
},
)
except Exception:
logger.debug("event_callback error on codex session:compress", exc_info=True)
return True
# ---------------------------------------------------------------------------
# Codex app-server → Hermes UI bridge (#33200)
#
# The codex_app_server runtime hands the entire turn to a subprocess and
# bypasses the normal Hermes tool loop. Without this bridge gateway
# adapters (Discord, Telegram, TUI) never see live tool-progress bubbles
# or interim assistant commentary while codex is working — the user just
# stares at a quiet channel until the final answer lands. The bridge
# translates raw codex JSON-RPC notifications into the same three agent
# callbacks the standard runtime fires:
# - tool_progress_callback("tool.started"|"tool.completed", name, ...)
# - _fire_stream_delta(text) for streaming agentMessage chunks
# - _emit_interim_assistant_message({...}) for completed agentMessages
# ---------------------------------------------------------------------------
# Codex item types that map to a Hermes tool_call in the projector (and
# therefore deserve a tool_progress bubble pair). The projector lives in
# agent/transports/codex_event_projector.py — keep these in sync so the
# tool name shown in the UI matches the name recorded in messages.
# webSearch is codex's built-in web search tool — it has no projector
# entry (codex handles it internally) but still deserves a bubble.
_CODEX_TOOL_ITEM_TYPES = frozenset(
{"commandExecution", "fileChange", "mcpToolCall", "dynamicToolCall", "webSearch"}
)
# Internal MCP server that wraps Hermes' native tools for codex. When
# codex calls back through it, the inner dispatch runs in a SEPARATE
# hermes-tools-mcp-server subprocess that has no access to the parent
# agent's tool_progress_callback — so the inner call can never surface
# its own native progress event. The codex-level mcpToolCall event IS
# the display event for those calls; we strip the mcp.hermes-tools.*
# namespacing and emit the bare tool name (web_search, browser_navigate,
# vision_analyze, ...) since the user thinks of these as Hermes tools,
# not as MCP calls.
_INTERNAL_MCP_SERVER = "hermes-tools"
def _codex_item_to_tool_name(item: dict) -> str:
"""Synthetic Hermes tool name for a codex item. Mirrors
CodexEventProjector so the progress bubble and the projected
tool_calls entry use the same identifier."""
item_type = item.get("type") or ""
if item_type == "commandExecution":
return "exec_command"
if item_type == "fileChange":
return "apply_patch"
if item_type == "mcpToolCall":
server = item.get("server") or "mcp"
tool = item.get("tool") or "unknown"
if server == _INTERNAL_MCP_SERVER:
return tool
return f"mcp.{server}.{tool}"
if item_type == "dynamicToolCall":
return item.get("tool") or "dynamic"
if item_type == "webSearch":
return "web_search"
return item_type or "unknown"
def _codex_item_to_args(item: dict) -> dict:
"""Args dict surfaced to tool_progress_callback("tool.started", ...).
Mirrors the projector's _project_command / _project_file_change /
_project_mcp_tool_call / _project_dynamic_tool_call shapes."""
item_type = item.get("type") or ""
if item_type == "commandExecution":
return {"command": item.get("command") or "",
"cwd": item.get("cwd") or ""}
if item_type == "fileChange":
return {"changes": [
{"kind": (c.get("kind") or {}).get("type") or "update",
"path": c.get("path") or ""}
for c in (item.get("changes") or []) if isinstance(c, dict)
]}
if item_type in {"mcpToolCall", "dynamicToolCall"}:
args = item.get("arguments") or {}
return args if isinstance(args, dict) else {"arguments": args}
if item_type == "webSearch":
return {"query": item.get("query") or ""}
return {}
def _codex_item_to_preview(item: dict) -> Any:
"""Short human-readable preview for the tool.started bubble. Returns
None when no useful preview is available (Hermes' UI tolerates None)."""
item_type = item.get("type") or ""
if item_type == "commandExecution":
cmd = item.get("command") or ""
return cmd[:120] if cmd else None
if item_type == "fileChange":
paths = [c.get("path") for c in (item.get("changes") or [])
if isinstance(c, dict) and c.get("path")]
if not paths:
return None
preview = ", ".join(paths[:3])
if len(paths) > 3:
preview += f", +{len(paths) - 3} more"
return preview
if item_type in {"mcpToolCall", "dynamicToolCall"}:
args = item.get("arguments") or {}
if not isinstance(args, dict) or not args:
return None
try:
return json.dumps(args, ensure_ascii=False)[:120]
except (TypeError, ValueError):
return None
if item_type == "webSearch":
query = item.get("query") or ""
return query[:120] if query else None
return None
def _codex_item_completion_payload(item: dict) -> tuple[str, bool]:
"""Return (result_text, is_error) for a completed codex tool item.
Mirrors the projector's tool-result content so the bubble shows the
same outcome string that ends up in the messages list."""
item_type = item.get("type") or ""
if item_type == "commandExecution":
out = item.get("aggregatedOutput") or ""
exit_code = item.get("exitCode")
is_error = bool(exit_code is not None and exit_code != 0)
if is_error:
out = f"[exit {exit_code}]\n{out}"
return out, is_error
if item_type == "fileChange":
status = item.get("status") or "unknown"
n = len(item.get("changes") or [])
return (
f"apply_patch status={status}, {n} change(s)",
status not in {"completed", "applied", "success"},
)
if item_type == "mcpToolCall":
error = item.get("error")
if error:
return (
f"[error] {json.dumps(error, ensure_ascii=False)[:1000]}",
True,
)
result = item.get("result")
return (
json.dumps(result, ensure_ascii=False)[:4000]
if result is not None else "",
False,
)
if item_type == "dynamicToolCall":
content_items = item.get("contentItems") or []
if isinstance(content_items, list) and content_items:
return (
json.dumps(content_items, ensure_ascii=False)[:4000],
not bool(item.get("success", True)),
)
success = item.get("success", True)
return f"success={success}", not bool(success)
return "", False
def make_codex_app_server_event_bridge(agent) -> Callable[[dict], None]:
"""Build an ``on_event`` callback that wires codex app-server JSON-RPC
notifications into Hermes' gateway UI callbacks.
Returns a single-argument callable suitable for
``CodexAppServerSession(on_event=...)``.
Translation map:
* ``item/started`` for tool-shaped items → ``tool_progress_callback(
"tool.started", name, preview, args)``
* ``item/completed`` for tool-shaped items → ``tool_progress_callback(
"tool.completed", name, None, None, duration=..., is_error=...,
result=...)``
* ``item/agentMessage/delta`` → ``_fire_stream_delta(text)`` so chat
adapters can render the assistant's reply as it streams.
* ``item/reasoning/delta`` → ``_fire_reasoning_delta(text)``
* ``item/completed`` for ``agentMessage`` →
``_emit_interim_assistant_message({"role": "assistant",
"content": text})``. The gateway's ``already_streamed`` check
dedupes against any text the stream-delta callback already
rendered for the same message.
All callback invocations are guarded — a buggy display callback must
not tear down the codex turn loop. Errors are logged at DEBUG so the
notification stream keeps flowing regardless.
"""
# item_id -> (tool_name, args, started_wall_time). Populated on
# item/started and consumed on item/completed so duration is correct
# even when codex doesn't report durationMs.
started: dict[str, tuple[str, dict, float]] = {}
def _stable_call_id(item: dict, name: str) -> str:
"""Deterministic tool_call id mirroring CodexEventProjector, so a
live TUI tool card correlates with the same tool call after the
session is resumed and history is projected."""
from agent.transports.codex_event_projector import _deterministic_call_id
item_id = item.get("id") or ""
item_type = item.get("type") or ""
if item_type == "commandExecution":
return _deterministic_call_id("exec", item_id)
if item_type == "fileChange":
return _deterministic_call_id("apply_patch", item_id)
if item_type == "mcpToolCall":
server = item.get("server") or "mcp"
tool = item.get("tool") or "unknown"
return _deterministic_call_id(f"mcp__{server}__{tool}", item_id)
if item_type == "dynamicToolCall":
tool = item.get("tool") or "unknown"
return _deterministic_call_id(f"dyn_{tool}", item_id)
return _deterministic_call_id(name, item_id)
def _fire_tool_started(item: dict) -> None:
item_id = item.get("id") or ""
name = _codex_item_to_tool_name(item)
args = _codex_item_to_args(item)
if item_id:
started[item_id] = (name, args, time.monotonic())
cb = getattr(agent, "tool_progress_callback", None)
if cb is not None:
try:
cb("tool.started", name, _codex_item_to_preview(item), args)
except Exception:
logger.debug(
"tool_progress_callback raised on tool.started for %s",
name, exc_info=True,
)
# Authoritative stable-ID tool card (TUI / desktop). Fires
# alongside tool_progress so surfaces that render structured tool
# cards (not just progress bubbles) stay correlated with the
# projected history entry after a resume.
start_cb = getattr(agent, "tool_start_callback", None)
if start_cb is not None:
try:
start_cb(_stable_call_id(item, name), name, args)
except Exception:
logger.debug(
"tool_start_callback raised for %s", name, exc_info=True,
)
def _fire_tool_completed(item: dict) -> None:
item_id = item.get("id") or ""
name = _codex_item_to_tool_name(item)
prior = started.pop(item_id, None)
# Prefer codex's own durationMs when present so the bubble shows
# exact tool wall-time; fall back to our started timestamp; fall
# back to None if we never saw an item/started (some codex
# versions only emit completed for fast items).
duration: Any = None
codex_ms = item.get("durationMs")
if isinstance(codex_ms, (int, float)) and codex_ms >= 0:
duration = codex_ms / 1000.0
elif prior is not None:
duration = time.monotonic() - prior[2]
result, is_error = _codex_item_completion_payload(item)
cb = getattr(agent, "tool_progress_callback", None)
if cb is not None:
try:
cb("tool.completed", name, None, None,
duration=duration, is_error=is_error, result=result)
except Exception:
logger.debug(
"tool_progress_callback raised on tool.completed for %s",
name, exc_info=True,
)
complete_cb = getattr(agent, "tool_complete_callback", None)
if complete_cb is not None:
args = prior[1] if prior is not None else _codex_item_to_args(item)
try:
complete_cb(_stable_call_id(item, name), name, args, result)
except Exception:
logger.debug(
"tool_complete_callback raised for %s", name, exc_info=True,
)
def _fire_text_delta(params: dict) -> None:
text = params.get("delta") or params.get("text") or ""
if not isinstance(text, str) or not text:
return
fn = getattr(agent, "_fire_stream_delta", None)
if fn is None:
return
try:
fn(text)
except Exception:
logger.debug("_fire_stream_delta raised", exc_info=True)
def _fire_reasoning_delta(params: dict) -> None:
text = params.get("delta") or params.get("text") or ""
if not isinstance(text, str) or not text:
return
fn = getattr(agent, "_fire_reasoning_delta", None)
if fn is None:
return
try:
fn(text)
except Exception:
logger.debug("_fire_reasoning_delta raised", exc_info=True)
def _fire_agent_message_completed(item: dict) -> None:
text = item.get("text") or ""
if not isinstance(text, str) or not text.strip():
return
# display.show_commentary=false — mid-turn narration stays off the
# visible interim path on this runtime too (same contract as the
# codex_responses commentary channel).
if not getattr(agent, "show_commentary", True):
return
emit = getattr(agent, "_emit_interim_assistant_message", None)
if emit is None:
return
try:
emit({"role": "assistant", "content": text})
except Exception:
logger.debug(
"_emit_interim_assistant_message raised", exc_info=True,
)
def on_event(note: dict) -> None:
if not isinstance(note, dict):
return
method = note.get("method") or ""
params = note.get("params") or {}
if not isinstance(params, dict):
params = {}
if method == "item/agentMessage/delta":
_fire_text_delta(params)
return
if method in {"item/reasoning/delta", "item/reasoning/summaryDelta"}:
_fire_reasoning_delta(params)
return
item = params.get("item")
if not isinstance(item, dict):
return
item_type = item.get("type") or ""
if method == "item/started" and item_type in _CODEX_TOOL_ITEM_TYPES:
_fire_tool_started(item)
return
if method == "item/completed":
if item_type in _CODEX_TOOL_ITEM_TYPES:
_fire_tool_completed(item)
elif item_type == "agentMessage":
_fire_agent_message_completed(item)
return on_event
def run_codex_app_server_turn(
agent,
*,
@@ -628,10 +244,7 @@ def run_codex_app_server_turn(
Called from run_conversation() when agent.api_mode == "codex_app_server".
Returns the same dict shape as the chat_completions path.
"""
from agent.transports.codex_app_server_session import (
CodexAppServerSession,
_ServerRequestRouting,
)
from agent.transports.codex_app_server_session import CodexAppServerSession
# Lazy session: one CodexAppServerSession per AIAgent instance.
# Spawned on first turn, reused across turns, closed at AIAgent
@@ -649,42 +262,26 @@ def run_codex_app_server_turn(
except Exception:
approval_callback = None
# Gateway / cron contexts have no UI to surface codex's approval
# requests through, so codex app-server exec / apply_patch requests
# fail closed (silently decline) by default. When the user has
# explicitly opted out of Hermes approvals — via `approvals.mode: off`
# in config, the /yolo session toggle, or --yolo / HERMES_YOLO_MODE —
# honor that and let codex's own sandbox permission profile
# (~/.codex/config.toml) be the policy gate instead of double-gating
# with a missing Hermes UI. Defaults (manual/smart/unset) preserve the
# current fail-closed behavior — this is a no-op for those users.
auto_approve_requests = False
try:
from tools.approval import is_approval_bypass_active
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)
auto_approve_requests = is_approval_bypass_active()
except Exception:
logger.debug(
"codex app-server: approval-bypass lookup failed; "
"keeping fail-closed default",
exc_info=True,
)
# Bridge codex JSON-RPC notifications (item/started, item/completed,
# item/agentMessage/delta, ...) into Hermes' gateway UI callbacks
# (tool_progress_callback, _fire_stream_delta,
# _emit_interim_assistant_message). Without this, Discord/Telegram
# users see no live tool-progress or interim commentary while
# codex_app_server is running — only the final answer (#33200).
# Supersedes the narrower item/started-only bridge from #38835.
agent._codex_session = CodexAppServerSession(
cwd=cwd,
approval_callback=approval_callback,
request_routing=_ServerRequestRouting(
auto_approve_exec=auto_approve_requests,
auto_approve_apply_patch=auto_approve_requests,
),
on_event=make_codex_app_server_event_bridge(agent),
on_event=_on_codex_event,
)
# NOTE: the user message is ALREADY appended to messages by the
@@ -736,28 +333,6 @@ def run_codex_app_server_turn(
if turn.projected_messages:
messages.extend(turn.projected_messages)
# Persist the newly-projected assistant/tool messages ourselves.
# This path is an early return that bypasses conversation_loop, whose
# normal per-step _persist_session() calls would otherwise flush them.
# The inbound user turn was already flushed at turn start
# (turn_context.py _persist_session), and _flush_messages_to_session_db
# is idempotent via the intrinsic _DB_PERSISTED_MARKER — so this writes
# ONLY the new codex projected rows and does NOT re-write the user turn.
# Keeping the agent as the sole persister lets us return
# agent_persisted=True below, so the gateway skips its own DB write and
# we avoid the #860/#42039 duplicate user-message write (append_message
# is a raw INSERT with no dedup, so a gateway re-write would duplicate
# the already-flushed user turn). See gateway/run.py agent_persisted.
if getattr(agent, "_session_db", None) is not None:
try:
agent._flush_messages_to_session_db(messages)
except Exception:
logger.debug(
"codex app-server projected-message flush failed",
exc_info=True,
)
# Counter ticks for the agent-improvement loop.
# _turns_since_memory and _user_turn_count are ALREADY incremented
# in the run_conversation() pre-loop block (lines ~11793-11817) so we
@@ -768,7 +343,6 @@ def run_codex_app_server_turn(
agent._iters_since_skill = (
getattr(agent, "_iters_since_skill", 0) + turn.tool_iterations
)
_record_codex_app_server_compaction(agent, turn)
usage_result = _record_codex_app_server_usage(agent, turn)
api_calls = 1
@@ -820,18 +394,6 @@ def run_codex_app_server_turn(
"completed": not turn.interrupted and turn.error is None,
"partial": turn.interrupted or turn.error is not None,
"error": turn.error,
# The codex app-server runtime IS an early-return path that bypasses
# conversation_loop, but we flush the projected assistant/tool messages
# ourselves above (see the _flush_messages_to_session_db call after
# messages.extend). The inbound user turn was already flushed at turn
# start (turn_context._persist_session) and the flush dedups via
# _DB_PERSISTED_MARKER, so state.db ends up with each real message
# exactly once and session_search / conversation-distill see the full
# gateway conversation. Report agent_persisted=True so the gateway
# skips its own append_to_transcript DB write — writing again there
# would re-INSERT the already-flushed user turn (append_message has no
# dedup), reintroducing the #860 / #42039 duplicate-write bug.
"agent_persisted": True,
"codex_thread_id": turn.thread_id,
"codex_turn_id": turn.turn_id,
**usage_result,
@@ -876,48 +438,18 @@ def _event_field(event: Any, name: str, default: Any = None) -> Any:
return value if value is not None else default
def _item_field(item: Any, name: str, default: Any = None) -> Any:
"""Field access for nested Response items (attr-style SDK object or dict)."""
value = getattr(item, name, None)
if value is None and isinstance(item, dict):
value = item.get(name, default)
return value if value is not None else default
def _raise_stream_error(event: Any) -> None:
"""Raise a ``_StreamErrorEvent`` from a ``type=error`` SSE frame.
The Responses spec puts the failure details at the top level of the
frame (``{"type": "error", "code": ..., "message": ..., "param": ...}``),
but the official OpenAI SDK and several OpenAI-compatible proxies wrap
them in an HTTP-style nested envelope instead
(``{"type": "error", "error": {"code": ..., "message": ..., "param": ...}}``).
Read the top-level fields first, then fall back to the nested envelope so
the error classifier sees the provider's real code/message (rate-limit vs
context-overflow vs entitlement) rather than the generic placeholder.
Port of anomalyco/opencode#36130.
Imported lazily so this module stays importable from places that don't
pull in ``run_agent`` (e.g. plugin code, doc tools).
"""
from run_agent import _StreamErrorEvent
nested = _event_field(event, "error")
def _error_field(name: str) -> Any:
value = _event_field(event, name)
if value is None and nested is not None:
value = _item_field(nested, name)
return value
raw_message = _error_field("message")
if raw_message is not None and not isinstance(raw_message, str):
raw_message = str(raw_message)
message = (raw_message or "stream emitted error event").strip() or "stream emitted error event"
message = (_event_field(event, "message", "") or "stream emitted error event").strip()
raise _StreamErrorEvent(
message,
code=_error_field("code"),
param=_error_field("param"),
code=_event_field(event, "code"),
param=_event_field(event, "param"),
)
@@ -927,7 +459,6 @@ def _consume_codex_event_stream(
model: str,
on_text_delta=None,
on_reasoning_delta=None,
on_commentary_message=None,
on_first_delta=None,
on_event=None,
interrupt_check=None,
@@ -959,11 +490,7 @@ def _consume_codex_event_stream(
* ``on_text_delta(str)`` — fires per ``response.output_text.delta``, suppressed
once a function_call event is seen (so tool-call turns don't bleed text
into the chat).
* ``on_reasoning_delta(str)`` — fires per ``response.reasoning.*.delta`` and
``phase=analysis`` message deltas. When no dedicated commentary callback
is supplied, commentary also uses this legacy fallback.
* ``on_commentary_message(str)`` — fires once per completed
``phase=commentary`` message, before any following tool item executes.
* ``on_reasoning_delta(str)`` — fires per ``response.reasoning.*.delta``.
* ``on_first_delta()`` — one-shot, fires on the first text delta only.
* ``on_event(event)`` — fires for every event before any other processing.
Used for watchdog activity, debug logging, anything wire-shape-agnostic.
@@ -973,8 +500,6 @@ def _consume_codex_event_stream(
collected_text_deltas: List[str] = []
has_tool_calls = False
first_delta_fired = False
active_message_phase: str | None = None
commentary_text_deltas: List[str] = []
terminal_status: str = "completed"
terminal_usage: Any = None
terminal_response_id: str = None
@@ -1008,43 +533,9 @@ def _consume_codex_event_stream(
if event_type == "error":
_raise_stream_error(event)
# Track the phase of the active streamed message item. Codex/Harmony
# ``commentary``/``analysis`` text is mid-turn preamble/progress
# narration, never the final answer. We still collect completed output
# items for replay, but route those deltas to the reasoning callback so
# they display like thinking text instead of assistant content.
if event_type == "response.output_item.added":
item = _event_field(event, "item")
item_type = _item_field(item, "type", "")
if item_type == "message":
phase = _item_field(item, "phase", None)
active_message_phase = phase.strip().lower() if isinstance(phase, str) else None
if active_message_phase == "commentary":
commentary_text_deltas = []
else:
active_message_phase = None
if "function_call" in str(item_type):
has_tool_calls = True
continue
if "output_text.delta" in event_type or event_type == "response.output_text.delta":
delta_text = _event_field(event, "delta", "")
if delta_text and active_message_phase == "commentary":
commentary_text_deltas.append(delta_text)
# Preserve CLI/backward compatibility when no first-class
# commentary consumer is installed.
if on_commentary_message is None and on_reasoning_delta is not None:
try:
on_reasoning_delta(delta_text)
except Exception:
logger.debug("Codex stream on_reasoning_delta raised", exc_info=True)
elif delta_text and active_message_phase == "analysis":
if on_reasoning_delta is not None:
try:
on_reasoning_delta(delta_text)
except Exception:
logger.debug("Codex stream on_reasoning_delta raised", exc_info=True)
elif delta_text:
if delta_text:
collected_text_deltas.append(delta_text)
if not has_tool_calls:
if not first_delta_fired:
@@ -1078,27 +569,6 @@ def _consume_codex_event_stream(
done_item = _event_field(event, "item")
if done_item is not None:
collected_output_items.append(done_item)
done_phase = _item_field(done_item, "phase", None)
done_phase = done_phase.strip().lower() if isinstance(done_phase, str) else None
if done_phase == "commentary" and on_commentary_message is not None:
commentary_text = "".join(commentary_text_deltas).strip()
if not commentary_text:
content_parts = _item_field(done_item, "content", [])
if isinstance(content_parts, list):
commentary_text = "".join(
str(_item_field(part, "text", "") or "")
for part in content_parts
if _item_field(part, "type", "") == "output_text"
).strip()
if commentary_text:
try:
on_commentary_message(commentary_text)
except Exception:
logger.debug(
"Codex stream on_commentary_message raised",
exc_info=True,
)
commentary_text_deltas = []
continue
if event_type in _TERMINAL_EVENT_TYPES:
@@ -1199,14 +669,14 @@ def run_codex_stream(agent, api_kwargs: dict, client: Any = None, on_first_delta
def _on_reasoning_delta(text: str) -> None:
agent._fire_reasoning_delta(text)
def _on_commentary_message(text: str) -> None:
agent._fire_streamed_codex_commentary(text)
def _on_event(event: Any) -> None:
# TTFB watchdog and activity touch — runs once per SSE event.
agent._codex_stream_last_event_ts = time.time()
agent._touch_activity("receiving stream response")
def _interrupt_check() -> bool:
return bool(agent._interrupt_requested)
for attempt in range(max_stream_retries + 1):
if agent._interrupt_requested:
raise InterruptedError("Agent interrupted before Codex stream retry")
@@ -1226,27 +696,6 @@ def run_codex_stream(agent, api_kwargs: dict, client: Any = None, on_first_delta
continue
raise
# Claim the delta sink for THIS attempt (#65991) — parity with the
# chat_completions/anthropic/bedrock paths. If a prior attempt's
# stream is somehow still alive, this claim supersedes it so its
# late deltas are fenced out of the turn; conversely, a newer
# attempt supersedes us and the interrupt_check below stops our
# consumption immediately.
_writer_token = claim_stream_writer(agent)
def _interrupt_or_superseded(_tok=_writer_token) -> bool:
if agent._interrupt_requested:
return True
if not stream_writer_is_current(agent, _tok):
logger.warning(
"Codex streaming attempt superseded by a newer stream; "
"stopping consumption to preserve the single-writer "
"invariant (model=%s).",
api_kwargs.get("model", "unknown"),
)
return True
return False
try:
# Compatibility: some mocks/providers return a concrete response
# instead of an iterable. Pass it straight through.
@@ -1259,17 +708,9 @@ def run_codex_stream(agent, api_kwargs: dict, client: Any = None, on_first_delta
model=api_kwargs.get("model"),
on_text_delta=_on_text_delta,
on_reasoning_delta=_on_reasoning_delta,
on_commentary_message=(
_on_commentary_message
if (
getattr(agent, "interim_assistant_callback", None) is not None
and getattr(agent, "show_commentary", True)
)
else None
),
on_first_delta=on_first_delta,
on_event=_on_event,
interrupt_check=_interrupt_or_superseded,
interrupt_check=_interrupt_check,
)
except (_httpx.RemoteProtocolError, _httpx.ReadTimeout, _httpx.ConnectError, ConnectionError) as exc:
if attempt < max_stream_retries:
@@ -1318,5 +759,4 @@ __all__ = [
"run_codex_stream",
"run_codex_create_stream_fallback",
"_consume_codex_event_stream",
"make_codex_app_server_event_bridge",
]
+1 -10
View File
@@ -56,7 +56,6 @@ import logging
import os
import re
import subprocess
import tempfile
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional
@@ -413,18 +412,10 @@ def _marker_root(cwd: Path) -> Optional[Path]:
"""
current = cwd.resolve()
home = _home()
# Shared world-writable temp roots are never project roots: a stray
# manifest in /tmp (left by any process) must not flip every session
# whose cwd lives under the temp dir into the coding posture. Same
# reasoning as the $HOME skip below.
try:
temp_root = Path(tempfile.gettempdir()).resolve()
except Exception:
temp_root = None
for depth, parent in enumerate([current, *current.parents]):
if depth > 6:
break
if parent == home or (temp_root is not None and parent == temp_root):
if parent == home:
continue
for marker in _PROJECT_MARKERS:
if (parent / marker).exists():
+122 -1166
View File
File diff suppressed because it is too large Load Diff
+5 -42
View File
@@ -26,31 +26,7 @@ Lifecycle:
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from agent.redact import redact_sensitive_text
MEMORY_CONTEXT_MAX_CHARS = 6_000
_MEMORY_CONTEXT_HEAD_CHARS = 4_000
_MEMORY_CONTEXT_TAIL_CHARS = 1_500
_MEMORY_CONTEXT_TRUNCATION_MARKER = "\n...[memory provider context truncated]...\n"
def sanitize_memory_context(memory_context: str) -> str:
"""Prepare provider context for a context-engine/LLM egress boundary."""
sanitized = redact_sensitive_text(
memory_context.strip(),
force=True,
redact_url_credentials=True,
)
if len(sanitized) <= MEMORY_CONTEXT_MAX_CHARS:
return sanitized
return (
sanitized[:_MEMORY_CONTEXT_HEAD_CHARS]
+ _MEMORY_CONTEXT_TRUNCATION_MARKER
+ sanitized[-_MEMORY_CONTEXT_TAIL_CHARS:]
)
from typing import Any, Dict, List
class ContextEngine(ABC):
@@ -111,10 +87,8 @@ class ContextEngine(ABC):
def compress(
self,
messages: List[Dict[str, Any]],
current_tokens: Optional[int] = None,
focus_topic: Optional[str] = None,
force: bool = False,
memory_context: str = "",
current_tokens: int = None,
focus_topic: str = None,
) -> List[Dict[str, Any]]:
"""Compact the message list and return the new message list.
@@ -129,12 +103,6 @@ class ContextEngine(ABC):
Engines that support guided compression should prioritise
preserving information related to this topic. Engines that
don't support it may simply ignore this argument.
force: Whether a user-requested compression should bypass an
engine-owned cooldown. Engines without cooldowns may ignore it.
memory_context: Text returned by memory providers immediately before
compaction. Summarizing engines should include non-empty text in
their handoff prompt. Older engines may omit this parameter; the
host filters unsupported optional arguments by signature.
"""
# -- Optional: pre-flight check ----------------------------------------
@@ -226,17 +194,12 @@ class ContextEngine(ABC):
Default returns the standard fields run_agent.py expects.
"""
# Clamp the -1 "compression just ran, awaiting real usage" sentinel
# (set by conversation_compression) to 0 so status readers don't see a
# raw -1 or a negative usage_percent on the transitional turn. Mirrors
# the CLI/gateway status-bar paths (cli.py, tui_gateway/server.py).
last_prompt = self.last_prompt_tokens if self.last_prompt_tokens > 0 else 0
return {
"last_prompt_tokens": last_prompt,
"last_prompt_tokens": self.last_prompt_tokens,
"threshold_tokens": self.threshold_tokens,
"context_length": self.context_length,
"usage_percent": (
min(100, last_prompt / self.context_length * 100)
min(100, self.last_prompt_tokens / self.context_length * 100)
if self.context_length else 0
),
"compression_count": self.compression_count,
+6 -48
View File
@@ -152,24 +152,13 @@ async def preprocess_context_references_async(
blocks: list[str] = []
injected_tokens = 0
# Expand all references concurrently. Each _expand_reference is independent
# (no shared state during expansion) — a message with several @url: refs
# would otherwise pay one full web_extract round-trip per ref in series.
# gather preserves positional order, so we reassemble warnings/blocks in the
# original ref order exactly as the prior serial loop did; the token-budget
# check below is unchanged (it runs once, after all refs are expanded).
expanded = await asyncio.gather(
*(
_expand_reference(
ref,
cwd_path,
url_fetcher=url_fetcher,
allowed_root=allowed_root_path,
)
for ref in refs
for ref in refs:
warning, block = await _expand_reference(
ref,
cwd_path,
url_fetcher=url_fetcher,
allowed_root=allowed_root_path,
)
)
for warning, block in expanded:
if warning:
warnings.append(warning)
if block:
@@ -381,37 +370,6 @@ def _ensure_reference_path_allowed(path: Path) -> None:
continue
raise ValueError("path is a sensitive credential or internal Hermes path and cannot be attached")
# Anchor to the canonical read deny-list (agent/file_safety.get_read_block_error),
# the single source of truth used by the file/terminal read path. The narrow
# list above predates that guard and never caught the real credential stores:
# provider keys (auth.json), Anthropic OAuth tokens (.anthropic_oauth.json),
# MCP OAuth material (mcp-tokens/), webhook HMAC secrets, and project-local
# .env files. That gap matters because the gateway feeds UNTRUSTED remote
# message text into reference expansion, so `@file:~/.hermes/auth.json` from a
# chat peer would otherwise read the operator's keys straight into context.
# Routing through the canonical guard closes the gap today and keeps this path
# protected automatically whenever that deny-list grows.
try:
from agent.file_safety import get_read_block_error
if get_read_block_error(str(path)) is not None:
raise ValueError(
"path is a sensitive credential or internal Hermes path and cannot be attached"
)
except ValueError:
raise
except Exception:
# Fail CLOSED on the security path. This guard exists specifically to
# cover credential stores the narrow list above misses (auth.json,
# .anthropic_oauth.json, mcp-tokens/, ...). If the canonical lookup
# ever fails, silently falling through would re-open that exact hole —
# the gateway feeds untrusted remote text here, so a probe could then
# attach the operator's keys. Refuse instead: a spurious block on a
# legitimate file is a recoverable annoyance; a leaked credential is not.
raise ValueError(
"path could not be verified against the credential deny-list and cannot be attached"
)
def _strip_trailing_punctuation(value: str) -> str:
stripped = value.rstrip(TRAILING_PUNCTUATION)
File diff suppressed because it is too large Load Diff
+165 -1082
View File
File diff suppressed because it is too large Load Diff
+5 -4
View File
@@ -26,7 +26,7 @@ from openai.types.chat.chat_completion_message_tool_call import (
Function,
)
from agent.file_safety import get_read_block_error, get_write_denied_error
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
@@ -727,9 +727,10 @@ class CopilotACPClient:
elif method == "fs/write_text_file":
try:
path = _ensure_path_within_cwd(str(params.get("path") or ""), cwd)
denied = get_write_denied_error(str(path))
if denied:
raise PermissionError(denied)
if is_write_denied(str(path)):
raise PermissionError(
f"Write denied: '{path}' is a protected system/credential file."
)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(str(params.get("content") or ""))
response = {
+1 -1
View File
@@ -22,7 +22,7 @@ _PERSISTABLE_PROVIDER_SOURCES = frozenset({
("minimax-oauth", "oauth"),
("nous", "device_code"),
("openai-codex", "device_code"),
("xai-oauth", "device_code"),
("xai-oauth", "loopback_pkce"),
})
_SAFE_SECRETISH_METADATA_KEYS = frozenset({
+55 -340
View File
@@ -43,19 +43,11 @@ logger = logging.getLogger(__name__)
def _load_config_safe() -> Optional[dict]:
"""Load config.yaml read-only, returning None on any error.
Uses ``load_config_readonly()``: every consumer in this module only reads
(``get_pool_strategy``, ``_iter_custom_providers``, the model-config seed),
and the deepcopy that ``load_config()`` pays per call is what made
credential-pool checks the dominant cost of ``model.options`` — the picker
calls ``load_pool()`` once per provider row, each of which loaded (and
deep-copied) the full config again.
"""
"""Load config.yaml, returning None on any error."""
try:
from hermes_cli.config import load_config_readonly
from hermes_cli.config import load_config
return load_config_readonly()
return load_config()
except Exception:
return None
@@ -90,7 +82,7 @@ _TERMINAL_AUTH_REASONS = frozenset({
# without losing recoverability — the user always has the option to re-add
# via ``hermes auth add``.
#
# Singleton-seeded entries (``device_code``, ``claude_code``)
# Singleton-seeded entries (``device_code``, ``loopback_pkce``, ``claude_code``)
# are NOT pruned because ``_seed_from_singletons`` would just re-create them
# on the next ``load_pool()`` with the same stale singleton tokens, defeating
# the cleanup. They remain in the pool marked DEAD until an explicit re-auth
@@ -122,20 +114,6 @@ EXHAUSTED_TTL_401_SECONDS = 5 * 60 # 5 minutes
EXHAUSTED_TTL_429_SECONDS = 60 * 60 # 1 hour
EXHAUSTED_TTL_DEFAULT_SECONDS = 60 * 60 # 1 hour
# Throttle window for the "no available entries" INFO line. Credential
# selection runs on a hot path (every model call, plus auxiliary tasks like
# compression/moa/titles), so when a pool is empty or fully exhausted the
# un-throttled log fires on *every* selection. On Windows several Hermes
# processes share one rotating log guarded by concurrent-log-handler's
# cross-process lock; that per-selection volume storms the lock
# (``RuntimeError: Cannot acquire lock after 20 attempts``), pegs a core, and
# stalls the asyncio event loop long enough to fail the Desktop backend
# readiness handshake ("Timed out connecting to Hermes backend after
# 15000ms"). Logging the condition at most once per window preserves the
# signal while removing the storm — same class of fix as the warn-once
# dedup in #58265.
NO_AVAILABLE_ENTRIES_LOG_THROTTLE_SECONDS = 60.0
# Pool key prefix for custom OpenAI-compatible endpoints.
# Custom endpoints all share provider='custom' but are keyed by their
# custom_providers name: 'custom:<normalized_name>'.
@@ -150,17 +128,6 @@ _EXTRA_KEYS = frozenset({
})
def _normalize_pool_auth_type(provider: str, token: Any, auth_type: Any) -> str:
"""Infer pool auth metadata for token formats with one unambiguous meaning."""
if (
provider == "anthropic"
and isinstance(token, str)
and token.startswith("sk-ant-oat")
):
return AUTH_TYPE_OAUTH
return str(auth_type or AUTH_TYPE_API_KEY)
@dataclass
class PooledCredential:
provider: str
@@ -190,11 +157,6 @@ class PooledCredential:
def __post_init__(self):
if self.extra is None:
self.extra = {}
self.auth_type = _normalize_pool_auth_type(
self.provider,
self.access_token,
self.auth_type,
)
def __getattr__(self, name: str):
if name in _EXTRA_KEYS:
@@ -483,44 +445,6 @@ def get_pool_strategy(provider: str) -> str:
return STRATEGY_FILL_FIRST
def credential_pool_matches_provider(
pool_or_provider: Any,
provider: Optional[str],
*,
base_url: Optional[str] = None,
) -> bool:
"""Return whether a pool belongs to the requested runtime provider.
Named custom endpoints intentionally use two identities: the live agent is
``custom`` while its pool is keyed ``custom:<name>``. Accept that pair only
when the runtime base URL resolves to the exact same custom pool key.
Empty string identities fail closed. Legacy pool adapters without a
``provider`` attribute remain compatible; production pools are scoped.
"""
raw_pool_provider = getattr(pool_or_provider, "provider", None)
if raw_pool_provider is None:
if isinstance(pool_or_provider, str):
raw_pool_provider = pool_or_provider
else:
# Backward compatibility for lightweight/unscoped pool adapters.
# Production CredentialPool instances always carry ``provider``;
# old plugins and tests may expose only select()/has_credentials().
return True
pool_provider = str(raw_pool_provider or "").strip().lower()
provider_norm = str(provider or "").strip().lower()
if not pool_provider or not provider_norm:
return False
if pool_provider == provider_norm:
return True
if provider_norm != "custom" or not pool_provider.startswith(CUSTOM_POOL_PREFIX):
return False
try:
matched_pool = get_custom_provider_pool_key(base_url or "")
except Exception:
return False
return str(matched_pool or "").strip().lower() == pool_provider
DEFAULT_MAX_CONCURRENT_PER_CREDENTIAL = 1
@@ -565,12 +489,14 @@ def _write_through_provider_state_to_global_root(
except Exception:
return
try:
auth_mod._persist_provider_state_to_store(
provider_id,
state,
global_path,
set_active=False,
)
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",
@@ -588,12 +514,6 @@ class CredentialPool:
self._lock = threading.Lock()
self._active_leases: Dict[str, int] = {}
self._max_concurrent = DEFAULT_MAX_CONCURRENT_PER_CREDENTIAL
# Monotonic timestamp of the last "no available entries" log, used to
# throttle that message so an empty/exhausted pool cannot storm the
# shared rotating log (see NO_AVAILABLE_ENTRIES_LOG_THROTTLE_SECONDS).
# Re-armed to None on every successful selection so a recover→re-exhaust
# transition logs promptly instead of being swallowed by a stale window.
self._last_no_entries_log_at: Optional[float] = None
def has_credentials(self) -> bool:
return bool(self._entries)
@@ -696,32 +616,17 @@ class CredentialPool:
file_refresh = creds.get("refreshToken", "")
file_access = creds.get("accessToken", "")
file_expires = creds.get("expiresAt", 0)
# Sync when either token changed. Access tokens can be re-issued
# without a new refresh token (silent re-issue path), so checking
# only refresh_token misses that case and leaves a stale
# access_token in the pool → 401 on every request until the pool
# entry's exhausted TTL expires.
entry_access = entry.access_token or ""
entry_refresh = entry.refresh_token or ""
if (file_access or file_refresh) and (
(file_access and file_access != entry_access)
or (file_refresh and file_refresh != entry_refresh)
):
logger.debug(
"Pool entry %s: syncing tokens from credentials file (tokens changed)",
entry.id,
)
# If the credentials file has a different token pair, sync it
if file_refresh and file_refresh != entry.refresh_token:
logger.debug("Pool entry %s: syncing tokens from credentials file (refresh token changed)", entry.id)
updated = replace(
entry,
access_token=file_access or entry.access_token,
refresh_token=file_refresh or entry.refresh_token,
expires_at_ms=file_expires or entry.expires_at_ms,
access_token=file_access,
refresh_token=file_refresh,
expires_at_ms=file_expires,
last_status=None,
last_status_at=None,
last_error_code=None,
last_error_reason=None,
last_error_message=None,
last_error_reset_at=None,
)
self._replace_entry(entry, updated)
self._persist()
@@ -804,11 +709,11 @@ class CredentialPool:
keeps the consumed refresh_token and the next ``_refresh_entry`` call
would replay it and get a ``refresh_token_reused``-style 4xx.
Only applies to entries seeded from the singleton (``device_code``);
manually added entries are independent credentials with their own
refresh-token lifecycle.
Only applies to entries seeded from the singleton (``loopback_pkce``);
manually added entries (``manual:xai_pkce``) are independent
credentials with their own refresh-token lifecycle.
"""
if self.provider != "xai-oauth" or entry.source != "device_code":
if self.provider != "xai-oauth" or entry.source != "loopback_pkce":
return entry
try:
with _auth_store_lock():
@@ -852,45 +757,6 @@ class CredentialPool:
logger.debug("Failed to sync xAI OAuth entry from auth.json: %s", exc)
return entry
def _sync_xai_oauth_entry_from_pool_store(
self, entry: PooledCredential
) -> PooledCredential:
"""Adopt a token pair rotated by another pool instance.
Direct xAI integrations load a fresh ``CredentialPool`` for each
request. Their in-memory locks therefore cannot protect xAI's
single-use refresh token across concurrent requests or processes.
This helper is called while the shared auth-store lock is held and
re-reads the exact persisted row before a refresh POST is attempted.
"""
if self.provider != "xai-oauth":
return entry
try:
persisted = next(
(
payload
for payload in read_credential_pool(self.provider)
if isinstance(payload, dict) and payload.get("id") == entry.id
),
None,
)
if not isinstance(persisted, dict):
return entry
stored = PooledCredential.from_dict(self.provider, persisted)
if (
stored.access_token != entry.access_token
or stored.refresh_token != entry.refresh_token
):
logger.debug(
"Pool entry %s: adopting xAI OAuth tokens rotated by another pool instance",
entry.id,
)
self._replace_entry(entry, stored)
return stored
except Exception as exc:
logger.debug("Failed to sync xAI OAuth entry from credential pool: %s", exc)
return entry
def _sync_nous_entry_from_auth_store(self, entry: PooledCredential) -> PooledCredential:
"""Sync a Nous pool entry from auth.json if tokens differ.
@@ -987,9 +853,8 @@ class CredentialPool:
"""
# Only sync entries that were seeded *from* a singleton. Manually
# added pool entries (source="manual:*") are independent credentials
# and must not write back to the singleton. All singleton-seeded
# device-code sources (nous, openai-codex, xAI) use ``device_code``.
if entry.source != "device_code":
# and must not write back to the singleton.
if entry.source not in {"device_code", "loopback_pkce"}:
return
try:
with _auth_store_lock():
@@ -1084,61 +949,6 @@ class CredentialPool:
self._mark_exhausted(entry, None)
return None
# Codex and xAI OAuth refresh tokens are single-use. The
# sync→POST→write-back sequence below must run atomically across Hermes
# processes: otherwise two processes can both adopt the same on-disk
# token, both POST it, and the loser gets ``refresh_token_reused``.
# Serialize the whole sequence through the shared cross-process
# auth-store flock (the same lock and extended-timeout pattern used by
# resolve_codex_runtime_credentials()). When a waiter finally acquires
# the lock, the in-lock re-sync below picks up the rotated token the
# winner persisted and skips the POST.
if self.provider in ("openai-codex", "xai-oauth"):
sync_entry = (
self._sync_codex_entry_from_auth_store
if self.provider == "openai-codex"
else self._sync_xai_oauth_entry_from_pool_store
)
with _auth_store_lock(
timeout_seconds=self._single_use_refresh_lock_timeout()
):
synced = sync_entry(entry)
if self.provider == "openai-codex":
if synced is not entry:
entry = synced
if not force and not self._entry_needs_refresh(entry):
return entry
return self._refresh_entry_impl(entry, force=force)
if (
synced.access_token != entry.access_token
or synced.refresh_token != entry.refresh_token
):
return synced
return self._refresh_entry_impl(synced, force=force)
return self._refresh_entry_impl(entry, force=force)
def _single_use_refresh_lock_timeout(self) -> float:
"""Lock timeout for single-use-refresh-token providers.
Covers the configured refresh POST timeout plus a margin so a slow
token endpoint cannot make the flock give up before the refresh
resolves. Reads the provider's ``HERMES_*_REFRESH_TIMEOUT_SECONDS``
override.
"""
env_var = (
"HERMES_CODEX_REFRESH_TIMEOUT_SECONDS"
if self.provider == "openai-codex"
else "HERMES_XAI_REFRESH_TIMEOUT_SECONDS"
)
refresh_timeout_seconds = auth_mod.env_float(env_var, 20)
return max(
float(auth_mod.AUTH_LOCK_TIMEOUT_SECONDS),
float(refresh_timeout_seconds) + 5.0,
)
def _refresh_entry_impl(
self, entry: PooledCredential, *, force: bool
) -> Optional[PooledCredential]:
try:
if self.provider == "anthropic":
from agent.anthropic_adapter import refresh_anthropic_oauth_pure
@@ -1259,8 +1069,8 @@ class CredentialPool:
# consumed the refresh token between our proactive sync and the
# HTTP call. Re-check auth.json and adopt the fresh tokens if
# they have rotated since. Only meaningful for singleton-seeded
# (device_code) entries; manual entries don't share
# state with the singleton.
# (loopback_pkce) entries; manual entries don't share state with
# the singleton.
if self.provider == "xai-oauth":
synced = self._sync_xai_oauth_entry_from_auth_store(entry)
if synced.refresh_token != entry.refresh_token:
@@ -1282,8 +1092,8 @@ class CredentialPool:
# Terminal error: auth.json has no newer tokens — the stored
# refresh_token is dead. Clear it from auth.json so the next
# session does not re-seed the same revoked credentials, and
# remove all singleton-seeded xAI entries from the in-memory
# pool. Mirrors the Nous quarantine path above.
# remove all singleton-seeded (loopback_pkce) entries from the
# in-memory pool. Mirrors the Nous quarantine path above.
if auth_mod._is_terminal_xai_oauth_refresh_error(exc):
logger.debug(
"xAI OAuth refresh token is terminally invalid; clearing local token state"
@@ -1317,11 +1127,11 @@ class CredentialPool:
)
removed_ids = [
item.id for item in self._entries
if item.source == "device_code"
if item.source == "loopback_pkce"
]
self._entries = [
item for item in self._entries
if item.source != "device_code"
if item.source != "loopback_pkce"
]
if self._current_id == entry.id:
self._current_id = None
@@ -1499,7 +1309,7 @@ class CredentialPool:
if self.provider == "xai-oauth":
return auth_mod._xai_access_token_is_expiring(
entry.access_token,
auth_mod._xai_proactive_refresh_skew_seconds(entry.access_token),
auth_mod.XAI_ACCESS_TOKEN_REFRESH_SKEW_SECONDS,
)
if self.provider == "nous":
# Nous refresh can require network access and should happen when
@@ -1524,11 +1334,6 @@ class CredentialPool:
entries_to_prune: List[str] = []
available: List[PooledCredential] = []
for entry in self._entries:
# Borrowed credentials persist as metadata-only references and are
# hydrated from their live source on load. A stale duplicate row
# can remain unhydrated; never lease or select it as an empty key.
if entry.auth_type == AUTH_TYPE_API_KEY and not entry.runtime_api_key:
continue
# For anthropic claude_code entries, sync from the credentials file
# before any status/refresh checks. This picks up tokens refreshed
# by other processes (Claude Code CLI, other Hermes profiles).
@@ -1566,7 +1371,7 @@ class CredentialPool:
# tokens that another process (or a fresh `hermes model` ->
# xAI Grok OAuth login) has since rotated in auth.json.
if (self.provider == "xai-oauth"
and entry.source == "device_code"
and entry.source == "loopback_pkce"
and entry.last_status in {STATUS_EXHAUSTED, STATUS_DEAD}):
synced = self._sync_xai_oauth_entry_from_auth_store(entry)
if synced is not entry:
@@ -1632,32 +1437,13 @@ class CredentialPool:
self._persist(removed_ids=entries_to_prune)
return available
def _log_no_available_entries(self) -> None:
"""Emit the empty-pool INFO line at most once per throttle window.
Called on every selection while the pool is empty/exhausted. Without
throttling this storms the Windows cross-process log lock and stalls the
event loop (see NO_AVAILABLE_ENTRIES_LOG_THROTTLE_SECONDS).
"""
now = time.monotonic()
last = self._last_no_entries_log_at
if last is not None and (now - last) < NO_AVAILABLE_ENTRIES_LOG_THROTTLE_SECONDS:
return
self._last_no_entries_log_at = now
logger.info("credential pool: no available entries (all exhausted or empty)")
def _select_unlocked(self, *, refresh: bool = True) -> Optional[PooledCredential]:
available = self._available_entries(clear_expired=True, refresh=refresh)
def _select_unlocked(self) -> Optional[PooledCredential]:
available = self._available_entries(clear_expired=True, refresh=True)
if not available:
self._current_id = None
self._log_no_available_entries()
logger.info("credential pool: no available entries (all exhausted or empty)")
return None
# A successful selection means the pool recovered; re-arm the throttle
# so a later re-exhaustion logs immediately rather than being silenced
# by a window opened during the previous empty stretch.
self._last_no_entries_log_at = None
if self._strategy == STRATEGY_RANDOM:
entry = random.choice(available)
self._current_id = entry.id
@@ -1781,35 +1567,6 @@ class CredentialPool:
with self._lock:
return self._try_refresh_current_unlocked()
def try_refresh_matching(
self, api_key_hint: Optional[str] = None
) -> Optional[PooledCredential]:
"""Force-refresh the entry that supplied ``api_key_hint``.
Direct provider integrations may reload the pool after a request has
already failed, so they cannot rely on ``current_id`` identifying the
issuing credential. With no hint, select an entry without first doing
the normal proactive refresh; the forced refresh below must consume a
rotating refresh token exactly once.
"""
with self._lock:
entry = None
if api_key_hint:
entry = next(
(
candidate
for candidate in self._entries
if candidate.runtime_api_key == api_key_hint
),
None,
)
else:
entry = self.current() or self._select_unlocked(refresh=False)
if entry is None:
return None
self._current_id = entry.id
return self._try_refresh_current_unlocked()
def _try_refresh_current_unlocked(self) -> Optional[PooledCredential]:
entry = self.current()
if entry is None:
@@ -1893,15 +1650,11 @@ class CredentialPool:
def _upsert_entry(entries: List[PooledCredential], provider: str, source: str, payload: Dict[str, Any]) -> bool:
matching_indices = []
existing_idx = None
for idx, entry in enumerate(entries):
if entry.source == source:
matching_indices.append(idx)
existing_idx = matching_indices[0] if matching_indices else None
duplicate_indices = set(matching_indices[1:])
if duplicate_indices:
entries[:] = [entry for idx, entry in enumerate(entries) if idx not in duplicate_indices]
existing_idx = idx
break
if existing_idx is None:
payload.setdefault("id", uuid.uuid4().hex[:6])
@@ -1933,8 +1686,8 @@ def _upsert_entry(entries: List[PooledCredential], provider: str, source: str, p
# Runtime-only borrowed secret updates should refresh the in-memory
# entry without forcing auth.json churn when the disk-safe payload is
# unchanged (for example env keys with the same fingerprint).
return bool(duplicate_indices) or existing.to_dict() != updated.to_dict()
return bool(duplicate_indices)
return existing.to_dict() != updated.to_dict()
return False
def _normalize_pool_priorities(provider: str, entries: List[PooledCredential]) -> bool:
@@ -2131,16 +1884,11 @@ def _seed_from_singletons(provider: str, entries: List[PooledCredential]) -> Tup
from hermes_cli.copilot_auth import resolve_copilot_token, get_copilot_api_token
token, source = resolve_copilot_token()
if token:
api_token, enterprise_base_url = get_copilot_api_token(token)
api_token = get_copilot_api_token(token)
source_name = "gh_cli" if "gh" in source.lower() else f"env:{source}"
if not _is_suppressed(provider, source_name):
active_sources.add(source_name)
pconfig = PROVIDER_REGISTRY.get(provider)
# Use enterprise base URL from token exchange if available,
# otherwise fall back to the provider's default.
effective_base_url = enterprise_base_url or (
pconfig.inference_base_url if pconfig else ""
)
changed |= _upsert_entry(
entries,
provider,
@@ -2149,7 +1897,7 @@ def _seed_from_singletons(provider: str, entries: List[PooledCredential]) -> Tup
"source": source_name,
"auth_type": AUTH_TYPE_API_KEY,
"access_token": api_token,
"base_url": effective_base_url,
"base_url": pconfig.inference_base_url if pconfig else "",
"label": source,
},
)
@@ -2268,30 +2016,28 @@ def _seed_from_singletons(provider: str, entries: List[PooledCredential]) -> Tup
# (``providers["xai-oauth"]``). Surface them in the pool too so
# ``hermes auth list`` reflects the logged-in state and so the pool
# is the single source of truth for refresh during runtime resolution.
if _is_suppressed(provider, "loopback_pkce"):
return changed, active_sources
state = _load_provider_state(auth_store, "xai-oauth")
tokens = state.get("tokens") if isinstance(state, dict) else None
if isinstance(tokens, dict) and tokens.get("access_token"):
# Device code is the only supported xAI OAuth flow; the singleton is
# always surfaced as ``device_code`` (consistent with nous/codex).
source = "device_code"
if _is_suppressed(provider, source):
return changed, active_sources
active_sources.add(source)
active_sources.add("loopback_pkce")
from hermes_cli.auth import DEFAULT_XAI_OAUTH_BASE_URL
base_url = DEFAULT_XAI_OAUTH_BASE_URL
changed |= _upsert_entry(
entries,
provider,
source,
"loopback_pkce",
{
"source": source,
"source": "loopback_pkce",
"auth_type": AUTH_TYPE_OAUTH,
"access_token": tokens.get("access_token", ""),
"refresh_token": tokens.get("refresh_token"),
"base_url": base_url,
"last_refresh": state.get("last_refresh"),
"label": label_from_token(tokens.get("access_token", ""), source),
"label": label_from_token(tokens.get("access_token", ""), "loopback_pkce"),
},
)
@@ -2308,20 +2054,8 @@ 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()
raw = env_file.get(key, "").strip()
env_val = os.environ.get(key, "").strip()
# If .env contains an unresolved op:// reference, prefer the
# already-resolved value from os.environ (set by
# load_hermes_dotenv() -> apply_onepassword_secrets()). The raw
# "op://Vault/Item/field" string would otherwise win and every
# provider auth attempt would receive a URL instead of a key. This
# happens during a partial migration, or when the user wrote op://
# references straight into .env rather than the secrets.onepassword
# config block. For every non-op:// value the original
# .env-takes-precedence behaviour is preserved unchanged.
if raw.startswith("op://") and env_val:
return env_val
return raw or _get_secret(key, "") or env_val
val = env_file.get(key) or _get_secret(key, "") or ""
return val.strip()
# Honour user suppression — `hermes auth remove <provider> <N>` for an
# env-seeded credential marks the env:<VAR> source as suppressed so it
@@ -2408,6 +2142,7 @@ def _seed_from_env(provider: str, entries: List[PooledCredential]) -> Tuple[bool
if _is_source_suppressed(provider, source):
continue
active_sources.add(source)
auth_type = AUTH_TYPE_OAUTH if provider == "anthropic" and not token.startswith("sk-ant-api") else AUTH_TYPE_API_KEY
base_url = env_url or pconfig.inference_base_url
if provider == "kimi-coding":
base_url = _resolve_kimi_base_url(token, pconfig.inference_base_url, env_url)
@@ -2422,6 +2157,7 @@ def _seed_from_env(provider: str, entries: List[PooledCredential]) -> Tuple[bool
env_var=env_var,
token=token,
base_url=base_url,
auth_type=auth_type,
),
)
return changed, active_sources
@@ -2549,37 +2285,16 @@ def load_pool(provider: str) -> CredentialPool:
for payload in raw_entries
)
entries = [PooledCredential.from_dict(provider, payload) for payload in raw_entries]
raw_needs_auth_normalization = any(
isinstance(payload, dict)
and _normalize_pool_auth_type(
provider,
payload.get("access_token"),
payload.get("auth_type", AUTH_TYPE_API_KEY),
) != payload.get("auth_type", AUTH_TYPE_API_KEY)
for payload in raw_entries
)
if raw_needs_auth_normalization:
# A profile may be reading this provider from the global-root fallback.
# Keep that fallback read-only: only the store that owns these rows may
# rewrite them. Loading the default/root profile will heal global rows.
active_pool = _load_auth_store().get("credential_pool")
active_entries = active_pool.get(provider) if isinstance(active_pool, dict) else None
raw_needs_auth_normalization = bool(active_entries)
if provider.startswith(CUSTOM_POOL_PREFIX):
# Custom endpoint pool — seed from custom_providers config and model config
custom_changed, custom_sources = _seed_custom_pool(provider, entries)
changed = raw_needs_sanitization or raw_needs_auth_normalization or custom_changed
changed = raw_needs_sanitization or custom_changed
changed |= _prune_stale_seeded_entries(entries, custom_sources)
else:
singleton_changed, singleton_sources = _seed_from_singletons(provider, entries)
env_changed, env_sources = _seed_from_env(provider, entries)
changed = (
raw_needs_sanitization
or raw_needs_auth_normalization
or singleton_changed
or env_changed
)
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
+8 -3
View File
@@ -265,7 +265,7 @@ def _remove_minimax_oauth(provider: str, removed) -> RemovalResult:
return result
def _remove_xai_oauth_device_code(provider: str, removed) -> RemovalResult:
def _remove_xai_oauth_loopback_pkce(provider: str, removed) -> RemovalResult:
"""xAI OAuth tokens live in auth.json providers.xai-oauth — clear them.
Without this step, ``hermes auth remove xai-oauth <N>`` silently undoes
@@ -275,6 +275,11 @@ def _remove_xai_oauth_device_code(provider: str, removed) -> RemovalResult:
entry from the still-present singleton — credentials reappear with no
user feedback. Clearing the singleton in step with the suppression set
by the central dispatcher makes the removal stick.
Belt-and-braces against the manual entry path: ``hermes auth add
xai-oauth`` produces a ``manual:xai_pkce`` entry whose removal step
falls through to "unregistered → nothing to clean up" (correct —
manual entries are pool-only).
"""
result = RemovalResult()
if _clear_auth_store_provider(provider):
@@ -418,8 +423,8 @@ def _register_all_sources() -> None:
description="auth.json providers.openai-codex + ~/.codex/auth.json",
))
register(RemovalStep(
provider="xai-oauth", source_id="device_code",
remove_fn=_remove_xai_oauth_device_code,
provider="xai-oauth", source_id="loopback_pkce",
remove_fn=_remove_xai_oauth_loopback_pkce,
description="auth.json providers.xai-oauth",
))
register(RemovalStep(
+1 -1
View File
@@ -355,7 +355,7 @@ def evaluate_credits_notices(
if show_depleted and "credits.depleted" not in active:
to_show.append(
AgentNotice(
text="✕ Credit access paused · run /topup to top up",
text="✕ Credit access paused · run /credits to top up",
level="error",
kind=CREDITS_NOTICE_KIND,
key="credits.depleted",
+2 -42
View File
@@ -45,26 +45,12 @@ def _strip_aux_credential(value: Any) -> Optional[str]:
class _ReviewRuntimeBinding(NamedTuple):
"""Provider/model for the curator review fork plus per-slot overrides."""
"""Provider/model for the curator review fork plus optional per-slot overrides."""
provider: str
model: str
explicit_api_key: Optional[str]
explicit_base_url: Optional[str]
request_overrides: Dict[str, Any]
def _merge_request_overrides(
runtime_overrides: Any,
slot_extra_body: Any,
) -> Dict[str, Any]:
"""Merge resolver metadata with task-local request body fields."""
merged = dict(runtime_overrides or {})
if isinstance(slot_extra_body, dict) and slot_extra_body:
extra_body = dict(merged.get("extra_body") or {})
extra_body.update(slot_extra_body)
merged["extra_body"] = extra_body
return merged
DEFAULT_INTERVAL_HOURS = 24 * 7 # 7 days
@@ -1778,7 +1764,6 @@ def _resolve_review_runtime(cfg: Dict[str, Any]) -> _ReviewRuntimeBinding:
_task_model,
_strip_aux_credential(_cur_task.get("api_key")),
_strip_aux_credential(_cur_task.get("base_url")),
_merge_request_overrides({}, _cur_task.get("extra_body")),
)
# 2. Legacy curator.auxiliary.{provider,model} (deprecated, pre-unification)
@@ -1796,11 +1781,10 @@ def _resolve_review_runtime(cfg: Dict[str, Any]) -> _ReviewRuntimeBinding:
str(_legacy_model),
_strip_aux_credential(_legacy.get("api_key")),
_strip_aux_credential(_legacy.get("base_url")),
_merge_request_overrides({}, _legacy.get("extra_body")),
)
# 3. Fall through to the main chat model
return _ReviewRuntimeBinding(_main_provider, _main_model, None, None, {})
return _ReviewRuntimeBinding(_main_provider, _main_model, None, None)
def _resolve_review_model(cfg: Dict[str, Any]) -> tuple[str, str]:
@@ -1866,11 +1850,6 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
_base_url = None
_api_mode = None
_resolved_provider = None
_credential_pool = None
_request_overrides: Dict[str, Any] = {}
_max_tokens = None
_acp_command = None
_acp_args = None
_model_name = ""
try:
from hermes_cli.config import load_config
@@ -1888,16 +1867,6 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
_base_url = _rp.get("base_url")
_api_mode = _rp.get("api_mode")
_resolved_provider = _rp.get("provider") or _provider
_credential_pool = _rp.get("credential_pool")
_request_overrides = _merge_request_overrides(
_rp.get("request_overrides"),
_binding.request_overrides.get("extra_body"),
)
_max_tokens = _rp.get("max_output_tokens")
_acp_command = _rp.get("command")
_acp_args = list(_rp.get("args") or [])
if isinstance(_rp.get("model"), str) and _rp["model"].strip():
_model_name = _rp["model"].strip()
except Exception as e:
logger.debug("Curator provider resolution failed: %s", e, exc_info=True)
@@ -1906,21 +1875,12 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
review_agent = None
try:
_agent_kwargs: Dict[str, Any] = {}
if isinstance(_max_tokens, int):
_agent_kwargs["max_tokens"] = _max_tokens
if isinstance(_acp_command, str) and _acp_command:
_agent_kwargs["acp_command"] = _acp_command
_agent_kwargs["acp_args"] = _acp_args or []
review_agent = AIAgent(
model=_model_name,
provider=_resolved_provider,
api_key=_api_key,
base_url=_base_url,
api_mode=_api_mode,
credential_pool=_credential_pool,
request_overrides=_request_overrides,
**_agent_kwargs,
# Umbrella-building over a large skill collection is worth a
# high iteration ceiling — the pass typically takes 50-100
# API calls against hundreds of candidate skills. The
+2 -7
View File
@@ -98,12 +98,7 @@ def _backup_cron_jobs_into(dest: Path) -> Dict[str, Any]:
info["reason"] = "no cron/jobs.json present"
return info
try:
# utf-8-sig: same dialect as cron/jobs.load_jobs — a UTF-8 BOM left
# by Windows editors otherwise survives decoding as U+FEFF, breaks
# json.loads below, and misreports jobs_count as 0 with a spurious
# parse warning. The BOM-less text is also what gets written to the
# backup, so a later rollback restores a loadable file.
raw = src.read_text(encoding="utf-8-sig")
raw = src.read_text(encoding="utf-8")
except OSError as e:
logger.debug("Failed to read cron/jobs.json for backup: %s", e)
info["reason"] = f"read error: {e}"
@@ -561,7 +556,7 @@ def rollback(backup_id: Optional[str] = None) -> Tuple[bool, str, Optional[Path]
if target is None:
return (
False,
"no matching backup found"
f"no matching backup found"
+ (f" for id '{backup_id}'" if backup_id else "")
+ " (use `hermes curator rollback --list` to see available snapshots)",
None,
+7 -91
View File
@@ -27,14 +27,6 @@ logger = logging.getLogger(__name__)
_ANSI_RESET = "\033[0m"
def _display_url(value: Any) -> str:
"""Extract a display-only URL without assuming model argument types."""
if isinstance(value, dict):
value = value.get("url") or value.get("href")
return value.strip() if isinstance(value, str) else ""
# Diff colors — resolved lazily from the skin engine so they adapt
# to light/dark themes. Falls back to sensible defaults on import
# failure. We cache after first resolution for performance.
@@ -462,14 +454,13 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
sid = args.get("session_id", "")
data = args.get("data", "")
timeout_val = args.get("timeout")
parts = [str(action) if action else ""]
parts = [action]
if sid:
parts.append(str(sid)[:16])
parts.append(sid[:16])
if data:
parts.append(f'"{_oneline(str(data)[:20])}"')
parts.append(f'"{_oneline(data[:20])}"')
if timeout_val and action == "wait":
parts.append(f"{timeout_val}s")
parts = [p for p in parts if p]
return " ".join(parts) if parts else None
if tool_name == "todo":
@@ -524,16 +515,6 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
msg = msg[:17] + "..."
return f"to {target}: \"{msg}\""
if tool_name == "skill_view":
name = _oneline(str(args.get("name") or ""))
file_path = args.get("file_path")
if file_path:
file_path = _oneline(str(file_path))
preview = f"{name} → {file_path}" if name else file_path
else:
preview = name
return _truncate_preview(preview, max_len) if preview else None
key = primary_args.get(tool_name)
if not key:
for fallback_key in ("query", "text", "command", "path", "name", "prompt", "code", "goal"):
@@ -645,52 +626,6 @@ def verb_drops_preview(tool_name: str) -> bool:
return tool_name in _TOOL_VERBS_NO_PREVIEW
def build_status_phrase(tool_name: str, args: dict | None, max_len: int = 49) -> str | None:
"""Build a short present-tense status phrase for platform status surfaces.
Used by text-rendering "typing" indicators (Slack's
``assistant.threads.setStatus`` line) to show what the agent is doing
right now: ``is running scripts/run_tests.sh…`` instead of a static
``is thinking...``. The phrase is phrased to follow the bot's display
name ("Hermes is running …"), so it starts lowercase with "is".
Pass ``args=None`` for a verb-only phrase (``is running…``) — used when
``display.live_status`` is ``verb`` to keep argument previews out of
shared channels.
Returns None for the ``_thinking`` pseudo-tool and when friendly labels
are disabled (callers fall back to their static default). ``max_len``
caps the total phrase length; Slack truncates its status line around 50
characters, so the default stays just under that.
"""
if not tool_name or tool_name == "_thinking":
return None
if not _friendly_tool_labels:
return None
verb = _TOOL_VERBS.get(tool_name)
if verb:
head = f"is {verb[0].lower()}{verb[1:]}"
else:
# Custom / plugin / MCP tools: generic but still informative.
head = f"is using {tool_name}"
phrase = head
if args and verb and tool_name not in _TOOL_VERBS_NO_PREVIEW:
preview = build_tool_preview(tool_name, args, max_len=None)
if preview:
# Previews can contain newlines (terminal commands); keep the
# status to the first line.
preview = preview.splitlines()[0].strip()
phrase = f"{head}{tool_verb_connector(tool_name)}{preview}"
if len(phrase) > max_len - 1:
phrase = phrase[: max_len - 2].rstrip() + "…"
else:
phrase = phrase + "…"
return phrase
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.
@@ -1314,7 +1249,7 @@ def _detect_tool_failure(tool_name: str, result: str | None) -> tuple[bool, str]
return False, ""
def _get_cute_tool_message(
def get_cute_tool_message(
tool_name: str, args: dict, duration: float, result: str | None = None,
) -> str:
"""Generate a formatted tool completion line for CLI quiet mode.
@@ -1356,11 +1291,9 @@ def _get_cute_tool_message(
if tool_name == "web_extract":
urls = args.get("urls", [])
if urls:
url = _display_url(urls[0] if isinstance(urls, list) else urls)
if not url:
return _wrap(f"┊ 📄 fetch pages {dur}")
url = urls[0] if isinstance(urls, list) else str(urls)
domain = url.replace("https://", "").replace("http://", "").split("/")[0]
extra = f" +{len(urls)-1}" if isinstance(urls, list) and len(urls) > 1 else ""
extra = f" +{len(urls)-1}" if len(urls) > 1 else ""
return _wrap(f"┊ 📄 fetch {_trunc(domain, 35)}{extra} {dur}")
return _wrap(f"┊ 📄 fetch pages {dur}")
if tool_name == "terminal":
@@ -1451,11 +1384,7 @@ def _get_cute_tool_message(
if tool_name == "skills_list":
return _wrap(f"┊ 📚 skills list {args.get('category', 'all')} {dur}")
if tool_name == "skill_view":
label = args.get("name", "")
file_path = args.get("file_path")
if file_path:
label = f"{label} → {file_path}" if label else str(file_path)
return _wrap(f"┊ 📚 skill {_trunc(label, 44)} {dur}")
return _wrap(f"┊ 📚 skill {_trunc(args.get('name', ''), 30)} {dur}")
if tool_name == "image_generate":
return _wrap(f"┊ 🎨 create {_trunc(args.get('prompt', ''), 35)} {dur}")
if tool_name == "text_to_speech":
@@ -1490,19 +1419,6 @@ def _get_cute_tool_message(
return _wrap(f"┊ ⚡ {tool_name[:9]:9} {_trunc(preview, 35)} {dur}")
def get_cute_tool_message(
tool_name: str, args: dict, duration: float, result: str | None = None,
) -> str:
"""Render a completion label without letting cosmetic failures escape."""
try:
return _get_cute_tool_message(tool_name, args, duration, result=result)
except Exception as exc: # noqa: BLE001 — display must never abort a turn
logger.debug("Tool completion label failed for %s: %s", tool_name, exc)
safe_name = tool_name[:9] if isinstance(tool_name, str) and tool_name else "tool"
safe_duration = f"{duration:.1f}s" if isinstance(duration, (int, float)) else "done"
return f"┊ ⚡ {safe_name:9} completed {safe_duration}"
# =========================================================================
# Honcho session line (one-liner with clickable OSC 8 hyperlink)
# =========================================================================
+15 -266
View File
@@ -31,9 +31,6 @@ class FailoverReason(enum.Enum):
# Billing / quota
billing = "billing" # 402 or confirmed credit exhaustion — rotate immediately
rate_limit = "rate_limit" # 429 or quota-based throttling — backoff then rotate
# Upstream model rate-limited (aggregator 429) — fallback to a different
# model, NOT credential rotation. The user's key is healthy.
upstream_rate_limit = "upstream_rate_limit"
# Server-side
overloaded = "overloaded" # 503/529 — provider overloaded, backoff
@@ -41,11 +38,6 @@ class FailoverReason(enum.Enum):
# Transport
timeout = "timeout" # Connection/read timeout — rebuild client + retry
# TLS certificate verification failure — deterministic for the host
# (TLS-inspecting proxy, missing/expired CA bundle, self-signed cert).
# Retrying reproduces the identical handshake failure, so fail fast
# with actionable guidance instead of burning retries.
ssl_cert_verification = "ssl_cert_verification"
# Context / payload
context_overflow = "context_overflow" # Context too large — compress, not failover
@@ -115,7 +107,6 @@ _BILLING_PATTERNS = [
"exceeded your current quota",
"account is deactivated",
"plan does not include",
"out of extra usage", # Anthropic OAuth Pro/Max overage bucket depleted (HTTP 400)
"out of funds",
"run out of funds",
"balance_depleted",
@@ -123,25 +114,6 @@ _BILLING_PATTERNS = [
"not available on the free tier",
]
# xAI's explicit Grok credit-exhaustion code. Keep the HTTP 403 special case
# provider-scoped: other providers' generic billing codes historically remain
# auth failures when they arrive as 403.
_XAI_SPENDING_LIMIT_ERROR_CODE = "personal-team-blocked:spending-limit"
# Structured provider codes that mean the account cannot serve paid traffic
# until credits/subscription capacity is restored. xAI returns its explicit
# Grok spending-limit signal as HTTP 403 rather than 402.
_BILLING_ERROR_CODES = frozenset({
"insufficient_quota",
"billing_not_active",
"payment_required",
"insufficient_credits",
"no_usable_credits",
"balance_depleted",
"model_not_supported_on_free_tier",
_XAI_SPENDING_LIMIT_ERROR_CODE,
})
# Patterns that indicate rate limiting (transient, will resolve)
_RATE_LIMIT_PATTERNS = [
"rate limit",
@@ -269,11 +241,6 @@ _CONTEXT_OVERFLOW_PATTERNS = [
"context window",
"prompt is too long",
"prompt exceeds max length",
# NOTE: bare "max_tokens" is load-bearing — the output-cap-retry path keys
# off it (e.g. "max_tokens: 65536 > context_window: 200000 ..."). Do NOT
# remove it. Provider empty-response advisories also contain "very low
# max_tokens", but those are intercepted by _EMPTY_PROVIDER_RESPONSE_PATTERNS
# BEFORE this list is consulted, so they never mis-route into compression.
"max_tokens",
"maximum number of tokens",
# vLLM / local inference server patterns
@@ -291,8 +258,6 @@ _CONTEXT_OVERFLOW_PATTERNS = [
# Chinese error messages (some providers return these)
"超过最大长度",
"上下文长度",
# Z.AI / Zhipu GLM pattern (English form; error code 1210)
"tokens in request more than max tokens allowed",
# AWS Bedrock Converse API error patterns
"input is too long",
"max input token",
@@ -310,15 +275,6 @@ _MODEL_NOT_FOUND_PATTERNS = [
"no such model",
"unknown model",
"unsupported model",
# OpenRouter returns 404 with this message when none of the candidate
# endpoints for the selected model support tool/function calling.
# Classifying this as model_not_found triggers fallback to a different
# model or provider that does support tools. Without this entry the
# pattern falls through to ``unknown`` with ``retryable=True``, the
# retry loop burns all attempts on the same deterministic rejection,
# and the error surfaces as a confusing "model not found" message
# instead of automatically failing over. See PR #58446.
"no endpoints found that support tool use",
]
# Request-validation patterns — the request is malformed and will fail
@@ -431,19 +387,6 @@ _THINKING_SIG_PATTERNS = [
# the exception type is generic (e.g. RuntimeError from a local shim that
# wraps a subprocess timeout). Checked before the type-based transport
# heuristics so custom-provider "timed out" errors don't fall through to
# Provider empty-response advisories (OpenRouter / nano-gpt / similar).
# Checked before context-overflow matching because the advisory text often
# mentions "max_tokens" as a possible cause, which historically sat in
# _CONTEXT_OVERFLOW_PATTERNS and sent healthy sessions into a compression
# death spiral ending in "Cannot compress further".
_EMPTY_PROVIDER_RESPONSE_PATTERNS = [
"returned an empty response",
"empty response despite retries",
"provider returned an empty response",
"model returning empty responses",
"empty response stream",
]
# the unknown bucket and get misreported as empty responses.
_TIMEOUT_MESSAGE_PATTERNS = [
"timed out",
@@ -491,29 +434,6 @@ _SERVER_DISCONNECT_PATTERNS = [
"incomplete chunked read",
]
# SSL certificate verification failures — deterministic, NOT transient.
#
# A failed certificate chain (TLS-inspecting corporate proxy, missing
# custom CA in the trust store, expired certificate, self-signed cert)
# fails identically on every retry. Burning the retry budget before
# surfacing the error hides the actionable fix from the user for minutes.
# Inspired by Claude Code v2.1.199 (July 2026), which made SSL certificate
# errors fail immediately with a fix hint instead of retrying.
#
# Must be checked BEFORE _SSL_TRANSIENT_PATTERNS — "certificate verify
# failed" messages usually also contain "[SSL:" which would otherwise
# match the transient list and retry forever.
_SSL_CERT_VERIFY_PATTERNS = [
"certificate verify failed", # Python ssl module canonical text
"certificate_verify_failed", # OpenSSL error token
"unable to get local issuer certificate",
"self-signed certificate",
"self signed certificate",
"certificate has expired",
"hostname mismatch, certificate is not valid",
"unable to verify the first certificate", # Node/undici phrasing (MCP bridges)
]
# SSL/TLS transient failure patterns — intentionally distinct from
# _SERVER_DISCONNECT_PATTERNS above.
#
@@ -793,14 +713,6 @@ def classify_api_error(
if classified is not None:
return classified
# Local MoA config drift is deterministic: a persisted session can retain
# a preset name that was later renamed/deleted. Retrying the same lookup
# cannot recover and makes a clear config error look like an API outage.
from agent.errors import MoAPresetNotFoundError
if isinstance(error, MoAPresetNotFoundError):
return _result(FailoverReason.model_not_found, retryable=False)
# ── 3. Error code classification ────────────────────────────────
if error_code:
@@ -819,22 +731,7 @@ def classify_api_error(
if classified is not None:
return classified
# ── 5. SSL certificate verification failures → fail fast ────────
# A broken certificate chain (TLS-inspecting proxy, missing custom CA,
# expired/self-signed cert) is deterministic for the host — every retry
# reproduces the identical handshake failure. Fail immediately with
# actionable guidance instead of burning the retry budget first.
# Checked BEFORE the transient-SSL patterns: cert-verify messages also
# contain "[ssl:" which would otherwise match the transient list.
# Inspired by Claude Code v2.1.199 (July 2026).
if any(p in error_msg for p in _SSL_CERT_VERIFY_PATTERNS):
return _result(
FailoverReason.ssl_cert_verification,
retryable=False,
should_fallback=False,
)
# ── 5b. SSL/TLS transient errors → retry as timeout (not compression) ──
# ── 5. SSL/TLS transient errors → retry as timeout (not compression) ──
# SSL alerts mid-stream are transport hiccups, not server-side context
# overflow signals. Classify before the disconnect check so a large
# session doesn't incorrectly trigger context compression when the real
@@ -887,34 +784,12 @@ def classify_api_error(
)
return _result(FailoverReason.timeout, retryable=True)
# ── 7b. Stale-call circuit breaker → failover immediately ──────
# _check_stale_giveup() in agent/chat_completion_helpers.py raises a
# RuntimeError when the provider has been unresponsive for N
# consecutive stale attempts (default 5). The error is NOT a transport
# timeout — the circuit breaker fires *before* any network call to avoid
# an indefinite stall. Without this classification the RuntimeError
# falls through to FailoverReason.unknown (retryable=True), which burns
# all max_retries against the same dead provider (each retry hitting the
# circuit breaker instantly with zero network overhead) before fallback
# is attempted. Classify as non-retryable + should_fallback so the
# retry loop activates the next fallback provider on the first hit.
if (
error_type == "RuntimeError"
and "consecutive stale attempts" in error_msg
and "aborting this call" in error_msg
):
return _result(
FailoverReason.timeout,
retryable=False,
should_fallback=True,
)
# ── 8. Transport / timeout heuristics ───────────────────────────
# ── 7. Transport / timeout heuristics ───────────────────────────
if error_type in _TRANSPORT_ERROR_TYPES or isinstance(error, (TimeoutError, ConnectionError, OSError)):
return _result(FailoverReason.timeout, retryable=True)
# ── 9. Fallback: unknown ────────────────────────────────────────
# ── 8. Fallback: unknown ────────────────────────────────────────
return _result(FailoverReason.unknown, retryable=True)
@@ -953,11 +828,7 @@ def _classify_by_status(
# OpenRouter 403 "key limit exceeded" is actually billing. Other
# providers also use 403 for account-plan or credit exhaustion.
if (
(
provider == "xai-oauth"
and error_code.lower() == _XAI_SPENDING_LIMIT_ERROR_CODE
)
or "key limit exceeded" in error_msg
"key limit exceeded" in error_msg
or "spending limit" in error_msg
or any(p in error_msg for p in _BILLING_PATTERNS)
):
@@ -1038,22 +909,6 @@ def _classify_by_status(
FailoverReason.overloaded,
retryable=True,
)
# Distinguish an OpenRouter-aggregator upstream 429 (an upstream model
# like DeepSeek rate-limited OpenRouter's aggregate traffic) from an
# account-level 429 (the user's key is actually throttled). OpenRouter
# wraps upstream errors with the outer message "Provider returned
# error" — the user's key is healthy, so marking it exhausted / rotating
# is wrong and burns the key for ~24min. Fall back to a different model.
if _is_openrouter_upstream_error(body, provider):
upstream_provider = _extract_upstream_provider_name(body)
ctx = {"upstream_provider": upstream_provider} if upstream_provider else {}
return result_fn(
FailoverReason.upstream_rate_limit,
retryable=True,
should_rotate_credential=False,
should_fallback=True,
error_context=ctx,
)
return result_fn(
FailoverReason.rate_limit,
retryable=True,
@@ -1089,58 +944,11 @@ def _classify_by_status(
retryable=False,
should_fallback=True,
)
# Some local inference servers (notably llama.cpp / llama-server)
# report context overflow with an HTTP 500 instead of the standard
# 400/413. The request-validation guard above already ran, so any
# remaining explicit context-overflow signal routes into the
# compression-and-retry path (mirroring _classify_400) instead of
# blind server_error retries that exhaust and drop the turn.
# Empty-response advisories that mention "max_tokens" must not enter
# that compression path.
if any(p in error_msg for p in _EMPTY_PROVIDER_RESPONSE_PATTERNS):
return result_fn(
FailoverReason.server_error,
retryable=True,
should_compress=False,
)
if any(p in error_msg for p in _CONTEXT_OVERFLOW_PATTERNS):
return result_fn(
FailoverReason.context_overflow,
retryable=True,
should_compress=True,
)
return result_fn(FailoverReason.server_error, retryable=True)
if status_code in {503, 529}:
# Same overflow-as-5xx variant (server busy / model-load OOM, or a
# Cloudflare/Tailscale hop relabeling the status). Route explicit
# overflow bodies into compression; otherwise treat as transient
# overload and retry.
if any(p in error_msg for p in _EMPTY_PROVIDER_RESPONSE_PATTERNS):
return result_fn(
FailoverReason.server_error,
retryable=True,
should_compress=False,
)
if any(p in error_msg for p in _CONTEXT_OVERFLOW_PATTERNS):
return result_fn(
FailoverReason.context_overflow,
retryable=True,
should_compress=True,
)
return result_fn(FailoverReason.overloaded, retryable=True)
# 408 Request Timeout — a transient timing failure the server itself flags
# as safe to retry (RFC 9110 §15.5.9), not a malformed request. Commonly
# emitted by reverse proxies sitting in front of self-hosted backends
# (llama.cpp / Ollama / vLLM) when a long generation outruns the proxy's
# request-read window. Route to the dedicated ``timeout`` reason (rebuild
# client + retry) instead of falling through to the generic 4xx bucket
# below, which would abort the turn on a retry-safe error the same way it
# aborts a 400 Bad Request.
if status_code == 408:
return result_fn(FailoverReason.timeout, retryable=True)
# Other 4xx — non-retryable
if 400 <= status_code < 500:
return result_fn(
@@ -1234,7 +1042,6 @@ def _classify_400(
"encrypted content for item" in error_msg
and "could not be verified" in error_msg
)
or "could not decrypt the provided encrypted_content" in error_msg
):
return result_fn(
FailoverReason.invalid_encrypted_content,
@@ -1247,8 +1054,8 @@ def _classify_400(
# 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 historically
# sat in _CONTEXT_OVERFLOW_PATTERNS — so without this guard the 400 is
# 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
@@ -1270,17 +1077,6 @@ def _classify_400(
should_fallback=True,
)
# Empty-provider-response advisories must not enter compression. They
# often mention "max_tokens" as a possible cause and used to match the
# bare overflow pattern, then thrash compress until "Cannot compress
# further" on an otherwise healthy session (custom endpoints / nano-gpt).
if any(p in error_msg for p in _EMPTY_PROVIDER_RESPONSE_PATTERNS):
return result_fn(
FailoverReason.server_error,
retryable=True,
should_compress=False,
)
# Context overflow from 400
if any(p in error_msg for p in _CONTEXT_OVERFLOW_PATTERNS):
return result_fn(
@@ -1368,7 +1164,15 @@ def _classify_by_error_code(
should_rotate_credential=True,
)
if code_lower in _BILLING_ERROR_CODES:
if code_lower in {
"insufficient_quota",
"billing_not_active",
"payment_required",
"insufficient_credits",
"no_usable_credits",
"balance_depleted",
"model_not_supported_on_free_tier",
}:
return result_fn(
FailoverReason.billing,
retryable=False,
@@ -1484,15 +1288,6 @@ def _classify_by_message(
should_fallback=True,
)
# Empty-provider-response advisories (often mention "max_tokens") must
# retry without compression — see the matching 400-path guard above.
if any(p in error_msg for p in _EMPTY_PROVIDER_RESPONSE_PATTERNS):
return result_fn(
FailoverReason.server_error,
retryable=True,
should_compress=False,
)
# Context overflow patterns
if any(p in error_msg for p in _CONTEXT_OVERFLOW_PATTERNS):
return result_fn(
@@ -1650,49 +1445,3 @@ def _extract_message(error: Exception, body: dict) -> str:
return msg.strip()[:500]
# Fallback to str(error)
return str(error)[:500]
def _is_openrouter_upstream_error(body: Any, provider: str) -> bool:
"""Detect OpenRouter's aggregator-wrapped upstream provider errors.
OpenRouter returns errors from upstream model providers (DeepSeek,
Anthropic, etc.) wrapped with the outer message "Provider returned error"
and the real error nested in ``metadata.raw``. This signal means the
user's OpenRouter key is healthy — the upstream provider is the one that
failed — so credential rotation is the wrong recovery.
"""
if not isinstance(body, dict):
return False
provider_lower = (provider or "").strip().lower()
err = body.get("error")
if not isinstance(err, dict):
return False
outer_msg = str(err.get("message") or "").strip().lower()
if outer_msg != "provider returned error":
return False
# Require either the explicit OpenRouter provider OR the metadata shape
# that only OpenRouter produces (metadata.raw / metadata.provider_name).
if provider_lower == "openrouter":
return True
metadata = err.get("metadata")
if isinstance(metadata, dict) and (
"raw" in metadata or "provider_name" in metadata
):
return True
return False
def _extract_upstream_provider_name(body: Any) -> Optional[str]:
"""Pull the upstream provider name out of OpenRouter's error metadata."""
if not isinstance(body, dict):
return None
err = body.get("error")
if not isinstance(err, dict):
return None
metadata = err.get("metadata")
if not isinstance(metadata, dict):
return None
name = metadata.get("provider_name")
if isinstance(name, str) and name.strip():
return name.strip()
return None
-10
View File
@@ -1,13 +1,3 @@
class SSLConfigurationError(Exception):
"""Raised when SSL/TLS certificate bundle configuration fails."""
pass
class EmptyStreamError(RuntimeError):
"""Raised when a provider closes a stream without yielding a response."""
pass
class MoAPresetNotFoundError(ValueError):
"""Raised when a persisted MoA preset no longer exists in config."""
+9 -63
View File
@@ -95,16 +95,16 @@ def get_safe_write_roots() -> set[str]:
return roots
def _classify_write_denial(path: str) -> Optional[str]:
"""Return ``'credential'``, ``'safe_root'``, or ``None`` if writes are allowed."""
def is_write_denied(path: str) -> bool:
"""Return True if path is blocked by the write denylist or safe root."""
home = os.path.realpath(os.path.expanduser("~"))
resolved = os.path.realpath(os.path.expanduser(str(path)))
if resolved in build_write_denied_paths(home):
return "credential"
return True
for prefix in build_write_denied_prefixes(home):
if resolved.startswith(prefix):
return "credential"
return True
mcp_tokens_dir_name = "mcp-tokens"
@@ -118,27 +118,16 @@ def _classify_write_denial(path: str) -> Optional[str]:
continue
for base_real in hermes_dirs:
# Session transcripts are application-owned state. Letting the agent's
# generic file tools rewrite state.db or legacy JSON snapshots can
# falsify conversation history and invalidate resume/compression state.
try:
if resolved == os.path.realpath(os.path.join(base_real, "state.db")):
return True
sessions_real = os.path.realpath(os.path.join(base_real, "sessions"))
if resolved == sessions_real or resolved.startswith(sessions_real + os.sep):
return True
except Exception:
pass
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):
return "credential"
return True
except Exception:
pass
try:
pairing_real = os.path.realpath(os.path.join(base_real, "pairing"))
if resolved == pairing_real or resolved.startswith(pairing_real + os.sep):
return "credential"
return True
except Exception:
pass
@@ -150,28 +139,9 @@ def _classify_write_denial(path: str) -> Optional[str]:
allowed = True
break
if not allowed:
return "safe_root"
return True
return None
def is_write_denied(path: str) -> bool:
"""Return True if path is blocked by the write denylist or safe root."""
return _classify_write_denial(path) is not None
def get_write_denied_error(path: str, *, verb: str = "Write") -> Optional[str]:
"""Return a user/model-facing error when writes to ``path`` are blocked."""
denial = _classify_write_denial(path)
if denial is None:
return None
if denial == "safe_root":
roots_display = os.pathsep.join(sorted(get_safe_write_roots()))
return (
f"{verb} denied: '{path}' is outside HERMES_WRITE_SAFE_ROOT "
f"({roots_display}). Unset the variable or add this path's directory prefix."
)
return f"{verb} denied: '{path}' is a protected system/credential file."
return False
# Common secret-bearing project-local environment file basenames.
@@ -323,7 +293,7 @@ def get_read_block_error(path: str) -> Optional[str]:
# .env contents — .env.example is the documented-shape substitute. The
# terminal tool can still ``cat .env``; this is defense-in-depth, not a
# boundary (see module docstring).
if resolved.name.lower() in _BLOCKED_PROJECT_ENV_BASENAMES:
if resolved.name in _BLOCKED_PROJECT_ENV_BASENAMES:
return (
f"Access denied: {path} is a secret-bearing environment file "
"and cannot be read to prevent credential leakage. "
@@ -334,30 +304,6 @@ def get_read_block_error(path: str) -> Optional[str]:
return None
def raise_if_read_blocked(path: str) -> None:
"""Raise ``ValueError`` if ``path`` is a denied Hermes read (see
:func:`get_read_block_error`), else return.
Shared chokepoint for provider input-loading sites that read a local
file the model/tool supplied (e.g. image-gen ``image_url`` /
``reference_image_urls`` paths). Centralizes the guard so every provider
enforces the same read boundary with identical semantics instead of each
open-coding the try/except block (#57698).
Best-effort by design: if ``agent.file_safety`` machinery is somehow
unavailable at the call site the guard no-ops rather than breaking local
image loading — consistent with the defense-in-depth (not security
boundary) framing of the denylist itself. The blocking ``ValueError`` from
a real hit still propagates; only unexpected internal errors are swallowed.
"""
try:
blocked = get_read_block_error(path)
except Exception: # noqa: BLE001 - guard must never break local-file loading
return
if blocked:
raise ValueError(blocked)
# ---------------------------------------------------------------------------
# Cross-profile write guard (#TBD)
#
+10 -44
View File
@@ -27,18 +27,10 @@ from typing import Any, Dict, Iterator, List, Optional
import httpx
from agent.bounded_response import read_streaming_error_body
from agent.gemini_schema import sanitize_gemini_tool_parameters
logger = logging.getLogger(__name__)
try:
import hermes_cli as _hermes_cli
_HERMES_VERSION = str(_hermes_cli.__version__)
except Exception:
_HERMES_VERSION = "0.0.0"
DEFAULT_GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
# Published max output-token ceiling shared by every current Gemini text model
@@ -106,10 +98,7 @@ def probe_gemini_tier(
url,
params={"key": key},
json=payload,
headers={
"Content-Type": "application/json",
"X-Goog-Api-Client": f"hermes-agent/{_HERMES_VERSION}",
},
headers={"Content-Type": "application/json"},
)
except Exception as exc:
logger.debug("probe_gemini_tier: network error: %s", exc)
@@ -348,22 +337,6 @@ def _build_gemini_contents(messages: List[Dict[str, Any]]) -> tuple[List[Dict[st
if parts:
contents.append({"role": gemini_role, "parts": parts})
# Gemini's generateContent requires strict user/model alternation;
# consecutive same-role contents are rejected with HTTP 400 "Please ensure
# that multiturn requests alternate between user and model". The loop above
# emits one content per source message, so parallel tool calls (N tool
# results become N user functionResponse contents), back-to-back user turns,
# or merged assistant turns would each violate that. Merge adjacent
# same-role contents by concatenating their parts. For parallel calls this
# also produces the grouped multi-functionResponse turn Gemini expects.
merged_contents: List[Dict[str, Any]] = []
for content in contents:
if merged_contents and merged_contents[-1]["role"] == content["role"]:
merged_contents[-1]["parts"].extend(content["parts"])
else:
merged_contents.append(content)
contents = merged_contents
system_instruction = None
joined_system = "\n".join(part for part in system_text_parts if part).strip()
if joined_system:
@@ -753,17 +726,14 @@ def translate_stream_event(event: Dict[str, Any], model: str, tool_call_indices:
return chunks
def gemini_http_error(
response: httpx.Response, *, body_text: Optional[str] = None
) -> GeminiAPIError:
def gemini_http_error(response: httpx.Response) -> GeminiAPIError:
status = response.status_code
body_text = ""
body_json: Dict[str, Any] = {}
if body_text is None:
try:
body_text = response.text
except Exception:
body_text = ""
body_text = body_text or ""
try:
body_text = response.text
except Exception:
body_text = ""
if body_text:
try:
parsed = json.loads(body_text)
@@ -911,11 +881,7 @@ class GeminiNativeClient:
"Content-Type": "application/json",
"Accept": "application/json",
"x-goog-api-key": self.api_key,
# Include Hermes client context following Gemini's partner
# integration guidance.
# See https://ai.google.dev/gemini-api/docs/partner-integration
"User-Agent": f"hermes-agent/{_HERMES_VERSION} (gemini-native)",
"X-Goog-Api-Client": f"hermes-agent/{_HERMES_VERSION}",
"User-Agent": "hermes-agent (gemini-native)",
}
headers.update(self._default_headers)
return headers
@@ -986,8 +952,8 @@ class GeminiNativeClient:
try:
with self._http.stream("POST", url, json=request, headers=stream_headers, timeout=timeout) as response:
if response.status_code != 200:
body_text = read_streaming_error_body(response)
raise gemini_http_error(response, body_text=body_text)
response.read()
raise gemini_http_error(response)
tool_call_indices: Dict[str, Dict[str, Any]] = {}
for event in _iter_sse_events(response):
for chunk in translate_stream_event(event, model, tool_call_indices):
-24
View File
@@ -87,30 +87,6 @@ def sanitize_gemini_schema(schema: Any) -> Dict[str, Any]:
if any(not isinstance(item, str) for item in enum_val):
cleaned.pop("enum", None)
# Gemini validates ``required`` strictly against the same node's
# ``properties`` — GenerateContentRequest fails with HTTP 400
# "...items.required[0]: property is not defined" when a required name
# has no matching property in that node. MCP servers routinely emit
# this shape (e.g. the GitHub remote MCP's array item schemas carry
# ``required`` without ``properties``), and one bad tool schema fails
# the ENTIRE request before any model output. Filter ``required`` to
# names that exist in this node's ``properties`` and drop it when
# nothing valid remains. The tool handler still validates required
# fields at execution time, so this only removes what Gemini couldn't
# accept anyway. (Port of Kilo-Org/kilocode#11955.)
required_val = cleaned.get("required")
if isinstance(required_val, list):
props_val = cleaned.get("properties")
prop_names = set(props_val.keys()) if isinstance(props_val, dict) else set()
valid_required = [
name for name in required_val
if isinstance(name, str) and name in prop_names
]
if not valid_required:
cleaned.pop("required", None)
elif len(valid_required) != len(required_val):
cleaned["required"] = valid_required
return cleaned
+19 -46
View File
@@ -17,17 +17,13 @@ It reads ``agent.image_input_mode`` from config.yaml (``auto`` | ``native``
| ``text``, default ``auto``) and the active model's capability metadata.
In ``auto`` mode:
- If the active model reports ``supports_vision=True`` (via config
override or models.dev metadata), we attach natively — vision-capable
main models should always see the original pixels, even when an
auxiliary vision backend is configured. That auxiliary backend then
acts as a *fallback* for sessions whose main model can't take images.
- Otherwise, if the user has explicitly configured ``auxiliary.vision``
(provider/model/base_url not ``auto``/empty), we route through the
text pipeline so the auxiliary vision backend can describe the image
for the text-only main model.
- Otherwise (non-vision model, no explicit override), we fall back to
text via the default vision_analyze flow.
- If the user has explicitly configured ``auxiliary.vision.provider``
(i.e. not ``auto`` and not empty), we assume they want the text pipeline
regardless of the main model — they've opted in to a specific vision
backend for a reason (cost, quality, local-only, etc.).
- Otherwise, if the active model reports ``supports_vision=True`` in its
models.dev metadata, we attach natively.
- Otherwise (non-vision model, no explicit override), we fall back to text.
This keeps ``vision_analyze`` surfaced as a tool in every session — skills
and agent flows that chain it (browser screenshots, deeper inspection of
@@ -189,8 +185,7 @@ def _supports_vision_override(
2. ``providers.<provider>.models.<model>.supports_vision``
(named custom providers — ``provider`` may be the runtime-resolved
value ``"custom"`` and/or the user-declared name under
``model.provider``; both are tried. For ``custom:<name>`` syntax,
the stripped ``<name>`` is also tried as a provider key.)
``model.provider``; both are tried)
Returns None when no override is set, so the caller falls through to
models.dev. Returns False explicitly only when the user wrote a
@@ -210,16 +205,11 @@ def _supports_vision_override(
# get rewritten to provider="custom" at runtime
# (hermes_cli/runtime_provider.py:_resolve_named_custom_runtime), so the
# config still holds the user-declared name under model.provider. Try
# both as candidate provider keys, plus the stripped suffix from
# "custom:<name>" (where <name> is the key under providers:).
# both as candidate provider keys.
config_provider = str(model_cfg.get("provider") or "").strip()
# Extract the stripped name from "custom:<name>" if present
stripped_suffix = ""
if config_provider.startswith("custom:"):
stripped_suffix = config_provider[len("custom:"):]
providers_raw = cfg.get("providers")
providers_cfg: Dict[str, Any] = providers_raw if isinstance(providers_raw, dict) else {}
for p in dict.fromkeys(filter(None, (provider, config_provider, stripped_suffix))):
for p in dict.fromkeys(filter(None, (provider, config_provider))):
entry_raw = providers_cfg.get(p)
entry: Dict[str, Any] = entry_raw if isinstance(entry_raw, dict) else {}
models_raw = entry.get("models")
@@ -267,12 +257,10 @@ def _resolve_inference_base_url(
) -> str:
"""Best-effort base URL for the active inference provider."""
try:
from agent.auxiliary_client import _runtime_main_value
from agent.auxiliary_client import _RUNTIME_MAIN_BASE_URL
runtime = str(_runtime_main_value("base_url") or "").strip()
runtime_provider = str(_runtime_main_value("provider") or "").strip().lower()
requested_provider = str(provider or "").strip().lower()
if runtime and (not requested_provider or requested_provider == runtime_provider):
runtime = str(_RUNTIME_MAIN_BASE_URL or "").strip()
if runtime:
return runtime
except Exception:
pass
@@ -348,10 +336,8 @@ def _coerce_mode(raw: Any) -> str:
def _explicit_aux_vision_override(cfg: Optional[Dict[str, Any]]) -> bool:
"""True when the user configured a specific auxiliary vision backend.
An explicit override means the user has a dedicated vision backend
available; it's used as a *fallback* when the main model can't take
images natively. In ``auto`` mode, native vision on a vision-capable
main model still wins over this fallback — see issue #29135.
An explicit override means the user *wants* the text pipeline (they're
paying for a dedicated vision model), so we don't silently bypass it.
"""
if not isinstance(cfg, dict):
return False
@@ -440,15 +426,13 @@ def decide_image_input_mode(
if mode_cfg == "text":
return "text"
# auto: prefer native vision when the main model supports it. An
# explicit auxiliary.vision config acts as a *fallback* for text-only
# main models — it should not preempt native vision on a model that
# can natively inspect the pixels (issue #29135).
# auto
if _explicit_aux_vision_override(cfg):
return "text"
supports = _lookup_supports_vision(provider, model, cfg)
if supports is True:
return "native"
if _explicit_aux_vision_override(cfg):
return "text"
return "text"
@@ -634,17 +618,6 @@ def _file_to_data_url(path: Path) -> Optional[str]:
caller reports those paths in ``skipped`` and the rest of the turn
proceeds.
"""
try:
from agent.file_safety import raise_if_read_blocked
raise_if_read_blocked(str(path))
except ValueError as exc:
logger.warning("image_routing: blocked local image attachment %s -- %s", path, exc)
return None
except Exception:
# Keep attachment routing best-effort if the guard itself is unavailable.
pass
try:
raw = path.read_bytes()
except Exception as exc:
+23 -194
View File
@@ -17,7 +17,6 @@ Usage:
"""
import json
import sqlite3
import time
from collections import Counter, defaultdict
from datetime import datetime
@@ -142,8 +141,8 @@ class InsightsEngine:
}
# Compute insights
models = self._compute_model_breakdown(sessions, cutoff, source)
overview = self._compute_overview(sessions, message_stats, models)
overview = self._compute_overview(sessions, message_stats)
models = self._compute_model_breakdown(sessions)
platforms = self._compute_platform_breakdown(sessions)
tools = self._compute_tool_breakdown(tool_usage)
skills = self._compute_skill_breakdown(skill_usage)
@@ -173,7 +172,7 @@ class InsightsEngine:
"message_count, tool_call_count, input_tokens, output_tokens, "
"cache_read_tokens, cache_write_tokens, billing_provider, "
"billing_base_url, billing_mode, estimated_cost_usd, "
"actual_cost_usd, cost_status, cost_source, api_call_count")
"actual_cost_usd, cost_status, cost_source")
# Pre-computed query strings — f-string evaluated once at class definition,
# not at runtime, so no user-controlled value can alter the query structure.
@@ -400,12 +399,7 @@ class InsightsEngine:
# Computation
# =========================================================================
def _compute_overview(
self,
sessions: List[Dict],
message_stats: Dict,
models: Optional[List[Dict]] = None,
) -> Dict:
def _compute_overview(self, sessions: List[Dict], message_stats: Dict) -> Dict:
"""Compute high-level overview statistics."""
total_input = sum(s.get("input_tokens") or 0 for s in sessions)
total_output = sum(s.get("output_tokens") or 0 for s in sessions)
@@ -437,21 +431,6 @@ class InsightsEngine:
else:
models_without_pricing.add(display)
if models:
total_cost = sum(float(m.get("cost") or 0.0) for m in models)
# Token totals likewise: the per-model breakdown includes
# auxiliary usage rows (vision/compression/titles — task
# dimension in session_model_usage, #23270) plus reconciled
# residuals, while the sessions counters carry main-loop usage
# only. Summing the breakdown keeps overview totals consistent
# with the per-model table and stops `hermes insights`
# undercounting aux spend (#58592, #9979).
total_input = sum(int(m.get("input_tokens") or 0) for m in models)
total_output = sum(int(m.get("output_tokens") or 0) for m in models)
total_cache_read = sum(int(m.get("cache_read_tokens") or 0) for m in models)
total_cache_write = sum(int(m.get("cache_write_tokens") or 0) for m in models)
total_tokens = total_input + total_output + total_cache_read + total_cache_write
# Session duration stats (guard against negative durations from clock drift)
durations = []
for s in sessions:
@@ -494,189 +473,39 @@ class InsightsEngine:
"included_cost_sessions": included_cost_sessions,
}
_GET_MODEL_USAGE_WITH_SOURCE = (
"SELECT u.session_id, u.model, u.billing_provider, u.billing_base_url,"
" u.api_call_count, u.input_tokens, u.output_tokens,"
" u.cache_read_tokens, u.cache_write_tokens, u.reasoning_tokens,"
" u.estimated_cost_usd, u.actual_cost_usd, u.cost_status,"
" u.cost_source, u.billing_mode"
" FROM session_model_usage u"
" JOIN sessions s ON s.id = u.session_id"
" WHERE s.started_at >= ? AND s.source = ?"
)
_GET_MODEL_USAGE_ALL = (
"SELECT u.session_id, u.model, u.billing_provider, u.billing_base_url,"
" u.api_call_count, u.input_tokens, u.output_tokens,"
" u.cache_read_tokens, u.cache_write_tokens, u.reasoning_tokens,"
" u.estimated_cost_usd, u.actual_cost_usd, u.cost_status,"
" u.cost_source, u.billing_mode"
" FROM session_model_usage u"
" JOIN sessions s ON s.id = u.session_id"
" WHERE s.started_at >= ?"
)
def _get_model_usage(self, cutoff: float, source: str = None) -> List[Dict]:
"""Fetch per-model usage rows within the window (issue #51607).
Returns an empty list when the table is missing (e.g. a DB opened by
older code that never created it) so the caller can fall back to the
per-session aggregate.
"""
try:
if source:
cursor = self._conn.execute(
self._GET_MODEL_USAGE_WITH_SOURCE, (cutoff, source)
)
else:
cursor = self._conn.execute(self._GET_MODEL_USAGE_ALL, (cutoff,))
return [dict(row) for row in cursor.fetchall()]
except sqlite3.OperationalError:
return []
def _compute_model_breakdown(
self, sessions: List[Dict], cutoff: float, source: str = None
) -> List[Dict]:
"""Break down token usage and cost by model.
Tokens and cost are attributed per model from session_model_usage, so a
session that switched models mid-flight (via ``/model``) splits across
every model it used instead of dumping everything on the initial model
(issue #51607). Sessions without per-model rows — e.g. data written
before this table existed and not yet backfilled — fall back to their
single recorded (model, billing_provider) aggregate so nothing is lost.
Tool calls aren't tied to a specific API invocation, so they stay
attributed to the session's recorded model.
"""
def _compute_model_breakdown(self, sessions: List[Dict]) -> List[Dict]:
"""Break down usage by model."""
model_data = defaultdict(lambda: {
"sessions": set(), "input_tokens": 0, "output_tokens": 0,
"sessions": 0, "input_tokens": 0, "output_tokens": 0,
"cache_read_tokens": 0, "cache_write_tokens": 0,
"reasoning_tokens": 0, "total_tokens": 0, "api_calls": 0,
"tool_calls": 0, "cost": 0.0, "actual_cost": 0.0,
"total_tokens": 0, "tool_calls": 0, "cost": 0.0,
})
def _accumulate(model, provider, base_url, session_id, inp, out,
cache_read, cache_write, reasoning, *,
stored_cost=None, actual_cost=None, cost_status=None):
model = model or "unknown"
for s in sessions:
model = s.get("model") or "unknown"
# Normalize: strip provider prefix for display
display_model = model.split("/")[-1] if "/" in model else model
d: Dict[str, Any] = model_data[display_model]
d["sessions"].add(session_id)
d = model_data[display_model]
d["sessions"] += 1
inp = s.get("input_tokens") or 0
out = s.get("output_tokens") or 0
cache_read = s.get("cache_read_tokens") or 0
cache_write = s.get("cache_write_tokens") or 0
d["input_tokens"] += inp
d["output_tokens"] += out
d["cache_read_tokens"] += cache_read
d["cache_write_tokens"] += cache_write
d["reasoning_tokens"] += reasoning
d["total_tokens"] += inp + out + cache_read + cache_write
if stored_cost is None:
estimate, status = _estimate_cost(
model, inp, out,
cache_read_tokens=cache_read, cache_write_tokens=cache_write,
provider=provider or None, base_url=base_url,
)
else:
estimate = float(stored_cost or 0.0)
status = cost_status or "unknown"
d["tool_calls"] += s.get("tool_call_count") or 0
estimate, status = _estimate_cost(s)
d["cost"] += estimate
d["actual_cost"] += float(actual_cost or 0.0)
d["has_pricing"] = has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
d["cost_status"] = status
if has_known_pricing(model, provider or None, base_url):
d["has_pricing"] = True
else:
d.setdefault("has_pricing", False)
return display_model
usage_rows = self._get_model_usage(cutoff, source)
usage_totals = defaultdict(lambda: {
"input_tokens": 0, "output_tokens": 0, "cache_read_tokens": 0,
"cache_write_tokens": 0, "reasoning_tokens": 0,
"api_call_count": 0, "estimated_cost_usd": 0.0,
"actual_cost_usd": 0.0,
})
for r in usage_rows:
totals: Dict[str, Any] = usage_totals[r["session_id"]]
for key in (
"input_tokens", "output_tokens", "cache_read_tokens",
"cache_write_tokens", "reasoning_tokens", "api_call_count",
):
totals[key] += r[key] or 0
totals["estimated_cost_usd"] += r["estimated_cost_usd"] or 0.0
totals["actual_cost_usd"] += r["actual_cost_usd"] or 0.0
d = _accumulate(
r["model"], r["billing_provider"], r.get("billing_base_url"),
r["session_id"], r["input_tokens"] or 0, r["output_tokens"] or 0,
r["cache_read_tokens"] or 0, r["cache_write_tokens"] or 0,
r["reasoning_tokens"] or 0,
stored_cost=(
r["estimated_cost_usd"]
if r.get("cost_status") or r.get("cost_source")
else None
),
actual_cost=r["actual_cost_usd"],
cost_status=r.get("cost_status"),
)
model_data[d]["api_calls"] += r["api_call_count"] or 0
# Reconcile against the aggregate row. This covers legacy sessions,
# interrupted migrations, and absolute cumulative updates without
# double-counting already-attributed route deltas.
for s in sessions:
totals = usage_totals[s["id"]]
inp = max(0, (s.get("input_tokens") or 0) - totals["input_tokens"])
out = max(0, (s.get("output_tokens") or 0) - totals["output_tokens"])
cache_read = max(
0, (s.get("cache_read_tokens") or 0) - totals["cache_read_tokens"]
)
cache_write = max(
0, (s.get("cache_write_tokens") or 0) - totals["cache_write_tokens"]
)
residual_cost = max(
0.0, float(s.get("estimated_cost_usd") or 0.0)
- totals["estimated_cost_usd"],
)
residual_actual = max(
0.0, float(s.get("actual_cost_usd") or 0.0)
- totals["actual_cost_usd"],
)
residual_calls = max(
0, (s.get("api_call_count") or 0) - totals["api_call_count"]
)
if not (
inp or out or cache_read or cache_write or residual_cost
or residual_actual or residual_calls
):
continue
d = _accumulate(
s.get("model"), s.get("billing_provider"),
s.get("billing_base_url"), s["id"],
inp, out, cache_read, cache_write, 0,
stored_cost=residual_cost,
actual_cost=residual_actual,
cost_status=s.get("cost_status"),
)
residual_bucket: Dict[str, Any] = model_data[d]
residual_bucket["api_calls"] += residual_calls
# Tool calls are attributed by the session's recorded model.
for s in sessions:
tool_calls = s.get("tool_call_count") or 0
if not tool_calls:
continue
model = s.get("model") or "unknown"
display_model = model.split("/")[-1] if "/" in model else model
model_data[display_model]["tool_calls"] += tool_calls
result = []
for model, data in model_data.items():
entry = {"model": model, **data}
entry["sessions"] = len(data["sessions"])
# Models that surfaced only via tool-call attribution (no token
# rows) won't have these set by _accumulate — default them so the
# output shape is uniform for downstream/JSON consumers.
entry.setdefault("has_pricing", False)
entry.setdefault("cost_status", "unknown")
result.append(entry)
result = [
{"model": model, **data}
for model, data in model_data.items()
]
# Sort by tokens first, fall back to session count when tokens are 0
result.sort(key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
return result
-108
View File
@@ -1,108 +0,0 @@
"""Turn-end guard for kanban workers.
Kanban workers must end with ``kanban_complete`` or ``kanban_block``. Models
(especially GLM / Qwen families) sometimes narrate the next step
("Let me write the report now") and stop with ``finish_reason=stop`` and no
tool calls. Hermes treats that as a clean exit → ``rc=0`` → dispatcher
``protocol_violation``.
This module is policy-only: when a kanban worker tries to finish without a
terminal board tool, return a bounded synthetic nudge so the conversation
loop continues instead of exiting.
"""
from __future__ import annotations
import os
from typing import Any, Iterable, Optional
_TERMINAL_KANBAN_TOOLS = frozenset({"kanban_complete", "kanban_block"})
_DEFAULT_MAX_ATTEMPTS = 2
def kanban_stop_nudge_enabled() -> bool:
"""Return whether the kanban stop-guard is active for this process.
On when ``HERMES_KANBAN_TASK`` is set (dispatcher-spawned worker), unless
``HERMES_KANBAN_STOP_NUDGE`` explicitly disables it.
"""
env = os.environ.get("HERMES_KANBAN_STOP_NUDGE")
if env is not None and env.strip().lower() in {"0", "false", "no", "off"}:
return False
task = (os.environ.get("HERMES_KANBAN_TASK") or "").strip()
return bool(task)
def _tool_call_name(tc: Any) -> str:
if isinstance(tc, dict):
fn = tc.get("function")
if isinstance(fn, dict):
return str(fn.get("name") or "")
return str(tc.get("name") or "")
fn = getattr(tc, "function", None)
if fn is not None:
return str(getattr(fn, "name", "") or "")
return str(getattr(tc, "name", "") or "")
def session_called_kanban_terminal(messages: Iterable[dict] | None) -> bool:
"""True if this conversation already invoked a terminal kanban tool."""
if not messages:
return False
for msg in messages:
if not isinstance(msg, dict):
continue
role = msg.get("role")
if role == "assistant":
for tc in msg.get("tool_calls") or []:
if _tool_call_name(tc) in _TERMINAL_KANBAN_TOOLS:
return True
elif role == "tool":
name = str(msg.get("name") or "")
if name in _TERMINAL_KANBAN_TOOLS:
return True
return False
def build_kanban_stop_nudge(
*,
messages: Iterable[dict] | None = None,
attempts: int = 0,
max_attempts: int = _DEFAULT_MAX_ATTEMPTS,
task_id: Optional[str] = None,
) -> Optional[str]:
"""Return a synthetic follow-up when a kanban worker exits without a terminal tool.
Returns ``None`` when the guard should not fire (not a kanban worker,
already completed/blocked, or nudge budget exhausted).
"""
if not kanban_stop_nudge_enabled():
return None
if attempts >= max_attempts:
return None
if session_called_kanban_terminal(messages):
return None
tid = (task_id or os.environ.get("HERMES_KANBAN_TASK") or "").strip() or "this task"
return (
"[System: You are a Hermes kanban worker. A plain-text reply is NOT a "
"terminal state for the board.\n\n"
f"Task `{tid}` is still `running`. Ending now without a board tool "
"causes a protocol violation (clean exit with no "
"`kanban_complete` / `kanban_block`).\n\n"
"Do this immediately in your next response — do not narrate intent:\n"
"1. Finish any remaining deliverable (write the required file(s) now).\n"
"2. Call `kanban_complete(summary=..., artifacts=[...])` if the work "
"is done, OR `kanban_block(reason=...)` if you are blocked.\n\n"
"Never end a turn with only a promise of future action. Repeated "
"protocol violations will block this task and require manual intervention.]"
)
__all__ = [
"build_kanban_stop_nudge",
"kanban_stop_nudge_enabled",
"session_called_kanban_terminal",
]
+8 -22
View File
@@ -117,29 +117,15 @@ def build_learn_prompt(user_request: str) -> str:
return (
"[/learn] The user wants you to learn a reusable skill from the "
"request below, and save it.\n\n"
f"THE REQUEST:\n{req}\n\n"
"The request is open-ended and may mix two kinds of content, in any "
"order: SOURCES to gather (directories, file paths, URLs, \"what we "
"just did\", pasted notes) AND REQUIREMENTS that shape the skill "
"(what to focus on, what to leave out, scope, naming, the angle to "
"take). Treat EVERY part of the request as load-bearing. In "
"particular, prose that comes after a path or link is NOT incidental "
"— it is the user telling you what they want from that source. A "
"request like `<url> focus on the auth flow, skip the deprecated "
"endpoints` means: gather the URL AND honor \"focus on auth, skip "
"deprecated\" as authoring requirements. Never fetch the first source "
"and ignore the rest.\n\n"
"source(s) they described below, and save it.\n\n"
f"WHAT TO LEARN FROM:\n{req}\n\n"
"Do this:\n"
"1. Gather every source 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"
"1b. Apply every requirement, focus, and constraint in the request to "
"the skill you author — these govern what the SKILL.md covers and "
"emphasizes, not just which sources you read.\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 "
-328
View File
@@ -1,328 +0,0 @@
"""Assemble the "learning made visible" graph for desktop.
This graph is intentionally scoped to what a user actually learns over time:
- non-base, learned/profile skills (agent-created or used),
- memory chunks from ``MEMORY.md`` / ``USER.md`` as first-class nodes.
Skill links come from declared ``related_skills``. Memory-to-skill links are
derived from lexical overlap so the graph can answer "which learned skills are
connected to the things I remember?".
Run as a module to print edge-density stats against real data:
python -m agent.learning_graph
"""
from __future__ import annotations
import json
import re
from dataclasses import dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Optional
from hermes_constants import get_hermes_home
@dataclass
class SkillNode:
name: str
category: str
source: str = "profile"
timestamp: Optional[int] = None
use_count: int = 0
state: str = "active"
created_by: Optional[str] = None
pinned: bool = False
related: list[str] = field(default_factory=list)
def _frontmatter(text: str) -> dict[str, Any]:
try:
from agent.skill_utils import parse_frontmatter
fm, _ = parse_frontmatter(text)
return fm or {}
except Exception:
return {}
def _hermes_meta(fm: dict[str, Any]) -> dict[str, Any]:
"""``metadata.hermes`` as a dict, tolerant of the string-valued frontmatter
that ``parse_frontmatter``'s malformed-YAML fallback produces."""
meta = fm.get("metadata")
hermes = meta.get("hermes") if isinstance(meta, dict) else None
return hermes if isinstance(hermes, dict) else {}
def _related(fm: dict[str, Any]) -> list[str]:
raw = fm.get("related_skills") or _hermes_meta(fm).get("related_skills")
if isinstance(raw, list):
return [str(r).strip() for r in raw if str(r).strip()]
if isinstance(raw, str):
return [r.strip() for r in raw.strip("[]").split(",") if r.strip()]
return []
def _category(fm: dict[str, Any], skill_md: Path) -> str:
cat = fm.get("category") or _hermes_meta(fm).get("category")
if cat:
return str(cat)
# …/skills/<category>/<skill>/SKILL.md
parts = skill_md.parts
return parts[-3] if len(parts) >= 3 else "general"
def _iter_skill_files(roots: list[tuple[str, Path]]):
for source, root in roots:
if root.exists():
for path in root.rglob("SKILL.md"):
yield source, path
def _load_usage() -> dict[str, dict[str, Any]]:
try:
from tools.skill_usage import load_usage
return load_usage()
except Exception:
path = get_hermes_home() / "skills" / ".usage.json"
try:
return json.loads(path.read_text(encoding="utf-8"))
except Exception:
return {}
def _to_int_ts(value: Any) -> Optional[int]:
try:
if value is None:
return None
if isinstance(value, (int, float)):
return int(value)
s = str(value).strip()
if not s:
return None
try:
return int(float(s))
except ValueError:
parsed = datetime.fromisoformat(s.replace("Z", "+00:00"))
if parsed.tzinfo is None:
parsed = parsed.replace(tzinfo=timezone.utc)
return int(parsed.timestamp())
except Exception:
return None
def _usage_timestamp(rec: dict[str, Any]) -> Optional[int]:
for key in ("last_activity_at", "last_used_at", "last_viewed_at", "last_patched_at", "created_at"):
ts = _to_int_ts(rec.get(key))
if ts is not None:
return ts
return None
def build_skill_nodes(skill_roots: list[tuple[str, Path]]) -> dict[str, SkillNode]:
usage = _load_usage()
nodes: dict[str, SkillNode] = {}
for source, skill_md in _iter_skill_files(skill_roots):
if any(p in {".archive", ".hub", "node_modules", ".git"} for p in skill_md.parts):
continue
try:
fm = _frontmatter(skill_md.read_text(encoding="utf-8")[:4000])
except OSError:
continue
name = str(fm.get("name") or skill_md.parent.name).strip()
if not name or name in nodes:
continue
rec = usage.get(name, {})
last_activity = _usage_timestamp(rec)
file_ts = _to_int_ts(skill_md.stat().st_mtime)
nodes[name] = SkillNode(
name=name,
category=_category(fm, skill_md),
source=source,
timestamp=last_activity or file_ts,
use_count=int(rec.get("use_count", 0) or 0),
state=str(rec.get("state", "active") or "active"),
created_by=rec.get("created_by"),
pinned=bool(rec.get("pinned", False)),
related=_related(fm),
)
return nodes
def build_edges(nodes: dict[str, SkillNode]) -> list[tuple[str, str]]:
"""Undirected related_skills edges where BOTH endpoints exist (deduped)."""
seen: set[tuple[str, str]] = set()
edges: list[tuple[str, str]] = []
for node in nodes.values():
for target in node.related:
if target in nodes and target != node.name:
a, b = sorted((node.name, target))
key = (a, b)
if key not in seen:
seen.add(key)
edges.append(key)
return edges
def density_stats(nodes: dict[str, SkillNode], edges: list[tuple[str, str]]) -> dict[str, Any]:
linked: set[str] = set()
for a, b in edges:
linked.add(a)
linked.add(b)
cats: dict[str, int] = {}
for n in nodes.values():
cats[n.category] = cats.get(n.category, 0) + 1
n = len(nodes) or 1
return {
"nodes": len(nodes),
"related_edges": len(edges),
"edges_per_node": round(len(edges) / n, 3),
"linked_nodes": len(linked),
"isolated_pct": round(100 * (n - len(linked)) / n, 1),
"categories": len(cats),
"agent_created": sum(1 for x in nodes.values() if x.created_by == "agent"),
"used": sum(1 for x in nodes.values() if x.use_count > 0),
"top_categories": sorted(cats.items(), key=lambda kv: -kv[1])[:8],
}
def _memory_cards() -> list[dict[str, Any]]:
"""Freeform memory as readable cards.
``MEMORY.md`` / ``USER.md`` are prose split on bare ``§`` separators; each
chunk becomes one card. Every chunk is surfaced — the graph shows everything.
"""
base = get_hermes_home() / "memories"
cards: list[dict[str, Any]] = []
for fname, source in (("MEMORY.md", "memory"), ("USER.md", "profile")):
path = base / fname
try:
text = path.read_text(encoding="utf-8").strip()
file_ts = _to_int_ts(path.stat().st_mtime)
except OSError:
continue
for chunk_idx, chunk in enumerate(c.strip() for c in text.split("\n§\n")):
if not chunk:
continue
first = chunk.splitlines()[0].strip().lstrip("# ").strip()
cards.append(
{
"source": source,
"timestamp": file_ts + chunk_idx if file_ts is not None else None,
"title": (first[:80] + "…") if len(first) > 80 else first,
"body": chunk[:1200],
}
)
return cards
def _tokenize(text: str) -> set[str]:
return {t for t in re.split(r"[^a-z0-9]+", text.lower()) if len(t) >= 3}
def _memory_skill_edges(memory_cards: list[dict[str, Any]], skills: list[SkillNode]) -> list[tuple[str, str]]:
edges: list[tuple[str, str]] = []
skill_meta = [(s, _tokenize(s.name), s.name.lower()) for s in skills]
for idx, card in enumerate(memory_cards):
mem_id = f"memory:{card['source']}:{idx}"
text = f"{card.get('title', '')}\n{card.get('body', '')}".lower()
text_tokens = _tokenize(text)
scored: list[tuple[int, str]] = []
for skill, tokens, skill_name_lower in skill_meta:
score = 0
if skill_name_lower in text:
score += 6
score += len(tokens & text_tokens)
if score > 0:
scored.append((score, skill.name))
scored.sort(key=lambda x: (-x[0], x[1]))
for _, skill_name in scored[:4]:
edges.append((mem_id, skill_name))
return edges
def _skill_roots() -> list[tuple[str, Path]]:
repo = Path(__file__).resolve().parent.parent
home_skills = get_hermes_home() / "skills"
return [("base", repo / "skills"), ("profile", home_skills)]
def build_learning_graph() -> dict[str, Any]:
"""Full payload for the desktop learning panel.
Focus on what is profile-learned and actionable:
- skills that are NOT base-installed and show real learning signal
(agent-created or used),
- memory chunks as first-class graph nodes connected to those learned skills.
"""
all_skills = build_skill_nodes(_skill_roots())
learned_skills = {
name: node
for name, node in all_skills.items()
if node.source != "base" and (node.created_by == "agent" or node.use_count > 0)
}
skill_edges = build_edges(learned_skills)
memory_cards = _memory_cards()
memory_edges = _memory_skill_edges(memory_cards, list(learned_skills.values()))
edges = skill_edges + memory_edges
clusters: dict[str, int] = {}
for node in learned_skills.values():
clusters[node.category] = clusters.get(node.category, 0) + 1
if memory_cards:
clusters["memory"] = len(memory_cards)
graph_nodes = [
{
"id": n.name,
"label": n.name,
"kind": "skill",
"timestamp": n.timestamp,
"category": n.category,
"useCount": n.use_count,
"state": n.state,
"createdBy": n.created_by,
"pinned": n.pinned,
}
for n in learned_skills.values()
]
for i, card in enumerate(memory_cards):
graph_nodes.append(
{
"id": f"memory:{card['source']}:{i}",
"label": card["title"],
"kind": "memory",
"memorySource": card["source"],
"timestamp": card.get("timestamp"),
"category": "memory",
"useCount": 0,
"state": "active",
"createdBy": "memory",
"pinned": False,
}
)
return {
"nodes": graph_nodes,
"edges": [{"source": a, "target": b} for a, b in edges],
"clusters": [
{"category": c, "count": n}
for c, n in sorted(clusters.items(), key=lambda kv: -kv[1])
],
"memory": memory_cards,
"stats": {
**density_stats(learned_skills, skill_edges),
"memory_nodes": len(memory_cards),
"memory_skill_edges": len(memory_edges),
"learned_skills": len(learned_skills),
},
}
if __name__ == "__main__":
nodes = build_skill_nodes(_skill_roots())
print(json.dumps(density_stats(nodes, build_edges(nodes)), indent=2))
-659
View File
@@ -1,659 +0,0 @@
"""Terminal renderer for the learning timeline (learned skills + memories).
The desktop app (``apps/desktop/src/app/starmap``) paints a GPU radial
constellation; a terminal can't, so this is a *rendition* of the same data as a
timeline bar chart — date rows, proportional skill/memory bars colored by the
day's dominant category, and a cumulative trajectory sparkline — plus per-slice
bucket metadata the TUI walks as a tree. The age gradient and complementary
memory ink are ported from the desktop source, not guessed.
Grids are emitted as style runs — ``[text, style, alpha, hex?]`` — so each
consumer maps the semantic style + brightness onto its own palette; the
optional 4th element overrides the base color (category heatmap). Pure,
stdlib-only.
"""
from __future__ import annotations
import math
from datetime import datetime, timezone
from typing import Any, Iterable, Optional
# time-axis.ts LEAD_IN: the oldest node sits just off recency 0.
LEAD_IN = 0.06
# constants.ts AGE_GRADIENT — old quiet, recent bright.
AGE_OLD_INK = 0.42
AGE_MID_INK = 0.74
AGE_NEW_INK = 0.95
AGE_MID = 0.52
# Style keys consumers map to base colors (brightness = the run alpha).
STYLE_BG = "bg"
STYLE_SKILL = "skill"
STYLE_MEMORY = "memory"
STYLE_LABEL = "label"
STYLE_DIM = "dim"
# Legend glyphs mirror NODE_SHAPE (skill = circle, memory = diamond).
SKILL_GLYPH = "●"
MEMORY_GLYPH = "◆"
_LABEL_KEYS = tuple("123456789abc")
Run = list # [text, style, alpha, hex?]
Row = list # list[Run]
Grid = list # list[Row]
def _to_ts(value: Any) -> Optional[float]:
try:
return None if value is None else float(value)
except (TypeError, ValueError):
return None
def _clamp(v: float, lo: float, hi: float) -> float:
return lo if v < lo else hi if v > hi else v
def _smoothstep(p: float) -> float:
p = _clamp(p, 0.0, 1.0)
return p * p * (3 - 2 * p)
def recency_ink(rec: float) -> float:
"""Port of geometry.ts ``recencyInk`` — smoothstep age → ink alpha."""
t = _clamp(rec, 0.0, 1.0)
if t <= AGE_MID:
return AGE_OLD_INK + (AGE_MID_INK - AGE_OLD_INK) * _smoothstep(t / AGE_MID)
return AGE_MID_INK + (AGE_NEW_INK - AGE_MID_INK) * _smoothstep((t - AGE_MID) / (1 - AGE_MID))
def format_date(ts: Optional[float]) -> str:
if not ts:
return "unknown"
try:
dt = datetime.fromtimestamp(float(ts), tz=timezone.utc)
return f"{dt.day} {dt.strftime('%b %Y')}"
except (ValueError, OSError, OverflowError):
return "unknown"
def compute_recency(nodes: list[dict[str, Any]]) -> dict[str, Any]:
"""Port of time-axis.ts ``computeRecency`` (id → recency ratio, timed flag)."""
known = [t for t in (_to_ts(n.get("timestamp")) for n in nodes) if t is not None]
min_ts = min(known) if known else None
max_ts = max(known) if known else None
timed = min_ts is not None and max_ts is not None and max_ts > min_ts
ordered = sorted(
nodes,
key=lambda n: (
_to_ts(n.get("timestamp")) if _to_ts(n.get("timestamp")) is not None else math.inf,
str(n.get("id", "")),
),
)
last = max(len(ordered) - 1, 1)
ord_ratio = {str(n.get("id", "")): (i / last if len(ordered) > 1 else 0.0) for i, n in enumerate(ordered)}
rec: dict[str, float] = {}
for n in nodes:
nid = str(n.get("id", ""))
ts = _to_ts(n.get("timestamp"))
if timed and ts is not None and min_ts is not None and max_ts is not None:
ratio = (ts - min_ts) / (max_ts - min_ts)
else:
ratio = ord_ratio.get(nid, 0.0)
rec[nid] = LEAD_IN + (1 - LEAD_IN) * _clamp(ratio, 0.0, 1.0)
return {"rec": rec, "timed": timed, "minTs": min_ts, "maxTs": max_ts}
def _date_at(rec: dict[str, Any], reveal: float) -> Optional[float]:
if not rec.get("timed"):
return None
lo, hi = rec.get("minTs"), rec.get("maxTs")
if lo is None or hi is None:
return None
return round(lo + _clamp(reveal, 0, 1) * (hi - lo))
# ── Color: ported from color.ts so memory ink + age fade match the desktop ──
def hex_to_rgb(s: str) -> tuple[int, int, int]:
s = s.strip().lstrip("#")
if len(s) == 3:
s = "".join(c * 2 for c in s)
try:
return int(s[0:2], 16), int(s[2:4], 16), int(s[4:6], 16)
except (ValueError, IndexError):
return 255, 215, 0
def rgb_to_hex(c: tuple) -> str:
return "#{:02X}{:02X}{:02X}".format(*(int(_clamp(v, 0, 255)) for v in c))
def mix_rgb(a: tuple, b: tuple, t: float) -> tuple[int, int, int]:
p = _clamp(t, 0.0, 1.0)
return tuple(round(a[i] + (b[i] - a[i]) * p) for i in range(3)) # type: ignore[return-value]
def _rgb_to_hsl(c: tuple) -> tuple[float, float, float]:
r, g, b = (x / 255 for x in c)
mx, mn = max(r, g, b), min(r, g, b)
light = (mx + mn) / 2
d = mx - mn
if not d:
return 0.0, 0.0, light
s = d / (2 - mx - mn) if light > 0.5 else d / (mx + mn)
if mx == r:
h = (g - b) / d + (6 if g < b else 0)
elif mx == g:
h = (b - r) / d + 2
else:
h = (r - g) / d + 4
return h * 60, s, light
def _hsl_to_rgb(h: float, s: float, light: float) -> tuple[int, int, int]:
hue = ((h % 360) + 360) % 360
c = (1 - abs(2 * light - 1)) * s
x = c * (1 - abs(((hue / 60) % 2) - 1))
m = light - c / 2
if hue < 60:
r, g, b = c, x, 0.0
elif hue < 120:
r, g, b = x, c, 0.0
elif hue < 180:
r, g, b = 0.0, c, x
elif hue < 240:
r, g, b = 0.0, x, c
elif hue < 300:
r, g, b = x, 0.0, c
else:
r, g, b = c, 0.0, x
return round((r + m) * 255), round((g + m) * 255), round((b + m) * 255)
def _complementary_ink(c: tuple) -> tuple[int, int, int]:
h, s, light = _rgb_to_hsl(c)
return _hsl_to_rgb(h + 165, max(s, 0.5), _clamp(light, 0.5, 0.7))
def derive_palette(primary_hex: str, *, dark: bool = True) -> dict[str, str]:
"""Port of color.ts ``computePalette`` (the bits a terminal needs)."""
primary = hex_to_rgb(primary_hex)
base = (255, 255, 255) if dark else (0, 0, 0)
bg = (8, 8, 12) if dark else (250, 250, 250)
return {
"primary": primary_hex,
# Memories are drillable → primary "clickable" ink; skills are dead-ends
# → muted complement.
"memory": rgb_to_hex(mix_rgb(primary, base, 0.12 if dark else 0.18)),
"skill": rgb_to_hex(mix_rgb(_complementary_ink(primary), bg, 0.45)),
"label": rgb_to_hex(mix_rgb(base, bg, 0.35)),
"dim": rgb_to_hex(mix_rgb(base, bg, 0.7)),
"bg": rgb_to_hex(bg),
}
def _node_score(node: dict[str, Any], rec: float) -> float:
"""Pick which visible objects deserve map markers + label rows."""
if node.get("kind") == "memory":
return 3.5 + rec
use = float(node.get("useCount", 0) or 0)
return rec * 2 + math.sqrt(max(0.0, use)) + (2.0 if node.get("pinned") else 0.0)
def _node_label(node: dict[str, Any]) -> str:
text = str(node.get("label") or node.get("id") or "unknown").strip()
return text if len(text) <= 26 else text[:23].rstrip() + "…"
def _node_meta(node: dict[str, Any]) -> str:
if node.get("kind") == "memory":
source = "profile memory" if node.get("memorySource") == "profile" else "memory"
return f"{source} · {format_date(_to_ts(node.get('timestamp')))}"
bits = [str(node.get("category") or "skill"), format_date(_to_ts(node.get("timestamp")))]
count = int(node.get("useCount", 0) or 0)
if count:
bits.append(f"x{count}")
if node.get("pinned"):
bits.append("pinned")
return " · ".join(bits)
# ── Timeline chart frame ─────────────────────────────────────────────────────
class _ChartBucket:
__slots__ = ("label", "ts", "skills", "memories", "nodes", "rec")
def __init__(self, label: str, ts: float):
self.label = label
self.ts = ts
self.skills = 0
self.memories = 0
self.nodes: list[dict[str, Any]] = []
self.rec = 1.0
@property
def total(self) -> int:
return self.skills + self.memories
def _period_key(ts: float, granularity: str) -> tuple[int, ...]:
dt = datetime.fromtimestamp(ts, tz=timezone.utc)
if granularity == "day":
return (dt.year, dt.month, dt.day)
if granularity == "month":
return (dt.year, dt.month)
return (dt.year,)
def _period_label(ts: float, granularity: str) -> str:
dt = datetime.fromtimestamp(ts, tz=timezone.utc)
if granularity == "day":
return f"{dt.day} {dt.strftime('%b')}"
if granularity == "month":
return dt.strftime("%b %Y")
return dt.strftime("%Y")
def _build_chart_buckets(nodes: list[dict[str, Any]], rec: dict[str, Any], max_rows: int) -> list[_ChartBucket]:
"""Timeline rows: finest date granularity that fits, oldest → newest."""
if not nodes:
return []
if not rec["timed"]:
ordered = sorted(nodes, key=lambda n: rec["rec"].get(str(n.get("id", "")), 0.0))
n_bins = min(max_rows, max(1, len(ordered)))
buckets = [_ChartBucket(f"#{i + 1}", float(i)) for i in range(n_bins)]
for node in ordered:
idx = int(_clamp(math.floor(rec["rec"].get(str(node.get("id", "")), 0.0) * n_bins), 0, n_bins - 1))
b = buckets[idx]
b.nodes.append(node)
if node.get("kind") == "memory":
b.memories += 1
else:
b.skills += 1
return buckets
chosen: Optional[list[_ChartBucket]] = None
for granularity in ("day", "month", "year"):
groups: dict[tuple[int, ...], _ChartBucket] = {}
for node in nodes:
ts = _to_ts(node.get("timestamp"))
if ts is None:
continue
key = _period_key(ts, granularity)
bucket = groups.get(key)
if bucket is None:
bucket = _ChartBucket(_period_label(ts, granularity), ts)
groups[key] = bucket
bucket.nodes.append(node)
if node.get("kind") == "memory":
bucket.memories += 1
else:
bucket.skills += 1
# For short spans, keep the useful day-by-day graph even when the caller
# asked for fewer rows; terminal scrollback is better than collapsing a
# month of activity into one unreadable bar.
if len(groups) <= max_rows or (granularity == "day" and len(groups) <= 32):
chosen = [groups[key] for key in sorted(groups)]
break
if chosen is None:
# If even yearly buckets overflow, fall back to even time bins.
min_ts, max_ts = rec.get("minTs"), rec.get("maxTs")
n_bins = max(1, max_rows)
chosen = []
for i in range(n_bins):
ts = min_ts + (i / max(1, n_bins - 1)) * (max_ts - min_ts) if min_ts and max_ts else float(i)
chosen.append(_ChartBucket(format_date(ts), ts))
for node in nodes:
r = rec["rec"].get(str(node.get("id", "")), 0.0)
idx = int(_clamp(math.floor(r * n_bins), 0, n_bins - 1))
b = chosen[idx]
b.nodes.append(node)
if node.get("kind") == "memory":
b.memories += 1
else:
b.skills += 1
min_ts, max_ts = rec.get("minTs"), rec.get("maxTs")
span = (max_ts - min_ts) if min_ts is not None and max_ts is not None and max_ts > min_ts else 0
for bucket in chosen:
bucket.rec = LEAD_IN + (1 - LEAD_IN) * ((bucket.ts - min_ts) / span) if span else 1.0
return chosen
def _bucket_label_node(bucket: _ChartBucket) -> Optional[dict[str, Any]]:
if not bucket.nodes:
return None
return max(bucket.nodes, key=lambda node: _node_score(node, _to_ts(node.get("timestamp")) or bucket.ts))
def _bucket_nodes(bucket: _ChartBucket, memory_lookup: Optional[dict[str, dict[str, Any]]] = None) -> list[dict[str, Any]]:
out: list[dict[str, Any]] = []
# Chronological within the slice so the TUI tree reads oldest → newest.
ordered = sorted(bucket.nodes, key=lambda n: _to_ts(n.get("timestamp")) or bucket.ts)
for node in ordered:
style = STYLE_MEMORY if node.get("kind") == "memory" else STYLE_SKILL
raw_label = str(node.get("label") or node.get("id") or "unknown").strip()
memory = (memory_lookup or {}).get(str(node.get("id", "")))
out.append(
{
"id": str(node.get("id", "")),
"glyph": MEMORY_GLYPH if node.get("kind") == "memory" else SKILL_GLYPH,
"label": _node_label(node),
"fullLabel": raw_label,
"meta": _node_meta(node),
"body": str(memory.get("body", "")) if memory else "",
"style": style,
}
)
return out
def _bucket_rows(buckets: list[_ChartBucket], payload: dict[str, Any]) -> list[dict[str, Any]]:
cmap = category_color_map(payload)
memory_lookup = {
f"memory:{card.get('source')}:{idx}": card
for idx, card in enumerate(payload.get("memory", []) or [])
if isinstance(card, dict)
}
rows: list[dict[str, Any]] = []
for idx, bucket in enumerate(buckets):
cat = _bucket_category(bucket)
rows.append(
{
"index": idx,
"label": bucket.label,
"date": format_date(bucket.ts),
"skills": bucket.skills,
"memories": bucket.memories,
"total": bucket.total,
"category": cat,
"color": cmap.get(cat) if cat else None,
"nodes": _bucket_nodes(bucket, memory_lookup),
}
)
return rows
def _category_counts(payload: dict[str, Any]) -> list[tuple[str, int]]:
clusters = [
(str(c.get("category")), int(c.get("count", 0)))
for c in payload.get("clusters", []) or []
if c.get("category") and c.get("category") != "memory"
]
if clusters:
return clusters
counts: dict[str, int] = {}
for node in payload.get("nodes", []):
if node.get("kind") == "memory":
continue
cat = str(node.get("category") or "skill")
counts[cat] = counts.get(cat, 0) + 1
return sorted(counts.items(), key=lambda kv: (-kv[1], kv[0]))
def category_color_map(payload: dict[str, Any]) -> dict[str, str]:
"""Deterministic, evenly-spread hue per skill category (theme-independent)."""
clusters = _category_counts(payload)
n = max(1, len(clusters))
# Golden-angle hue spacing so adjacent categories never collide in color.
return {cat: rgb_to_hex(_hsl_to_rgb((i * 137.508) % 360, 0.55, 0.62)) for i, (cat, _c) in enumerate(clusters)}
def category_legend(payload: dict[str, Any], limit: int = 4) -> list[dict[str, Any]]:
cmap = category_color_map(payload)
cats = _category_counts(payload)
shown = cats[:limit]
hidden = max(0, len(cats) - len(shown))
return [
{"glyph": "●", "color": cmap.get(cat, ""), "label": f"{cat} ({count})"}
for cat, count in shown
] + ([{"glyph": "·", "color": "", "label": f"+{hidden}"}] if hidden else [])
def _bucket_category(bucket: _ChartBucket) -> Optional[str]:
counts: dict[str, int] = {}
for node in bucket.nodes:
if node.get("kind") == "memory":
continue
cat = str(node.get("category") or "skill")
counts[cat] = counts.get(cat, 0) + 1
return max(counts, key=lambda k: counts[k]) if counts else None
def _trajectory_row(buckets: list[_ChartBucket], width: int, reveal: float) -> Row:
"""Cumulative learning curve as a compact star-path sparkline."""
if not buckets:
return []
total = sum(b.total for b in buckets) or 1
visible = int(_clamp(math.ceil(reveal * len(buckets)), 0, len(buckets)))
acc = 0
points: list[int] = []
for b in buckets[:visible]:
acc += b.total
points.append(round((acc / total) * (width - 1)))
cells = [" "] * width
last = 0
for p in points:
for x in range(min(last, p), max(last, p) + 1):
if 0 <= x < width and cells[x] == " ":
cells[x] = "·"
if 0 <= p < width:
cells[p] = "✦"
last = p
return [["trajectory ", STYLE_LABEL, 0.55], ["".join(cells), STYLE_SKILL, 0.48]]
def render_graph(payload: dict[str, Any], *, cols: int = 80, rows: int = 16, reveal: float = 1.0) -> dict[str, Any]:
"""Render one timeline frame at ``reveal`` (0→1).
Date rows with proportional skill/memory bars colored by the day's dominant
category, numbered markers tied to label rows, and a cumulative trajectory
sparkline underneath.
"""
reveal = _clamp(reveal, 0.0, 1.0)
cols = max(44, cols)
rows = max(14, rows)
nodes = list(payload.get("nodes", []))
if not nodes:
placeholder = [["no learning yet — keep using Hermes and it maps out here", STYLE_DIM, 0.7]]
return {"grid": [placeholder], "date": "", "reveal": reveal, "visible": 0}
rec = compute_recency(nodes)
cmap = category_color_map(payload)
buckets = _build_chart_buckets(nodes, rec, max_rows=max(4, rows - 3))
n_buckets = len(buckets)
visible_bucket_count = int(_clamp(math.ceil(reveal * n_buckets), 0, n_buckets))
max_total = max((b.total for b in buckets), default=1) or 1
label_w = min(9, max(len(b.label) for b in buckets))
bar_w = max(14, cols - label_w - 16)
grid: Grid = []
labels: list[dict[str, Any]] = []
visible = 0
for i, bucket in enumerate(buckets):
if i >= visible_bucket_count:
grid.append([])
continue
visible += bucket.total
ink = recency_ink(bucket.rec)
bar_len = max(1, round((bucket.total / max_total) * bar_w)) if bucket.total else 0
skill_len = round((bucket.skills / bucket.total) * bar_len) if bucket.total else 0
if bucket.skills and skill_len == 0:
skill_len = 1
memory_len = bar_len - skill_len
if bucket.memories and memory_len == 0 and bar_len > 1:
memory_len = 1
skill_len = bar_len - 1
node = _bucket_label_node(bucket)
marker = ""
if node and len(labels) < 6:
marker = _LABEL_KEYS[len(labels)]
style = STYLE_MEMORY if node.get("kind") == "memory" else STYLE_SKILL
labels.append(
{
"key": marker,
"glyph": MEMORY_GLYPH if node.get("kind") == "memory" else SKILL_GLYPH,
"label": _node_label(node),
"meta": _node_meta(node),
"style": style,
"alpha": round(ink, 3),
}
)
cat = _bucket_category(bucket)
cat_hex = cmap.get(cat) if cat else None
row: Row = [[f"{bucket.label:>{label_w}} ", STYLE_LABEL, ink], ["│ ", STYLE_DIM, 0.55]]
if marker:
row.append([marker, STYLE_LABEL, 0.95])
elif bucket.total:
head_hex = cat_hex if bucket.skills else None
row.append(["✦" if bucket.skills else "◆", STYLE_SKILL if bucket.skills else STYLE_MEMORY, ink, head_hex])
if skill_len:
# Bar colored by the day's dominant category — a learning heatmap.
row.append(["━" * skill_len, STYLE_SKILL, ink, cat_hex])
if memory_len:
if memory_len == 1:
mem_trail = "◆"
else:
mem_trail = "◆" + ("━" * (memory_len - 2)) + "◆"
row.append([mem_trail, STYLE_MEMORY, max(0.65, ink)])
if bar_len < bar_w:
# Empty space keeps counts aligned; starmap texture lives in the
# trajectory row below, where it reads as signal rather than noise.
row.append([" " * (bar_w - bar_len), STYLE_BG, 1.0])
row.append([" ", STYLE_BG, 1.0])
row.append([str(bucket.skills), STYLE_SKILL, max(0.72, ink)])
if bucket.memories:
row.append(["+", STYLE_DIM, 0.6])
row.append([str(bucket.memories), STYLE_MEMORY, max(0.72, ink)])
if i == visible_bucket_count - 1:
row.append([" ◀ now", STYLE_LABEL, 0.9])
elif bucket.total == max_total and max_total > 1:
row.append([" ☄ peak", STYLE_LABEL, 0.75])
grid.append(row)
# Cumulative learning trajectory underneath the rows.
grid.append([[(" " * (label_w + 2)), STYLE_BG, 1.0], *_trajectory_row(buckets, max(12, cols - label_w - 13), reveal)])
return {
"grid": grid,
"date": format_date(_date_at(rec, reveal)),
"reveal": reveal,
"visible": visible,
"labels": labels,
}
# ── Trimmings ──────────────────────────────────────────────────────────────
def build_legend(payload: dict[str, Any]) -> list[dict[str, Any]]:
nodes = payload.get("nodes", [])
skills = sum(1 for n in nodes if n.get("kind") != "memory")
memories = sum(1 for n in nodes if n.get("kind") == "memory")
return [
{"glyph": SKILL_GLYPH, "style": STYLE_SKILL, "label": f"skills ({skills})"},
{"glyph": MEMORY_GLYPH, "style": STYLE_MEMORY, "label": f"memories ({memories})"},
]
def axis_labels(payload: dict[str, Any]) -> dict[str, str]:
rec = compute_recency(list(payload.get("nodes", [])))
if not rec["timed"]:
return {"start": "oldest", "end": "now"}
return {"start": format_date(rec.get("minTs")), "end": format_date(rec.get("maxTs"))}
def _peak_day(payload: dict[str, Any]) -> Optional[str]:
counts: dict[tuple[int, ...], int] = {}
reps: dict[tuple[int, ...], float] = {}
for node in payload.get("nodes", []):
ts = _to_ts(node.get("timestamp"))
if ts is None:
continue
key = _period_key(ts, "day")
counts[key] = counts.get(key, 0) + 1
reps[key] = ts
if not counts:
return None
best = max(counts, key=lambda k: counts[k])
return f"busiest day {_period_label(reps[best], 'day')} · {counts[best]} learned"
def build_summary(payload: dict[str, Any]) -> list[str]:
stats = payload.get("stats", {}) or {}
lines: list[str] = []
learned = stats.get("learned_skills", stats.get("nodes", 0))
mem = stats.get("memory_nodes", 0)
edges = stats.get("related_edges", 0)
lines.append(f"{learned} learned skills · {mem} memories · {edges} skill links")
extra = []
if stats.get("memory_skill_edges"):
extra.append(f"{stats['memory_skill_edges']} memory↔skill links")
peak = _peak_day(payload)
if peak:
extra.append(peak)
if extra:
lines.append(" · ".join(extra))
return lines
def _merge_runs(cells: Iterable[Run]) -> Row:
out: Row = []
for run in cells:
text, style, alpha = run[0], run[1], (run[2] if len(run) > 2 else 1.0)
hex_override = run[3] if len(run) > 3 else None
prev_hex = out[-1][3] if out and len(out[-1]) > 3 else None
if out and out[-1][1] == style and abs(out[-1][2] - alpha) < 1e-6 and prev_hex == hex_override:
out[-1][0] += text
else:
merged: Run = [text, style, alpha]
if hex_override:
merged.append(hex_override)
out.append(merged)
return out
def render_frames(payload: dict[str, Any], *, cols: int = 80, rows: int = 16, frames: int = 48) -> dict[str, Any]:
"""Pre-render a full play-through (reveal 0→1) plus static legend/summary."""
frames = max(2, min(frames, 240))
nodes = list(payload.get("nodes", []))
rec = compute_recency(nodes)
# Mirror render_graph's bucketing so the interactive row list lines up with
# what the user sees.
buckets = _build_chart_buckets(nodes, rec, max_rows=max(4, rows - 3)) if nodes else []
out_frames = []
for i in range(frames):
reveal = i / (frames - 1)
frame = render_graph(payload, cols=cols, rows=rows, reveal=reveal)
out_frames.append(
{
"reveal": frame["reveal"],
"date": frame["date"],
"visible": frame["visible"],
"grid": frame["grid"],
"labels": frame.get("labels", []),
}
)
return {
"frames": out_frames,
"legend": build_legend(payload),
"categories": category_legend(payload),
"buckets": _bucket_rows(buckets, payload),
"summary": build_summary(payload),
"axis": axis_labels(payload),
"count": len(payload.get("nodes", [])),
"cols": cols,
"rows": rows,
}
-206
View File
@@ -1,206 +0,0 @@
"""User-initiated edit/delete for journey nodes (learned skills + memories).
The journey graph (``agent.learning_graph``) gives every node a stable id:
- **skills** → the skill name (e.g. ``"debugging-hermes-desktop"``)
- **memories** → ``memory:<source>:<index>`` where ``source`` is ``memory``
(``MEMORY.md``) or ``profile`` (``USER.md``) and ``index`` is the node's
position in the combined card list (``MEMORY.md`` cards first, then
``USER.md``).
This module maps a node id back to its on-disk home and performs the mutation,
shared by the CLI (``hermes journey delete|edit``), the TUI ``/journey`` overlay
(gateway RPCs), and the desktop GUI (REST). Deleting a skill *archives* it
(recoverable via ``hermes curator restore``); deleting a memory rewrites its
file. Pure stdlib + existing skill/memory helpers.
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
_MEMORY_FILES = {"memory": "MEMORY.md", "profile": "USER.md"}
def parse_node_kind(node_id: str) -> str:
return "memory" if node_id.startswith("memory:") else "skill"
def _memories_dir() -> Path:
from hermes_constants import get_hermes_home
return get_hermes_home() / "memories"
def _parse_memory_id(node_id: str) -> tuple[str, int]:
"""``memory:<source>:<index>`` → (source, global_index)."""
parts = node_id.split(":", 2)
if len(parts) != 3 or parts[0] != "memory" or parts[1] not in _MEMORY_FILES:
raise ValueError(f"bad memory node id: {node_id!r}")
try:
return parts[1], int(parts[2])
except ValueError as exc:
raise ValueError(f"bad memory node id: {node_id!r}") from exc
def _memory_local_index(source: str, global_index: int) -> int:
"""Global card index → position within the source's own file.
``_memory_cards`` emits all ``MEMORY.md`` cards before ``USER.md`` cards, so
a profile card's local index is its global index minus the memory count.
"""
from agent.learning_graph import _memory_cards
cards = _memory_cards()
if not 0 <= global_index < len(cards):
raise IndexError(f"memory index {global_index} out of range")
if cards[global_index].get("source") != source:
raise ValueError("memory node id is stale — refresh the graph")
if source == "memory":
return global_index
return global_index - sum(1 for c in cards if c.get("source") == "memory")
def _locate_memory(source: str, gidx: int) -> tuple[Path, list[str], int]:
"""Resolve a memory card to its file, all §-delimited entries, and local index.
Entries come from ``MemoryStore._read_file`` — the same parser the memory
tool uses — so journey indices stay aligned with what the graph renders.
"""
from tools.memory_tool import MemoryStore
path = _memories_dir() / _MEMORY_FILES[source]
if not path.exists():
raise ValueError(f"{path.name} not found")
chunks = MemoryStore._read_file(path)
local = _memory_local_index(source, gidx)
if not 0 <= local < len(chunks):
raise ValueError("memory node id is stale — refresh the graph")
return path, chunks, local
# ── Inspect (edit prefill) ──────────────────────────────────────────────────
def node_detail(node_id: str) -> dict[str, Any]:
"""Current content for an edit prefill. ``content`` is the full SKILL.md
(skills) or the raw memory chunk (memories)."""
try:
return _node_detail(node_id)
except (ValueError, IndexError) as exc:
return {"ok": False, "message": str(exc)}
def _node_detail(node_id: str) -> dict[str, Any]:
if parse_node_kind(node_id) == "memory":
source, gidx = _parse_memory_id(node_id)
_, chunks, local = _locate_memory(source, gidx)
body = chunks[local].strip()
return {"ok": True, "kind": "memory", "id": node_id, "label": body.splitlines()[0][:80], "content": body}
from tools.skill_manager_tool import _find_skill
found = _find_skill(node_id)
if not found:
return {"ok": False, "message": f"skill '{node_id}' not found"}
skill_md = Path(found["path"]) / "SKILL.md"
if not skill_md.exists():
return {"ok": False, "message": f"SKILL.md missing for '{node_id}'"}
return {
"ok": True,
"kind": "skill",
"id": node_id,
"label": node_id,
"content": skill_md.read_text(encoding="utf-8"),
}
# ── Delete ──────────────────────────────────────────────────────────────────
def delete_node(node_id: str) -> dict[str, Any]:
try:
return _delete_memory(node_id) if parse_node_kind(node_id) == "memory" else _delete_skill(node_id)
except (ValueError, IndexError) as exc:
return {"ok": False, "message": str(exc)}
def _delete_skill(name: str) -> dict[str, Any]:
from tools import skill_usage
if skill_usage.get_record(name).get("pinned"):
return {"ok": False, "message": f"'{name}' is pinned — unpin it first (hermes curator unpin {name})"}
ok, message = skill_usage.archive_skill(name)
if ok:
_clear_skill_cache()
return {"ok": ok, "message": f"archived '{name}' — restore with: hermes curator restore {name}" if ok else message}
def _delete_memory(node_id: str) -> dict[str, Any]:
source, gidx = _parse_memory_id(node_id)
path, chunks, local = _locate_memory(source, gidx)
del chunks[local]
_write_memory(path, chunks)
return {"ok": True, "message": f"deleted memory from {path.name}"}
# ── Edit ────────────────────────────────────────────────────────────────────
def edit_node(node_id: str, content: str) -> dict[str, Any]:
try:
return _edit_memory(node_id, content) if parse_node_kind(node_id) == "memory" else _edit_skill(node_id, content)
except (ValueError, IndexError) as exc:
return {"ok": False, "message": str(exc)}
def _edit_skill(name: str, content: str) -> dict[str, Any]:
from tools.skill_manager_tool import _edit_skill as _do_edit
result = _do_edit(name, content)
if result.get("success"):
_clear_skill_cache()
return {"ok": True, "message": f"updated '{name}'"}
return {"ok": False, "message": result.get("error", "edit failed")}
def _edit_memory(node_id: str, content: str) -> dict[str, Any]:
source, gidx = _parse_memory_id(node_id)
body = content.strip()
if not body:
return {"ok": False, "message": "empty memory — use delete to remove it"}
path, chunks, local = _locate_memory(source, gidx)
chunks[local] = body
_write_memory(path, chunks)
return {"ok": True, "message": f"updated memory in {path.name}"}
# ── Helpers ─────────────────────────────────────────────────────────────────
def _write_memory(path: Path, chunks: list[str]) -> None:
"""Atomic temp-file + rename via the memory tool, so a concurrent reader
never sees a half-written file (and the §-join stays single-sourced)."""
from tools.memory_tool import MemoryStore
MemoryStore._write_file(path, [c.strip() for c in chunks if c.strip()])
def _clear_skill_cache() -> None:
try:
from agent.prompt_builder import clear_skills_system_prompt_cache
clear_skills_system_prompt_cache(clear_snapshot=True)
except Exception:
pass
-12
View File
@@ -20,17 +20,6 @@ _LM_VALID_EFFORTS = {"none", "minimal", "low", "medium", "high", "xhigh"}
# Map them onto the OpenAI-compatible request vocabulary.
_LM_EFFORT_ALIASES = {"off": "none", "on": "medium"}
# Hermes' generic effort ladder grew past LM Studio's vocabulary ("max",
# "ultra"). Clamp the stronger generic levels onto LM Studio's ceiling: left
# alone they miss _LM_VALID_EFFORTS, keep the initialized "medium" default and
# are thereby conflated with unparseable input, so asking for more reasoning
# yields less than "xhigh". Mirrors the ceiling clamp every other provider
# applies (see agent/transports/codex.py).
#
# Deliberately separate from _LM_EFFORT_ALIASES: that mapping is also applied
# to the model's published allowed_options, which must not be rewritten.
_LM_EFFORT_CLAMP = {"max": "xhigh", "ultra": "xhigh"}
def resolve_lmstudio_effort(
reasoning_config: Optional[dict],
@@ -50,7 +39,6 @@ def resolve_lmstudio_effort(
else:
raw = (reasoning_config.get("effort") or "").strip().lower()
raw = _LM_EFFORT_ALIASES.get(raw, raw)
raw = _LM_EFFORT_CLAMP.get(raw, raw)
if raw in _LM_VALID_EFFORTS:
effort = raw
if allowed_options:
+71 -148
View File
@@ -18,15 +18,9 @@ into it via :func:`agent.lsp.manager.LSPService.touch_file`.
Implementation notes:
- All per-document state lives in one :class:`_DocState` keyed by
absolute path. Freshness is tracked with **document versions**,
not timestamps: every didChange bumps ``version``, and each stored
push/pull result is tagged with the version it describes. A
result is fresh iff its tag >= the version being waited on, so a
didChange implicitly invalidates everything older — no clearing,
no clock comparisons, no race windows. This is what prevents
"ghost diagnostics": a slow server's leftovers from the previous
edit can never masquerade as a verdict on the current content.
- Push diagnostics are stored per-URI in :attr:`_push_diagnostics` from
``textDocument/publishDiagnostics`` notifications. Pull diagnostics
go in :attr:`_pull_diagnostics`. The merged view dedupes by content.
- Whole-document sync. Even when the server advertises incremental
sync, we send a single ``contentChanges`` entry replacing the
@@ -51,7 +45,6 @@ import asyncio
import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Awaitable, Callable, Dict, List, Optional, Set
from urllib.parse import quote, unquote
@@ -131,40 +124,6 @@ def _end_position(text: str) -> Dict[str, int]:
return {"line": last_line, "character": last_col}
@dataclass
class _DocState:
"""Everything the client tracks for one open document.
``version`` is the LSP document version we last sent (didOpen=0,
each didChange +1). It doubles as the freshness token: stored
push/pull results are tagged with the version they describe
(``push_version`` / ``pull_version``), and a result is *fresh*
iff its tag has caught up to ``version``. Bumping the version on
didChange therefore invalidates all older results implicitly —
no store-clearing, no timestamps.
``push_version``/``pull_version`` start at -1 = "no data yet".
Servers that echo a document version in publishDiagnostics get
exact tagging; those that don't are credited with the current
version at receipt time (a push observed after we sent the
change describes the changed content or newer).
"""
version: int = 0
text: str = ""
push: List[Dict[str, Any]] = field(default_factory=list)
pull: List[Dict[str, Any]] = field(default_factory=list)
push_version: int = -1
pull_version: int = -1
seed_seen: bool = False
def fresh_push(self, version: Optional[int] = None) -> bool:
return self.push_version >= (self.version if version is None else version)
def fresh_pull(self, version: Optional[int] = None) -> bool:
return self.pull_version >= (self.version if version is None else version)
class LSPClient:
"""Async LSP client tied to one server process and one workspace root.
@@ -227,10 +186,18 @@ class LSPClient:
# is silently dropped by default.
}
# Per-document state (version, text, diagnostic stores, and
# their freshness tags), keyed by absolute file path (NOT URI).
# See _DocState for the version-based freshness model.
self._docs: Dict[str, _DocState] = {}
# Tracked file state — required for didChange version bumps.
self._files: Dict[str, Dict[str, Any]] = {}
# Diagnostic stores, keyed by file path (NOT URI).
self._push_diagnostics: Dict[str, List[Dict[str, Any]]] = {}
self._pull_diagnostics: Dict[str, List[Dict[str, Any]]] = {}
# Per-path "last published" time so wait-for-fresh logic works.
self._published: Dict[str, float] = {}
# Per-path version of the latest push (matches our didChange
# version when the server respects it).
self._published_version: Dict[str, int] = {}
# First-push seen flag, for typescript-style seed-on-first-push.
self._first_push_seen: Set[str] = set()
# Capability registrations — only diagnostic ones are tracked.
self._diagnostic_registrations: Dict[str, Dict[str, Any]] = {}
@@ -296,13 +263,6 @@ class LSPClient:
cmd = self._win_wrap_cmd(cmd)
try:
# start_new_session=True detaches the LSP server into its own
# process group / session. Without this, the LSP server inherits
# the gateway's pgid (= TUI parent PID). When mcp_tool's
# _kill_orphaned_mcp_children races with LSP spawn and sweeps the
# gateway's child set, it captures the LSP PID, records the
# inherited pgid, and killpg() then kills the TUI parent itself.
# See tui_gateway_crash.log "killpg → SIGTERM received" stacks.
self._proc = await asyncio.create_subprocess_exec(
cmd[0],
*cmd[1:],
@@ -311,7 +271,6 @@ class LSPClient:
stderr=asyncio.subprocess.PIPE,
env=env,
cwd=self._cwd,
start_new_session=True,
)
except FileNotFoundError as e:
raise LSPProtocolError(
@@ -680,25 +639,25 @@ class LSPClient:
if not isinstance(diagnostics, list):
diagnostics = []
version = params.get("version")
loop_time = asyncio.get_event_loop().time()
doc = self._docs.setdefault(path, _DocState(version=-1))
if self._seed_first_push and not doc.seed_seen:
# First push: seed the store WITHOUT a freshness tag. It
# arrives before the user-triggered didChange could've
# produced fresh diagnostics, so it must never satisfy a
# waiter — it's baseline data only.
doc.seed_seen = True
doc.push = diagnostics
if self._seed_first_push and path not in self._first_push_seen:
# First push: seed without firing the event so a waiter
# doesn't resolve on the very first push (which arrives
# before the user-triggered didChange could've produced
# fresh diagnostics).
self._first_push_seen.add(path)
self._push_diagnostics[path] = diagnostics
self._published[path] = loop_time
if isinstance(version, int):
self._published_version[path] = version
return
doc.seed_seen = True
doc.push = diagnostics
# Tag with the echoed document version when the server provides
# one; otherwise credit the current version — a push observed
# after we sent the change describes the changed content (or
# newer). Note doc.version is -1 for never-opened paths
# (e.g. relatedDocuments spillover), keeping them unfresh.
doc.push_version = version if isinstance(version, int) else doc.version
self._push_diagnostics[path] = diagnostics
self._published[path] = loop_time
if isinstance(version, int):
self._published_version[path] = version
self._first_push_seen.add(path)
# Bump the monotonic push counter and wake every waiter. We
# keep the Event sticky-set so any wait already in progress
# resolves; waiters re-check their predicate after waking and
@@ -727,16 +686,16 @@ class LSPClient:
raise LSPProtocolError(f"cannot read {abs_path}: {e}") from e
uri = file_uri(abs_path)
doc = self._docs.get(abs_path)
existing = self._files.get(abs_path)
if doc is not None and doc.version >= 0:
if existing is not None:
# Re-open: bump version, fire didChangeWatchedFiles + didChange.
await self._send_notification(
"workspace/didChangeWatchedFiles",
{"changes": [{"uri": uri, "type": 2}]}, # 2 = CHANGED
)
new_version = doc.version + 1
old_text = doc.text
new_version = existing["version"] + 1
old_text = existing["text"]
content_changes: List[Dict[str, Any]]
if self._sync_kind == 2:
content_changes = [
@@ -757,11 +716,7 @@ class LSPClient:
"contentChanges": content_changes,
},
)
# Bumping the version is the whole invalidation story:
# every stored result tagged with an older version is now
# stale by definition (see _DocState).
doc.version = new_version
doc.text = text
self._files[abs_path] = {"version": new_version, "text": text}
return new_version
# First open: didChangeWatchedFiles CREATED + didOpen.
@@ -769,9 +724,12 @@ class LSPClient:
"workspace/didChangeWatchedFiles",
{"changes": [{"uri": uri, "type": 1}]}, # 1 = CREATED
)
# Fresh doc state — anything stashed under this path by a
# pre-open push (relatedDocuments spillover etc.) is discarded.
self._docs[abs_path] = _DocState(version=0, text=text)
# Clear any stale push/pull entries — fresh open should start
# from scratch.
self._push_diagnostics.pop(abs_path, None)
self._pull_diagnostics.pop(abs_path, None)
self._published.pop(abs_path, None)
self._published_version.pop(abs_path, None)
await self._send_notification(
"textDocument/didOpen",
{
@@ -783,6 +741,7 @@ class LSPClient:
}
},
)
self._files[abs_path] = {"version": 0, "text": text}
return 0
async def save_file(self, path: str) -> None:
@@ -802,19 +761,12 @@ class LSPClient:
async def _pull_document_diagnostics(self, path: str) -> None:
"""Send ``textDocument/diagnostic`` for one file.
Stores results into the doc's pull store, tagged with the
document version captured at request send time. If a didChange
races past the in-flight request, the version bump makes the
stored result stale automatically — no explicit invalidation.
Silently no-ops on errors (server may not support the pull
endpoint).
Stores results into :attr:`_pull_diagnostics`. Silently
no-ops on errors (server may not support the pull endpoint).
"""
abs_path = os.path.abspath(path)
doc = self._docs.get(abs_path)
sent_version = doc.version if doc else -1
try:
params: Dict[str, Any] = {
"textDocument": {"uri": file_uri(abs_path)}
"textDocument": {"uri": file_uri(os.path.abspath(path))}
}
result = await self._send_request_with_retry(
"textDocument/diagnostic",
@@ -828,9 +780,7 @@ class LSPClient:
return
items = result.get("items")
if isinstance(items, list):
doc = self._docs.setdefault(abs_path, _DocState(version=-1))
doc.pull = items
doc.pull_version = sent_version
self._pull_diagnostics[os.path.abspath(path)] = items
related = result.get("relatedDocuments")
if isinstance(related, dict):
for uri, sub in related.items():
@@ -838,11 +788,7 @@ class LSPClient:
continue
sub_items = sub.get("items")
if isinstance(sub_items, list):
rel = self._docs.setdefault(uri_to_path(uri), _DocState(version=-1))
rel.pull = sub_items
# Same send-anchored tagging: fresh only if that
# doc hasn't changed since the request went out.
rel.pull_version = rel.version
self._pull_diagnostics[uri_to_path(uri)] = sub_items
async def wait_for_diagnostics(
self,
@@ -850,36 +796,22 @@ class LSPClient:
version: int,
*,
mode: str = "document",
timeout: Optional[float] = None,
) -> bool:
) -> None:
"""Wait for the server to publish diagnostics for ``path`` at ``version``.
``mode`` is ``"document"`` (5s budget, document pulls) or
``"full"`` (10s budget, also workspace pulls). ``timeout``
overrides the mode's default budget when provided — this is
how the user's ``lsp.wait_timeout`` config reaches the wait
loop (slow servers like tsserver on big projects need more
than the 5s default).
Returns ``True`` when *fresh* diagnostics arrived (a push at
or after our didChange, or a pull answered after it) and
``False`` on timeout. Callers must treat ``False`` as "no
data", NOT as "no errors" — the diagnostic stores may still
hold stale entries from the previous edit at that point.
Best-effort — never throws if the server doesn't support pull
diagnostics; we still get the push side.
``"full"`` (10s budget, also workspace pulls). Best-effort —
returns silently on timeout. Does NOT throw if the server
doesn't support pull diagnostics; we still get the push side.
"""
if timeout is not None and timeout > 0:
budget = timeout
else:
budget = DIAGNOSTICS_FULL_WAIT if mode == "full" else DIAGNOSTICS_DOCUMENT_WAIT
budget = DIAGNOSTICS_FULL_WAIT if mode == "full" else DIAGNOSTICS_DOCUMENT_WAIT
deadline = asyncio.get_event_loop().time() + budget
abs_path = os.path.abspath(path)
while True:
remaining = deadline - asyncio.get_event_loop().time()
if remaining <= 0:
return False
return
# Concurrent: document pull + push wait.
pull_task = asyncio.create_task(self._pull_document_diagnostics(abs_path))
@@ -898,24 +830,26 @@ class LSPClient:
pass
# If we got a fresh push for our version, we're done.
doc = self._docs.get(abs_path)
if doc and doc.fresh_push(version):
return True
current_v = self._published_version.get(abs_path)
if abs_path in self._published and (
current_v is None or current_v >= version
):
return
# Pull may have answered for the current version — that's
# also success.
if doc and doc.fresh_pull(version):
return True
# Pull may have populated _pull_diagnostics — that's also
# success.
if abs_path in self._pull_diagnostics:
return
# Loop until budget runs out.
async def _wait_for_fresh_push(self, path: str, version: int, timeout: float) -> None:
"""Wait until a fresh publishDiagnostics arrives for ``path`` at ``version``+."""
"""Wait until a publishDiagnostics arrives for ``path`` at ``version``+."""
deadline = asyncio.get_event_loop().time() + timeout
baseline = self._push_counter
while True:
doc = self._docs.get(path)
if doc and doc.fresh_push(version):
current_v = self._published_version.get(path)
if path in self._published and (current_v is None or current_v >= version):
# Debounce — wait a tick in case more diagnostics arrive
# immediately after. TS often emits in pairs. We
# snapshot the counter so we wake on a *new* push, not
@@ -946,28 +880,17 @@ class LSPClient:
except asyncio.TimeoutError:
continue
def diagnostics_for(self, path: str, *, fresh_only: bool = False) -> List[Dict[str, Any]]:
def diagnostics_for(self, path: str) -> List[Dict[str, Any]]:
"""Return current merged + deduped diagnostics for one file.
Diagnostics from push and pull stores are concatenated and
deduplicated by ``(severity, code, message, range)`` content
key. Empty list if the server hasn't published anything.
With ``fresh_only=True``, a store only contributes when its
version tag has caught up to the document's current version —
stale leftovers from the previous edit cycle are excluded.
This is what report paths should use: after an edit, "stale
errors" and "no errors" must not be conflated.
"""
doc = self._docs.get(os.path.abspath(path))
if doc is None:
return []
if fresh_only:
return _dedupe(
doc.push if doc.fresh_push() else [],
doc.pull if doc.fresh_pull() else [],
)
return _dedupe(doc.push, doc.pull)
abs_path = os.path.abspath(path)
push = self._push_diagnostics.get(abs_path) or []
pull = self._pull_diagnostics.get(abs_path) or []
return _dedupe(push, pull)
def _dedupe(*lists: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+7 -10
View File
@@ -102,11 +102,6 @@ INSTALL_RECIPES: Dict[str, Dict[str, Any]] = {
# Lua — manual (LuaLS is platform-specific binaries from GitHub
# releases; complex enough that we punt to the user)
"lua-language-server": {"strategy": "manual", "pkg": "", "bin": "lua-language-server"},
# PowerShell — PowerShellEditorServices ships as a GitHub release
# zip driven by a pwsh bootstrap script, not a single binary. We
# require a manual bundle install and probe for the pwsh host so
# `hermes lsp status` reports the host's presence.
"powershell": {"strategy": "manual", "pkg": "", "bin": "pwsh"},
}
@@ -348,15 +343,17 @@ def _install_pip(pkg: str, bin_name: str) -> Optional[str]:
pip_target.mkdir(parents=True, exist_ok=True)
try:
logger.info("[install] pip install --target %s %s", pip_target, pkg)
from hermes_cli.tools_config import _pip_install
proc = _pip_install(
["--target", str(pip_target), "--quiet", pkg],
proc = subprocess.run(
[sys.executable, "-m", "pip", "install", "--target", str(pip_target), "--quiet", pkg],
check=False,
capture_output=True,
text=True,
timeout=300,
stdin=subprocess.DEVNULL,
)
if proc.returncode != 0:
logger.warning(
"[install] pip install failed for %s: %s", pkg, (proc.stderr or "").strip()[:500]
"[install] pip install failed for %s: %s", pkg, proc.stderr.strip()[:500]
)
return None
except (subprocess.TimeoutExpired, OSError) as e:
+10 -41
View File
@@ -292,10 +292,7 @@ class LSPService:
if not self.enabled_for(file_path):
return
try:
# Outer join budget must exceed the inner wait budget or a
# slow-but-alive server gets falsely marked broken.
t = max(8.0, self._wait_timeout + 3.0)
diags = self._loop.run(self._snapshot_async(file_path), timeout=t)
diags = self._loop.run(self._snapshot_async(file_path), timeout=8.0)
self._delta_baseline[os.path.abspath(file_path)] = diags or []
except Exception as e: # noqa: BLE001
logger.debug("baseline snapshot failed for %s: %s", file_path, e)
@@ -344,7 +341,7 @@ class LSPService:
try:
t = timeout if timeout is not None else self._wait_timeout + 2.0
diags = self._loop.run(self._open_and_wait_async(file_path), timeout=t)
diags = self._loop.run(self._open_and_wait_async(file_path), timeout=t) or []
except asyncio.TimeoutError as e:
eventlog.log_timeout(server_id, file_path)
logger.debug("LSP diagnostics timeout for %s: %s", file_path, e)
@@ -356,17 +353,6 @@ class LSPService:
self._mark_broken_for_file(file_path, e)
return []
if diags is None:
# The server is alive but never produced diagnostics for the
# post-edit content within the wait budget (common for
# tsserver on large projects). Report "no data" rather than
# whatever stale state is in the stores — surfacing the
# previous edit's errors as if they were current is the
# ghost-diagnostics bug. The server is NOT marked broken:
# slow is not dead, and the next edit may well succeed.
eventlog.log_timeout(server_id, file_path, kind="fresh diagnostics")
return []
abs_path = os.path.abspath(file_path)
if delta:
baseline = self._delta_baseline.get(abs_path) or []
@@ -466,43 +452,26 @@ class LSPService:
return []
try:
version = await client.open_file(file_path, language_id=language_id_for(file_path))
fresh = await client.wait_for_diagnostics(file_path, version, mode=self._wait_mode)
await client.wait_for_diagnostics(file_path, version, mode=self._wait_mode)
except Exception as e: # noqa: BLE001
logger.debug("snapshot open/wait failed: %s", e)
return []
self._last_used[(client.server_id, client.workspace_root)] = time.time()
if not fresh:
# No fresh data for the pre-edit content — an empty baseline
# is safe: worst case the delta filter removes less, never
# more. Never seed the baseline from stale stores.
return []
return list(client.diagnostics_for(file_path, fresh_only=True))
return list(client.diagnostics_for(file_path))
async def _open_and_wait_async(self, file_path: str) -> Optional[List[Dict[str, Any]]]:
"""Open + wait for FRESH diagnostics.
Returns the fresh diagnostic list, or ``None`` when the server
never produced post-change data within the wait budget. The
distinction matters: ``[]`` means "server checked the new
content, it's clean", ``None`` means "no verdict" — the caller
must not substitute stale data for either.
"""
async def _open_and_wait_async(self, file_path: str) -> List[Dict[str, Any]]:
client = await self._get_or_spawn(file_path)
if client is None:
return None
return []
try:
version = await client.open_file(file_path, language_id=language_id_for(file_path))
await client.save_file(file_path)
fresh = await client.wait_for_diagnostics(
file_path, version, mode=self._wait_mode, timeout=self._wait_timeout
)
await client.wait_for_diagnostics(file_path, version, mode=self._wait_mode)
except Exception as e: # noqa: BLE001
logger.debug("open/wait failed for %s: %s", file_path, e)
return None
return []
self._last_used[(client.server_id, client.workspace_root)] = time.time()
if not fresh:
return None
return list(client.diagnostics_for(file_path, fresh_only=True))
return list(client.diagnostics_for(file_path))
async def _current_diags_async(self, file_path: str) -> List[Dict[str, Any]]:
ws, gated = resolve_workspace_for_file(file_path)
@@ -513,7 +482,7 @@ class LSPService:
client = self._clients.get((srv.server_id, ws))
if client is None:
return []
return list(client.diagnostics_for(file_path, fresh_only=True))
return list(client.diagnostics_for(file_path))
async def _get_or_spawn(self, file_path: str) -> Optional[LSPClient]:
srv = find_server_for_file(file_path)
+1 -1
View File
@@ -91,7 +91,7 @@ async def read_message(reader: asyncio.StreamReader) -> Optional[dict]:
header_bytes += len(line)
if header_bytes > 8192:
raise LSPProtocolError(
"LSP header block exceeded 8 KiB without terminator"
f"LSP header block exceeded 8 KiB without terminator"
)
line = line[:-2] # strip CRLF
if not line:
+6 -58
View File
@@ -8,7 +8,6 @@ OpenCode's ``lsp/diagnostic.ts`` and Claude Code's
"""
from __future__ import annotations
import html
from typing import Any, Dict, List
# Severity-1 only by default — warnings/info/hints would flood the
@@ -19,65 +18,18 @@ DEFAULT_SEVERITIES = frozenset({1}) # ERROR only
MAX_PER_FILE = 20
MAX_TOTAL_CHARS = 4000
# Per-field caps for diagnostic content sourced from the language server.
# These bound the length of any single attacker-controlled identifier that
# can ride into the model's tool output via an LSP diagnostic message.
MAX_MESSAGE_CHARS = 300
MAX_CODE_CHARS = 80
MAX_SOURCE_CHARS = 80
def _sanitize_field(value: Any, *, limit: int) -> str:
"""Make a language-server field safe to embed in a tool-result block.
Diagnostic ``message``, ``code``, and ``source`` originate from a
language server that has just parsed user-controlled source code, so
they're untrusted from the agent's point of view. A hostile repo can
place instruction-shaped text inside identifier names, type aliases,
or import paths so the resulting diagnostic echoes that text back
into the ``<diagnostics>`` block the model reads.
This helper:
* Collapses CR/LF so a raw newline can't synthesize a new line in the
formatted block.
* Drops non-printable ASCII control characters that have no business
in a single-line summary.
* Caps length per-field so a long identifier can't push past the
block boundary.
* HTML-escapes ``< > &`` so the result can't close ``<diagnostics>``
early or open a new tag.
Returns ``""`` for ``None`` / empty so the surrounding format string
naturally omits the part (mirrors the prior ``if code not in {None,
""}`` check at call sites).
"""
if value is None:
return ""
raw = str(value)
# Collapse newlines so identifier text with raw \n can't fake new lines.
raw = raw.replace("\r", " ").replace("\n", " ")
# Drop ASCII control chars; keep regular spaces.
raw = "".join(ch for ch in raw if ch == " " or ch.isprintable())
raw = raw.strip()[:limit]
return html.escape(raw, quote=False)
def format_diagnostic(d: Dict[str, Any]) -> str:
"""One-line representation of a single diagnostic.
``message``, ``code``, and ``source`` are sanitized before
interpolation — see ``_sanitize_field``.
"""
"""One-line representation of a single diagnostic."""
sev = SEVERITY_NAMES.get(d.get("severity") or 1, "ERROR")
rng = d.get("range") or {}
start = rng.get("start") or {}
line = int(start.get("line", 0)) + 1
col = int(start.get("character", 0)) + 1
msg = _sanitize_field(d.get("message"), limit=MAX_MESSAGE_CHARS)
code = _sanitize_field(d.get("code"), limit=MAX_CODE_CHARS)
code_part = f" [{code}]" if code else ""
source = _sanitize_field(d.get("source"), limit=MAX_SOURCE_CHARS)
msg = str(d.get("message") or "").rstrip()
code = d.get("code")
code_part = f" [{code}]" if code not in {None, ""} else ""
source = d.get("source")
source_part = f" ({source})" if source else ""
return f"{sev} [{line}:{col}] {msg}{code_part}{source_part}"
@@ -105,11 +57,7 @@ def report_for_file(
body = "\n".join(lines)
if extra > 0:
body += f"\n... and {extra} more"
# quote=True escapes both ``"`` and ``&`` so a crafted file name like
# ``foo"><script`` can't break out of the ``file="..."`` attribute and
# synthesize new tags inside the tool output.
safe_path = html.escape(file_path, quote=True)
return f"<diagnostics file=\"{safe_path}\">\n{body}\n</diagnostics>"
return f"<diagnostics file=\"{file_path}\">\n{body}\n</diagnostics>"
def truncate(s: str, *, limit: int = MAX_TOTAL_CHARS) -> str:
-147
View File
@@ -102,9 +102,6 @@ LANGUAGE_BY_EXT: Dict[str, str] = {
".zig": "zig",
".zon": "zig",
".dockerfile": "dockerfile",
".ps1": "powershell",
".psm1": "powershell",
".psd1": "powershell",
}
@@ -679,131 +676,6 @@ def _spawn_astro(root: str, ctx: ServerContext) -> Optional[SpawnSpec]:
)
_PSES_BUNDLE_WARNED = False
def _find_pses_bundle(ctx: ServerContext) -> Optional[str]:
"""Locate the PowerShellEditorServices module bundle directory.
PSES ships as a GitHub release zip (not an npm/go/pip package), so
there's no auto-install recipe — the user downloads it and points us
at the extracted bundle. Resolution order:
1. ``command`` override in config (``lsp.servers.powershell.command``) —
the FIRST element is treated as the bundle path when it's a
directory. This is the documented config knob.
2. ``init_overrides["powershell"]["bundlePath"]``.
3. ``PSES_BUNDLE_PATH`` env var.
4. ``<HERMES_HOME>/lsp/PowerShellEditorServices`` staging dir (where a
user-run unzip would naturally land).
Returns the bundle directory containing ``PowerShellEditorServices/``,
or ``None`` when it can't be found.
"""
candidates: List[str] = []
override = ctx.binary_overrides.get("powershell")
if override and override[0]:
candidates.append(override[0])
init = ctx.init_overrides.get("powershell", {})
if isinstance(init, dict) and init.get("bundlePath"):
candidates.append(str(init["bundlePath"]))
env_path = os.environ.get("PSES_BUNDLE_PATH")
if env_path:
candidates.append(env_path)
home = os.environ.get("HERMES_HOME") or os.path.join(
os.path.expanduser("~"), ".hermes"
)
candidates.append(os.path.join(home, "lsp", "PowerShellEditorServices"))
for cand in candidates:
if not cand:
continue
# Accept either the bundle root or the inner module dir.
start_script = os.path.join(
cand, "PowerShellEditorServices", "Start-EditorServices.ps1"
)
if os.path.isfile(start_script):
return cand
inner = os.path.join(cand, "Start-EditorServices.ps1")
if os.path.isfile(inner):
return os.path.dirname(cand)
return None
def _spawn_powershell_es(root: str, ctx: ServerContext) -> Optional[SpawnSpec]:
"""Spawn PowerShellEditorServices over stdio.
Unlike the single-binary servers, PSES is a PowerShell module driven
by a bootstrap script. We need both a PowerShell host (``pwsh`` for
PowerShell 7+, or Windows ``powershell``) and the PSES module bundle.
The bundle is manual-install (release zip) — see ``_find_pses_bundle``.
"""
pwsh = _which("pwsh", "powershell")
if pwsh is None:
return None
bundle = _find_pses_bundle(ctx)
if bundle is None:
global _PSES_BUNDLE_WARNED
if not _PSES_BUNDLE_WARNED:
_PSES_BUNDLE_WARNED = True
logger.warning(
"powershell: pwsh found but the PowerShellEditorServices "
"bundle is missing. Download the release zip from "
"https://github.com/PowerShell/PowerShellEditorServices/releases, "
"extract it, and either set lsp.servers.powershell.command "
"to the bundle path or unzip it to "
"<HERMES_HOME>/lsp/PowerShellEditorServices."
)
return None
start_script = os.path.join(
bundle, "PowerShellEditorServices", "Start-EditorServices.ps1"
)
# Session details file: PSES writes connection info here on startup.
session_path = os.path.join(
hermes_lsp_session_dir(), f"pses-session-{os.getpid()}.json"
)
log_path = os.path.join(hermes_lsp_session_dir(), "pses.log")
inner = (
f"& '{start_script}' "
f"-BundledModulesPath '{bundle}' "
f"-LogPath '{log_path}' "
f"-SessionDetailsPath '{session_path}' "
f"-FeatureFlags @() -AdditionalModules @() "
f"-HostName Hermes -HostProfileId hermes -HostVersion 1.0.0 "
f"-Stdio -LogLevel Normal"
)
return SpawnSpec(
command=[
pwsh,
"-NoLogo",
"-NoProfile",
"-NonInteractive",
"-ExecutionPolicy",
"Bypass",
"-Command",
inner,
],
workspace_root=root,
cwd=root,
env=ctx.env_overrides.get("powershell", {}),
initialization_options={
k: v
for k, v in ctx.init_overrides.get("powershell", {}).items()
if k != "bundlePath"
},
)
def hermes_lsp_session_dir() -> str:
"""Return (and create) the dir for PSES session/log scratch files."""
home = os.environ.get("HERMES_HOME") or os.path.join(
os.path.expanduser("~"), ".hermes"
)
d = os.path.join(home, "lsp", "pses")
os.makedirs(d, exist_ok=True)
return d
def _resolve_override(ctx: ServerContext, server_id: str) -> Optional[str]:
"""User can pin a binary path in config."""
override = ctx.binary_overrides.get(server_id)
@@ -951,18 +823,6 @@ def _root_java(file_path: str, workspace: str) -> Optional[str]:
)
def _root_powershell(file_path: str, workspace: str) -> Optional[str]:
# PowerShell projects rarely have a universal root marker. Use the
# PSScriptAnalyzer settings file when present, otherwise fall back to
# the git workspace root (nearest_root does exact-name matching only,
# so no globs here).
return _root_or_workspace(
file_path,
workspace,
["PSScriptAnalyzerSettings.psd1"],
)
# ---------------------------------------------------------------------------
# the registry
# ---------------------------------------------------------------------------
@@ -1152,13 +1012,6 @@ SERVERS: List[ServerDef] = [
build_spawn=_spawn_jdtls,
description="Java — Eclipse JDT Language Server",
),
ServerDef(
server_id="powershell",
extensions=(".ps1", ".psm1", ".psd1"),
resolve_root=_root_powershell,
build_spawn=_spawn_powershell_es,
description="PowerShell — PowerShellEditorServices (manual bundle)",
),
]
+11 -52
View File
@@ -4,86 +4,45 @@ from __future__ import annotations
from typing import Any, Sequence
from agent.redact import redact_sensitive_text
def summarize_manual_compression(
before_messages: Sequence[dict[str, Any]],
after_messages: Sequence[dict[str, Any]],
before_tokens: int,
after_tokens: int,
*,
compression_state: Any = None,
) -> dict[str, Any]:
"""Return consistent user-facing feedback for manual compression."""
before_count = len(before_messages)
after_count = len(after_messages)
noop = list(after_messages) == list(before_messages)
aborted = (
compression_state is not None
and getattr(compression_state, "_last_compress_aborted", False) is True
)
fallback_used = (
compression_state is not None
and getattr(compression_state, "_last_summary_fallback_used", False) is True
)
failure_reason = (
getattr(compression_state, "_last_summary_error", None)
if compression_state is not None
else None
)
if not isinstance(failure_reason, str) or not failure_reason.strip():
failure_reason = None
if aborted:
headline = f"Compression aborted: {before_count} messages preserved"
elif fallback_used:
headline = (
f"Compressed with fallback: {before_count} → {after_count} messages"
)
elif noop:
if noop:
headline = f"No changes from compression: {before_count} messages"
if after_tokens == before_tokens:
token_line = (
f"Approx request size: ~{before_tokens:,} tokens (unchanged)"
)
else:
token_line = (
f"Approx request size: ~{before_tokens:,} → "
f"~{after_tokens:,} tokens"
)
else:
headline = f"Compressed: {before_count} → {after_count} messages"
if noop and after_tokens == before_tokens:
token_line = f"Approx request size: ~{before_tokens:,} tokens (unchanged)"
else:
token_line = (
f"Approx request size: ~{before_tokens:,} → "
f"~{after_tokens:,} tokens"
)
note = None
if aborted:
note = "Summary generation failed; no messages were removed."
elif fallback_used:
dropped_count = getattr(
compression_state, "_last_summary_dropped_count", None
)
if not isinstance(dropped_count, int) or isinstance(dropped_count, bool):
dropped_count = max(before_count - after_count, 0)
note = (
"Summary generation failed; Hermes used limited fallback context "
f"and removed {dropped_count} message(s)."
)
elif not noop and after_count < before_count and after_tokens > before_tokens:
if not noop and after_count < before_count and after_tokens > before_tokens:
note = (
"Note: fewer messages can still raise this estimate when "
"compression rewrites the transcript into denser summaries."
)
if failure_reason and (aborted or fallback_used):
# This text crosses a user-facing UI boundary. Never let a disabled
# global redaction preference expose credentials embedded in provider
# exception text.
safe_reason = redact_sensitive_text(failure_reason.strip(), force=True)
note = f"{note} Reason: {safe_reason}"
return {
"noop": noop,
"aborted": aborted,
"fallback_used": fallback_used,
"headline": headline,
"token_line": token_line,
"note": note,
+57 -207
View File
@@ -30,7 +30,7 @@ import logging
import re
import inspect
import threading
from concurrent.futures import Future, ThreadPoolExecutor, wait
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Callable, Dict, List, Optional
from agent.memory_provider import MemoryProvider
@@ -44,7 +44,6 @@ logger = logging.getLogger(__name__)
# teardown indefinitely — the worker threads are daemon, so anything still
# running past this window dies with the interpreter.
_SYNC_DRAIN_TIMEOUT_S = 5.0
_EXTERNAL_PREFETCH_TIMEOUT_S = 8.0
def normalize_tool_schema(schema: Any) -> Optional[Dict[str, Any]]:
@@ -358,19 +357,10 @@ class MemoryManager:
provider is allowed. Failures in one provider never block the other.
"""
def __init__(self, *, external_prefetch_timeout: Optional[float] = None) -> None:
def __init__(self) -> None:
self._providers: List[MemoryProvider] = []
self._tool_to_provider: Dict[str, MemoryProvider] = {}
self._has_external: bool = False # True once a non-builtin provider is added
self._external_prefetch_timeout = (
_EXTERNAL_PREFETCH_TIMEOUT_S
if external_prefetch_timeout is None
else float(external_prefetch_timeout)
)
if self._external_prefetch_timeout <= 0:
raise ValueError("external_prefetch_timeout must be positive")
self._external_prefetch_threads: Dict[str, threading.Thread] = {}
self._external_prefetch_lock = threading.Lock()
# 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
@@ -378,16 +368,6 @@ class MemoryManager:
# _submit_background() and the sync_all/queue_prefetch_all rationale.
self._sync_executor: Optional[ThreadPoolExecutor] = None
self._sync_executor_lock = threading.Lock()
# Futures are tracked by durability class so shutdown can give writes
# a bounded FIFO drain, then explicitly report anything abandoned.
self._background_futures: Dict[Future, str] = {}
self._shutting_down = False
self._shutdown_drain_state: Dict[str, Any] = {
"status": "not_started",
"abandoned_writes": 0,
"abandoned_prefetches": 0,
"active_tasks": 0,
}
# -- Registration --------------------------------------------------------
@@ -524,7 +504,7 @@ class MemoryManager:
parts = []
for provider in self._providers:
try:
result = self._prefetch_provider(provider, clean_query, session_id=session_id)
result = provider.prefetch(clean_query, session_id=session_id)
if result and result.strip():
parts.append(result)
except Exception as e:
@@ -534,56 +514,6 @@ class MemoryManager:
)
return "\n\n".join(parts)
def _prefetch_provider(
self, provider: MemoryProvider, query: str, *, session_id: str = ""
) -> str:
if provider.name == "builtin":
return provider.prefetch(query, session_id=session_id)
result_box: Dict[str, str] = {}
error_box: Dict[str, Exception] = {}
def _run() -> None:
try:
result_box["value"] = provider.prefetch(query, session_id=session_id) or ""
except Exception as exc: # pragma: no cover - re-raised by caller
error_box["value"] = exc
thread = threading.Thread(
target=_run,
daemon=True,
name=f"memory-prefetch-{provider.name}",
)
with self._external_prefetch_lock:
existing = self._external_prefetch_threads.get(provider.name)
if existing is not None:
if existing.is_alive():
logger.debug(
"Memory provider '%s' prefetch is still running; skipping this turn",
provider.name,
)
return ""
self._external_prefetch_threads.pop(provider.name, None)
self._external_prefetch_threads[provider.name] = thread
thread.start()
thread.join(self._external_prefetch_timeout)
if thread.is_alive():
logger.warning(
"Memory provider '%s' prefetch timed out after %.1fs; skipping it until "
"the stuck call returns",
provider.name,
self._external_prefetch_timeout,
)
return ""
with self._external_prefetch_lock:
if self._external_prefetch_threads.get(provider.name) is thread:
self._external_prefetch_threads.pop(provider.name, None)
if error_box:
raise error_box["value"]
return result_box.get("value", "")
def queue_prefetch_all(self, query: str, *, session_id: str = "") -> None:
"""Queue background prefetch on all providers for the next turn.
@@ -609,7 +539,7 @@ class MemoryManager:
provider.name, e,
)
self._submit_background(_run, kind="prefetch")
self._submit_background(_run)
# -- Sync ----------------------------------------------------------------
@@ -685,59 +615,43 @@ class MemoryManager:
# -- Background dispatch -------------------------------------------------
def _submit_background(self, fn, *, kind: str = "write") -> None:
"""Queue ``fn`` on the serialized worker and track its durability class."""
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:
if self._shutting_down:
logger.warning("Memory manager is shutting down; rejecting late %s task", kind)
return
# Creation failure outside shutdown: preserve the historical
# fail-safe behavior and run the operation inline.
# Executor unavailable (shut down / creation failed) — run
# inline rather than drop the work. Slow, but correct.
try:
fn()
except Exception as e: # pragma: no cover - fn guards internally
logger.debug("Inline memory background task failed: %s", e)
return
try:
# Make submit+tracking atomic with the shutdown snapshot. The
# callback is attached after releasing the lock because an already
# completed future invokes callbacks synchronously.
with self._sync_executor_lock:
if self._shutting_down:
logger.warning("Memory manager is shutting down; rejecting late %s task", kind)
return
future = executor.submit(fn)
self._background_futures[future] = kind
future.add_done_callback(self._forget_background_future)
executor.submit(fn)
except RuntimeError:
if self._shutting_down:
logger.warning("Memory manager shut down during %s submission; task rejected", kind)
return
# 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 _forget_background_future(self, future: Future) -> None:
with self._sync_executor_lock:
self._background_futures.pop(future, None)
def _get_sync_executor(self) -> Optional[ThreadPoolExecutor]:
"""Lazily create the single-worker background executor."""
if self._shutting_down:
return None
if self._sync_executor is not None:
return self._sync_executor
with self._sync_executor_lock:
if self._shutting_down:
return None
if self._sync_executor is None:
try:
# Daemon workers (see tools.daemon_pool): a provider wedged
# on a network call must never block interpreter exit.
from tools.daemon_pool import DaemonThreadPoolExecutor
self._sync_executor = DaemonThreadPoolExecutor(
self._sync_executor = ThreadPoolExecutor(
max_workers=1,
thread_name_prefix="mem-sync",
)
@@ -864,55 +778,6 @@ class MemoryManager:
exc_info=True,
)
def commit_session_boundary_async(
self,
messages: List[Dict[str, Any]],
*,
new_session_id: str,
parent_session_id: str = "",
reason: str = "new_session",
) -> None:
"""Queue old-session extraction + provider rebinding as ONE serialized task.
Session rotation (/new) must deliver ``on_session_end`` (end-of-session
extraction — an LLM-bound call that can take seconds) strictly BEFORE
``on_session_switch`` (which rebinds provider-internal ``_session_id`` /
turn buffers to the new session). Running extraction inline blocked the
/new command for the whole LLM round-trip (#16454); running it on an
ad-hoc thread raced the inline switch — providers key off internal
state, so a late ``on_session_end`` ran against post-switch bindings
(transcript misattributed to the new session id, double-ingest of the
old turn buffer, new-session buffers cleared).
Submitting BOTH hooks as one task on the manager's single background
worker gives both properties at a single chokepoint: the caller returns
immediately, and the worker's FIFO order serializes end→switch against
every other provider write (per-turn ``sync_all``, prefetches), which
already share the same worker. If the executor is unavailable,
``_submit_background`` degrades to inline execution — the pre-#16454
synchronous behavior, slow but correct.
"""
if not self._providers:
return
snapshot = list(messages or [])
def _run() -> None:
try:
self.on_session_end(snapshot)
except Exception as e: # pragma: no cover - on_session_end guards per-provider
logger.warning("Session-boundary extraction failed: %s", e)
try:
self.on_session_switch(
new_session_id,
parent_session_id=parent_session_id,
reset=True,
reason=reason,
)
except Exception as e: # pragma: no cover - on_session_switch guards per-provider
logger.warning("Session-boundary switch failed: %s", e)
self._submit_background(_run)
def on_session_switch(
self,
new_session_id: str,
@@ -1150,66 +1015,51 @@ class MemoryManager:
provider.name, e,
)
@property
def shutdown_drain_state(self) -> Dict[str, Any]:
"""Snapshot of the most recent bounded shutdown drain outcome."""
with self._sync_executor_lock:
return dict(self._shutdown_drain_state)
def _drain_sync_executor(self) -> None:
"""Give queued FIFO work a bounded chance, then abandon explicitly."""
"""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:
self._shutting_down = True
executor = self._sync_executor
self._sync_executor = None
tracked = dict(self._background_futures)
self._shutdown_drain_state = {
"status": "draining" if executor is not None else "drained",
"abandoned_writes": 0,
"abandoned_prefetches": 0,
"active_tasks": sum(not future.done() for future in tracked),
}
if executor is None:
return
# shutdown(wait=False) closes submission without touching the FIFO.
# Waiting on the tracked futures lets the real single-worker executor
# run every queued write/boundary task in order up to the deadline.
executor.shutdown(wait=False, cancel_futures=False)
_, pending = wait(tuple(tracked), timeout=_SYNC_DRAIN_TIMEOUT_S)
if not pending:
with self._sync_executor_lock:
self._shutdown_drain_state.update(status="drained", active_tasks=0)
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
abandoned_writes = 0
abandoned_prefetches = 0
active_tasks = 0
for future in pending:
kind = tracked[future]
if future.cancel():
if kind == "prefetch":
abandoned_prefetches += 1
else:
abandoned_writes += 1
else:
active_tasks += 1
with self._sync_executor_lock:
self._shutdown_drain_state.update(
status="timed_out",
abandoned_writes=abandoned_writes,
abandoned_prefetches=abandoned_prefetches,
active_tasks=active_tasks,
)
logger.warning(
"Memory shutdown drain timed out after %.2fs; abandoning %d queued "
"memory write(s) and %d queued prefetch(es); %d active task(s) remain detached",
_SYNC_DRAIN_TIMEOUT_S,
abandoned_writes,
abandoned_prefetches,
active_tasks,
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.
+66 -662
View File
@@ -14,7 +14,6 @@ from concurrent.futures import ThreadPoolExecutor
from typing import Any
from agent.auxiliary_client import call_llm
from agent.message_content import flatten_message_text
from agent.transports import get_transport
logger = logging.getLogger(__name__)
@@ -27,60 +26,6 @@ logger = logging.getLogger(__name__)
# opening dozens of sockets at once.
_MAX_REFERENCE_WORKERS = 8
class _RefAccounting:
"""Per-reference token usage + estimated cost + full trace, carried as the
third slot of a reference-output tuple.
Kept as a tiny object (not a bare CanonicalUsage) because an advisor may
run on a different model/provider than the aggregator, so its cost MUST be
priced at its OWN model's rate — folding advisor tokens into the
aggregator's usage and pricing the sum at the aggregator's rate would
misprice every advisor. ``usage`` feeds accurate token counts;
``cost_usd`` feeds accurate cost.
``messages`` / ``output`` / ``model`` / ``provider`` / ``temperature``
carry the FULL reference input and output for trace persistence (the
display ``text`` is a truncated preview and is not enough to audit what an
advisor actually saw). They are only populated when tracing is on; they add
negligible cost otherwise.
"""
__slots__ = (
"usage",
"cost_usd",
"cost_status",
"cost_source",
"messages",
"output",
"model",
"provider",
"temperature",
)
def __init__(
self,
usage: Any,
cost_usd: Any = None,
cost_status: str | None = None,
cost_source: str | None = None,
*,
messages: Any = None,
output: str | None = None,
model: str | None = None,
provider: str | None = None,
temperature: Any = None,
):
self.usage = usage
self.cost_usd = cost_usd
self.cost_status = cost_status
self.cost_source = cost_source
self.messages = messages
self.output = output
self.model = model
self.provider = provider
self.temperature = temperature
# 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
@@ -120,54 +65,11 @@ _REFERENCE_SYSTEM_PROMPT = (
def _slot_label(slot: dict[str, Any]) -> str:
label = f"{(slot.get('provider') or '').strip()}:{(slot.get('model') or '').strip()}"
effort = str(slot.get("reasoning_effort") or "").strip()
return f"{label}[reasoning={effort}]" if effort else label
def _slot_label(slot: dict[str, str]) -> str:
return f"{slot.get('provider', '').strip()}:{slot.get('model', '').strip()}"
def _slot_reasoning_config(slot: dict[str, Any]) -> dict[str, Any] | None:
"""Translate optional per-MoA-slot reasoning_effort into runtime config."""
effort = slot.get("reasoning_effort")
try:
from hermes_constants import parse_reasoning_effort
return parse_reasoning_effort(effort)
except Exception: # pragma: no cover - defensive; bad config must not break MoA
return None
def _aggregator_reasoning_config(aggregator: dict[str, Any]) -> dict[str, Any] | None:
"""Resolve the aggregator's reasoning config: slot > per-model > global.
The aggregator is MoA's ACTING model, so when its slot doesn't pin a
reasoning_effort it must resolve exactly like any other acting model:
through the shared chokepoint (``resolve_reasoning_config``), which
applies ``agent.reasoning_overrides`` for the slot's model first, then
the global ``agent.reasoning_effort``. Without this the main loop's
reasoning gates (keyed to the virtual ``moa://local`` identity) never
fire, so the aggregator silently ran at the backend default (#64187).
Reference advisors intentionally do NOT get this fallback: they are side
calls (like auxiliary tasks), and inheriting a global ``xhigh`` into every
advisor fan-out would silently multiply cost. Their depth is slot-or-
provider-default only.
"""
cfg = _slot_reasoning_config(aggregator)
if cfg is not None:
return cfg
try:
from hermes_cli.config import load_config
from hermes_constants import resolve_reasoning_config
return resolve_reasoning_config(
load_config() or {}, str(aggregator.get("model") or "")
)
except Exception: # pragma: no cover - defensive; bad config must not break MoA
return None
def _slot_runtime(slot: dict[str, Any]) -> dict[str, Any]:
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
@@ -191,84 +93,35 @@ def _slot_runtime(slot: dict[str, Any]) -> dict[str, Any]:
from hermes_cli.runtime_provider import resolve_runtime_provider
rt = resolve_runtime_provider(requested=provider, target_model=model)
# Forward the resolved endpoint through to call_llm unconditionally.
# call_llm's _resolve_task_provider_model() is the single chokepoint that
# decides whether an explicit base_url collapses a call to the generic
# ``custom`` route or keeps the provider's real identity: it preserves
# identity for any first-class provider (via
# _preserve_provider_with_base_url, a provider-catalog capability check),
# so provider branches that add auth refresh / request metadata /
# request-shape adapters — anthropic OAuth (Bearer + anthropic-beta),
# openai-codex Responses wrapping + Cloudflare headers, xai-oauth,
# bedrock SigV4 signing, nous Portal tags — still fire. Those branches
# re-resolve their own credentials by name and ignore a forwarded
# base_url/api_key, so forwarding is safe even for a placeholder key
# (bedrock's "aws-sdk"). We used to maintain a name-preservation set here
# too; that duplicated the chokepoint and drifted out of sync, so the
# single source of truth now lives in call_llm.
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"]
if rt.get("api_mode"):
out["api_mode"] = rt["api_mode"]
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 _maybe_apply_moa_cache_control(
messages: list[dict[str, Any]],
runtime: dict[str, Any],
) -> list[dict[str, Any]]:
"""Decorate an advisor or aggregator request with cache_control when its
route honors it.
Reuses the SAME policy function as the main agent loop
(``anthropic_prompt_cache_policy``) resolved against the slot's own
provider/base_url/api_mode/model, and the SAME breakpoint layout
(``apply_anthropic_cache_control``, system_and_3). This keeps advisor and
aggregator calls decorated exactly like an acting agent on that provider
would be — no MoA-specific caching logic to drift.
Returns the messages unchanged on any resolution error or when the
policy says the route doesn't honor markers.
"""
try:
from types import SimpleNamespace
from agent.agent_runtime_helpers import anthropic_prompt_cache_policy
from agent.prompt_caching import apply_anthropic_cache_control
# The policy function reads agent.* only as fallbacks for kwargs we
# don't pass; provide a stub so the slot is judged purely on its own
# resolved runtime.
stub = SimpleNamespace(provider="", base_url="", api_mode="", model="")
should_cache, native_layout = anthropic_prompt_cache_policy(
stub,
provider=runtime.get("provider") or "",
base_url=runtime.get("base_url") or "",
api_mode=runtime.get("api_mode") or "",
model=runtime.get("model") or "",
)
if not should_cache:
return messages
return apply_anthropic_cache_control(
messages, native_anthropic=native_layout
)
except Exception as exc: # pragma: no cover - decoration must never break a call
logger.debug("MoA cache_control decoration skipped: %s", exc)
return messages
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, Any]:
"""Call one reference model and return ``(label, text, usage)``.
) -> 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
@@ -279,103 +132,29 @@ def _run_reference(
real maximum); ``temperature`` is only the user's configured preset value,
which call_llm may still override per model.
The reference's token usage is normalized with the slot's OWN resolved
provider/api_mode (advisors may run on a different provider than the
aggregator, with different usage wire shapes) and returned as a
``CanonicalUsage`` so the caller can fold advisor spend into session
accounting. Without this, the entire reference fan-out — often the bulk of
a MoA turn's token spend — is invisible to cost tracking, which only ever
saw the aggregator's usage.
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.
"""
from agent.usage_pricing import CanonicalUsage, estimate_usage_cost, normalize_usage
label = _slot_label(slot)
runtime = _slot_runtime(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]
# Apply the same Anthropic-style prompt-caching decoration the main
# agent loop applies (system_and_3 breakpoints). The advisory view is
# append-only across iterations (new turns append before the trailing
# synthetic marker), so on cache-honoring routes (Claude via
# OpenRouter/native, MiniMax, Qwen/DashScope) iteration N+1's prefix
# replays iteration N's cached prefix. Without this, Claude advisors
# served ZERO cache reads across an entire benchmark run (measured:
# 0/1227 calls, 11.5M re-billed input tokens) because Anthropic
# caching is opt-in per request. OpenAI-family advisors are untouched
# (their caching is automatic; markers are ignored harmlessly, but we
# only decorate when the policy says the route honors them).
messages = _maybe_apply_moa_cache_control(messages, runtime)
response = call_llm(
task="moa_reference",
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
reasoning_config=_slot_reasoning_config(slot),
**runtime,
**_slot_runtime(slot),
)
usage = CanonicalUsage()
raw_usage = getattr(response, "usage", None)
if raw_usage:
try:
usage = normalize_usage(
raw_usage,
provider=runtime.get("provider"),
api_mode=runtime.get("api_mode"),
)
except Exception: # pragma: no cover - defensive
usage = CanonicalUsage()
# Price this advisor at ITS OWN model/provider rate (with correct
# cache-read/cache-write split), not the aggregator's. This is why
# advisor cost is summed as dollars rather than by folding tokens into
# the aggregator's usage.
cost_usd = None
cost_status = None
cost_source = None
try:
cost = estimate_usage_cost(
slot.get("model") or "",
usage,
provider=runtime.get("provider"),
base_url=runtime.get("base_url"),
api_key=runtime.get("api_key"),
)
cost_usd = cost.amount_usd
cost_status = cost.status
cost_source = cost.source
except Exception: # pragma: no cover - defensive
pass
_output_text = _extract_text(response) or "(empty response)"
acct = _RefAccounting(
usage,
cost_usd,
cost_status,
cost_source,
messages=messages,
output=_output_text,
model=slot.get("model"),
provider=runtime.get("provider") or slot.get("provider"),
temperature=temperature,
)
return label, _output_text, acct
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}]", _RefAccounting(
CanonicalUsage(),
messages=[{"role": "system", "content": _REFERENCE_SYSTEM_PROMPT}, *ref_messages],
output=f"[failed: {exc}]",
model=slot.get("model"),
provider=runtime.get("provider") or slot.get("provider"),
temperature=temperature,
)
return label, f"[failed: {exc}]"
def _run_references_parallel(
@@ -384,7 +163,7 @@ def _run_references_parallel(
*,
temperature: float | None = None,
max_tokens: int | None = None,
) -> list[tuple[str, str, Any]]:
) -> 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
@@ -392,36 +171,24 @@ def _run_references_parallel(
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.
Each element is ``(label, text, usage)`` where usage is a
``CanonicalUsage`` (zeroed for skipped/failed references).
"""
from agent.usage_pricing import CanonicalUsage
if not reference_models:
return []
results: list[tuple[str, str, Any] | None] = [None] * len(reference_models)
results: list[tuple[str, str] | None] = [None] * len(reference_models)
futures = {}
workers = min(_MAX_REFERENCE_WORKERS, len(reference_models))
# Reference slots run on bare executor threads, which start with an empty
# contextvars.Context — propagate the parent turn's context (approval
# callbacks + the Nous Portal conversation tag) into each worker so
# advisor calls attribute to the same conversation as the acting turn.
from tools.thread_context import propagate_context_to_thread
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]",
_RefAccounting(CanonicalUsage()),
)
continue
futures[
executor.submit(
propagate_context_to_thread(_run_reference),
_run_reference,
slot,
ref_messages,
temperature=temperature,
@@ -477,14 +244,6 @@ def _render_tool_calls(tool_calls: Any) -> str:
return "\n".join(lines)
_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.]"
)
def _reference_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Build an advisory view of the conversation for reference models.
@@ -516,57 +275,25 @@ def _reference_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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")
# Flatten structured content (lists of parts) to visible text. Content
# arrives as a list — not a string — in two common cases:
# 1. Anthropic prompt-cache decoration: conversation_loop runs
# apply_anthropic_cache_control BEFORE the MoA facade, converting
# string content to [{"type": "text", "text": ..., "cache_control":
# ...}]. A str-only read here flattened the user's ENTIRE prompt to
# "" — Claude references then 400'd ("messages: at least one
# message is required") while tolerant models answered "no user
# request is present".
# 2. Multimodal turns (pasted image → text + image_url parts) and
# multimodal tool results (screenshots).
# flatten_message_text extracts the text parts and skips image parts,
# and returns strings unchanged — so a decorated and an undecorated
# transcript produce a byte-identical advisory view (which keeps the
# advisory prefix stable across iterations for advisor prompt caching).
text = flatten_message_text(content)
text = content if isinstance(content, str) else ""
if role == "system":
continue
if role == "user":
if not text.strip() and isinstance(content, list) and content:
# Structured content with no extractable text (e.g. an
# image-only turn). Emitting an empty user message would be
# dropped/rejected by strict providers (Anthropic 400s on
# empty text blocks — the original "closed" preset failure
# mode), and silently skipping the turn would break
# user/assistant alternation in the advisory view. Substitute
# a placeholder so the reference knows a non-text turn
# happened. Only structured content qualifies — an empty or
# whitespace-only STRING turn carries nothing and is dropped
# below instead.
text = "[user sent non-text content (e.g. an image attachment)]"
if not text.strip():
# Genuinely empty user turn (content="" / None). It carries
# nothing advisory, and strict providers (Kimi/Moonshot, ZAI,
# and others that enforce non-empty user content) reject it
# with 400 "message ... with role 'user' must not be empty" —
# the same way the assistant branch below drops turns with no
# parts. Lenient providers (DeepSeek) accept the empty turn,
# which is why a MoA fan-out would fail on one reference and
# pass on another for the identical rendered view. The
# advisory view is already not strictly alternating (adjacent
# assistant turns occur in every tool loop), so dropping a
# contentless turn is safe.
continue
last_user_content = text
if text.strip():
last_user_content = text
rendered.append({"role": "user", "content": text})
elif role == "assistant":
parts: list[str] = []
@@ -596,7 +323,7 @@ def _reference_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
# 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})
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.
@@ -607,10 +334,8 @@ def _reference_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
if last_user_content is not None:
return [{"role": "user", "content": last_user_content}]
for msg in reversed(messages):
if msg.get("role") == "user":
fallback_text = flatten_message_text(msg.get("content"))
if fallback_text.strip():
return [{"role": "user", "content": fallback_text}]
if msg.get("role") == "user" and isinstance(msg.get("content"), str):
return [{"role": "user", "content": msg["content"]}]
return rendered
@@ -627,47 +352,20 @@ def _extract_text(response: Any) -> str:
except Exception:
pass
try:
message = response.choices[0].message
if isinstance(message, dict):
content = message.get("content")
else:
content = getattr(message, "content", message)
if not isinstance(content, str):
content = str(content) if content else ""
return content.strip()
content = response.choices[0].message.content
return (content or "").strip()
except Exception:
return ""
def _preset_temperature(preset: dict[str, Any], key: str) -> float | None:
"""Read an optional temperature from a preset.
Returns None when the key is absent, empty, or explicitly null — meaning
"don't send temperature; let the provider default apply", exactly like a
single-model Hermes agent (which never sends temperature unless
configured). The old coercion ``float(preset.get(key, 0.6) or 0.6)``
made unset impossible: absent, null, and even 0 all collapsed to the
hardcoded default, so MoA advisors/aggregator always ran at 0.6/0.4
while the same model running solo used the provider default.
"""
value = preset.get(key)
if value is None or (isinstance(value, str) and not value.strip()):
return None
try:
return float(value)
except (TypeError, ValueError):
logger.warning("ignoring non-numeric %s=%r in MoA preset", key, value)
return None
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 | None = None,
aggregator_temperature: float | None = None,
temperature: float = 0.6,
aggregator_temperature: float = 0.4,
max_tokens: int | None = None,
) -> str:
"""Run configured reference models and synthesize their advice.
@@ -680,13 +378,8 @@ def aggregate_moa_context(
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.
``temperature`` / ``aggregator_temperature`` are ``None`` by default:
like max_tokens, ``call_llm`` omits temperature when None so the
provider default applies — matching single-model agent behavior. Presets
may still pin explicit values.
"""
reference_outputs: list[tuple[str, str, Any]] = []
reference_outputs: list[tuple[str, str]] = []
ref_messages = _reference_messages(api_messages)
reference_outputs = _run_references_parallel(
reference_models,
@@ -697,7 +390,7 @@ def aggregate_moa_context(
joined = "\n\n".join(
f"Reference {idx} — {label}:\n{text}"
for idx, (label, text, _usage) in enumerate(reference_outputs, start=1)
for idx, (label, text) in enumerate(reference_outputs, start=1)
)
synth_prompt = (
"You are the aggregator in a Mixture of Agents process. Synthesize the "
@@ -710,28 +403,13 @@ def aggregate_moa_context(
)
agg_label = _slot_label(aggregator)
agg_runtime = _slot_runtime(aggregator)
try:
# Same cache_control decoration as _run_reference's advisor calls
# (see _maybe_apply_moa_cache_control) — this synthesis call is a
# third, independent MoA call path that 22c5048d9 did not cover (it
# only restored caching for the acting-aggregator turn in the
# persistent `provider: moa` model and for advisor fan-out). Without
# it, the one-shot `/moa <prompt>` command's synthesis call re-bills
# its full input (system-less prompt containing every joined
# reference output) on every invocation with zero cache_control
# breakpoints, even when the resolved aggregator slot is a
# cache-honoring route (e.g. Claude on OpenRouter/native Anthropic).
agg_messages = _maybe_apply_moa_cache_control(
[{"role": "user", "content": synth_prompt}], agg_runtime
)
response = call_llm(
task="moa_aggregator",
messages=agg_messages,
messages=[{"role": "user", "content": synth_prompt}],
temperature=aggregator_temperature,
max_tokens=max_tokens,
reasoning_config=_aggregator_reasoning_config(aggregator),
**agg_runtime,
**_slot_runtime(aggregator),
)
synthesis = _extract_text(response)
except Exception as exc:
@@ -751,43 +429,6 @@ def aggregate_moa_context(
)
def _attach_reference_guidance(agg_messages: list[dict[str, Any]], guidance: str) -> None:
"""Attach the per-turn reference block at the END of the aggregator prompt.
The reference text differs on every tool-loop iteration. In an agentic loop
the most recent ``user`` message is the *original task* sitting near the TOP
of the context (everything after it is assistant/tool turns), so merging the
turn-varying reference block into it diverges the prompt prefix early — the
server's KV cache cannot be reused and the entire conversation re-prefills on
every step (full prefill each tool call, dominating latency on long contexts).
Appending at the very end keeps the ``[system][task][tool-history]`` prefix
stable and cache-reusable (only the new block re-prefills), and gives the
aggregator the references with recency. Merge into the last message only when
it is already a trailing ``user`` turn (plain chat — still at the end).
A trailing user turn's content may be a STRING or a LIST of content parts —
Anthropic prompt-cache decoration (which runs before the MoA facade)
converts string content to ``[{"type": "text", ..., "cache_control": ...}]``,
and multimodal turns are lists natively. Both shapes are merged in place:
appending a new text part AFTER the cache_control-marked part keeps the
cached prefix byte-stable (the marker still terminates it) while the
turn-varying guidance rides outside the cached span. Appending a SEPARATE
user message here instead would produce two consecutive user turns —
strict providers reject that.
"""
last = agg_messages[-1] if agg_messages else None
if last is not None and last.get("role") == "user":
last_content = last.get("content")
if isinstance(last_content, str):
last["content"] = last_content + "\n\n" + guidance
return
if isinstance(last_content, list):
last["content"] = [*last_content, {"type": "text", "text": "\n\n" + guidance}]
return
agg_messages.append({"role": "user", "content": guidance})
class MoAChatCompletions:
"""OpenAI-chat-compatible facade where the aggregator is the acting model."""
@@ -813,88 +454,7 @@ class MoAChatCompletions:
# 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, Any]] = []
# Token usage + estimated cost of the reference fan-out from the most
# recent cache-MISS create() call, awaiting consumption by session
# accounting. Set on every create() (zeroed on a cache HIT so per-turn
# advisor spend is counted exactly once). Consumed via
# ``consume_reference_usage``.
from agent.usage_pricing import CanonicalUsage
self._pending_reference_usage: Any = CanonicalUsage()
self._pending_reference_cost: Any = None
# Resolved aggregator slot ({provider, model, ...}) from the most recent
# create(); read by session cost accounting to price the aggregator's
# acting turn at its real model instead of the virtual preset name.
self.last_aggregator_slot: Any = None
# Full-turn trace parts stashed on a cache-MISS create(), awaiting the
# caller to stitch in the live session_id + resolved aggregator output
# and flush to the trace file (only when moa.save_traces is on).
self._pending_trace: Any = None
def consume_reference_usage(self) -> tuple[Any, Any]:
"""Pop pending reference-fan-out usage + cost, resetting both to empty.
Returns ``(CanonicalUsage, cost_usd_or_None)`` for the most recent
``create()`` and clears the pending values, so a subsequent read (e.g.
a streaming retry re-entering accounting) cannot double-count. Usage is
always a ``CanonicalUsage`` (zeroed if none); cost is a summed-dollars
float or ``None`` when no advisor could be priced.
"""
from agent.usage_pricing import CanonicalUsage
usage = self._pending_reference_usage or CanonicalUsage()
cost = self._pending_reference_cost
self._pending_reference_usage = CanonicalUsage()
self._pending_reference_cost = None
return usage, cost
def consume_and_save_trace(
self, session_id: Any = None, aggregator_output_fallback: Any = None
) -> None:
"""Flush the pending full-turn trace to disk, if one is pending.
No-op when tracing is off (``save_moa_turn`` checks the config), when
there is no pending trace (a cache-HIT iteration ran no references), or
when the aggregator input was never recorded. Clears the pending trace
so a repeat consume cannot double-write. Best-effort — never raises.
``aggregator_output_fallback`` is the aggregator's resolved acting text
as the caller already holds it in memory (the streamed assistant text).
On the streaming path the aggregator's output could not be captured
inline at ``create()`` time (the raw token stream was handed to the live
consumer), so ``pending["aggregator_output"]`` is None; we fold the
caller's resolved text in here so the trace is self-contained in BOTH
streaming and non-streaming modes. Non-streaming already has the inline
output and ignores the fallback.
"""
pending = self._pending_trace
self._pending_trace = None
if not pending or "aggregator_input_messages" not in pending:
return
try:
from agent.moa_trace import save_moa_turn
agg_slot = pending.get("aggregator_slot") or {}
# Prefer the inline capture (non-streaming); fall back to the
# caller's resolved streamed text when streaming left it None.
agg_output = pending.get("aggregator_output")
if agg_output is None and aggregator_output_fallback:
agg_output = aggregator_output_fallback
save_moa_turn(
session_id=session_id,
preset_name=pending.get("preset", ""),
reference_outputs=pending.get("reference_outputs", []),
aggregator_label=pending.get("aggregator_label", ""),
aggregator_model=agg_slot.get("model"),
aggregator_provider=agg_slot.get("provider"),
aggregator_temperature=pending.get("aggregator_temperature"),
aggregator_input_messages=pending.get("aggregator_input_messages"),
aggregator_output=agg_output,
aggregator_streamed=bool(pending.get("aggregator_streamed")),
)
except Exception as exc: # pragma: no cover - tracing must never break a turn
logger.debug("MoA trace flush failed: %s", exc)
self._ref_cache_outputs: list[tuple[str, str]] = []
def _emit(self, event: str, **kwargs: Any) -> None:
cb = self.reference_callback
@@ -913,33 +473,12 @@ class MoAChatCompletions:
messages = list(api_kwargs.get("messages") or [])
reference_models = preset.get("reference_models") or []
aggregator = preset.get("aggregator") or {}
# Expose the resolved aggregator slot so session cost accounting can
# price the aggregator's acting turn at its REAL model/provider. The
# agent's model/provider on the MoA path are the virtual preset name
# ("closed") and "moa", which have no pricing entry — without this the
# aggregator's spend (often the bulk of the turn) is silently dropped
# and the session cost reflects advisor fan-out only.
self.last_aggregator_slot = dict(aggregator) if aggregator else None
# By default MoA does not cap reference or aggregator output: each model
# uses its own maximum (max_tokens=None → call_llm omits the parameter,
# so a long aggregator synthesis is never truncated and providers that
# reject max_tokens don't 400). A preset MAY set reference_max_tokens to
# cap ADVISOR output only — advisor generation is the dominant MoA
# latency (turn latency correlates ~0.88 with output tokens), and the
# aggregator only needs the gist of each advisor's judgement, so a cap
# (e.g. 600) measurably cuts per-turn wall time (~44% on a sample task).
# The acting aggregator is never capped here (its output is the
# user-visible answer).
reference_max_tokens = preset.get("reference_max_tokens")
# None (the default) = don't send temperature; provider default
# applies, matching single-model agent behavior. Presets may pin
# explicit values. See _preset_temperature.
temperature = _preset_temperature(preset, "reference_temperature")
aggregator_temperature = _preset_temperature(preset, "aggregator_temperature")
if aggregator_temperature is None and api_kwargs.get("temperature") is not None:
# The acting agent's own configured temperature (if any) still
# applies to the aggregator, which IS the acting model.
aggregator_temperature = api_kwargs.get("temperature")
# 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
@@ -947,46 +486,16 @@ class MoAChatCompletions:
if not preset.get("enabled", True):
reference_models = []
from agent.usage_pricing import CanonicalUsage
reference_outputs: list[tuple[str, str, Any]] = []
reference_outputs: list[tuple[str, str]] = []
ref_messages = _reference_messages(messages)
# Fan-out cadence. "per_iteration" (default): advisors re-run whenever
# the advisory view changes — i.e. every tool iteration, since the
# view grows with each tool result. "user_turn": advisors run ONCE per
# user turn; subsequent tool iterations reuse that turn's advice and
# the aggregator acts alone (the original MoA shape: synthesize at the
# start, then let the acting model work). Implemented by hashing only
# the prefix up to the LAST USER message so mid-turn growth doesn't
# change the signature — iteration 2+ becomes a cache HIT.
fanout_mode = str(preset.get("fanout") or "per_iteration").strip().lower()
sig_messages = ref_messages
if fanout_mode == "user_turn":
# Find the last REAL user message. The advisory view appends a
# synthetic user marker (_ADVISORY_INSTRUCTION) when it ends on an
# assistant turn — i.e. on every tool iteration after the first —
# so that marker must not count as a user turn or the prefix
# would include the grown mid-turn context and the signature
# would change every iteration (defeating the once-per-turn
# cadence entirely).
last_user_idx = None
for _i in range(len(ref_messages) - 1, -1, -1):
_m = ref_messages[_i]
if _m.get("role") == "user" and _m.get("content") != _ADVISORY_INSTRUCTION:
last_user_idx = _i
break
if last_user_idx is not None:
sig_messages = ref_messages[: last_user_idx + 1]
# 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; in user_turn mode the
# signature is stable across those iterations (prefix hash above), so
# the fan-out runs once per user turn and iterations reuse the advice.
# 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 sig_messages
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))
@@ -994,54 +503,15 @@ class MoAChatCompletions:
if _refs_from_cache:
reference_outputs = list(self._ref_cache_outputs)
# References already ran (and were accounted) earlier this turn;
# this create() is a repeat tool-iteration reusing the cached
# advice. Charging their tokens/cost again here would multiply
# advisor spend by the tool-iteration count, so pending is zero.
self._pending_reference_usage = CanonicalUsage()
self._pending_reference_cost = None
# Likewise no trace on a cache HIT — the full turn was already
# traced on the MISS that ran the references. A repeat iteration is
# not a new MoA turn.
self._pending_trace = None
else:
reference_outputs = _run_references_parallel(
reference_models,
ref_messages,
temperature=temperature,
max_tokens=reference_max_tokens,
max_tokens=None,
)
self._ref_cache_key = _cache_key
self._ref_cache_outputs = list(reference_outputs)
# Sum the advisor fan-out's token usage AND cost so the caller can
# fold advisor spend into session accounting exactly once per turn.
# Only the freshly run references (cache MISS) contribute; a cache
# HIT above zeroes this. Token counts sum directly (each already
# normalized per-advisor provider/api_mode); cost sums in dollars
# because each advisor was priced at its OWN model rate — advisors
# may be cheaper/pricier than the aggregator, so their tokens must
# NOT be repriced at the aggregator's rate.
_ref_usage = CanonicalUsage()
_ref_cost: Any = None
for _lbl, _txt, _acct in reference_outputs:
if isinstance(_acct, _RefAccounting):
if isinstance(_acct.usage, CanonicalUsage):
_ref_usage = _ref_usage + _acct.usage
if _acct.cost_usd is not None:
_ref_cost = (_ref_cost or 0) + _acct.cost_usd
self._pending_reference_usage = _ref_usage
self._pending_reference_cost = _ref_cost
# Stash the full reference fan-out for trace persistence. The
# aggregator input/label are filled in below once agg_messages is
# built; the aggregator OUTPUT is stitched in by the caller
# (consume_and_save_trace) once the response resolves — the caller
# holds the live session_id and the resolved aggregator response.
self._pending_trace = {
"preset": self.preset_name,
"reference_outputs": list(reference_outputs),
"aggregator_slot": aggregator,
"aggregator_temperature": aggregator_temperature,
}
# Surface each reference model's answer to the display BEFORE the
# aggregator acts — once per turn (only on the iteration that
@@ -1050,7 +520,7 @@ class MoAChatCompletions:
# visible rather than a silent pause. Best-effort: never blocks the
# turn.
_ref_count = len(reference_outputs)
for _idx, (_label, _text, _usage) in enumerate(reference_outputs, start=1):
for _idx, (_label, _text) in enumerate(reference_outputs, start=1):
self._emit(
"moa.reference",
index=_idx,
@@ -1069,29 +539,28 @@ class MoAChatCompletions:
if reference_outputs:
joined = "\n\n".join(
f"Reference {idx} — {label}:\n{text}"
for idx, (label, text, _usage) in enumerate(reference_outputs, start=1)
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"
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}"
)
_attach_reference_guidance(agg_messages, guidance)
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
# Record the exact aggregator INPUT (incl. the injected reference
# context) into the pending trace so a trace captures what the
# aggregator actually saw, not a reconstruction.
if self._pending_trace is not None:
self._pending_trace["aggregator_input_messages"] = agg_messages
self._pending_trace["aggregator_label"] = _slot_label(aggregator)
# 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
@@ -1100,83 +569,18 @@ class MoAChatCompletions:
# 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.
# When the agent's streaming consumer calls us with stream=True, run the
# references first (above) and then return the aggregator's RAW token
# stream so the acting model's output reaches the user live. The consumer
# reassembles chunks + tool_calls, runs stale-stream detection, and falls
# back to a non-streaming retry on error. The non-streaming path
# (stream=False) is unchanged — no stream/stream_options/timeout are
# forwarded, so its behavior is byte-for-byte identical to before.
stream = bool(api_kwargs.get("stream"))
stream_kwargs: dict[str, Any] = {}
if stream:
stream_kwargs["stream"] = True
stream_kwargs["stream_options"] = (
api_kwargs.get("stream_options") or {"include_usage": True}
)
# Forward the consumer's per-request (stream read) timeout so it
# actually governs the aggregator stream, not just call_llm's default.
if api_kwargs.get("timeout") is not None:
stream_kwargs["timeout"] = api_kwargs["timeout"]
_agg_response = call_llm(
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"),
reasoning_config=_aggregator_reasoning_config(aggregator),
**stream_kwargs,
**_slot_runtime(aggregator),
)
# Non-streaming path (quiet mode / eval / subagents): the aggregator
# output is available inline, so capture it into the pending trace now.
# Streaming path: the aggregator's raw token stream is returned to the
# consumer live and its acting output lands as the turn's assistant
# message; the trace marks it streamed and points there.
if self._pending_trace is not None:
if stream:
self._pending_trace["aggregator_streamed"] = True
self._pending_trace["aggregator_output"] = None
else:
self._pending_trace["aggregator_streamed"] = False
try:
self._pending_trace["aggregator_output"] = _extract_text(_agg_response)
except Exception: # pragma: no cover - defensive
self._pending_trace["aggregator_output"] = None
return _agg_response
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)
def consume_reference_usage(self) -> Any:
"""Pop the pending reference-fan-out usage from the completions facade.
Lets session accounting fold the MoA advisor tokens into the turn's
usage without reaching into ``.chat.completions`` internals.
"""
return self.chat.completions.consume_reference_usage()
@property
def last_aggregator_slot(self) -> Any:
"""Resolved aggregator slot ({provider, model, ...}) from the most
recent create(), or None. Read by session cost accounting to price the
aggregator's acting turn at its real model instead of the virtual
preset name."""
return getattr(self.chat.completions, "last_aggregator_slot", None)
def consume_and_save_trace(
self, session_id: Any = None, aggregator_output_fallback: Any = None
) -> None:
"""Flush the pending full-turn MoA trace via the completions facade.
No-op unless ``moa.save_traces`` is enabled and a turn is pending.
``aggregator_output_fallback`` supplies the resolved acting text so the
streaming path's trace is self-contained (see the facade docstring).
"""
return self.chat.completions.consume_and_save_trace(
session_id, aggregator_output_fallback=aggregator_output_fallback
)
-167
View File
@@ -1,167 +0,0 @@
"""Full MoA turn trace persistence (opt-in via config ``moa.save_traces``).
When enabled, every Mixture-of-Agents turn that actually runs the reference
fan-out (a cache MISS in ``MoAChatCompletions.create``) appends one JSON line
to ``<hermes_home>/moa-traces/<session_id>.jsonl``. The record is the TRUE
FULL turn — the exact messages array each reference model received (system
prompt + advisory view, not the truncated display preview), each reference's
full output, and the exact messages array the aggregator received (including
the injected reference-context guidance block) plus its output when available
— so a run can be audited end-to-end offline: what every model saw, what every
model said, and what it cost.
This is a side-channel trace. It is NOT the conversation ``messages`` table and
never enters message history or replay — MoA references are advisory side-calls
with their own system prompt, not conversation turns, so persisting them as
message rows would corrupt role alternation / replay. Traces live in their own
files, keyed by session id, and are safe to delete.
Cost model note: gated OFF by default. When off, the only overhead is the
``_traces_enabled()`` config read (cheap) — no file I/O, no serialization.
"""
from __future__ import annotations
import json
import logging
import os
import time
from pathlib import Path
from typing import Any, Optional
from hermes_constants import get_hermes_home
logger = logging.getLogger(__name__)
def _traces_enabled_and_dir() -> Optional[Path]:
"""Return the trace directory if ``moa.save_traces`` is on, else None.
Reads config lazily per call (config is cheap to load and this only runs on
a cache-MISS MoA turn, i.e. once per user turn, not per tool iteration).
``moa.trace_dir`` overrides the default ``<hermes_home>/moa-traces/``.
"""
try:
from hermes_cli.config import load_config
moa_cfg = (load_config() or {}).get("moa") or {}
except Exception: # pragma: no cover - defensive: never break a turn over tracing
return None
if not moa_cfg.get("save_traces"):
return None
override = moa_cfg.get("trace_dir")
if override:
base = Path(os.path.expandvars(os.path.expanduser(str(override))))
else:
base = get_hermes_home() / "moa-traces"
return base
def _sanitize_session_id(session_id: Optional[str]) -> str:
"""Make a session id safe as a filename component."""
if not session_id:
return "unknown-session"
return "".join(c if (c.isalnum() or c in "-_.") else "_" for c in str(session_id))
def _slot_trace(acct: Any, label: str) -> dict[str, Any]:
"""Render one reference's _RefAccounting into a full trace dict.
Includes the FULL input messages the reference received and its FULL
output — not the truncated display preview.
"""
usage = getattr(acct, "usage", None)
usage_dict: dict[str, Any] = {}
if usage is not None:
usage_dict = {
"input_tokens": getattr(usage, "input_tokens", 0),
"output_tokens": getattr(usage, "output_tokens", 0),
"cache_read_tokens": getattr(usage, "cache_read_tokens", 0),
"cache_write_tokens": getattr(usage, "cache_write_tokens", 0),
"reasoning_tokens": getattr(usage, "reasoning_tokens", 0),
}
return {
"label": label,
"model": getattr(acct, "model", None),
"provider": getattr(acct, "provider", None),
"temperature": getattr(acct, "temperature", None),
"input_messages": getattr(acct, "messages", None),
"output": getattr(acct, "output", None),
"usage": usage_dict,
"cost_usd": getattr(acct, "cost_usd", None),
"cost_status": getattr(acct, "cost_status", None),
"cost_source": getattr(acct, "cost_source", None),
}
def save_moa_turn(
*,
session_id: Optional[str],
preset_name: str,
reference_outputs: list[tuple[str, str, Any]],
aggregator_label: str,
aggregator_model: Optional[str],
aggregator_provider: Optional[str],
aggregator_temperature: Any,
aggregator_input_messages: Any,
aggregator_output: Optional[str],
aggregator_streamed: bool,
) -> None:
"""Append one full MoA turn record to the session's trace JSONL, if enabled.
Best-effort: any failure is logged at debug and swallowed — tracing must
never break a live turn. Called once per turn on a reference cache MISS.
``aggregator_output`` is the aggregator's synthesized text. On the
non-streaming path (eval / quiet-mode / subagents) it was captured inline
at call time. On the streaming path it is captured after the fact from the
caller's resolved assistant text (``aggregator_output_fallback`` in
``consume_and_save_trace``) so the trace is self-contained either way; if
that resolved text was unavailable, it falls back to None and the record
points at the session store via ``output_location``.
"""
base = _traces_enabled_and_dir()
if base is None:
return
try:
base.mkdir(parents=True, exist_ok=True)
path = base / f"{_sanitize_session_id(session_id)}.jsonl"
# output_location tells an offline reader where the acting text lives:
# embedded here when we have it (both non-streaming inline capture and
# streaming after-the-fact capture), else the session-db assistant row.
_have_output = bool(aggregator_output)
if not aggregator_streamed:
_output_location = "inline"
elif _have_output:
_output_location = "inline_from_stream"
else:
_output_location = "assistant_message_in_session_db"
record = {
"ts": time.time(),
"session_id": session_id,
"preset": preset_name,
"references": [
_slot_trace(acct, label)
for label, _text, acct in reference_outputs
],
"aggregator": {
"label": aggregator_label,
"model": aggregator_model,
"provider": aggregator_provider,
"temperature": aggregator_temperature,
"input_messages": aggregator_input_messages,
"output": aggregator_output,
"streamed": aggregator_streamed,
# Where the aggregator's acting output lives for this record.
# "inline" — non-streaming inline capture
# "inline_from_stream" — streamed, then captured from the
# caller's resolved assistant text
# "assistant_message_in_session_db" — streamed and the resolved
# text was unavailable at flush time
"output_location": _output_location,
},
}
with path.open("a", encoding="utf-8") as f:
f.write(json.dumps(record, ensure_ascii=False, default=str) + "\n")
except Exception as exc: # pragma: no cover - tracing must never break a turn
logger.debug("MoA trace write failed (session=%s): %s", session_id, exc)
+89 -695
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File diff suppressed because it is too large Load Diff
+2 -33
View File
@@ -15,9 +15,6 @@ and MoonshotAI/kimi-cli#1595:
2. When ``anyOf`` is used, ``type`` must be on the ``anyOf`` children, not
the parent. Presence of both causes "type should be defined in anyOf
items instead of the parent schema".
3. Every object schema must carry a ``required`` array, even an empty one.
Standard JSON Schema allows omitting it; Moonshot 400s with
"required must be an array".
The ``#/definitions/...`` → ``#/$defs/...`` rewrite for draft-07 refs is
handled separately in ``tools/mcp_tool._normalize_mcp_input_schema`` so it
@@ -133,32 +130,9 @@ def _repair_schema(node: Any, is_schema: bool = True) -> Any:
else:
repaired.pop("enum")
# Rule 4: object schemas must carry a `required` array, even when empty.
if repaired.get("type") == "object":
repaired = _ensure_required_array(repaired)
return repaired
def _ensure_required_array(node: Dict[str, Any]) -> Dict[str, Any]:
"""Guarantee an object schema carries a ``required`` array (Moonshot rule).
Standard JSON Schema lets you omit ``required`` when nothing is required;
Moonshot 400s on that ("required must be an array"). Ensure the key is a
list. When ``properties`` is known, prune ``required`` entries that don't
name a real property — defensive against dangling names, which Moonshot
also rejects. Mutates and returns ``node``.
"""
props = node.get("properties")
req = node.get("required")
if isinstance(req, list):
if isinstance(props, dict):
node["required"] = [r for r in req if r in props]
else:
node["required"] = []
return node
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")
@@ -200,18 +174,17 @@ def sanitize_moonshot_tool_parameters(parameters: Any) -> Dict[str, Any]:
applied. Input is not mutated.
"""
if not isinstance(parameters, dict):
return {"type": "object", "properties": {}, "required": []}
return {"type": "object", "properties": {}}
repaired = _repair_schema(copy.deepcopy(parameters), is_schema=True)
if not isinstance(repaired, dict):
return {"type": "object", "properties": {}, "required": []}
return {"type": "object", "properties": {}}
# Top-level must be an object schema
if repaired.get("type") != "object":
repaired["type"] = "object"
if "properties" not in repaired:
repaired["properties"] = {}
_ensure_required_array(repaired)
return repaired
@@ -259,10 +232,6 @@ def is_moonshot_model(model: str | None) -> bool:
tail = bare.rsplit("/", 1)[-1]
if tail.startswith("kimi-") or tail == "kimi":
return True
# Kimi Coding Plan serves K3 under the bare slug ``k3`` (plus dated /
# suffixed variants like ``k3.1`` or ``k3-turbo``).
if tail == "k3" or tail.startswith(("k3.", "k3-")):
return True
# Vendor-prefixed forms commonly used on aggregators
if "moonshot" in bare or "/kimi" in bare or bare.startswith("kimi"):
return True
+2 -2
View File
@@ -209,7 +209,7 @@ def mark_seen(config_path: Path, flag: str) -> bool:
"""
try:
import yaml
from hermes_cli.config import atomic_config_write
from utils import atomic_yaml_write
except Exception as e: # pragma: no cover — dependency issue
logger.debug("onboarding: failed to import yaml/utils: %s", e)
return False
@@ -228,7 +228,7 @@ def mark_seen(config_path: Path, flag: str) -> bool:
if seen.get(flag) is True:
return True # already marked — nothing to do
seen[flag] = True
atomic_config_write(config_path, cfg)
atomic_yaml_write(config_path, cfg)
return True
except Exception as e:
logger.debug("onboarding: failed to mark flag %s: %s", flag, e)
+1 -65
View File
@@ -230,68 +230,6 @@ def _png_bytes(frame) -> bytes:
return buf.getvalue()
def _union_alpha_bbox(frames) -> tuple[int, int, int, int] | None:
"""Union opaque-pixel bbox across *frames* (a stable trim for animation)."""
left = top = right = bottom = None
for frame in frames:
try:
bbox = frame.getchannel("A").getbbox()
except Exception: # noqa: BLE001 - cosmetic; fail open
bbox = None
if not bbox:
continue
l, t, r, b = bbox
left = l if left is None else min(left, l)
top = t if top is None else min(top, t)
right = r if right is None else max(right, r)
bottom = b if bottom is None else max(bottom, b)
if left is None or top is None or right is None or bottom is None:
return None
return (left, top, right, bottom)
def _crop_frames_to_alpha_union(frames):
"""Crop every frame to the union opaque bbox so the sprite hugs its box.
kitty paints the whole transmitted rectangle, transparent margins included,
which makes the visible pet look small and adrift inside a larger cell box.
Trimming to the visible bounds keeps the pet tight in its corner.
"""
bbox = _union_alpha_bbox(frames)
if not bbox:
return frames
return [f.crop(bbox) for f in frames]
# Nominal terminal cell size in pixels. kitty fits an image to its cell
# rectangle preserving aspect, so a frame whose pixel size isn't a whole
# multiple of the cell rounds up — which makes the terminal clip the bottom row
# (the "clipped feet") and letterbox a blank row. Snapping each frame to an
# exact cell multiple avoids that. (See ratatui-image #57: "render in multiples
# of the font-size, to avoid stale character artifacts.")
_CELL_W = 8
_CELL_H = 16
def _snap_frames_to_cell_grid(frames):
"""Resize frames so width/height are exact multiples of the cell box.
Removes the sub-cell remainder kitty would otherwise round up + clip. All
frames share the union-cropped size, so they snap to the same cell grid.
"""
if not frames:
return frames
from PIL import Image
w, h = frames[0].size
cols = max(1, round(w / _CELL_W))
rows = max(1, round(h / _CELL_H))
target = (cols * _CELL_W, rows * _CELL_H)
if (w, h) == target:
return frames
return [f.resize(target, Image.LANCZOS) for f in frames]
def _kitty_apc(ctrl: str, data: str) -> str:
"""Emit a kitty APC escape for *data*, chunked into ≤4096-byte ``m`` pieces."""
chunk = 4096
@@ -423,7 +361,7 @@ def _encode_iterm(frame, *, cell_cols: int | None = None, cell_rows: int | None
"""Encode one frame as an iTerm2 inline image (OSC 1337 File)."""
payload = base64.standard_b64encode(_png_bytes(frame)).decode("ascii")
size = len(payload)
args = ["inline=1", f"size={size}", "preserveAspectRatio=1"]
args = [f"inline=1", f"size={size}", "preserveAspectRatio=1"]
if cell_cols:
args.append(f"width={cell_cols}")
if cell_rows:
@@ -625,8 +563,6 @@ class PetRenderer:
frames = self._frames(state)
if not frames:
return None
frames = _crop_frames_to_alpha_union(frames)
frames = _snap_frames_to_cell_grid(frames)
cols, rows = self._cell_box(frames[0])
return {
"cols": cols,
+3 -83
View File
@@ -31,55 +31,7 @@ version can change at runtime (editable installs, hot-reload tooling), and
from __future__ import annotations
from contextvars import ContextVar
from typing import List, Optional
# ── Ambient conversation context ─────────────────────────────────────────────
#
# The main agent loop knows its ``session_id``; the dozens of auxiliary call
# sites (compression, title generation, vision, web_extract, session_search,
# MoA reference/aggregator slots, curator, kanban helpers, ...) do not — they
# funnel through ``agent.auxiliary_client.call_llm`` which has no session
# handle. Rather than threading a ``session_id`` parameter through every one
# of those call sites (and every future one), the agent loop publishes the
# active conversation id here and ``nous_portal_tags()`` picks it up as a
# fallback whenever no explicit ``session_id`` is passed.
#
# ContextVar (not a module global) so concurrent agents in one process —
# gateway sessions, delegate_task subagents, batch runners — never see each
# other's conversation id. Worker threads spawned via
# ``tools.thread_context.propagate_context_to_thread`` (background review,
# MoA fan-out, tool executor) inherit it through the copied Context; bare
# threads (title generator) capture it explicitly at spawn time.
_conversation_id: ContextVar[Optional[str]] = ContextVar(
"nous_portal_conversation_id", default=None
)
def set_conversation_context(conversation_id: Optional[str]):
"""Publish the active conversation id for ambient Portal tagging.
Called by the agent loop at turn entry with the conversation's stable
id (the session-lineage ROOT id, so the tag survives context-compression
session rotation). Pass ``None`` to clear. Returns the ContextVar token
so callers can ``reset_conversation_context(token)`` on turn exit.
"""
return _conversation_id.set(conversation_id or None)
def reset_conversation_context(token) -> None:
"""Restore the previous conversation context (pair with ``set_...``)."""
try:
_conversation_id.reset(token)
except Exception:
# Token from another Context (e.g. reset on a different thread) —
# fall back to clearing rather than raising in cleanup paths.
_conversation_id.set(None)
def get_conversation_context() -> Optional[str]:
"""Return the ambient conversation id, or ``None`` when unset."""
return _conversation_id.get()
from typing import List
def _hermes_version() -> str:
@@ -103,42 +55,10 @@ def hermes_client_tag() -> str:
return f"client=hermes-client-v{_hermes_version()}"
def conversation_tag(session_id: str) -> str:
"""Return the ``conversation=...`` tag for a Hermes session/conversation.
Format: ``conversation=<session_id>``. ``session_id`` is the canonical
Hermes conversation identifier (``AIAgent.session_id``) — the same value
used for ``~/.hermes/sessions/`` storage, session logs, and lineage.
Unlike the product/client tags this is high-cardinality (one value per
conversation), so it is only appended when a session id is actually
available — never as part of the always-on base tag set.
"""
return f"conversation={session_id}"
def nous_portal_tags(session_id: str | None = None) -> List[str]:
def nous_portal_tags() -> List[str]:
"""Return the canonical list of Nous Portal product tags.
Always returns a fresh list so callers can mutate it freely
(e.g. ``merged_extra.setdefault("tags", []).extend(nous_portal_tags())``).
When ``session_id`` is provided, a ``conversation=<session_id>`` tag is
appended so Portal usage can be attributed to a specific Hermes
conversation. When it is omitted, the ambient conversation context
(``set_conversation_context``, published by the agent loop at turn
entry) is used instead — this is how auxiliary calls (compression,
titles, vision, MoA slots, ...) inherit the conversation tag without
per-call-site plumbing. Callers outside any conversation (e.g. the
auxiliary client's import-time base tags) get the canonical two-tag set.
"""
tags = ["product=hermes-agent", hermes_client_tag()]
# Ambient context first: the agent loop publishes the lineage ROOT id
# (stable across context-compression rotation and delegate subagent
# trees), which is the better conversation key than a per-segment
# session_id passed explicitly. The explicit argument remains as a
# fallback for callers running outside any agent turn.
effective = get_conversation_context() or session_id
if effective:
tags.append(conversation_tag(effective))
return tags
return ["product=hermes-agent", hermes_client_tag()]
+16 -35
View File
@@ -146,56 +146,37 @@ def build_keepalive_http_client(
base_url: str = "",
*,
async_mode: bool = False,
verify: Any = True,
) -> Optional[Any]:
"""Build an httpx client for OpenAI SDK calls with env-only proxy policy.
Uses explicit ``HTTPS_PROXY`` / ``NO_PROXY`` env vars via
``_get_proxy_for_base_url``. Plain no-proxy mounts disable httpx's default
``trust_env`` proxy path, so macOS system proxy settings from
``_get_proxy_for_base_url``. A custom transport disables httpx's default
``trust_env`` path, so macOS system proxy settings from
``urllib.request.getproxies()`` (which omit the ExceptionsList) are not
applied. Mirrors ``AIAgent._build_keepalive_http_client``.
Connection lifecycle is managed at the HTTP pool layer
(``keepalive_expiry=20.0`` reaps idle connections before reverse proxies'
typical 30-60 s timeouts) instead of the former custom
``socket_options`` transport, which broke streaming behind reverse
proxies (#54049, #12952) and stalled TLS handshakes by stripping
``TCP_NODELAY``.
``verify`` is forwarded to httpx so auxiliary-client calls (compression,
vision, web_extract, title generation, etc.) honor the same per-provider
``ssl_ca_cert`` / ``ssl_verify`` and ``HERMES_CA_BUNDLE`` settings the main
client uses. It is passed on the client AND on the plain no-proxy mounts
(a mounted transport owns the SSL context for its scheme).
"""
try:
import httpx
import socket
if "api.githubcopilot.com" in str(base_url or "").lower():
client_cls = httpx.AsyncClient if async_mode else httpx.Client
return client_cls()
sock_opts = [(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)]
if hasattr(socket, "TCP_KEEPIDLE"):
sock_opts.append((socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, 30))
sock_opts.append((socket.IPPROTO_TCP, socket.TCP_KEEPINTVL, 10))
sock_opts.append((socket.IPPROTO_TCP, socket.TCP_KEEPCNT, 3))
elif hasattr(socket, "TCP_KEEPALIVE"):
sock_opts.append((socket.IPPROTO_TCP, socket.TCP_KEEPALIVE, 30))
proxy = _get_proxy_for_base_url(base_url)
limits = httpx.Limits(
max_keepalive_connections=20,
max_connections=100,
keepalive_expiry=20.0,
)
# Generous read=None for SSE streaming endpoints.
timeout = httpx.Timeout(connect=15.0, read=None, write=15.0, pool=10.0)
transport_cls = httpx.AsyncHTTPTransport if async_mode else httpx.HTTPTransport
client_cls = httpx.AsyncClient if async_mode else httpx.Client
mounts = {}
if proxy is None:
mounts = {
"http://": transport_cls(verify=verify),
"https://": transport_cls(verify=verify),
}
return client_cls(
limits=limits,
timeout=timeout,
transport=transport_cls(socket_options=sock_opts),
proxy=proxy,
mounts=mounts or None,
verify=verify,
)
except Exception:
return None
+50 -156
View File
@@ -7,7 +7,6 @@ assemble pieces, then combines them with memory and ephemeral prompts.
import json
import logging
import os
import sys
import threading
import contextvars
from collections import OrderedDict
@@ -18,8 +17,6 @@ from typing import Optional
from agent.runtime_cwd import resolve_agent_cwd
from agent.skill_utils import (
EXCLUDED_SKILL_DIRS,
SKILL_SUPPORT_DIRS,
extract_skill_conditions,
extract_skill_description,
get_all_skills_dirs,
@@ -28,7 +25,6 @@ from agent.skill_utils import (
parse_frontmatter,
skill_matches_environment,
skill_matches_platform,
skill_matches_platform_list,
)
from utils import atomic_json_write
@@ -58,14 +54,6 @@ def _scan_context_content(content: str, filename: str) -> str:
BLOCKED at this layer because the file would otherwise enter the
system prompt verbatim and the user has no chance to intervene.
"""
# Editors (Windows Notepad, PowerShell Out-File without -Encoding
# utf8NoBOM, some VS Code profiles) prefix a UTF-8 BOM as an encoding
# artifact, not a prompt injection. Strip a leading U+FEFF silently so a
# context file (SOUL.md, AGENTS.md, ...) is not blocked wholesale; BOMs
# elsewhere in the content remain subject to the threat scan below.
if content.startswith("\ufeff"):
content = content[1:]
findings = _scan_for_threats(content, scope="context")
if findings:
logger.warning("Context file %s blocked: %s", filename, ", ".join(findings))
@@ -122,7 +110,6 @@ def _strip_yaml_frontmatter(content: str) -> str:
strip it so only the human-readable markdown body is injected into the
system prompt.
"""
content = content.lstrip("\ufeff") # tolerate UTF-8 BOM (Windows editors)
if content.startswith("---"):
end = content.find("\n---", 3)
if end != -1:
@@ -266,10 +253,6 @@ KANBAN_GUIDANCE = (
"- **Deliverables.** Files a human wants go in "
"`kanban_complete(artifacts=[<absolute paths>])` (top-level param; paths in "
"`metadata` are NOT uploaded). Files must exist at completion.\n"
"- **Attachments.** Attach real downloadable artifacts instead of pasting "
"links in comments: `kanban_attach` (base64) or `kanban_attach_url` "
"(server-side public http(s) fetch); 25 MB cap, `kanban_attachments` "
"lists them. Workers may only attach to their own task.\n"
"- **Created cards.** List ids in `kanban_complete(created_cards=[...])` "
"ONLY when captured from a successful `kanban_create` return — never invent "
"or paste ids; the kernel rejects the completion on any phantom id.\n"
@@ -557,29 +540,6 @@ def computer_use_guidance(platform_name: Optional[str] = None) -> str:
"4. After any state-changing action, re-capture to verify. You can "
"pass `capture_after=true` to get the follow-up screenshot in one "
"round-trip.\n\n"
"## Verify → escalate ladder (background-first, NOT background-only)\n"
"Background delivery is the DEFAULT and the co-work path, but it is "
"the first rung, not the only one. Read each action's structured "
"result and climb only when the driver tells you to:\n"
"- `effect: 'confirmed'` + `verified: true` — the driver read the "
"result back. Done.\n"
"- `effect: 'unverifiable'` — the input was delivered but the driver "
"can't confirm it. Re-capture and check the screenshot/tree yourself "
"before deciding it worked.\n"
"- `effect: 'suspected_noop'`, `code: 'background_unavailable'`, or an "
"`escalation.recommended` field — the action did NOT land. Follow "
"`escalation.recommended`:\n"
" - `'px'` → re-issue addressing the target by `coordinate=[x,y]` "
"read off the screenshot instead of `element`.\n"
" - `'foreground'` (or a pixel click still didn't land) → re-issue "
"the SAME action with `delivery_mode='foreground'`. This briefly "
"raises the window; it needs its own approval and is only appropriate "
"when the user isn't actively working. Common for Electron/Chromium "
"consent dialogs, DirectInput games, and raw-input canvases.\n"
"- Escalate to foreground as a REACTION to a returned signal, never "
"as a prediction from the app being Electron/Chromium/GTK. Do not "
"silently retry the same rung expecting a different result, and do "
"not conclude 'cua-driver can't drive this app' — climb the ladder.\n\n"
"## Background mode rules\n"
"- Do NOT use `raise_window=true` on `focus_app` unless the user "
"explicitly asked you to bring a window to front. Input routing to "
@@ -695,7 +655,19 @@ PLATFORM_HINTS = {
"Standard Markdown is automatically converted to Telegram formatting. "
"Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, "
"`inline code`, ```code blocks```, [links](url), and ## headers. "
"Prefer bullet lists and labeled key:value pairs for structured data. "
"Telegram now supports rich Markdown, so lean into it: whenever it "
"makes the answer clearer or easier to scan, actively reach for real "
"Markdown tables (pipe `| col | col |` syntax), bullet and numbered "
"lists, task lists (`- [ ]` / `- [x]`), headings, nested blockquotes, "
"collapsible details, footnotes/references, math/formulas (`$...$`, "
"`$$...$$`), underline, subscript/superscript, marked (highlighted) "
"text, and anchors. Default to structured formatting over dense "
"paragraphs for any comparison, set of steps, key/value summary, or "
"tabular data. Prefer real Markdown tables and task lists over "
"hand-built bullet substitutes when presenting structured data; these "
"degrade gracefully (tables become readable bullet groups) when rich "
"rendering is unavailable, but advanced constructs like math and "
"collapsible details may render as plain source text in that case. "
"You can send media files natively: to deliver a file to the user, "
"include MEDIA:/absolute/path/to/file in your response. Images "
"(.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice "
@@ -771,17 +743,6 @@ PLATFORM_HINTS = {
"or 'all'). Do not promise the user that a deliver='origin' or "
"default-deliver cron job will message them in this session."
),
"desktop": (
"You are chatting inside the Hermes desktop app — a graphical chat "
"surface, not a terminal. Use markdown freely: it renders with full "
"GitHub flavor (tables, code blocks with syntax highlighting, math "
"via $...$, task lists, blockquote callouts). "
"You can deliver files natively — include MEDIA:/absolute/path/to/file "
"in your response. Images (.png, .jpg, .webp) appear inline, audio and "
"video play inline, and other files arrive as download links. You can "
"also include image URLs in markdown format ![alt](url) and they "
"render inline as photos."
),
"sms": (
"You are communicating via SMS. Keep responses concise and use plain text "
"only — no markdown, no formatting. SMS messages are limited to ~1600 "
@@ -805,19 +766,8 @@ PLATFORM_HINTS = {
),
"matrix": (
"You are in a Matrix room communicating with your user. "
"The adapter converts your Markdown to HTML for rich display — bold, "
"italic, inline code, fenced code blocks, headings, bullet and "
"numbered lists, blockquotes, and links all render.\n\n"
"Do NOT use Markdown tables: many popular Matrix clients (Element X, "
"Beeper, most mobile apps) do not render HTML tables, so the cells "
"collapse into one continuous run of text. Present tabular data as "
"labeled '**Label:** value' lines or bullet lists instead.\n\n"
"Avoid ||spoiler|| tags, ~~strikethrough~~, and checkboxes "
"(- [ ] / - [x]) — they are not converted and appear as literal "
"characters.\n\n"
"LINKS: prefer [descriptive link text](url) over bare URLs. When "
"referencing something with an associated URL (events, sources, "
"people), make the name a clickable link.\n\n"
"Matrix renders Markdown — bold, italic, code blocks, and links work; "
"the adapter converts your Markdown to HTML for rich display. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.jpg, .png, .webp) are sent as inline photos, "
"audio (.ogg, .mp3) as voice/audio messages, video (.mp4) inline, "
@@ -900,27 +850,6 @@ PLATFORM_HINTS = {
),
}
# Telegram rich-messages extension — only injected when the user has opted in
# to ``platforms.telegram.extra.rich_messages: true``. The base
# PLATFORM_HINTS["telegram"] covers MarkdownV2-compatible constructs; this
# extension adds the Bot API 10.1 rich-Markdown guidance (tables, task lists,
# collapsible details, math, etc.).
TELEGRAM_RICH_MESSAGES_HINT = (
"Telegram now supports rich Markdown, so lean into it: whenever it "
"makes the answer clearer or easier to scan, actively reach for real "
"Markdown tables (pipe `| col | col |` syntax), bullet and numbered "
"lists, task lists (`- [ ]` / `- [x]`), headings, nested blockquotes, "
"collapsible details, footnotes/references, math/formulas (`$...$`, "
"`$$...$$`), underline, subscript/superscript, marked (highlighted) "
"text, and anchors. Default to structured formatting over dense "
"paragraphs for any comparison, set of steps, key/value summary, or "
"tabular data. Prefer real Markdown tables and task lists over "
"hand-built bullet substitutes when presenting structured data; these "
"degrade gracefully (tables become readable bullet groups) when rich "
"rendering is unavailable, but advanced constructs like math and "
"collapsible details may render as plain source text in that case. "
)
# ---------------------------------------------------------------------------
# Environment hints — execution-environment awareness for the agent.
# Unlike PLATFORM_HINTS (which describe the messaging channel), these describe
@@ -1198,6 +1127,22 @@ def build_environment_hints() -> str:
f"`uname -a && whoami && pwd`."
)
# Hermes desktop GUI — any agent running under the desktop app should know
# it. HERMES_DESKTOP marks the backend powering the chat; HERMES_DESKTOP_TERMINAL
# marks a hermes launched in the embedded terminal pane. Both set by main.cjs.
_truthy = ("1", "true", "yes")
_in_desktop = (os.getenv("HERMES_DESKTOP") or "").strip().lower() in _truthy
_in_desktop_term = (os.getenv("HERMES_DESKTOP_TERMINAL") or "").strip().lower() in _truthy
if _in_desktop or _in_desktop_term:
_desktop_hint = "Runtime surface: you're running inside the Hermes desktop GUI app."
if _in_desktop_term:
_desktop_hint += (
" You're in its embedded terminal pane, beside the GUI chat — the user can "
"select your output (⌥-drag on macOS, Shift-drag elsewhere) and press "
"⌘/Ctrl+L to send it to the chat composer."
)
hints.append(_desktop_hint)
if is_wsl():
hints.append(WSL_ENVIRONMENT_HINT)
@@ -1331,26 +1276,13 @@ def clear_skills_system_prompt_cache(*, clear_snapshot: bool = False) -> None:
def _build_skills_manifest(skills_dir: Path) -> dict[str, list[int]]:
"""Build an mtime/size manifest of all SKILL.md and DESCRIPTION.md files."""
manifest: dict[str, list[int]] = {}
skills_dir_str = str(skills_dir)
base = os.path.join(skills_dir_str, "")
prefix_len = len(base)
for root, dirs, files in os.walk(skills_dir_str, followlinks=True):
has_skill_md = "SKILL.md" in files
dirs[:] = [
d
for d in dirs
if d not in EXCLUDED_SKILL_DIRS
and not (has_skill_md and d in SKILL_SUPPORT_DIRS)
]
for filename in ("SKILL.md", "DESCRIPTION.md"):
if filename not in files:
continue
path = os.path.join(root, filename)
for filename in ("SKILL.md", "DESCRIPTION.md"):
for path in iter_skill_index_files(skills_dir, filename):
try:
st = os.stat(path)
st = path.stat()
except OSError:
continue
manifest[path[prefix_len:]] = [st.st_mtime_ns, st.st_size]
manifest[str(path.relative_to(skills_dir))] = [st.st_mtime_ns, st.st_size]
return manifest
@@ -1482,22 +1414,6 @@ def _skill_should_show(
return True
def _current_session_platform_hint() -> str:
"""Return the active platform without importing the gateway package on CLI startup."""
platform = os.environ.get("HERMES_PLATFORM") or os.environ.get("HERMES_SESSION_PLATFORM")
if platform:
return platform
session_context = sys.modules.get("gateway.session_context")
get_session_env = getattr(session_context, "get_session_env", None) if session_context else None
if get_session_env is None:
return ""
try:
return get_session_env("HERMES_SESSION_PLATFORM") or ""
except Exception:
return ""
def build_skills_system_prompt(
available_tools: "set[str] | None" = None,
available_toolsets: "set[str] | None" = None,
@@ -1532,10 +1448,15 @@ def build_skills_system_prompt(
# ── Layer 1: in-process LRU cache ─────────────────────────────────
# Include the resolved platform so per-platform disabled-skill lists
# produce distinct cache entries (gateway serves multiple platforms).
_platform_hint = _current_session_platform_hint()
from gateway.session_context import get_session_env
_platform_hint = (
os.environ.get("HERMES_PLATFORM")
or get_session_env("HERMES_SESSION_PLATFORM")
or ""
)
disabled = get_disabled_skill_names(_platform_hint or None)
cache_key = (
str(skills_dir),
str(skills_dir.resolve()),
tuple(str(d) for d in external_dirs),
tuple(sorted(str(t) for t in (available_tools or set()))),
tuple(sorted(str(ts) for ts in (available_toolsets or set()))),
@@ -1564,7 +1485,7 @@ def build_skills_system_prompt(
category = entry.get("category") or "general"
frontmatter_name = entry.get("frontmatter_name") or skill_name
platforms = entry.get("platforms") or []
if not skill_matches_platform_list(platforms):
if not skill_matches_platform({"platforms": platforms}):
continue
if frontmatter_name in disabled or skill_name in disabled:
continue
@@ -2004,7 +1925,6 @@ def build_context_files_prompt(
cwd: Optional[str] = None,
skip_soul: bool = False,
context_length: Optional[int] = None,
allow_install_tree_fallback: bool = False,
) -> str:
"""Discover and load context files for the system prompt.
@@ -2026,43 +1946,17 @@ def build_context_files_prompt(
"""
if cwd is None:
cwd = os.getcwd()
cwd_is_fallback = True
else:
cwd_is_fallback = False
cwd_path = Path(cwd).resolve()
sections = []
# Never let a FALLBACK-picked directory inside the Hermes install/source
# tree gain system-prompt authority. A backend that self-spawns into that
# tree (the desktop app default) would otherwise load this repo's
# contributor AGENTS.md as authoritative project context (#64590). An
# explicitly configured cwd is honored verbatim — the Hermes tree is a
# legitimate workspace when the user deliberately points a session at it —
# and CLI-style surfaces pass allow_install_tree_fallback=True because
# their launch dir IS the user's shell cwd (developing Hermes in-tree).
from agent.runtime_cwd import _is_install_tree
if (
cwd_is_fallback
and not allow_install_tree_fallback
and _is_install_tree(cwd_path)
):
logger.warning(
"skipping project-context discovery: working-directory resolution "
"fell back to the Hermes install tree (%s) — set terminal.cwd to "
"your project directory",
cwd_path,
)
project_context = ""
else:
# Priority-based project context: first match wins
project_context = (
_load_hermes_md(cwd_path, context_length)
or _load_agents_md(cwd_path, context_length)
or _load_claude_md(cwd_path, context_length)
or _load_cursorrules(cwd_path, context_length)
)
# Priority-based project context: first match wins
project_context = (
_load_hermes_md(cwd_path, context_length)
or _load_agents_md(cwd_path, context_length)
or _load_claude_md(cwd_path, context_length)
or _load_cursorrules(cwd_path, context_length)
)
if project_context:
sections.append(project_context)
+4 -44
View File
@@ -17,23 +17,12 @@ def _apply_cache_marker(msg: dict, cache_marker: dict, native_anthropic: bool =
role = msg.get("role", "")
content = msg.get("content")
if role == "tool" and native_anthropic:
# Native Anthropic layout: top-level marker; the adapter moves it
# inside the tool_result block.
msg["cache_control"] = cache_marker
if role == "tool":
if native_anthropic:
msg["cache_control"] = cache_marker
return
if content is None or content == "":
if role == "tool" and not native_anthropic:
# OpenRouter rejects top-level cache_control on role:tool (silent
# hang) and an empty message has no content part to carry the
# marker — skip. Non-empty tool content falls through below and
# gets the marker on a content part, which OpenRouter honors.
return
if role == "assistant" and not native_anthropic:
# Empty assistant turns are pure tool_calls. A top-level marker
# here is ignored on the envelope layout, so skip.
return
msg["cache_control"] = cache_marker
return
@@ -49,30 +38,6 @@ def _apply_cache_marker(msg: dict, cache_marker: dict, native_anthropic: bool =
last["cache_control"] = cache_marker
def _can_carry_marker(msg: dict, native_anthropic: bool) -> bool:
"""True if a marker on this message is actually honored by the provider.
On the native Anthropic layout every message works (top-level markers are
relocated by the adapter). On the envelope layout (OpenRouter et al.) only
markers inside content parts are honored: empty-content messages (e.g.
assistant turns that are pure tool_calls) and empty tool messages would
receive a top-level marker the provider ignores — wasting one of the four
breakpoints. Skip those so the breakpoints land on messages that count.
"""
if native_anthropic:
return True
content = msg.get("content")
if content is None or content == "":
return False
if isinstance(content, list):
# _apply_cache_marker only marks the LAST content part, so the carrier
# predicate must agree: a list whose last element isn't a dict cannot
# actually receive a marker and would waste a breakpoint. Mirror the
# `content` truthiness + last-element-dict check in _apply_cache_marker.
return bool(content) and isinstance(content[-1], dict)
return isinstance(content, str)
def _build_marker(ttl: str) -> Dict[str, str]:
"""Build a cache_control marker dict for the given TTL ('5m' or '1h')."""
marker: Dict[str, str] = {"type": "ephemeral"}
@@ -107,12 +72,7 @@ def apply_anthropic_cache_control(
breakpoints_used += 1
remaining = 4 - breakpoints_used
non_sys = [
i
for i in range(len(messages))
if messages[i].get("role") != "system"
and _can_carry_marker(messages[i], native_anthropic=native_anthropic)
]
non_sys = [i for i in range(len(messages)) if messages[i].get("role") != "system"]
for idx in non_sys[-remaining:]:
_apply_cache_marker(messages[idx], marker, native_anthropic=native_anthropic)

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