The live smoke exposed a harness trap: driving each turn through its own
asyncio.run() binds the engine asyncio primitives to the first loop, and
every later stream silently takes the interrupted path - full provider
replies persisted as empty assistant messages. The scripted smoke had
the same latent artifact and did not assert reply content, so it stayed
green. Now the whole scenario runs on ONE loop (like the real server)
and every turn asserts a real reply.
A long multi-turn session driven through the real SessionManager with a
forced 3k-token cap: repeated compactions advance the boundary, later
summaries fold the previous one in, the provider verifiably receives the
compacted view (summary block + verbatim tail, bounded) while the
canonical transcript keeps every turn, state survives a mid-conversation
rebuild, and the persisted record round-trips the final boundary.
Scripted stand-in for the live-model smoke: intent survival across a
real summarizer (prompt tuning) still needs a configured provider key.
Settings -> Models grows a Context compaction card next to Token savings:
the trigger % of the context window (10-95), the absolute token cap
(clamped 10k-2M), and the summarizer-model pin (default: the session's
own model). POST /v1/settings/compaction persists them; engines read the
knobs live per check, so changes apply to running sessions immediately.
The "context compacted" divider rides the existing notice machinery: the
persisted `compacted` notice replays on reload (itemsFromMessages) and
the live COMPACTED event appends the same info notice mid-turn. The
transcript itself stays intact - outbound-only by construction.
Covered by vitest (marker replay), a settings-card e2e (defaults +
clamped POSTs + model pin), and a mid-session divider e2e driven by the
fixtures' scripted `compacted` event.
Minimal engine footprint: a checkpoint at each iteration top (between tool
turns and before a new turn), the usage signal captured per round-trip
(context_tokens; chars/4 estimate when never reported), and
_outbound_messages consulting the boundary. The summarizer runs off-loop
through the normal provider router, so the Settings model pin is just an
id.
Failure policy per spec: retry once in both modes; attended sessions get
the Retry / Trim-oldest-10% prompt (via the ask_user plumbing, gated by an
is_attended callback the WS surface wires); unattended runs auto-trim and
continue — never parked on internal bookkeeping. Raw context-overflow 400s
from the main model route into the same policy, progress-guarded so a
still-overflowing model terminates in the error path.
CompactionState persists on the session record (new sqlite column, same
defensive parse as grants), so reloads keep the compacted view. A
persisted compacted notice + a new COMPACTED event mark the spot for
the GUI divider (rendered in commit 3).
Trigger math (usage signal, chars/4 estimate fallback, min(80% x window,
250k cap) with overridable knobs), boundary picking that never splits a
turn (user-message starts preferred, iteration starts inside a giant tool
loop), the 8-section summarizer prompt with the continuation contract,
mechanical working-state extraction from tool records, deterministic
user-message preservation, the trim-oldest fallback, outbound-view
application, and context-overflow detection. Injectable provider seam;
no engine changes yet.
Options accept {label, description, recommended, preview} objects (plain
strings unchanged — old sessions render as today's pills), and `questions`
groups up to 4 questions into one call, rendered as a stepper via the
header chips. Any option preview switches the card to a two-pane layout:
options left, monospace pane right, following hover/focus.
Grouped calls resolve with a JSON map keyed by header-or-question and
return {answers: {...}} to the agent (single stays {answer: ...});
a grouped item's first question doubles as its title/options so channel
mirrors and legacy surfaces degrade sensibly. Channel buttons use option
labels; grouped items mirror as text with the open-the-app hint.
openai_provider is now the compat workhorse (vendors, resellers, Ollama,
custom endpoints); the effort pin stays for GPT-5.6 reached through a
custom endpoint. base.py lists the current provider set.
_build_openai: no custom base_url -> OpenAIResponsesProvider; a custom
endpoint (Azure /openai/v1, vLLM, compat gateways) keeps the Chat
Completions OpenAIProvider, as do Ollama and every compat vendor. Verify
path (raw GET /models) and matrix ids are untouched.
/v1/chat/completions rejects function tools with any reasoning_effort
other than none on GPT-5.6, so native OpenAI has run with reasoning OFF.
OpenAIResponsesProvider speaks /v1/responses instead: reasoning + tools
at real effort, streamed reasoning summaries into the existing
reasoning_delta plumbing, and CoT continuity across tool round-trips via
store:false + encrypted reasoning replayed through a new _openai message
sidecar (same extras contract as _anthropic/_gemini). Not routed yet.
Coworkers remember durable things you tell them and use them in future sessions.
One Settings screen lists everything remembered - edit, delete, or stop new saves; standing instructions ride along.
Knowledge is session-stable, the save switch is per-message; sqlite gains a summary column via in-place migration.
Devika Verma