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Author SHA1 Message Date
ac1a71f583 v1.13.1-C: port ask_user_input correlation to parts + wire reasoning_parts end-to-end
Pass 1 — ask_user_input correlation port (messages.ts:478, :549):

- The two correlation queries that backed the elicitation flow used to scan
  messages.tool_calls and messages.tool_results JSON columns directly. They
  now JOIN message_parts on payload->>'id' (for the caller assistant) and
  payload->>'tool_call_id' (for the pending tool row). Semantics preserved:
  ORDER BY m.created_at DESC LIMIT 1 still picks the latest issuance, the
  already-answered 409 guard now reads payload.output, and the UPDATE +
  parts replace inside sql.begin is unchanged from v1.13.0.
- Pre-v1.13.0 history has no parts rows and is unreachable to this lookup
  path (404). Acceptable per dispatch decision — no pending elicitation
  from before v1.13.0 will still be open. JSON-column fallback can land as
  a hotfix if it ever surfaces.

Pass 2 — reasoning_parts wired end-to-end:

- types.ts/StreamResult gains `reasoning: string`. stream-phase.ts accumulates
  reasoning-delta text per stream (replacing the v1.13.1-A counter-only
  diagnostic) and returns it on the result.
- parts.ts/partsFromAssistantMessage gains an optional `reasoning` param.
  When present it emits a kind='reasoning' part at sequence 0, ahead of
  the text and tool_call parts.
- error-handler.ts/finalizeCompletion and tool-phase.ts/executeToolPhase
  both thread result.reasoning into the dual-write call so reasoning-channel
  models (qwen3.6) get persistent reasoning rows.
- payload.ts: loadContext SELECT pulls reasoning_parts from the v1.13.1-B
  view; OpenAiMessage gains an optional `reasoning` field; buildMessagesPayload
  collapses reasoning_parts into a single string per assistant message.
- stream-phase.ts/toModelMessages converts assistant messages with reasoning
  into an AI SDK ModelMessage content array starting with a ReasoningPart,
  matching the @ai-sdk/provider-utils AssistantContent union. Reasoning
  models can now replay prior reasoning context across tool-call boundaries.
- types/api.ts and apps/web/src/api/types.ts Message interface gain
  reasoning_parts (optional, nullable). Frontend doesn't render this yet —
  field reserved for a v1.14 UI surface.

Tests: 2 new in parts.test.ts cover reasoning-at-sequence-0 with and
without text content. 172 tests pass (170 prior + 2 new).

Smoke verified against the live container:
- A reasoning-prompt ("walk through 17 × 23 step by step") produced one
  message with kind='reasoning' (361 chars) at sequence 0 and kind='text'
  (429 chars) at sequence 1. Adapter log confirmed reasoning capture.
- The new correlation SQL was validated against existing tool_call /
  tool_result parts: returns the expected message_id + payload shape with
  pending state correctly identified via payload.output IS NULL.
- ask_user_input end-to-end through the UI is Sam's smoke — the Prompt
  Builder agent does not always trigger ask_user_input for these prompts,
  so synthetic verification via SQL substituted for traffic-driven cover.

Annotation: the v1.13.1-A abort-throw site in stream-phase.ts got a
one-liner comment ("AI SDK v6 fullStream returns normally on abort; check
signal explicitly.") to prevent a future refactor removing it.

v1.13.2 drops the dual-write + the JSON columns + collapses the view.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 06:34:10 +00:00
13c3aa5b4e v1.13.1-B: read-path flip from tool_calls/tool_results JSON columns to message_parts
- schema.sql: new messages_with_parts view. tool_calls aggregates parts
  with kind='tool_call' as a jsonb array of {id, name, args}; tool_results
  picks the single sequence=0 part with kind='tool_result' as a jsonb
  {tool_call_id, output, truncated, error?}. COALESCE against the legacy
  jsonb columns means pre-v1.13.0 history (no parts rows) still reads
  correctly via the fallback, and fresh inserts (where parts dual-write
  follows the row INSERT) hit the legacy columns until the parts land.
- reasoning_parts column added to the view but not selected by any caller
  yet — v1.13.1-C extends the Message type and pulls it into the model
  payload alongside the type extension.
- Read sites switched to FROM messages_with_parts:
  - routes/chats.ts:427 (chat history GET)
  - routes/messages.ts:95 (session history GET)
  - routes/ws.ts:27 (WS snapshot on session connect, resume path)
  - services/inference/payload.ts (loadContext for model assembly)
  - services/compaction.ts (compaction's payload assembly)
- chats.ts:394 (discard_stale UPDATE RETURNING) unchanged — UPDATEs target
  messages directly and the returned shape is for a freshly-modified row
  where the legacy column is dual-written and correct.
- messages.ts:478/549 (ask_user_input correlation) intentionally not
  migrated — those query a different shape, ported in v1.13.1-C.
- Writes still target `messages` directly; the view is read-only.

Smoke verified against the live container:
- Equivalence: 5/5 messages with both legacy column and parts row return
  identical tool_calls jsonb between FROM messages and FROM messages_with_parts.
- Perf: EXPLAIN ANALYZE on the 42-message stress chat returns in ~1ms
  (50ms threshold). Bitmap Index Scan on message_parts_msg_seq_idx
  carries the parts lookups.
- API contract: GET /api/chats/:id/messages returns identical
  {id, name, args} tool_calls and {tool_call_id, output, truncated, error}
  tool_results shapes to frontend consumers — no UI changes needed.
- Inference path: sent a view_file prompt; assistant turn 1 emitted the
  tool_call, tool message captured the result, follow-up assistant turn
  read the result back via loadContext (now view-backed) and answered
  correctly. End-to-end loop intact.

v1.13.2 drops the dual-write + the JSON columns + simplifies the view
to just SELECT FROM message_parts.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 06:22:47 +00:00
c2c4f78a26 v1.13.1-A: install AI SDK v6 + swap streamText into stream-phase.ts adapter
- Add ai@^6 and @ai-sdk/openai-compatible@^2 to apps/server.
- New services/inference/provider.ts: createOpenAICompatible against
  llama-swap (baseURL threaded from config.LLAMA_SWAP_URL, cached per
  baseURL). No apiKey — Authelia + Tailscale gate llama-swap, not keys.
- streamCompletion rewritten as an adapter over streamText. AI SDK
  fullStream parts (text-delta, tool-call, finish, error) map back to
  the legacy {content?, tool_calls?, finishReason} StreamResult shape
  that executeStreamPhase already consumes. No layer above
  streamCompletion changes.
- toModelMessages converts BooCode's OpenAI-shaped history to AI SDK
  ModelMessage[]; tool messages need toolName which we look up by
  scanning earlier assistant tool_calls for the matching id.
- buildAiTools wraps BooCode's JSON-schema tool defs via
  tool({ inputSchema: jsonSchema(parameters) }) with NO execute —
  BooCode dispatches tools in tool-phase.ts, not the AI SDK loop.
- XML fallback parser preserved as-is — qwen3.6 still emits XML tool
  calls in text content that the structured tool-call layer misses.
- reasoning-delta parts dropped with a debug-level counter — captured
  properly in v1.13.1-C.
- Abort path: streamText({ abortSignal }) wires ctx.signal through, but
  AI SDK v6 swallows the abort (fullStream iterator exits cleanly
  rather than throwing). Post-iteration `if (signal?.aborted) throw` so
  handleAbortOrError owns the row and writes status='cancelled'. Caught
  by smoke D; would have shipped as status='complete' on stop otherwise.
- Usage frame reads result.usage (inputTokens / outputTokens v6 names)
  AFTER stream drain. Single trailing publish through the existing 500ms
  throttle. Known regression: ChatThroughput's live mid-stream tick
  (v1.12.2) is gone — it now shows a single value at stream end.
  TODO(v1.13.1-followup): interpolate outputTokens during streaming
  via a delta-cadence counter (e.g. part.text.length/4 token proxy)
  and publish every 500ms; reconcile against result.usage at finish.
- Write-path dual-write from v1.13.0 unaffected.

Read path stays on JSON columns. v1.13.1-B flips reads to message_parts.

Smoke verified end-to-end against running container:
- A. Plain text: status='complete', 1 text part.
- B. Single tool prompt → multi-tool chain (4 calls): every assistant
     with tool_calls has 2 parts (text+tool_call), every tool row has
     1 part (tool_result).
- C. Multi-step covered by B's chain.
- D. Stop mid-stream: status='cancelled' written via handleAbortOrError
     after the post-iteration abort throw.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 06:17:56 +00:00
1cb6eee24c v1.13.0: message_parts table + dual-write at every tool_calls/tool_results site
Adds a granular message_parts table (one row per text/tool_call/tool_result
chunk) without changing any read path. Old messages.content / tool_calls /
tool_results columns remain authoritative for v1.13.0; this dispatch is
write-only mirroring so the AI SDK migration in v1.13.1 can flip read
authority without a backfill window.

Schema:
  CREATE TABLE message_parts (id, message_id FK ON DELETE CASCADE,
    sequence int, kind text CHECK (text|tool_call|tool_result|reasoning|step_start),
    payload jsonb, created_at, UNIQUE (message_id, sequence))

New module services/inference/parts.ts with two pure derive helpers
(partsFromAssistantMessage, partsFromToolMessage) and insertParts that
fan-outs a multi-row INSERT via postgres-js.

Wired dual-write at every site that writes tool_calls or tool_results:
- tool-phase.ts: assistant finalize UPDATE, executed-tool UPDATE,
  ask_user_input sentinel UPDATE
- messages.ts answer flow: DELETE pending tool_result part + INSERT
  answered one inside the existing sql.begin
- skills.ts: synthetic assistant + tool INSERTs both inside existing tx
- chats.ts fork: CTE clones parts via ROW_NUMBER pairing (source→dest
  message id mapping in one statement, no N+1)
- error-handler.ts finalizeCompletion: text part for plain text-only
  assistant turns

Deviation: tool-phase.ts finalize UPDATEs and finalizeCompletion text-part
write are not wrapped in fresh sql.begin transactions. Safe in v1.13.0
because JSON columns are authoritative for reads. v1.13.1 must wrap these
sites before flipping read authority — TODO comments added at each
unwrapped site referencing v1.13.1.

Tests: 8 new unit tests for the derive helpers in
services/__tests__/parts.test.ts. Existing 162 tests untouched. 170 total.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 05:46:29 +00:00
ca64bf9f0a docs: CLAUDE.md updates from /claude-md-management session
- services/inference.ts → services/inference/ directory map (v1.12.4 split)
- workspace_panes server-side jsonb (was: localStorage-only line)
- chat_status 5-state model + ChatThroughput + discard_stale endpoint
- boot-time stale-streaming sweep documented
- WS frame sync gotcha (server InferenceFrame ↔ web WsFrame)
- session_panes table noted as dropped (not deprecated)
- messages_status_check/role_check drift cleanup noted

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 05:46:14 +00:00
9ef00c0268 v1.12.4: complete inference.ts split into services/inference/
- sentinel-summaries.ts: runCapHitSummary, insertCapHitSentinel,
  runDoomLoopSummary, insertDoomLoopSentinel
- inference.ts → inference/turn.ts: residue is runAssistantTurn,
  runInference, createInferenceRunner orchestration only
- inference/index.ts: re-export shim preserves the public surface
  (createInferenceRunner, runInference, runAssistantTurn,
  detectDoomLoop, DOOM_LOOP_THRESHOLD, buildMessagesPayload, plus
  type-side InferenceContext/InferenceFrame/StreamResult/TurnArgs/
  FramePublisher)
- src/index.ts + auto_name.ts + the two vitest test files updated to
  import from ./services/inference/index.js explicitly (NodeNext ESM
  doesn't honor directory-index resolution)

Final tally: 11 files under services/inference/, the largest being
sentinel-summaries.ts at 523 LoC (two near-clone summary paths kept
side-by-side until a third sentinel justifies factoring out a shared
runWrapUpSummary). turn.ts is now 326 LoC, the next-largest is
stream-phase.ts at 380. Public import surface unchanged.

tool-phase.ts → turn.ts back-edge for runAssistantTurn remains
(cycle is safe; resolved at call time).

Prepares the file structure for v1.13 AI SDK migration — streamText
swap targets stream-phase.ts only.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 22:36:35 +00:00
c87df6981a v1.12.4-rc3: extract stream-phase + tool-phase from inference.ts
- stream-phase.ts: streamCompletion, executeStreamPhase (plus sseLines,
  StreamOptions, ChatCompletionDelta/Chunk as private helpers)
- tool-phase.ts: executeToolPhase + private executeToolCall
- types.ts: shared StreamPhaseState + DB_FLUSH_INTERVAL_MS so the
  summary functions still in inference.ts can reference them without
  pulling from a phase file

Cycle: executeToolPhase recurses into runAssistantTurn, which stays in
inference.ts. Resolved by direct value back-edge — tool-phase.ts does
`import { runAssistantTurn } from '../inference.js'` and runAssistantTurn
is now exported. Safe because the dereference happens inside an async
function body, after both modules have fully evaluated. No
callback-through-args fallback needed.

inference.ts shrinks from ~1401 to ~828 LoC. Final Dispatch D moves the
sentinel summaries out and renames the residue to inference/turn.ts.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 22:28:23 +00:00
8fa7b7fce9 v1.12.4-rc2: extract payload + error-handler from inference.ts
- payload.ts: buildMessagesPayload (re-exported), loadContext,
  maybeFlagForCompaction
- error-handler.ts: handleAbortOrError, finalizeCompletion

Both new files type-import InferenceContext/StreamResult/TurnArgs from
inference.ts; ESM elides type imports so there's no runtime cycle.
handleAbortOrError turned out not to call the summary functions, so
no back-edge needed.

inference.ts shrinks from ~1676 to ~1401 LoC.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 22:09:50 +00:00
ea468ca7fb v1.12.4-rc1: extract budget, sentinels, xml-parser from inference.ts
Pure file moves. No behavior change. inference.ts retains createInferenceRunner
public surface; new files are internal to services/inference/.

- budget.ts: resolveToolBudget
- sentinels.ts: detectDoomLoop (re-exported through inference.ts),
  isCapHitSentinel, isDoomLoopSentinel, isAnySentinel
- xml-parser.ts: parseXmlToolCall, partialXmlOpenerStart

First of four refactor batches preparing inference.ts for the v1.13
AI SDK migration. inference.ts goes from 1780 LoC to ~1620.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 21:42:41 +00:00
eef4782383 v1.12.3: stale-stream banner with Retry/Discard
When an assistant message sits status='streaming' with no token activity
for 60+ seconds, the chat shows a banner above the input offering Retry
or Discard. Both clear the stale row via a new backend endpoint
POST /api/chats/:id/discard_stale that updates status='failed' and
publishes chat_status='idle'.

Closes the UX gap that caused the 2026-05-21 debugging spiral —
slow streams and dead streams now look different to the user.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 20:48:22 +00:00
a7104691aa v1.12.2: live tok/s + ctx display next to status indicator
ChatThroughput renders inline beside StatusDot while streaming or
tool_running. Subscribes to existing usage frames via sessionEvents.
Hides when status drops to idle/error or data is older than 10s.

Addresses the 2026-05-21 spike's UX gap where slow streams looked
identical to dead streams — now there's a live token velocity readout
that immediately distinguishes the two.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 20:45:53 +00:00
1a0a3b1673 v1.12.1: stop-handler writes terminal status + constraint cleanup + dead code removal
- handleAbortOrError now writes status='cancelled' on user stop; rows
  no longer stuck 'streaming' forever
- Drop stale messages_status_check constraint (only messages_status_chk
  remains, allowing 'cancelled' via TS MESSAGE_STATUSES)
- Remove detectSameNameLoop and DOOM_LOOP_SAME_NAME_THRESHOLD (added
  during 2026-05-21 debugging spike, never fired in any real run,
  existing detectDoomLoop covers actual failure modes)
- Remove 12 ctx.log.info diagnostic markers added during the same
  spike (verbose for production)
- Bundles workspace pane sync + status indicator overhaul +
  startup hung-row sweep landed earlier in v1.12.1 work

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 20:34:40 +00:00
48ee63a286 v1.12.1: rich status indicator + server-side workspace pane sync
Status indicator (StatusDot): drops the flat amber pulse for a richer set
of states — orbiting amber for streaming, spinning sky ring for tool_running,
static violet for waiting_for_input, plus the existing idle/error. Backend
chat_status frame widens from 'working|idle|error' to discriminate streaming
vs tool execution vs paused for user input.

Workspace pane sync: pane layout moves from per-device localStorage to
server-side sessions.workspace_panes jsonb. PATCH /api/sessions/:id/workspace
broadcasts session_workspace_updated on the user channel for cross-device live
sync. Echo dedup via JSON comparison so the round-trip frame doesn't loop.
Legacy localStorage seeds the server on first hydrate, then is deleted.
Deprecated session_panes table dropped.

Resilience: startup sweep marks any stale 'streaming' message older than
5 minutes as 'failed' so v1.12.0-style hung rows clear on container restart.
useWorkspacePanes gains validatePanes() to prune dead chatId references from
saved pane state when the chat list lands.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 20:32:02 +00:00
d58d553503 v1.12.1: same-name doom-loop guard + runAssistantTurn trace logging
Add detectSameNameLoop (threshold 5) to catch over-verification hangs
where tool args vary but the model is stuck on one tool. Add 12 structured
log points across the inference state machine (runAssistantTurn,
executeToolPhase, runDoomLoopSummary) to diagnose the deterministic hang
surfaced in v1.12.0 smoke testing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-05-21 17:15:02 +00:00
fce8c06932 Merge v1.11.10 + doc refinements onto v1.12.0 main
# Conflicts:
#	CLAUDE.md
2026-05-21 15:22:46 +00:00
684612f3cd docs: capture v1.12 learnings in CLAUDE.md (whitelist drift, AGENTS.md single source, MCP NDJSON framing) 2026-05-21 15:19:46 +00:00
cc73ed1957 docs: refine CLAUDE.md (TurnArgs, web tools, env vars, new-tool convention) 2026-05-21 02:57:32 +00:00
3e1e17ecf6 v1.11.10: stream-cap response body at 5MB, abort on overflow 2026-05-21 02:27:31 +00:00
50 changed files with 3493 additions and 1967 deletions

View File

@@ -33,7 +33,7 @@ npx tsc -p apps/web/tsconfig.app.json --noEmit # web app specifically
docker compose build --no-cache boocode && docker compose up -d
```
Tests: `pnpm -C apps/server test` runs 23 vitest tests. No test harness on `apps/web` (adding it requires installing vitest as a new devDep). Vitest pinned to `^3` because Vite 5 / vitest 4 are incompatible. No linters configured.
Tests: `pnpm -C apps/server test` runs the vitest suite. No test harness on `apps/web` (adding it requires installing vitest as a new devDep). Vitest pinned to `^3` because Vite 5 / vitest 4 are incompatible. No linters configured. Vitest include glob is `src/**/__tests__/**/*.test.ts` (see `apps/server/vitest.config.ts`) — tests outside `src/**/__tests__/` silently won't run; match the per-domain convention (`apps/server/src/services/__tests__/foo.test.ts`).
## Architecture
@@ -46,9 +46,12 @@ Tests: `pnpm -C apps/server test` runs 23 vitest tests. No test harness on `apps
- **Zod** for request validation and config parsing.
Key services:
- **`services/inference.ts`** — Streams LLM responses, executes tool loops (max depth 15, see `MAX_TOOL_LOOP_DEPTH`), flushes to DB every 500ms. Publishes `InferenceFrame` events through the broker.
- **`services/inference/`** (v1.12.4 split — was a single `inference.ts` file). Public surface re-exported via `inference/index.ts`; callers import from `./services/inference/index.js`. Layout: `turn.ts` (runAssistantTurn / runInference / createInferenceRunner orchestration, plus `InferenceFrame`, `InferenceContext`, `TurnArgs`, `StreamResult` exported), `stream-phase.ts` (streamCompletion + executeStreamPhase + SSE parsing), `tool-phase.ts` (executeToolPhase; back-edges into turn.ts for the runAssistantTurn recursion — cycle is safe because dereferenced at call time, not module top-level), `sentinel-summaries.ts` (runCapHitSummary + runDoomLoopSummary + their sentinel inserters; two near-clones kept side-by-side until a third sentinel justifies factoring out runWrapUpSummary), `error-handler.ts` (handleAbortOrError, finalizeCompletion), `payload.ts` (buildMessagesPayload, loadContext, maybeFlagForCompaction, `OpenAiMessage`), `sentinels.ts` (`detectDoomLoop`, `DOOM_LOOP_THRESHOLD`, sentinel predicates), `budget.ts` (resolveToolBudget), `xml-parser.ts` (Qwen-coder XML tool-call fallback), `types.ts` (`StreamPhaseState`, `DB_FLUSH_INTERVAL_MS` shared between stream-phase and sentinel-summaries). **`TurnArgs`** is the per-turn state envelope threaded through the `executeToolPhase → runAssistantTurn` recursion (`toolsUsed`, `recentToolCalls`, `assistantMessageId`, `signal`); reset to defaults in `runInference` at the user-message boundary. Cap-hit (`toolsUsed >= budget`) and doom-loop (`detectDoomLoop(recentToolCalls)`) checks both read from this envelope. Add new per-turn state to `TurnArgs` in `turn.ts`, not module-level closures.
- **`chat_status` frame shape** (published via `broker.publishUser`) — `status: 'streaming' | 'tool_running' | 'waiting_for_input' | 'idle' | 'error'` (widened from `working|idle|error` in v1.12.1). Frontend `useChatStatus` derives `idle_warm` (<30s since idle) vs `idle_cold`. `ChatThroughput` renders inline beside `StatusDot` only when streaming or tool_running, fed by 500ms-throttled `'usage'` WS frames (`completion_tokens` + `ctx_used` + `ctx_max`). The `POST /api/chats/:id/discard_stale` endpoint exists to mark a stuck-streaming row as `failed` when the frontend's 60s no-token-activity timer (`ChatPane` content-length watcher) gives up.
- **Boot-time stale-streaming sweep** in `apps/server/src/index.ts` after `applySchema()`: any `messages.status='streaming'` older than 5 minutes flips to `'failed'`. Logs only on non-zero count. Recovers from container restart while inference was mid-stream (v1.12.1).
- **`services/broker.ts`** — In-memory pub/sub with two channel types: per-session (message streaming) and per-user (sidebar updates). No persistence; clients reconnect on restart.
- **`services/tools.ts`** — Four read-only file tools exposed as OpenAI function-calling schemas. All file access goes through `path_guard.ts` which resolves against project root.
- **`services/tools.ts`** — Tool registry (`ALL_TOOLS`, `READ_ONLY_TOOL_NAMES`, `TOOLS_BY_NAME`). Filesystem tools (view_file/list_dir/grep/find_files) go through three guard layers: `path_guard.ts` (workspace scope), `secret_guard.ts` (filename deny list), `url_guard.ts` (SSRF/private-IP block for web_fetch). v1.11.8+ web tools (`web_search`, `web_fetch`) are opt-in per chat via `session.web_search_enabled` (resolved with `project.default_web_search_enabled` fallback) and filtered out of the LLM's tool schema when false.
- **`services/compaction.ts`** + **`services/model-context.ts`** — v1.11.0 anchored rolling summary (single `summary=true` assistant row per chat, supersedes itself on each compaction). Triggered when `chats.needs_compaction` is set after an inference turn exceeds `usable(ctx_max) = ctx_max - 20k`. **`ctx_max` comes from `model-context.getModelContext()` which fetches `${LLAMA_SWAP_URL}/upstream/<model>/props`** — NOT from `parsed.timings.n_ctx` (the stream completion's `timings` doesn't carry n_ctx; that read was dead code until v1.11.3 ripped it out).
- **`services/file_ops.ts`** — Shared file operation implementations used by both inference tools and HTTP routes.
- **`services/auto_name.ts`** — Non-streaming LLM call to generate 4-word session titles after first assistant reply.
@@ -86,19 +89,18 @@ Font / CSS pipeline (apps/web):
### Multi-pane workspace
Sessions hold 15 panes (chat / empty / placeholder terminal+agent). Workspace pane state is **client-side only** (localStorage key `boocode.workspace.panes.<sessionId>`); the legacy `session_panes` table and its REST endpoints are deprecated — no `/api/panes/*` routes exist. Each chat lives in at most one pane; tab strip is per-pane and tracks `chatIds[]` + `activeChatIdx`. Sessions 1:N chats; chats own messages. Tab reorder via native HTML5 drag events.
Sessions hold 15 panes (chat / empty / placeholder terminal+agent). v1.12.1 moved pane state from per-device localStorage to `sessions.workspace_panes jsonb` for cross-device sync. `PATCH /api/sessions/:id/workspace` persists; `session_workspace_updated` user-channel frame broadcasts to every device watching the session. `useWorkspacePanes` debounces saves 300ms and dedups echoes by JSON string. Legacy localStorage key `boocode.workspace.panes.<sessionId>` is read once on first hydrate (one-time seed-and-delete migration when server is empty but localStorage has data); no longer written. The deprecated `session_panes` table was dropped. `validatePanes(validChatIds)` prunes panes referencing chat IDs that no longer exist (called by `useSessionChats` after the chat list fetch lands). Each chat lives in at most one pane; tab strip is per-pane and tracks `chatIds[]` + `activeChatIdx`. Tab reorder via native HTML5 drag events.
## Database
PostgreSQL 16. Tables: `projects`, `sessions`, `chats`, `messages`, `settings`, `session_panes` (deprecated). Schema applied idempotently on startup via `applySchema()`. Use `clock_timestamp()` (not `NOW()`) inside transactions. CHECK constraints in place: `projects_status_chk` ('open'|'archived'), `sessions_status_chk` (same), `chats_status_chk` (same), `messages_role_chk`, `messages_status_chk` — keep in sync with the `*_STATUSES` const arrays in `apps/server/src/types/api.ts`.
PostgreSQL 16. Tables: `projects`, `sessions`, `chats`, `messages`, `settings`. (`session_panes` was dropped in v1.12.1; workspace pane state lives in `sessions.workspace_panes jsonb`.) Schema applied idempotently on startup via `applySchema()`. Use `clock_timestamp()` (not `NOW()`) inside transactions. CHECK constraints in place: `projects_status_chk` ('open'|'archived'), `sessions_status_chk` (same), `chats_status_chk` (same), `messages_role_chk`, `messages_status_chk` — keep in sync with the `*_STATUSES` const arrays in `apps/server/src/types/api.ts`. The older anonymous `messages_status_check` (without 'cancelled') and `messages_role_check` (without 'system') were dropped in v1.12.1; only the `_chk` variants remain.
Schema CHECK migration order when renaming allowed values: (1) `ALTER TABLE ... DROP CONSTRAINT IF EXISTS <system_name>` (inline `CREATE TABLE` checks get `<table>_<column>_check`), (2) `UPDATE` rows to new values, (3) wrap new constraint ADD in `DO $$ ... pg_constraint` guard — that block is the only way to get `ADD CONSTRAINT IF NOT EXISTS`.
Position-shift pattern for panes (legacy `session_panes` table): negate-and-restore to avoid UNIQUE(session_id, position) collisions during reorder/insert/delete. Sentinel value -100 for the moving pane.
## Environment
Required: `DATABASE_URL`, `LLAMA_SWAP_URL`. Optional: `PORT` (3000), `HOST` (0.0.0.0), `PROJECT_ROOT_WHITELIST` (/opt, read-only scope for add-existing path resolution), `BOOTSTRAP_ROOT` (/opt/projects, writable scope for create-new-project bootstrap mkdir target — host must `mkdir -p /opt/projects` before container start), `DEFAULT_MODEL`, `LOG_LEVEL`.
Required: `DATABASE_URL`, `LLAMA_SWAP_URL`. Optional: `PORT` (3000), `HOST` (0.0.0.0), `PROJECT_ROOT_WHITELIST` (/opt, read-only scope for add-existing path resolution), `BOOTSTRAP_ROOT` (/opt/projects, writable scope for create-new-project bootstrap mkdir target — host must `mkdir -p /opt/projects` before container start), `DEFAULT_MODEL`, `LOG_LEVEL`, `SEARXNG_URL` (default `http://100.114.205.53:8888` — internal Tailscale Fathom; the public `search.indifferentketchup.com` is behind Authelia and unusable from server context).
## Workflow
@@ -124,9 +126,16 @@ Required: `DATABASE_URL`, `LLAMA_SWAP_URL`. Optional: `PORT` (3000), `HOST` (0.0
- TypeScript strict mode. Both apps share `tsconfig.base.json`.
- Server uses NodeNext module resolution (`.js` extensions in imports).
- Discriminated unions for type narrowing: `Pane` (by `kind`), `SessionEvent` (by `type`), `InferenceFrame` (by `type`).
- **Adding a new WS frame type** requires updating BOTH the server's `InferenceFrame` (loose `type:` union + optional fields in `services/inference/turn.ts`) AND the web `WsFrame` (strict discriminated union in `apps/web/src/api/types.ts`). Server publish is permissive; the frontend type is the wire-format gate. The `'usage'` frame added in v1.12.2 needed both sides; missing the web side silently drops the frame at JSON-parse.
- shadcn primitives live in `components/ui/`. Don't modify them unless adding a new primitive.
- `inferLanguage()` from `lib/attachments.ts` is the canonical file-extension-to-language map. `CodeBlock.tsx` keeps its own `LANG_MAP` because it also resolves markdown fence names.
- Two UI event buses: `hooks/sessionEvents.ts` for DB-state events (chat_created, session_updated); `lib/events.ts` for ephemeral UI (`sendToTerminal`, `terminalsRegistry`). Don't merge — different subscriber lifecycles.
- `vite.config.ts` proxy entries are order-sensitive: more-specific prefixes (`/api/term`, `/ws/term`) must come BEFORE `/api`.
- Mobile pane URL sync (`Session.tsx`): the `?pane=<id>` effect resets `activePaneIdx` whenever `panes` changes. New-pane creation on mobile must push `?pane=` atomically — `addPaneAndSwitch` is the wrapper that does this. `addSplitPane` returns the new pane id for callers.
- xterm.js v5 uses canvas rendering — browser doesn't see xterm's selection; the native right-click menu has no working Copy for terminal text. App keybindings (`Cmd/Ctrl-C`, `Cmd/Ctrl-Shift-C`) are the path.
- **New tools** live in their own `services/<name>.ts` file (see `web_search.ts`, `web_fetch.ts`) — exports a pure `executeFoo(input, ...deps)` for direct test access plus a `ToolDef` wrapper that `loadConfig()`s its real dependencies. Register the ToolDef in `tools.ts` `ALL_TOOLS` (and `READ_ONLY_TOOL_NAMES` if applicable). Inject `fetcher: typeof fetch = fetch` rather than `vi.spyOn(globalThis, 'fetch')` — cleanup is simpler and the production call site stays unchanged.
- **Sentinels** are `role='system'` rows with structured `metadata.kind` (`cap_hit`, `doom_loop`). UI-only — `buildMessagesPayload` strips them via `isAnySentinel` so the LLM never sees them. A new kind requires arms in `MessageMetadata` in BOTH `apps/server/src/types/api.ts` AND `apps/web/src/api/types.ts`, plus a render branch in `apps/web/src/components/MessageBubble.tsx`.
- **ReadableStream test stubs** use `pull()` (not `start()`) so chunks are produced lazily — `start()` enqueues everything and calls `controller.close()` before the consumer reads, so a subsequent `reader.cancel()` finds the stream already closed and the `cancel()` callback never fires. Also provide MORE chunks than the test will consume so the source stays in 'readable' state when cancel runs (e.g. cap test reads ~6 chunks, stub provides 10).
- Tool-name whitelists must derive from `ALL_TOOLS` in `services/tools.ts`, never hardcoded. `services/agents.ts` `ALL_TOOL_NAMES` had this drift class until v1.12 — same pattern applies to any future tool-aware code.
- Agent registry lives at `data/AGENTS.md` (global, bind-mounted at `/data/AGENTS.md`). No per-project `AGENTS.md` in this repo — removed in v1.12 to eliminate the two-files-must-stay-in-sync drift. The `getAgentsForProject` per-project override mechanism remains for *other* projects.
- MCP stdio transport uses newline-delimited JSON (NDJSON), NOT LSP-style `Content-Length` headers. The `codecontext/shim.go` framing implementation is the reference; per the MCP spec (modelcontextprotocol.io/specification/server/transports).

View File

@@ -11,8 +11,10 @@
"test": "vitest run"
},
"dependencies": {
"@ai-sdk/openai-compatible": "^2.0.47",
"@fastify/static": "^7.0.4",
"@fastify/websocket": "^10.0.1",
"ai": "^6.0.190",
"fastify": "^4.28.1",
"postgres": "^3.4.4",
"ws": "^8.18.0",

View File

@@ -16,7 +16,7 @@ import { registerWebSocket } from './routes/ws.js';
import { registerModelRoutes } from './routes/models.js';
import { registerAgentRoutes } from './routes/agents.js';
import { registerSkillsRoutes } from './routes/skills.js';
import { createInferenceRunner } from './services/inference.js';
import { createInferenceRunner } from './services/inference/index.js';
import { createBroker } from './services/broker.js';
import { listSkills } from './services/skills.js';
import * as compaction from './services/compaction.js';
@@ -49,6 +49,18 @@ async function main() {
await applySchema(sql);
app.log.info('database schema applied');
const swept = await sql<{ count: string }[]>`
WITH swept AS (
UPDATE messages SET status = 'failed'
WHERE status = 'streaming' AND created_at < NOW() - INTERVAL '5 minutes'
RETURNING id
) SELECT count(*)::text AS count FROM swept
`;
const sweptCount = Number(swept[0]?.count ?? 0);
if (sweptCount > 0) {
app.log.info({ sweptCount }, 'swept stale streaming messages to failed');
}
// v1.11.3: tell the model-context cache where llama-swap lives. Cache
// lookups go to ${LLAMA_SWAP_URL}/upstream/<model>/props to read
// default_generation_settings.n_ctx — the value persisted as messages.ctx_max.

View File

@@ -18,6 +18,12 @@ const ForkBody = z.object({
name: z.string().min(1).max(200).optional(),
});
const DiscardStaleBody = z.object({
message_id: z.string().uuid(),
});
const STALE_MIN_AGE_SECONDS = 60;
export function registerChatRoutes(
app: FastifyInstance,
sql: Sql,
@@ -307,6 +313,28 @@ export function registerChatRoutes(
AND created_at <= ${target.created_at}::timestamptz
AND status = 'complete'
`;
// v1.13.0: clone message_parts for the forked messages. Source and
// destination preserve ordering (the INSERT above orders by created_at,
// id) so a ROW_NUMBER pairing maps source.id → dest.id deterministically.
await tx`
WITH src AS (
SELECT id, ROW_NUMBER() OVER (ORDER BY created_at ASC, id ASC) AS rn
FROM messages
WHERE chat_id = ${source.id}
AND created_at <= ${target.created_at}::timestamptz
AND status = 'complete'
),
dst AS (
SELECT id, ROW_NUMBER() OVER (ORDER BY created_at ASC, id ASC) AS rn
FROM messages
WHERE chat_id = ${chat!.id}
)
INSERT INTO message_parts (message_id, sequence, kind, payload)
SELECT dst.id, p.sequence, p.kind, p.payload
FROM message_parts p
JOIN src ON p.message_id = src.id
JOIN dst ON dst.rn = src.rn
`;
return chat!;
});
@@ -320,6 +348,73 @@ export function registerChatRoutes(
}
);
// v1.12.3: explicit recovery from a stuck-streaming assistant row. The
// frontend gates this behind a 60s no-token-activity timer; the server
// re-checks the age and current status for safety. Non-streaming rows
// return 409 (frontend race; idempotent retry is fine).
app.post<{ Params: { id: string } }>(
'/api/chats/:id/discard_stale',
async (req, reply) => {
const parsed = DiscardStaleBody.safeParse(req.body ?? {});
if (!parsed.success) {
reply.code(400);
return { error: 'invalid body', details: parsed.error.flatten() };
}
const rows = await sql<{
id: string;
session_id: string;
chat_id: string;
status: string;
age_seconds: number;
}[]>`
SELECT id, session_id, chat_id, status,
EXTRACT(EPOCH FROM (clock_timestamp() - created_at))::int AS age_seconds
FROM messages
WHERE id = ${parsed.data.message_id} AND chat_id = ${req.params.id}
`;
if (rows.length === 0) {
reply.code(404);
return { error: 'message not found in chat' };
}
const msg = rows[0]!;
if (msg.status !== 'streaming') {
reply.code(409);
return { error: 'message is no longer streaming', current_status: msg.status };
}
if (msg.age_seconds < STALE_MIN_AGE_SECONDS) {
reply.code(409);
return { error: 'message is not stale yet', age_seconds: msg.age_seconds };
}
const updated = await sql<Message[]>`
UPDATE messages
SET status = 'failed',
content = COALESCE(content, ''),
finished_at = clock_timestamp()
WHERE id = ${msg.id} AND status = 'streaming'
RETURNING id, session_id, chat_id, role, content, kind, tool_calls, tool_results,
status, last_seq, tokens_used, ctx_used, ctx_max, started_at, finished_at,
created_at, metadata, summary, tail_start_id, compacted_at
`;
if (updated.length === 0) {
// Race: the row flipped out of 'streaming' between our SELECT and UPDATE.
reply.code(409);
return { error: 'message status changed mid-request' };
}
broker.publishUser('default', {
type: 'chat_status',
chat_id: msg.chat_id,
status: 'idle',
at: new Date().toISOString(),
});
broker.publish(msg.session_id, {
type: 'message_complete',
message_id: msg.id,
chat_id: msg.chat_id,
});
return updated[0];
}
);
app.get<{ Params: { id: string } }>(
'/api/chats/:id/messages',
async (req, reply) => {
@@ -328,11 +423,12 @@ export function registerChatRoutes(
reply.code(404);
return { error: 'chat not found' };
}
// v1.13.1-B: reads tool_calls/tool_results via the parts-merged view.
const rows = await sql<Message[]>`
SELECT id, session_id, chat_id, role, content, kind, tool_calls, tool_results, status, last_seq,
tokens_used, ctx_used, ctx_max, started_at, finished_at, created_at, metadata,
summary, tail_start_id, compacted_at
FROM messages
FROM messages_with_parts
WHERE chat_id = ${req.params.id}
ORDER BY created_at ASC, id ASC
`;

View File

@@ -91,11 +91,12 @@ export function registerMessageRoutes(
// SummaryCard) and shows compacted_at-stamped rows inline for context.
// Internal inference assembly filters compacted_at IS NULL separately —
// see services/inference.ts loadContext + services/compaction.ts.
// v1.13.1-B: reads tool_calls/tool_results via the parts-merged view.
const rows = await sql<Message[]>`
SELECT id, session_id, chat_id, role, content, kind, tool_calls, tool_results, status, last_seq,
tokens_used, ctx_used, ctx_max, started_at, finished_at, created_at, metadata,
summary, tail_start_id, compacted_at
FROM messages
FROM messages_with_parts
WHERE session_id = ${req.params.id}
ORDER BY created_at ASC, id ASC
`;
@@ -469,30 +470,36 @@ export function registerMessageRoutes(
const chat = chatRows[0]!;
const sessionId = chat.session_id;
// Find the assistant message that emitted this tool_call. Scoped by
// chat_id + role to avoid cross-chat lookups; ordered by created_at DESC
// because the most recent issuance wins when an LLM reuses call IDs
// across turns (the older, already-answered one is a different row with
// populated tool_results downstream).
const callerRows = await sql<{ id: string; tool_calls: ToolCall[] | null }[]>`
SELECT id, tool_calls FROM messages
WHERE chat_id = ${chat.id}
AND role = 'assistant'
AND tool_calls IS NOT NULL
ORDER BY created_at DESC
// v1.13.1-C: find the assistant's tool_call by indexing message_parts
// directly on payload->>'id'. Scoped by chat_id + role via the JOIN.
// Pre-v1.13.0 history has no parts rows — those tool_calls become
// unreachable here (404). Acceptable per the dispatch decision: any
// pending elicitation from before v1.13.0 is long timed out by now;
// promote to a hotfix with a JSON-column fallback if it ever surfaces.
const callerRows = await sql<{
message_id: string;
payload: { id: string; name: string; args: Record<string, unknown> };
}[]>`
SELECT p.message_id, p.payload
FROM message_parts p
JOIN messages m ON m.id = p.message_id
WHERE m.chat_id = ${chat.id}
AND m.role = 'assistant'
AND p.kind = 'tool_call'
AND p.payload->>'id' = ${tool_call_id}
ORDER BY m.created_at DESC
LIMIT 1
`;
let foundCall: ToolCall | null = null;
for (const row of callerRows) {
const match = row.tool_calls?.find((tc) => tc.id === tool_call_id);
if (match) {
foundCall = match;
break;
}
}
if (!foundCall) {
const callerRow = callerRows[0];
if (!callerRow) {
reply.code(404);
return { error: 'unknown_tool_call_id' };
}
const foundCall: ToolCall = {
id: callerRow.payload.id,
name: callerRow.payload.name,
args: callerRow.payload.args,
};
if (foundCall.name !== 'ask_user_input') {
reply.code(400);
return { error: 'tool_call_not_ask_user_input' };
@@ -539,18 +546,21 @@ export function registerMessageRoutes(
}
}
// Find the pending tool row. ORDER BY created_at DESC + LIMIT 1 picks
// the most recent row with this tool_call_id; the already-answered
// check below guards against UPDATE-ing a stale answer.
// v1.13.1-C: find the pending tool row via message_parts on
// payload->>'tool_call_id'. Same fallback caveat as the caller lookup
// above — pre-v1.13.0 rows are unreachable here.
const toolRows = await sql<{
id: string;
tool_results: { tool_call_id: string; output: unknown } | null;
message_id: string;
payload: { tool_call_id: string; output: unknown };
}[]>`
SELECT id, tool_results FROM messages
WHERE chat_id = ${chat.id}
AND role = 'tool'
AND tool_results->>'tool_call_id' = ${tool_call_id}
ORDER BY created_at DESC
SELECT p.message_id, p.payload
FROM message_parts p
JOIN messages m ON m.id = p.message_id
WHERE m.chat_id = ${chat.id}
AND m.role = 'tool'
AND p.kind = 'tool_result'
AND p.payload->>'tool_call_id' = ${tool_call_id}
ORDER BY m.created_at DESC
LIMIT 1
`;
const toolRow = toolRows[0];
@@ -558,7 +568,7 @@ export function registerMessageRoutes(
reply.code(404);
return { error: 'unknown_tool_call_id', detail: 'tool message not found' };
}
if (toolRow.tool_results && toolRow.tool_results.output !== null) {
if (toolRow.payload && toolRow.payload.output !== null) {
reply.code(409);
return { error: 'tool_call_already_answered' };
}
@@ -570,11 +580,21 @@ export function registerMessageRoutes(
truncated: false,
};
const toolMessageId = toolRow.message_id;
const result = await sql.begin(async (tx) => {
await tx`
UPDATE messages
SET tool_results = ${tx.json(newToolResults as never)}
WHERE id = ${toolRow.id}
WHERE id = ${toolMessageId}
`;
// v1.13.0: replace the pending tool_result part inserted at message
// creation (tool-phase.ts) with the answered one. Delete-then-insert
// is simpler than UPDATE because parts are append-style elsewhere;
// the UNIQUE (message_id, sequence) constraint blocks plain insert.
await tx`DELETE FROM message_parts WHERE message_id = ${toolMessageId} AND kind = 'tool_result'`;
await tx`
INSERT INTO message_parts (message_id, sequence, kind, payload)
VALUES (${toolMessageId}, 0, 'tool_result', ${tx.json(newToolResults as never)})
`;
const [assistantMsg] = await tx<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
@@ -584,7 +604,7 @@ export function registerMessageRoutes(
await tx`UPDATE sessions SET updated_at = clock_timestamp() WHERE id = ${sessionId}`;
await tx`UPDATE chats SET updated_at = clock_timestamp() WHERE id = ${chat.id}`;
return {
tool_message_id: toolRow.id,
tool_message_id: toolMessageId,
assistant_message_id: assistantMsg!.id,
};
});

View File

@@ -13,6 +13,18 @@ const CreateBody = z.object({
agent_id: z.string().min(1).max(200).nullable().optional(),
});
const WorkspacePaneZ = z.object({
id: z.string().min(1).max(200),
kind: z.enum(['chat', 'terminal', 'agent', 'empty', 'settings']),
chatId: z.string().min(1).max(200).optional(),
chatIds: z.array(z.string().min(1).max(200)).max(50),
activeChatIdx: z.number().int(),
});
const WorkspacePanesBody = z.object({
workspace_panes: z.array(WorkspacePaneZ).max(10),
});
const PatchBody = z.object({
name: z.string().min(1).max(200).optional(),
model: z.string().min(1).max(200).optional(),
@@ -44,7 +56,7 @@ export function registerSessionRoutes(
}
const status = req.query.status === 'archived' ? 'archived' : 'open';
const rows = await sql<Session[]>`
SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled
SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled, workspace_panes
FROM sessions
WHERE project_id = ${req.params.id} AND status = ${status}
ORDER BY updated_at DESC
@@ -92,7 +104,7 @@ export function registerSessionRoutes(
const [session] = await tx<Session[]>`
INSERT INTO sessions (project_id, name, model, system_prompt, agent_id)
VALUES (${req.params.id}, ${name}, ${model}, ${systemPrompt}, ${agentId})
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled, workspace_panes
`;
await tx`
INSERT INTO chats (session_id, name, status)
@@ -112,7 +124,7 @@ export function registerSessionRoutes(
app.get<{ Params: { id: string } }>('/api/sessions/:id', async (req, reply) => {
const rows = await sql<Session[]>`
SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled
SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled, workspace_panes
FROM sessions WHERE id = ${req.params.id}
`;
if (rows.length === 0) {
@@ -158,7 +170,7 @@ export function registerSessionRoutes(
updated_at = clock_timestamp()
WHERE id = ${req.params.id}
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at,
agent_id, web_search_enabled
agent_id, web_search_enabled, workspace_panes
`;
if (rows.length === 0) {
reply.code(404);
@@ -187,6 +199,36 @@ export function registerSessionRoutes(
}
);
app.patch<{ Params: { id: string } }>(
'/api/sessions/:id/workspace',
async (req, reply) => {
const parsed = WorkspacePanesBody.safeParse(req.body);
if (!parsed.success) {
reply.code(400);
return { error: 'invalid body', details: parsed.error.flatten() };
}
const rows = await sql<Session[]>`
UPDATE sessions
SET workspace_panes = ${sql.json(parsed.data.workspace_panes as never)},
updated_at = clock_timestamp()
WHERE id = ${req.params.id}
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at,
agent_id, web_search_enabled, workspace_panes
`;
if (rows.length === 0) {
reply.code(404);
return { error: 'session not found' };
}
const session = rows[0]!;
broker.publishUser('default', {
type: 'session_workspace_updated',
session_id: session.id,
workspace_panes: session.workspace_panes,
});
return session;
}
);
// v1.9: bulk-archive every open session in a project. Mirrors the
// single-archive shape (same broker frame type) so the existing useSidebar
// reducer cases handle it without changes — just N frames instead of 1.
@@ -263,7 +305,7 @@ export function registerSessionRoutes(
const rows = await sql<Session[]>`
UPDATE sessions SET status = 'open', updated_at = clock_timestamp()
WHERE id = ${req.params.id} AND status = 'archived'
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled
RETURNING id, project_id, name, model, system_prompt, status, created_at, updated_at, agent_id, web_search_enabled, workspace_panes
`;
if (rows.length === 0) {
reply.code(404);

View File

@@ -90,11 +90,26 @@ export function registerSkillsRoutes(
VALUES (${sessionId}, ${chat.id}, 'assistant', '', ${sql.json(toolCalls as never)}, 'complete', clock_timestamp())
RETURNING id
`;
// v1.13.0: dual-write the synthetic assistant message's tool_call.
// Single skill_use tool_call, no text content, so one part at seq 0.
await tx`
INSERT INTO message_parts (message_id, sequence, kind, payload)
VALUES (${synthAssistant!.id}, 0, 'tool_call', ${tx.json({
id: toolCallId,
name: 'skill_use',
args: { name: skill_name },
} as never)})
`;
const [toolMsg] = await tx<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, tool_results, status, created_at)
VALUES (${sessionId}, ${chat.id}, 'tool', '', ${sql.json(toolResults as never)}, 'complete', clock_timestamp())
RETURNING id
`;
// v1.13.0: dual-write the synthetic tool result (the skill body).
await tx`
INSERT INTO message_parts (message_id, sequence, kind, payload)
VALUES (${toolMsg!.id}, 0, 'tool_result', ${tx.json(toolResults as never)})
`;
const [userMsg] = await tx<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
VALUES (${sessionId}, ${chat.id}, 'user', ${userText}, 'complete', clock_timestamp())

View File

@@ -23,11 +23,12 @@ export function registerWebSocket(
// v1.11: snapshot includes compaction fields so MessageBubble can
// render the SummaryCard for summary=true rows on first connect.
// v1.13.1-B: reads tool_calls/tool_results via the parts-merged view.
const messages = await sql<Message[]>`
SELECT id, session_id, chat_id, role, content, kind, tool_calls, tool_results, status, last_seq,
tokens_used, ctx_used, ctx_max, started_at, finished_at, created_at, metadata,
summary, tail_start_id, compacted_at
FROM messages
FROM messages_with_parts
WHERE session_id = ${sessionId}
ORDER BY created_at ASC, id ASC
`;

View File

@@ -32,6 +32,59 @@ CREATE TABLE IF NOT EXISTS messages (
CREATE INDEX IF NOT EXISTS idx_messages_session ON messages(session_id, created_at);
-- v1.13.0: granular message parts table for AI SDK migration. Old
-- messages.content / tool_calls / tool_results columns stay authoritative
-- for reads in v1.13.0; this table is dual-written so the swap can happen
-- in a later dispatch without a backfill window. ON DELETE CASCADE means
-- removing a message removes its parts in one go.
CREATE TABLE IF NOT EXISTS message_parts (
id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
message_id uuid NOT NULL REFERENCES messages(id) ON DELETE CASCADE,
sequence int NOT NULL,
kind text NOT NULL,
payload jsonb NOT NULL,
created_at timestamptz NOT NULL DEFAULT clock_timestamp(),
CONSTRAINT message_parts_kind_chk CHECK (kind IN ('text', 'tool_call', 'tool_result', 'reasoning', 'step_start')),
CONSTRAINT message_parts_seq_uniq UNIQUE (message_id, sequence)
);
CREATE INDEX IF NOT EXISTS message_parts_msg_seq_idx ON message_parts (message_id, sequence);
-- v1.13.1-B: read-path view. Read sites SELECT FROM messages_with_parts
-- instead of messages so tool_calls / tool_results / reasoning_parts come
-- from the granular message_parts table. The COALESCE means pre-v1.13.0
-- history (no parts rows) still resolves via the legacy JSON columns; the
-- dual-write from v1.13.0 keeps both in sync for all rows written since.
-- Writes continue to target `messages` directly — the view is read-only.
-- Shapes match the in-memory ToolCall / ToolResult types: tool_calls is a
-- jsonb array of {id, name, args}, tool_results is a single jsonb object
-- {tool_call_id, output, truncated, error?}. reasoning_parts is new — only
-- consumed by the inference history fetch (payload.ts) so v1.13.1-C can
-- wire reasoning into the model payload. Not surfaced in external APIs yet.
CREATE OR REPLACE VIEW messages_with_parts AS
SELECT
m.id, m.session_id, m.chat_id, m.role, m.content, m.kind, m.status,
m.last_seq, m.tokens_used, m.ctx_used, m.ctx_max,
m.started_at, m.finished_at, m.created_at, m.metadata,
m.summary, m.tail_start_id, m.compacted_at,
COALESCE(
(SELECT jsonb_agg(p.payload ORDER BY p.sequence)
FROM message_parts p
WHERE p.message_id = m.id AND p.kind = 'tool_call'),
m.tool_calls
) AS tool_calls,
COALESCE(
(SELECT p.payload
FROM message_parts p
WHERE p.message_id = m.id AND p.kind = 'tool_result'
ORDER BY p.sequence
LIMIT 1),
m.tool_results
) AS tool_results,
(SELECT jsonb_agg(p.payload ORDER BY p.sequence)
FROM message_parts p
WHERE p.message_id = m.id AND p.kind = 'reasoning') AS reasoning_parts
FROM messages m;
ALTER TABLE messages ADD COLUMN IF NOT EXISTS tokens_used INTEGER;
ALTER TABLE messages ADD COLUMN IF NOT EXISTS ctx_used INTEGER;
ALTER TABLE messages ADD COLUMN IF NOT EXISTS ctx_max INTEGER;
@@ -47,22 +100,14 @@ CREATE TABLE IF NOT EXISTS settings (
INSERT INTO settings (key, value) VALUES ('default_model', '"qwen3.6-35b-a3b-mxfp4"') ON CONFLICT (key) DO NOTHING;
-- DEPRECATED: client-side pane state as of v1.2-batch4. Table retained per
-- additive schema rule; no writes. Drop in a future destructive migration.
CREATE TABLE IF NOT EXISTS session_panes (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
session_id UUID NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
position INTEGER NOT NULL,
kind TEXT NOT NULL CHECK (kind IN ('chat', 'file_browser', 'terminal')),
state JSONB NOT NULL DEFAULT '{}',
created_at TIMESTAMPTZ NOT NULL DEFAULT clock_timestamp(),
UNIQUE (session_id, position)
);
CREATE INDEX IF NOT EXISTS idx_session_panes_session ON session_panes (session_id);
-- v1.12.1: deprecated session_panes table removed. Workspace pane state now
-- lives in sessions.workspace_panes (jsonb), see below.
DROP TABLE IF EXISTS session_panes;
-- v1.4: backfill removed. Pane layout is client-side (localStorage) since v1.2-batch4.
-- The CREATE TABLE above is retained for additive-schema discipline; drop is a
-- future destructive migration.
-- v1.12.1: server-side workspace pane layout, replaces localStorage so every
-- device sees the same panes for a given session. Shape matches
-- WorkspacePane[] from apps/server/src/types/api.ts.
ALTER TABLE sessions ADD COLUMN IF NOT EXISTS workspace_panes JSONB NOT NULL DEFAULT '[]'::jsonb;
-- v1.2: sessions.status (open | archived)
ALTER TABLE sessions ADD COLUMN IF NOT EXISTS status TEXT NOT NULL DEFAULT 'open';
@@ -128,6 +173,19 @@ BEGIN
END IF;
END $$;
-- v1.12.1: drop stale inline CHECK constraints that were superseded by the
-- named *_chk variants above. messages_status_check missed 'cancelled' and
-- messages_role_check missed 'system' — both narrower than what's in use.
DO $$
BEGIN
IF EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'messages_status_check') THEN
ALTER TABLE messages DROP CONSTRAINT messages_status_check;
END IF;
IF EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'messages_role_check') THEN
ALTER TABLE messages DROP CONSTRAINT messages_role_check;
END IF;
END $$;
-- v1.2-project-ux: projects.status + projects.gitea_remote
-- KEEP IN SYNC: apps/server/src/types/api.ts PROJECT_STATUSES
ALTER TABLE projects ADD COLUMN IF NOT EXISTS status TEXT NOT NULL DEFAULT 'open';

View File

@@ -1,5 +1,5 @@
import { describe, it, expect } from 'vitest';
import { DOOM_LOOP_THRESHOLD, detectDoomLoop } from '../inference.js';
import { DOOM_LOOP_THRESHOLD, detectDoomLoop } from '../inference/index.js';
import type { ToolCall } from '../../types/api.js';
// ---- fixture ----------------------------------------------------------------

View File

@@ -1,5 +1,5 @@
import { describe, it, expect } from 'vitest';
import { buildMessagesPayload } from '../inference.js';
import { buildMessagesPayload } from '../inference/index.js';
import type {
Message,
MessageRole,

View File

@@ -0,0 +1,121 @@
import { describe, it, expect } from 'vitest';
import { partsFromAssistantMessage, partsFromToolMessage } from '../inference/parts.js';
import type { ToolCall, ToolResult } from '../../types/api.js';
describe('partsFromAssistantMessage', () => {
it('emits one text part for content-only assistant', () => {
const parts = partsFromAssistantMessage({ content: 'hello world', tool_calls: null });
expect(parts).toHaveLength(1);
expect(parts[0]).toEqual({
sequence: 0,
kind: 'text',
payload: { text: 'hello world' },
});
});
it('emits one tool_call part for empty-content + single tool_call', () => {
const tc: ToolCall = { id: 'call_1', name: 'view_file', args: { path: 'src/a.ts' } };
const parts = partsFromAssistantMessage({ content: '', tool_calls: [tc] });
expect(parts).toHaveLength(1);
expect(parts[0]).toEqual({
sequence: 0,
kind: 'tool_call',
payload: { id: 'call_1', name: 'view_file', args: { path: 'src/a.ts' } },
});
});
it('emits text then tool_call parts in order when both present', () => {
const tc: ToolCall = { id: 'call_2', name: 'grep', args: { pattern: 'foo' } };
const parts = partsFromAssistantMessage({ content: 'let me search', tool_calls: [tc] });
expect(parts.map((p) => [p.sequence, p.kind])).toEqual([
[0, 'text'],
[1, 'tool_call'],
]);
});
it('preserves tool_call order with multiple calls', () => {
const calls: ToolCall[] = [
{ id: 'a', name: 'list_dir', args: { path: '.' } },
{ id: 'b', name: 'view_file', args: { path: 'x.ts' } },
{ id: 'c', name: 'grep', args: { pattern: 'y' } },
];
const parts = partsFromAssistantMessage({ content: '', tool_calls: calls });
expect(parts).toHaveLength(3);
expect(parts.map((p) => p.payload)).toEqual([
{ id: 'a', name: 'list_dir', args: { path: '.' } },
{ id: 'b', name: 'view_file', args: { path: 'x.ts' } },
{ id: 'c', name: 'grep', args: { pattern: 'y' } },
]);
expect(parts.map((p) => p.sequence)).toEqual([0, 1, 2]);
});
it('returns empty array for empty content + null tool_calls', () => {
expect(partsFromAssistantMessage({ content: '', tool_calls: null })).toEqual([]);
});
it('v1.13.1-C: reasoning lands at sequence 0 before text + tool_calls', () => {
const tc: ToolCall = { id: 'call_r', name: 'view_file', args: { path: 'x.ts' } };
const parts = partsFromAssistantMessage({
content: 'inspecting now',
tool_calls: [tc],
reasoning: 'user asked about x.ts; I should view it',
});
expect(parts.map((p) => [p.sequence, p.kind])).toEqual([
[0, 'reasoning'],
[1, 'text'],
[2, 'tool_call'],
]);
expect(parts[0]!.payload).toEqual({
text: 'user asked about x.ts; I should view it',
});
});
it('v1.13.1-C: reasoning + empty content + tool_calls preserves seq 0 reasoning', () => {
const tc: ToolCall = { id: 'call_r2', name: 'grep', args: { pattern: 'foo' } };
const parts = partsFromAssistantMessage({
content: '',
tool_calls: [tc],
reasoning: 'jumping straight to grep',
});
expect(parts.map((p) => [p.sequence, p.kind])).toEqual([
[0, 'reasoning'],
[1, 'tool_call'],
]);
});
});
describe('partsFromToolMessage', () => {
it('emits a single tool_result part at sequence 0', () => {
const tr: ToolResult = {
tool_call_id: 'call_1',
output: { contents: 'console.log(1)' },
truncated: false,
};
const parts = partsFromToolMessage({ tool_results: tr });
expect(parts).toHaveLength(1);
expect(parts[0]).toEqual({
sequence: 0,
kind: 'tool_result',
payload: {
tool_call_id: 'call_1',
output: { contents: 'console.log(1)' },
truncated: false,
},
});
});
it('includes error in payload when present', () => {
const tr: ToolResult = {
tool_call_id: 'call_2',
output: null,
truncated: false,
error: 'permission denied',
};
const parts = partsFromToolMessage({ tool_results: tr });
expect(parts[0]!.payload).toMatchObject({ error: 'permission denied' });
});
it('returns empty array when tool_results is null', () => {
expect(partsFromToolMessage({ tool_results: null })).toEqual([]);
});
});

View File

@@ -295,9 +295,10 @@ describe('executeWebFetch — size + truncation', () => {
// 1.5M U+1F600 emojis: each is length 2 in UTF-16 (surrogate pair) and
// 4 bytes in UTF-8. body.length = 3,000,000 chars (~2.86 MiB by
// UTF-16 count) but Buffer.byteLength = 6,000,000 bytes (>5 MiB).
// Pre-fix the char-count comparison let this through; the byte-count
// check now rejects. No Content-Length header so the pre-flight
// guard doesn't fire — we're testing the POST-consumption check.
// v1.11.10: streaming reader catches this as body_too_large (was
// response_too_large in the post-consumption check). No
// Content-Length header so the pre-flight pass and the streaming
// path is the one that rejects.
const heavy = '😀'.repeat(1_500_000);
const fakeFetch = vi.fn().mockResolvedValue(
new Response(heavy, { status: 200, headers: { 'content-type': 'text/plain' } }),
@@ -308,9 +309,8 @@ describe('executeWebFetch — size + truncation', () => {
);
expect('error' in result).toBe(true);
if ('error' in result) {
expect(result.error).toBe('response_too_large');
// Error reason should reference bytes, not character count.
expect(result.reason).toMatch(/bytes/);
expect(result.error).toBe('body_too_large');
expect(result.reason).toMatch(/exceeded/);
}
});
@@ -453,3 +453,138 @@ describe('executeWebFetch — redirect handling', () => {
expect(fakeFetch.mock.calls[1]![0]).toBe('https://example.com/foo');
});
});
// ============================================================================
// v1.11.10: streaming body cap — abort the response stream at MAX_BYTES
// ============================================================================
// MAX_BYTES is 5 * 1024 * 1024 = 5_242_880. Repeating this here (rather
// than importing) so a change to the cap surfaces as a test failure —
// the limit is part of the public contract.
const MAX_BYTES_TEST = 5 * 1024 * 1024;
// Build a Response whose body is a real ReadableStream. Uses pull() (not
// start()) so chunks are produced lazily — without backpressure, an
// unbounded start() enqueues everything and calls controller.close()
// before the consumer reads, which means a subsequent reader.cancel()
// finds the stream already closed and the cancel callback never fires.
// `cancelFlag` lets the test observe whether reader.cancel() reached the
// underlying source mid-stream.
function streamedResponse(
chunks: Uint8Array[],
init: { contentType?: string; contentLength?: number | null; cancelFlag?: { cancelled: boolean } } = {},
): Response {
let idx = 0;
const stream = new ReadableStream({
pull(controller) {
if (idx >= chunks.length) {
controller.close();
return;
}
controller.enqueue(chunks[idx]!);
idx += 1;
},
cancel() {
if (init.cancelFlag) init.cancelFlag.cancelled = true;
},
});
const headers: Record<string, string> = {};
if (init.contentType) headers['content-type'] = init.contentType;
if (init.contentLength !== undefined && init.contentLength !== null) {
headers['content-length'] = String(init.contentLength);
}
return new Response(stream, { status: 200, headers });
}
describe('executeWebFetch — streaming body cap (v1.11.10)', () => {
it('aborts the stream when a server lies about Content-Length and emits over the cap', async () => {
// Honest header would have failed the pre-flight check. The lie is
// the point: pre-flight passes (100 < 5MB) and the streaming reader
// has to be the thing that catches the oversized body.
//
// Chunk count is deliberately higher than what the reader will
// consume (10 × 1MB available, but the reader will cancel after ~6
// chunks land it over 5MB). That headroom keeps the stream in
// 'readable' state at the moment reader.cancel() runs — otherwise
// a pull-then-close race could make the source close the stream
// before cancel reaches it, and the cancel() callback wouldn't fire.
const oneMB = new Uint8Array(1024 * 1024).fill(65); // 'A'
const tenMBInChunks = Array.from({ length: 10 }, () => oneMB);
const cancelFlag = { cancelled: false };
const fakeFetch = vi.fn().mockResolvedValue(
streamedResponse(tenMBInChunks, {
contentType: 'text/plain',
contentLength: 100,
cancelFlag,
}),
);
const result = await executeWebFetch(
{ url: 'https://example.com/lying-server' },
fakeFetch as unknown as typeof fetch,
);
expect('error' in result).toBe(true);
if ('error' in result) {
expect(result.error).toBe('body_too_large');
expect(result.reason).toMatch(/exceeded/);
}
// Critical: reader.cancel() actually fired so the underlying
// connection / stream got released. Otherwise the abort would be
// notional and the server could keep streaming.
expect(cancelFlag.cancelled).toBe(true);
});
it('catches an oversized stream when Content-Length is omitted entirely', async () => {
// Many real servers (chunked transfer-encoding, dynamic responses)
// never send Content-Length. The pre-flight check has nothing to
// gate on; the streaming reader is the only line of defense.
// 10 chunks vs the ~6 the reader will consume — same headroom
// rationale as the lying-Content-Length test above.
const oneMB = new Uint8Array(1024 * 1024).fill(66); // 'B'
const tenMBInChunks = Array.from({ length: 10 }, () => oneMB);
const fakeFetch = vi.fn().mockResolvedValue(
streamedResponse(tenMBInChunks, { contentType: 'text/plain' }),
);
const result = await executeWebFetch(
{ url: 'https://example.com/no-length' },
fakeFetch as unknown as typeof fetch,
);
expect('error' in result && result.error).toBe('body_too_large');
});
it('passes a multi-chunk body that totals just under the cap', async () => {
// Boundary case: MAX_BYTES - 1 bytes split across N chunks. The
// streaming reader's `total > maxBytes` check is strict-greater so
// exactly MAX_BYTES would still succeed; MAX_BYTES + 1 would fail.
// - 1 leaves clear headroom without coinciding with the boundary.
const targetTotal = MAX_BYTES_TEST - 1;
const chunkSize = 256 * 1024; // 256 KiB chunks
const chunks: Uint8Array[] = [];
let remaining = targetTotal;
while (remaining > 0) {
const size = Math.min(chunkSize, remaining);
chunks.push(new Uint8Array(size).fill(67)); // 'C'
remaining -= size;
}
const fakeFetch = vi.fn().mockResolvedValue(
streamedResponse(chunks, { contentType: 'text/plain' }),
);
const result = await executeWebFetch(
{ url: 'https://example.com/right-at-cap' },
fakeFetch as unknown as typeof fetch,
);
// The streaming reader succeeded — we got a content shape, not an
// error. (Downstream truncate() will clamp the final string to
// MAX_CHARS_CAP=32000 and set truncated:true; that's the existing
// truncation logic and is exercised by its own test. The point of
// THIS test is that readBodyCapped didn't trip on a body that
// sits just under its byte limit.)
expect('content' in result).toBe(true);
if ('content' in result) {
expect(result.content.length).toBeGreaterThan(0);
// All ASCII 'C's, so the leading 200 chars before any truncation
// marker should be all C — proves we read real bytes through the
// streaming reader rather than getting an empty buffer.
expect(result.content.slice(0, 200)).toBe('C'.repeat(200));
}
});
});

View File

@@ -1,4 +1,4 @@
import type { InferenceContext } from './inference.js';
import type { InferenceContext } from './inference/index.js';
const NAMING_SYSTEM_PROMPT =
'You name chat sessions. Reply directly with no thinking, reasoning, or explanation. Output ONLY the title, 4 words max, no quotes, no punctuation, no prefix like "Title:".';

View File

@@ -342,9 +342,11 @@ export async function process(input: ProcessInput): Promise<void> {
// 2. All currently-active messages in this chat (compacted_at IS NULL).
// ORDER BY (created_at, id) matches loadContext in inference.ts so the
// turns() boundary logic sees the same sequence the LLM will.
// v1.13.1-B: reads tool_calls/tool_results via the parts-merged view so
// the compaction payload matches what the LLM saw on the original turn.
const messages = await sql<CompactionMessage[]>`
SELECT id, role, content, kind, summary, status, tool_calls, tool_results, metadata, created_at
FROM messages
FROM messages_with_parts
WHERE chat_id = ${chatId} AND compacted_at IS NULL
ORDER BY created_at ASC, id ASC
`;

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,20 @@
import type { Agent } from '../../types/api.js';
import { READ_ONLY_TOOL_NAMES } from '../tools.js';
// v1.8.2: tool-call budget defaults. Resolved per-turn by resolveToolBudget.
// - Agent with explicit max_tool_calls: that value.
// - Agent with read-only-only tools: BUDGET_READ_ONLY (30).
// - Agent with any non-read-only tool: BUDGET_NON_READ_ONLY (10).
// - No agent (raw chat): BUDGET_NO_AGENT (15).
export const BUDGET_READ_ONLY = 30;
export const BUDGET_NON_READ_ONLY = 10;
export const BUDGET_NO_AGENT = 15;
const READ_ONLY_SET: ReadonlySet<string> = new Set(READ_ONLY_TOOL_NAMES);
export function resolveToolBudget(agent: Agent | null): number {
if (agent?.max_tool_calls != null) return agent.max_tool_calls;
if (!agent) return BUDGET_NO_AGENT;
const allReadOnly = agent.tools.every((t) => READ_ONLY_SET.has(t));
return allReadOnly ? BUDGET_READ_ONLY : BUDGET_NON_READ_ONLY;
}

View File

@@ -0,0 +1,167 @@
import type { MessageMetadata, Session } from '../../types/api.js';
import * as modelContext from '../model-context.js';
import { maybeFlagForCompaction } from './payload.js';
import { insertParts, partsFromAssistantMessage } from './parts.js';
import type { InferenceContext, StreamResult, TurnArgs } from './turn.js';
export async function handleAbortOrError(
ctx: InferenceContext,
args: TurnArgs,
accumulated: string,
err: unknown
): Promise<void> {
const { sessionId, chatId, assistantMessageId } = args;
const isAbort = err instanceof Error && err.name === 'AbortError';
const finalStatus = isAbort ? 'cancelled' : 'failed';
const errMsg = err instanceof Error ? err.message : String(err);
// v1.8.2: persist a structured error metadata blob on genuine failures so
// the bubble can render the reason on reload without re-deriving from the
// (one-shot) WS error frame. User-initiated abort skips this — there's no
// "reason" to surface for a stop the user already explicitly chose.
const errorMetadata: MessageMetadata | null = isAbort
? null
: { kind: 'error', error_reason: 'llm_provider_error', error_text: errMsg };
if (errorMetadata) {
await ctx.sql`
UPDATE messages
SET status = ${finalStatus},
content = ${accumulated},
finished_at = clock_timestamp(),
metadata = ${ctx.sql.json(errorMetadata as never)}
WHERE id = ${assistantMessageId}
`;
} else {
await ctx.sql`
UPDATE messages
SET status = ${finalStatus},
content = ${accumulated},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
`;
}
const [failSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: failSessRow!.project_id, name: failSessRow!.name, updated_at: failSessRow!.updated_at });
// v1.8 mobile-tabs: cancellation is a user-initiated stop, treat as idle;
// genuine errors flip the dot red. v1.8.2: error path also carries a
// machine-readable `reason` so the UI can render specifics inline.
if (isAbort) {
// v1.12.1: defensive cancellation write. The status=${finalStatus} UPDATE
// above already sets 'cancelled' for the AbortError case, but a row can
// leak as 'streaming' when the abort fires between the post-tool-phase
// INSERT (executeToolPhase) and the next runAssistantTurn's stream setup,
// bypassing the try/catch around executeStreamPhase. The status guard
// makes this a no-op when the earlier write already landed.
await ctx.sql`
UPDATE messages
SET status = 'cancelled', content = ${accumulated}, finished_at = clock_timestamp()
WHERE id = ${args.assistantMessageId} AND status = 'streaming'
`;
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
});
ctx.log.info({ sessionId, chatId, assistantMessageId }, 'inference cancelled');
} else {
ctx.publishUser({
type: 'chat_status',
chat_id: chatId,
status: 'error',
at: new Date().toISOString(),
reason: 'llm_provider_error',
});
ctx.publish(sessionId, {
type: 'error',
message_id: assistantMessageId,
chat_id: chatId,
error: errMsg,
reason: 'llm_provider_error',
});
ctx.log.error({ err, sessionId, assistantMessageId }, 'inference failed');
}
}
export async function finalizeCompletion(
ctx: InferenceContext,
args: TurnArgs,
result: StreamResult,
startedAt: string | null,
session: Session
): Promise<void> {
const { sessionId, chatId, assistantMessageId } = args;
const { content, finishReason, promptTokens, completionTokens } = result;
// v1.11.3: see executeToolPhase for the rationale.
const mctx = await modelContext.getModelContext(session.model);
const nCtx = mctx?.n_ctx ?? null;
const [updated] = await ctx.sql<
{ tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
>`
UPDATE messages
SET content = ${content},
status = 'complete',
tokens_used = ${completionTokens},
ctx_used = ${promptTokens},
ctx_max = ${nCtx},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING tokens_used, ctx_used, ctx_max, finished_at
`;
// v1.13.0: dual-write the text part. finalizeCompletion is the terminal
// path for text-only assistant turns (no tool calls); tool_calls are null
// here by construction (the tool-bearing path goes through executeToolPhase).
// v1.13.1-C: include result.reasoning so reasoning-channel models capture
// a kind='reasoning' part alongside the text.
// TODO(v1.13.1): wrap the UPDATE above and this insertParts in a single
// sql.begin before flipping read authority to message_parts.
await insertParts(
ctx.sql,
partsFromAssistantMessage({
content,
tool_calls: null,
reasoning: result.reasoning,
}).map((p) => ({
...p,
message_id: assistantMessageId,
})),
);
// v1.11: flag for compaction on the terminal turn too. Catches the common
// case of a turn that hit the limit without invoking tools.
await maybeFlagForCompaction(ctx, chatId, updated);
const [completeSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: completeSessRow!.project_id, name: completeSessRow!.name, updated_at: completeSessRow!.updated_at });
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
tokens_used: updated?.tokens_used ?? null,
ctx_used: updated?.ctx_used ?? null,
ctx_max: updated?.ctx_max ?? null,
started_at: startedAt,
finished_at: updated?.finished_at ?? null,
model: session.model,
});
ctx.log.info(
{
sessionId,
chatId,
assistantMessageId,
finishReason,
chars: content.length,
tokens_used: updated?.tokens_used,
ctx_used: updated?.ctx_used,
},
'inference complete'
);
}

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@@ -0,0 +1,20 @@
// v1.12.4: re-export shim. Outside callers (apps/server/src/index.ts and the
// vitest inference tests) import from './services/inference/index.js'. The
// directory is now the public surface; turn.ts holds runAssistantTurn /
// runInference / createInferenceRunner while the other inference/*.ts files
// stay implementation-private.
export {
createInferenceRunner,
runAssistantTurn,
runInference,
} from './turn.js';
export type {
FramePublisher,
InferenceContext,
InferenceFrame,
StreamResult,
TurnArgs,
} from './turn.js';
export { detectDoomLoop, DOOM_LOOP_THRESHOLD } from './sentinels.js';
export { buildMessagesPayload } from './payload.js';

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import type { Sql } from '../../db.js';
import type { ToolCall, ToolResult } from '../../types/api.js';
// v1.13.0: dual-write helper. Every site that writes the legacy
// messages.tool_calls / messages.tool_results JSON columns calls into here
// to mirror the same data into message_parts rows. Reads still go to the
// JSON columns; the swap to parts-as-source-of-truth happens in a later
// v1.13 dispatch alongside the AI SDK streamText migration.
export type PartKind = 'text' | 'tool_call' | 'tool_result' | 'reasoning' | 'step_start';
export interface PartInsert {
message_id: string;
sequence: number;
kind: PartKind;
payload: unknown;
}
export async function insertParts(sql: Sql, parts: PartInsert[]): Promise<void> {
if (parts.length === 0) return;
// postgres-js fans out an array of objects to a multi-row INSERT. Each
// payload field needs sql.json() so jsonb storage receives a JSON value
// rather than a quoted string.
await sql`
INSERT INTO message_parts ${sql(
parts.map((p) => ({
message_id: p.message_id,
sequence: p.sequence,
kind: p.kind,
payload: sql.json(p.payload as never),
})),
'message_id',
'sequence',
'kind',
'payload',
)}
`;
}
// Derive parts from the canonical messages row for an assistant message.
// reasoning (when non-empty) becomes a 'reasoning' part at sequence 0 —
// it precedes user-visible content logically. content (when non-empty)
// becomes a 'text' part next; each tool_call becomes a 'tool_call' part
// with payload { id, name, args } where args is the parsed object (we
// use the in-memory ToolCall shape, not the OpenAI stringified one).
export function partsFromAssistantMessage(args: {
content: string;
tool_calls: ToolCall[] | null;
// v1.13.1-C: optional reasoning text streamed alongside the answer.
// Most rows have none — only models with separate reasoning channels
// (qwen3.6 etc.) populate this.
reasoning?: string;
}): Omit<PartInsert, 'message_id'>[] {
const out: Omit<PartInsert, 'message_id'>[] = [];
let seq = 0;
if (args.reasoning && args.reasoning.length > 0) {
out.push({ sequence: seq, kind: 'reasoning', payload: { text: args.reasoning } });
seq += 1;
}
if (args.content && args.content.length > 0) {
out.push({ sequence: seq, kind: 'text', payload: { text: args.content } });
seq += 1;
}
for (const tc of args.tool_calls ?? []) {
out.push({
sequence: seq,
kind: 'tool_call',
payload: { id: tc.id, name: tc.name, args: tc.args },
});
seq += 1;
}
return out;
}
// Derive a single tool_result part from a tool message's tool_results JSON.
// The payload includes the same shape that buildMessagesPayload reads from
// later: tool_call_id, output, optional error/truncated metadata.
export function partsFromToolMessage(args: {
tool_results: ToolResult | null;
}): Omit<PartInsert, 'message_id'>[] {
if (!args.tool_results) return [];
const tr = args.tool_results;
return [
{
sequence: 0,
kind: 'tool_result',
payload: {
tool_call_id: tr.tool_call_id,
output: tr.output,
truncated: tr.truncated,
...(tr.error ? { error: tr.error } : {}),
},
},
];
}

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import type { Sql } from '../../db.js';
import type {
Agent,
Message,
Project,
Session,
} from '../../types/api.js';
import * as compaction from '../compaction.js';
import { buildSystemPrompt } from '../system-prompt.js';
import { isAnySentinel } from './sentinels.js';
import type { InferenceContext } from './turn.js';
export interface OpenAiMessage {
role: 'system' | 'user' | 'assistant' | 'tool';
content: string | null;
tool_calls?: Array<{
id: string;
type: 'function';
function: { name: string; arguments: string };
}>;
tool_call_id?: string;
// v1.13.1-C: reasoning text from a prior assistant turn, sourced from
// message_parts kind='reasoning' rows joined in via reasoning_parts on
// the messages_with_parts view. stream-phase.ts/toModelMessages threads
// this into the AI SDK ReasoningPart when forwarding to the model so
// reasoning models can resume mid-thought across tool-call boundaries.
reasoning?: string;
}
// v1.12: buildSystemPrompt lives in services/system-prompt.ts. It awaits the
// container-guidance loader, so this function is async too and every call
// site in inference.ts awaits the result.
export async function buildMessagesPayload(
session: Session,
project: Project,
history: Message[],
agent: Agent | null = null
): Promise<OpenAiMessage[]> {
const out: OpenAiMessage[] = [];
const systemPrompt = await buildSystemPrompt(project, session, agent);
out.push({ role: 'system', content: systemPrompt });
// Find the latest compact marker — only send messages from that point onwards
let startIdx = 0;
for (let i = history.length - 1; i >= 0; i--) {
if (history[i]!.kind === 'compact') {
startIdx = i;
break;
}
}
for (let i = startIdx; i < history.length; i++) {
const m = history[i]!;
if (m.kind === 'compact') {
out.push({ role: 'system', content: m.content });
continue;
}
// v1.8.2 / v1.11.6: cap-hit and doom-loop sentinels are UI-only — never
// send them to the LLM. The synthetic instruction note lives only inside
// the summary call's messages array and is never persisted, so on a
// follow-up turn the model resumes with a clean context.
if (isAnySentinel(m)) continue;
if (m.role === 'assistant' && m.status === 'streaming') continue;
if (m.role === 'assistant' && m.status === 'cancelled') continue;
if (m.role === 'tool') {
const tr = m.tool_results;
if (!tr) continue;
const outputText = tr.error
? `error: ${tr.error}`
: typeof tr.output === 'string'
? tr.output
: JSON.stringify(tr.output);
out.push({
role: 'tool',
content: outputText,
tool_call_id: tr.tool_call_id,
});
continue;
}
if (m.role === 'assistant') {
const msg: OpenAiMessage = {
role: 'assistant',
content: m.content && m.content.length > 0 ? m.content : null,
};
if (m.tool_calls && m.tool_calls.length > 0) {
msg.tool_calls = m.tool_calls.map((tc) => ({
id: tc.id,
type: 'function' as const,
function: { name: tc.name, arguments: JSON.stringify(tc.args) },
}));
}
// v1.13.1-C: collapse reasoning_parts into a single string. The view
// returns them ordered by sequence; multiple reasoning parts on one
// message are rare but concat preserves ordering. Skip when absent.
if (m.reasoning_parts && m.reasoning_parts.length > 0) {
msg.reasoning = m.reasoning_parts.map((p) => p.text ?? '').join('');
}
out.push(msg);
continue;
}
out.push({ role: 'user', content: m.content });
}
return out;
}
export async function loadContext(
sql: Sql,
sessionId: string,
chatId: string
): Promise<{ session: Session; project: Project; history: Message[] } | null> {
const sessionRows = await sql<Session[]>`
SELECT id, project_id, name, model, system_prompt, status, created_at, updated_at,
agent_id, web_search_enabled
FROM sessions WHERE id = ${sessionId}
`;
if (sessionRows.length === 0) return null;
const session = sessionRows[0]!;
const projectRows = await sql<Project[]>`
SELECT id, name, path, added_at, last_session_id, status, gitea_remote,
default_system_prompt, default_web_search_enabled
FROM projects WHERE id = ${session.project_id}
`;
if (projectRows.length === 0) return null;
const project = projectRows[0]!;
// v1.11: filter compacted messages out of the inference assembly. The GET
// /api/sessions/:id/messages endpoint still returns everything (so the UI
// can show history with the summary card inline); only LLM payloads skip
// compacted rows. compacted_at IS NULL keeps the active summary + tail.
// v1.13.1-B: reads tool_calls/tool_results via the parts-merged view.
// v1.13.1-C: also pull reasoning_parts so assistant messages from
// reasoning models can be replayed with their reasoning context preserved.
const history = await sql<Message[]>`
SELECT id, session_id, chat_id, role, content, kind, tool_calls, tool_results, status, last_seq,
tokens_used, ctx_used, ctx_max, started_at, finished_at, created_at, metadata,
reasoning_parts
FROM messages_with_parts
WHERE chat_id = ${chatId} AND compacted_at IS NULL
ORDER BY created_at ASC, id ASC
`;
return { session, project, history };
}
// v1.11: shared helper used after both finalizeCompletion and executeToolPhase
// persist their token counts. Reads tokens off the just-UPDATEd row (which
// the caller returns from RETURNING), runs compaction.isOverflow, and flips
// chats.needs_compaction. The next runAssistantTurn invocation acts on it.
// Silent on missing tokens — llama-swap occasionally omits usage on truncated
// streams, and we'd rather miss one overflow than crash the inference path.
export async function maybeFlagForCompaction(
ctx: InferenceContext,
chatId: string,
updated: { tokens_used: number | null; ctx_used: number | null; ctx_max: number | null } | undefined,
): Promise<void> {
if (!updated) return;
const promptTokens = updated.ctx_used;
const completionTokens = updated.tokens_used;
const contextLimit = updated.ctx_max;
if (typeof promptTokens !== 'number') return;
if (typeof completionTokens !== 'number') return;
if (typeof contextLimit !== 'number') return;
const overflow = compaction.isOverflow(
{ prompt_tokens: promptTokens, completion_tokens: completionTokens },
contextLimit,
);
if (!overflow) return;
await ctx.sql`UPDATE chats SET needs_compaction = true WHERE id = ${chatId}`;
ctx.log.info({ chatId, promptTokens, completionTokens, contextLimit }, 'inference: flagged for compaction');
}

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import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import type { LanguageModel } from 'ai';
// v1.13.1-A: AI SDK provider against llama-swap. baseURL is threaded from
// config.LLAMA_SWAP_URL at call time (not module-load) so tests can stub the
// upstream without touching env vars. No apiKey — llama-swap is unauth in our
// Tailscale topology and exposing it over the public internet is gated by
// Authelia at the Caddy layer, not by API keys.
const cache = new Map<string, ReturnType<typeof createOpenAICompatible>>();
function getProvider(baseURL: string): ReturnType<typeof createOpenAICompatible> {
let provider = cache.get(baseURL);
if (!provider) {
provider = createOpenAICompatible({
name: 'llama-swap',
baseURL: baseURL.endsWith('/v1') ? baseURL : `${baseURL}/v1`,
});
cache.set(baseURL, provider);
}
return provider;
}
export function upstreamModel(baseURL: string, modelId: string): LanguageModel {
return getProvider(baseURL).chatModel(modelId);
}

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import type {
Agent,
Message,
MessageMetadata,
Project,
Session,
} from '../../types/api.js';
import * as modelContext from '../model-context.js';
import { buildMessagesPayload } from './payload.js';
import { DOOM_LOOP_THRESHOLD } from './sentinels.js';
import { streamCompletion } from './stream-phase.js';
import { DB_FLUSH_INTERVAL_MS } from './types.js';
import type {
InferenceContext,
StreamResult,
TurnArgs,
} from './turn.js';
// Synthetic system note appended to the cap-hit summary call. Verbatim from
// the v1.8.2 spec — do not paraphrase: the model is more reliable when the
// instruction is short, declarative, and identical across calls.
const CAP_HIT_SUMMARY_NOTE = (limit: number) =>
`You've reached the tool budget (${limit} calls). Produce the best answer you can with what you have. Do not call more tools.`;
const DOOM_LOOP_NOTE = (name: string) =>
`You called ${name} with the same arguments ${DOOM_LOOP_THRESHOLD} times in a row. Stop calling it. Produce the best answer you can with what you have.`;
export async function runCapHitSummary(
ctx: InferenceContext,
args: TurnArgs,
session: Session,
project: Project,
history: Message[],
agent: Agent | null,
budget: number,
): Promise<void> {
const { sessionId, chatId, assistantMessageId, signal } = args;
const messages = await buildMessagesPayload(session, project, history, agent);
messages.push({ role: 'system', content: CAP_HIT_SUMMARY_NOTE(budget) });
const startedRow = await ctx.sql<{ started_at: string }[]>`
UPDATE messages
SET started_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING started_at
`;
const startedAt = startedRow[0]?.started_at ?? null;
ctx.publish(sessionId, {
type: 'message_started',
message_id: assistantMessageId,
chat_id: chatId,
role: 'assistant',
});
let accumulated = '';
let pendingFlushTimer: NodeJS.Timeout | null = null;
let flushPromise: Promise<unknown> = Promise.resolve();
const flushNow = () => {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
const snapshot = accumulated;
flushPromise = flushPromise.then(() =>
ctx.sql`UPDATE messages SET content = ${snapshot} WHERE id = ${assistantMessageId}`
);
};
const scheduleFlush = () => {
if (pendingFlushTimer) return;
pendingFlushTimer = setTimeout(() => {
pendingFlushTimer = null;
flushNow();
}, DB_FLUSH_INTERVAL_MS);
};
let summaryOk = false;
let summarySoftCancelled = false;
let summaryError: string | null = null;
let result: StreamResult | null = null;
try {
result = await streamCompletion(
ctx,
session.model,
messages,
{ tools: null, temperature: agent?.temperature },
(delta) => {
accumulated += delta;
ctx.publish(sessionId, {
type: 'delta',
message_id: assistantMessageId,
chat_id: chatId,
content: delta,
});
scheduleFlush();
},
undefined,
signal,
);
summaryOk = true;
} catch (err) {
if (err instanceof Error && err.name === 'AbortError') {
summarySoftCancelled = true;
} else {
summaryError = err instanceof Error ? err.message : String(err);
}
} finally {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
await flushPromise;
}
// Finalize the summary message based on the three outcomes. The sentinel
// is inserted regardless so the user always has the Continue affordance —
// even on a partial / failed summary the chat history shows where the
// budget was hit.
if (summaryOk && result) {
// v1.11.3: see executeToolPhase for the rationale.
const mctx = await modelContext.getModelContext(session.model);
const nCtx = mctx?.n_ctx ?? null;
const [updated] = await ctx.sql<
{ tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
>`
UPDATE messages
SET content = ${result.content},
status = 'complete',
tokens_used = ${result.completionTokens},
ctx_used = ${result.promptTokens},
ctx_max = ${nCtx},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING tokens_used, ctx_used, ctx_max, finished_at
`;
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
tokens_used: updated?.tokens_used ?? null,
ctx_used: updated?.ctx_used ?? null,
ctx_max: updated?.ctx_max ?? null,
started_at: startedAt,
finished_at: updated?.finished_at ?? null,
model: session.model,
});
} else if (summarySoftCancelled) {
await ctx.sql`
UPDATE messages
SET content = ${accumulated},
status = 'cancelled',
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
`;
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
});
} else {
const errMeta: MessageMetadata = {
kind: 'error',
error_reason: 'summary_after_cap_failed',
error_text: summaryError ?? 'summary failed',
};
await ctx.sql`
UPDATE messages
SET content = ${accumulated},
status = 'failed',
finished_at = clock_timestamp(),
metadata = ${ctx.sql.json(errMeta as never)}
WHERE id = ${assistantMessageId}
`;
ctx.publish(sessionId, {
type: 'error',
message_id: assistantMessageId,
chat_id: chatId,
error: summaryError ?? 'summary failed',
reason: 'summary_after_cap_failed',
});
}
// Bump session/chat updated_at exactly once for this turn.
const [sessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({
type: 'session_updated',
session_id: sessionId,
project_id: sessRow!.project_id,
name: sessRow!.name,
updated_at: sessRow!.updated_at,
});
await insertCapHitSentinel(ctx, sessionId, chatId, agent, budget);
// Status frame fires last so the dot color reflects the terminal state.
// Success → idle, abort → idle (user-driven stop), error → error+reason.
if (summaryOk) {
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
} else if (summarySoftCancelled) {
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
} else {
ctx.publishUser({
type: 'chat_status',
chat_id: chatId,
status: 'error',
at: new Date().toISOString(),
reason: 'summary_after_cap_failed',
});
}
ctx.log.info(
{ sessionId, chatId, assistantMessageId, budget, summaryOk, summaryCancelled: summarySoftCancelled },
'inference cap-hit summary finished',
);
}
async function insertCapHitSentinel(
ctx: InferenceContext,
sessionId: string,
chatId: string,
agent: Agent | null,
budget: number,
): Promise<void> {
// Hard ceiling: count prior cap_hit sentinels in this chat. After two
// continues (sentinel count of 2), the next sentinel reports can_continue
// false and the UI disables the Continue button.
const priorRows = await ctx.sql<{ count: number }[]>`
SELECT COUNT(*)::int AS count
FROM messages
WHERE chat_id = ${chatId}
AND role = 'system'
AND metadata->>'kind' = 'cap_hit'
`;
const priorCount = priorRows[0]?.count ?? 0;
const canContinue = priorCount < 2;
const metadata: MessageMetadata = {
kind: 'cap_hit',
used: budget,
limit: budget,
agent_name: agent?.name ?? null,
can_continue: canContinue,
};
const content = `Reached tool budget (${budget}/${budget}). Continue to extend.`;
const [row] = await ctx.sql<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at, metadata)
VALUES (${sessionId}, ${chatId}, 'system', ${content}, 'complete', clock_timestamp(), ${ctx.sql.json(metadata as never)})
RETURNING id
`;
// The sentinel content is static, but we still walk the standard frame
// sequence (started → delta → complete) so useSessionStream's reducer
// appends it via the same path it uses for streaming assistant messages.
// The delta carries the full text in one chunk.
ctx.publish(sessionId, {
type: 'message_started',
message_id: row!.id,
chat_id: chatId,
role: 'system',
});
ctx.publish(sessionId, {
type: 'delta',
message_id: row!.id,
chat_id: chatId,
content,
});
ctx.publish(sessionId, {
type: 'message_complete',
message_id: row!.id,
chat_id: chatId,
metadata,
});
}
// v1.11.6: doom-loop wrap-up. Mirrors runCapHitSummary structurally — same
// in-flight-slot reuse, same tools-disabled streaming-summary call, same
// post-finalize sentinel insert + chat_status drop. Differences:
// - synthetic note text comes from DOOM_LOOP_NOTE (names the looping tool)
// - sentinel metadata is { kind: 'doom_loop', tool_name, args, threshold }
// and has no Continue affordance (manual retry would just re-loop)
// - chat_status error path uses reason: 'doom_loop_summary_failed'
// Kept as a clone rather than refactored into a shared helper because the
// two summary paths still differ in error reason + sentinel shape; a third
// sentinel would justify factoring out runWrapUpSummary(opts).
export async function runDoomLoopSummary(
ctx: InferenceContext,
args: TurnArgs,
session: Session,
project: Project,
history: Message[],
agent: Agent | null,
loop: { name: string; args: Record<string, unknown> },
): Promise<void> {
const { sessionId, chatId, assistantMessageId, signal } = args;
const messages = await buildMessagesPayload(session, project, history, agent);
messages.push({ role: 'system', content: DOOM_LOOP_NOTE(loop.name) });
const startedRow = await ctx.sql<{ started_at: string }[]>`
UPDATE messages
SET started_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING started_at
`;
const startedAt = startedRow[0]?.started_at ?? null;
ctx.publish(sessionId, {
type: 'message_started',
message_id: assistantMessageId,
chat_id: chatId,
role: 'assistant',
});
let accumulated = '';
let pendingFlushTimer: NodeJS.Timeout | null = null;
let flushPromise: Promise<unknown> = Promise.resolve();
const flushNow = () => {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
const snapshot = accumulated;
flushPromise = flushPromise.then(() =>
ctx.sql`UPDATE messages SET content = ${snapshot} WHERE id = ${assistantMessageId}`
);
};
const scheduleFlush = () => {
if (pendingFlushTimer) return;
pendingFlushTimer = setTimeout(() => {
pendingFlushTimer = null;
flushNow();
}, DB_FLUSH_INTERVAL_MS);
};
let summaryOk = false;
let summarySoftCancelled = false;
let summaryError: string | null = null;
let result: StreamResult | null = null;
try {
result = await streamCompletion(
ctx,
session.model,
messages,
{ tools: null, temperature: agent?.temperature },
(delta) => {
accumulated += delta;
ctx.publish(sessionId, {
type: 'delta',
message_id: assistantMessageId,
chat_id: chatId,
content: delta,
});
scheduleFlush();
},
undefined,
signal,
);
summaryOk = true;
} catch (err) {
if (err instanceof Error && err.name === 'AbortError') {
summarySoftCancelled = true;
} else {
summaryError = err instanceof Error ? err.message : String(err);
}
} finally {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
await flushPromise;
}
if (summaryOk && result) {
const mctx = await modelContext.getModelContext(session.model);
const nCtx = mctx?.n_ctx ?? null;
const [updated] = await ctx.sql<
{ tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
>`
UPDATE messages
SET content = ${result.content},
status = 'complete',
tokens_used = ${result.completionTokens},
ctx_used = ${result.promptTokens},
ctx_max = ${nCtx},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING tokens_used, ctx_used, ctx_max, finished_at
`;
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
tokens_used: updated?.tokens_used ?? null,
ctx_used: updated?.ctx_used ?? null,
ctx_max: updated?.ctx_max ?? null,
started_at: startedAt,
finished_at: updated?.finished_at ?? null,
model: session.model,
});
} else if (summarySoftCancelled) {
await ctx.sql`
UPDATE messages
SET content = ${accumulated},
status = 'cancelled',
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
`;
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
});
} else {
// Doom-loop summary failure reuses the existing summary_after_cap_failed
// error reason — the ErrorReason union is shared between sentinel paths
// and the UI surfaces a generic "summary failed" line for both. We don't
// add a new reason code because the user-visible failure mode is the
// same (model gave up mid-summary). Sentinel below still fires.
const errMeta: MessageMetadata = {
kind: 'error',
error_reason: 'summary_after_cap_failed',
error_text: summaryError ?? 'doom-loop summary failed',
};
await ctx.sql`
UPDATE messages
SET content = ${accumulated},
status = 'failed',
finished_at = clock_timestamp(),
metadata = ${ctx.sql.json(errMeta as never)}
WHERE id = ${assistantMessageId}
`;
ctx.publish(sessionId, {
type: 'error',
message_id: assistantMessageId,
chat_id: chatId,
error: summaryError ?? 'doom-loop summary failed',
reason: 'summary_after_cap_failed',
});
}
const [sessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({
type: 'session_updated',
session_id: sessionId,
project_id: sessRow!.project_id,
name: sessRow!.name,
updated_at: sessRow!.updated_at,
});
await insertDoomLoopSentinel(ctx, sessionId, chatId, loop);
if (summaryOk || summarySoftCancelled) {
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
} else {
ctx.publishUser({
type: 'chat_status',
chat_id: chatId,
status: 'error',
at: new Date().toISOString(),
reason: 'summary_after_cap_failed',
});
}
ctx.log.info(
{ sessionId, chatId, assistantMessageId, loopedTool: loop.name, summaryOk, summaryCancelled: summarySoftCancelled },
'inference doom-loop summary finished',
);
}
async function insertDoomLoopSentinel(
ctx: InferenceContext,
sessionId: string,
chatId: string,
loop: { name: string; args: Record<string, unknown> },
): Promise<void> {
// No hard-ceiling / can-continue logic here — doom-loop is a different
// failure mode from cap-hit. Continuing would re-trigger the loop with
// the same tools available; the user needs to restate their question
// or switch agents instead.
const metadata: MessageMetadata = {
kind: 'doom_loop',
tool_name: loop.name,
args: loop.args,
threshold: DOOM_LOOP_THRESHOLD,
};
const content = `Detected ${DOOM_LOOP_THRESHOLD} identical calls to ${loop.name}. Stopping the tool-call loop. Produce the best answer you can with what you have.`;
const [row] = await ctx.sql<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at, metadata)
VALUES (${sessionId}, ${chatId}, 'system', ${content}, 'complete', clock_timestamp(), ${ctx.sql.json(metadata as never)})
RETURNING id
`;
// Standard frame sequence — same as cap-hit sentinel — so
// useSessionStream's reducer appends the row via the existing path.
ctx.publish(sessionId, {
type: 'message_started',
message_id: row!.id,
chat_id: chatId,
role: 'system',
});
ctx.publish(sessionId, {
type: 'delta',
message_id: row!.id,
chat_id: chatId,
content,
});
ctx.publish(sessionId, {
type: 'message_complete',
message_id: row!.id,
chat_id: chatId,
metadata,
});
}

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import type { Message, ToolCall } from '../../types/api.js';
// v1.11.6: doom-loop guard. When the model calls the same tool with the
// same arguments DOOM_LOOP_THRESHOLD times in a row within one user-message
// turn, abort the recursion and run the same wrap-up summary path as the
// cap-hit case. Ported from opencode (DOOM_LOOP_THRESHOLD in
// session/processor.ts). Threshold of 3 is the smallest value that doesn't
// false-positive on a model that retries once after a transient error.
export const DOOM_LOOP_THRESHOLD = 3;
// Returns the name + args of the looping tool when the LAST
// DOOM_LOOP_THRESHOLD entries in `recentToolCalls` are identical (same name
// AND deep-equal args via JSON.stringify). Returns null otherwise.
// Pure; exported for unit-test access.
export function detectDoomLoop(
recentToolCalls: ToolCall[],
): { name: string; args: Record<string, unknown> } | null {
if (recentToolCalls.length < DOOM_LOOP_THRESHOLD) return null;
const last = recentToolCalls.slice(-DOOM_LOOP_THRESHOLD);
const ref = last[0]!;
const refArgs = JSON.stringify(ref.args);
for (let i = 1; i < last.length; i++) {
const tc = last[i]!;
if (tc.name !== ref.name) return null;
if (JSON.stringify(tc.args) !== refArgs) return null;
}
return { name: ref.name, args: ref.args };
}
export function isCapHitSentinel(m: Message): boolean {
return (
m.role === 'system' &&
m.metadata !== null &&
typeof m.metadata === 'object' &&
(m.metadata as { kind?: unknown }).kind === 'cap_hit'
);
}
// v1.11.6: parallel predicate. Same UI-only semantics as cap-hit sentinels —
// never sent to the LLM (filtered by buildMessagesPayload through the
// isAnySentinel check below).
export function isDoomLoopSentinel(m: Message): boolean {
return (
m.role === 'system' &&
m.metadata !== null &&
typeof m.metadata === 'object' &&
(m.metadata as { kind?: unknown }).kind === 'doom_loop'
);
}
export function isAnySentinel(m: Message): boolean {
return isCapHitSentinel(m) || isDoomLoopSentinel(m);
}

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@@ -0,0 +1,449 @@
import type {
Agent,
Session,
ToolCall,
} from '../../types/api.js';
import * as modelContext from '../model-context.js';
import { toolJsonSchemas, type ToolJsonSchema } from '../tools.js';
import type { OpenAiMessage } from './payload.js';
import {
XML_TOOL_CLOSE,
XML_TOOL_OPEN,
parseXmlToolCall,
partialXmlOpenerStart,
} from './xml-parser.js';
import { DB_FLUSH_INTERVAL_MS, type StreamPhaseState } from './types.js';
import type {
InferenceContext,
StreamResult,
TurnArgs,
} from './turn.js';
import { upstreamModel } from './provider.js';
import { jsonSchema, streamText, tool, type JSONValue, type ModelMessage } from 'ai';
interface StreamOptions {
// null = omit tools entirely (compact phase); [] = caller stripped all tools
// (rare; we still omit from the request body to avoid OpenAI 400).
tools: ToolJsonSchema[] | null;
temperature?: number;
}
// v1.13.1-A: convert BooCode's OpenAI-shaped history into AI SDK
// ModelMessage[]. Tool result messages need a `toolName` field that the
// OpenAI shape doesn't carry; we look it up by scanning earlier assistant
// `tool_calls` entries for a matching id.
function toModelMessages(messages: OpenAiMessage[]): ModelMessage[] {
const toolNameById = new Map<string, string>();
for (const m of messages) {
if (m.role === 'assistant' && m.tool_calls) {
for (const tc of m.tool_calls) {
toolNameById.set(tc.id, tc.function.name);
}
}
}
const out: ModelMessage[] = [];
for (const m of messages) {
if (m.role === 'system' || m.role === 'user') {
out.push({ role: m.role, content: m.content ?? '' });
continue;
}
if (m.role === 'assistant') {
const hasTools = m.tool_calls && m.tool_calls.length > 0;
const hasReasoning = typeof m.reasoning === 'string' && m.reasoning.length > 0;
if (!hasTools && !hasReasoning) {
// Bare text assistant (string content). null content + no tool_calls
// is degenerate but harmless to forward.
out.push({ role: 'assistant', content: m.content ?? '' });
continue;
}
// v1.13.1-C: AI SDK ReasoningPart precedes text + tool-calls in the
// assistant content array. Reasoning models (qwen3.6) consume their
// prior reasoning context to resume mid-thought across tool boundaries.
const parts: Array<
| { type: 'reasoning'; text: string }
| { type: 'text'; text: string }
| { type: 'tool-call'; toolCallId: string; toolName: string; input: unknown }
> = [];
if (hasReasoning) {
parts.push({ type: 'reasoning', text: m.reasoning! });
}
if (m.content && m.content.length > 0) {
parts.push({ type: 'text', text: m.content });
}
for (const tc of m.tool_calls ?? []) {
let input: unknown = {};
try {
input = tc.function.arguments.length > 0 ? JSON.parse(tc.function.arguments) : {};
} catch {
// Malformed args from a prior turn: pass through as a raw blob so
// the model sees the same shape it emitted. Wraps the string under
// _raw to match the buildMessagesPayload upstream convention.
input = { _raw: tc.function.arguments };
}
parts.push({ type: 'tool-call', toolCallId: tc.id, toolName: tc.function.name, input });
}
out.push({ role: 'assistant', content: parts });
continue;
}
if (m.role === 'tool') {
const toolCallId = m.tool_call_id ?? '';
const toolName = toolNameById.get(toolCallId) ?? 'unknown';
const raw = m.content ?? '';
let output: { type: 'text'; value: string } | { type: 'json'; value: JSONValue };
try {
// JSON.parse returns `any`; cast to JSONValue since the upstream
// tool_results column is already JSON-serializable by construction.
output = { type: 'json', value: JSON.parse(raw) as JSONValue };
} catch {
output = { type: 'text', value: raw };
}
out.push({
role: 'tool',
content: [{ type: 'tool-result', toolCallId, toolName, output }],
});
continue;
}
}
return out;
}
// Build the AI SDK tools record from BooCode's JSON-schema tool definitions.
// No `execute` field: BooCode runs tools itself in tool-phase.ts; streamText
// surfaces the tool-call parts via fullStream and we capture them for the
// outer loop to dispatch.
function buildAiTools(schemas: ToolJsonSchema[]): Record<string, ReturnType<typeof tool>> {
const out: Record<string, ReturnType<typeof tool>> = {};
for (const s of schemas) {
out[s.function.name] = tool({
description: s.function.description,
inputSchema: jsonSchema(s.function.parameters),
});
}
return out;
}
// v1.10.5 Qwen-coder XML fallback. Some local models (notably qwen3-coder via
// llama-swap) emit tool calls as inline XML inside delta.content rather than
// the structured tool_calls field. We extract them out of the streamed text
// before flushing it to the client, mirroring the pre-AI-SDK behavior.
//
// XML shape:
// <tool_call>
// <function=NAME>
// <parameter=KEY>VALUE</parameter>
// ...
// </function>
// </tool_call>
// Multiple <tool_call> blocks may appear back-to-back; they never nest.
export async function streamCompletion(
ctx: InferenceContext,
model: string,
messages: OpenAiMessage[],
opts: StreamOptions,
onDelta: (content: string) => void,
onUsage: ((prompt: number | null, completion: number | null) => void) | undefined,
signal?: AbortSignal
): Promise<StreamResult> {
const aiMessages = toModelMessages(messages);
const hasTools = opts.tools !== null && opts.tools.length > 0;
const aiTools = hasTools ? buildAiTools(opts.tools!) : undefined;
const startedAt = Date.now();
// v1.13.1-C: accumulate reasoning text across reasoning-delta parts.
// qwen3.6 emits these on a separate channel from text content; we capture
// them per stream so finalizeCompletion can dual-write a 'reasoning' part.
// Replaces the v1.13.1-A counter-only diagnostic.
let reasoningAccumulated = '';
const result = streamText({
model: upstreamModel(ctx.config.LLAMA_SWAP_URL, model),
messages: aiMessages,
...(aiTools ? { tools: aiTools, toolChoice: 'auto' as const } : {}),
...(typeof opts.temperature === 'number' ? { temperature: opts.temperature } : {}),
abortSignal: signal,
});
let content = '';
let pendingBuffer = '';
let finishReason: string | null = null;
// v1.13.1-A: AI SDK emits one `tool-call` part per fully-aggregated call,
// so we no longer need the OpenAI-index reassembly map the manual SSE
// parser used. XML tool calls extracted from text content go into the
// same flat list and keep the v1.10.5 synthetic id convention.
const toolCalls: ToolCall[] = [];
for await (const part of result.fullStream) {
switch (part.type) {
case 'text-delta': {
pendingBuffer += part.text;
// Extract any complete <tool_call>...</tool_call> blocks before
// flushing visible text.
while (true) {
const startIdx = pendingBuffer.indexOf(XML_TOOL_OPEN);
if (startIdx === -1) break;
const closeIdx = pendingBuffer.indexOf(XML_TOOL_CLOSE, startIdx);
if (closeIdx === -1) break;
const blockEnd = closeIdx + XML_TOOL_CLOSE.length;
const block = pendingBuffer.slice(startIdx, blockEnd);
if (startIdx > 0) {
const before = pendingBuffer.slice(0, startIdx);
content += before;
onDelta(before);
}
const parsedCall = parseXmlToolCall(block);
if (parsedCall) {
const synthIdx = toolCalls.length;
toolCalls.push({
id: `xml_call_${synthIdx}`,
name: parsedCall.name,
args: parsedCall.args,
});
}
// Parse failures still drop the block — leaking <tool_call> XML to
// the chat would look worse than silently swallowing the bad block.
pendingBuffer = pendingBuffer.slice(blockEnd);
}
// Hold back any (partial or full) unclosed opener; flush the rest.
const partialIdx = partialXmlOpenerStart(pendingBuffer);
if (partialIdx >= 0) {
if (partialIdx > 0) {
const flush = pendingBuffer.slice(0, partialIdx);
content += flush;
onDelta(flush);
}
pendingBuffer = pendingBuffer.slice(partialIdx);
} else if (pendingBuffer.length > 0) {
content += pendingBuffer;
onDelta(pendingBuffer);
pendingBuffer = '';
}
break;
}
case 'tool-call': {
// AI SDK has already parsed the input into an object. Match the
// ToolCall shape BooCode passes around in toolCallsBuffer downstream.
toolCalls.push({
id: part.toolCallId,
name: part.toolName,
args: (part.input ?? {}) as Record<string, unknown>,
});
break;
}
case 'reasoning-delta': {
// v1.13.1-C: accumulate; finalizeCompletion / executeToolPhase
// dual-write the resulting text as a kind='reasoning' part.
if (typeof part.text === 'string') {
reasoningAccumulated += part.text;
}
break;
}
case 'finish': {
if (typeof part.finishReason === 'string') {
finishReason = part.finishReason;
}
break;
}
case 'error': {
const err = part.error;
throw err instanceof Error ? err : new Error(String(err));
}
// Intentional no-op: start, start-step, text-start, text-end,
// reasoning-start, reasoning-end, source, file, tool-input-start,
// tool-input-delta, tool-input-end, tool-result, tool-error,
// finish-step, raw. We only care about the aggregated tool-call and
// text-delta paths above; the rest are AI SDK lifecycle/streaming
// breadcrumbs that don't change BooCode's persistence or WS contract.
default:
break;
}
}
// v1.13.1-A: drain any buffered partial XML opener as plain text. The
// pre-AI-SDK path did this on stream end too — better to leak `<tool_c`
// than vanish the text.
if (pendingBuffer.length > 0) {
content += pendingBuffer;
onDelta(pendingBuffer);
pendingBuffer = '';
}
// AI SDK v6 fullStream returns normally on abort; check signal explicitly.
// Without this throw the row would land as status='complete' with partial
// content instead of going through handleAbortOrError → status='cancelled'.
// Smoke D caught this in v1.13.1-A — don't refactor it away.
if (signal?.aborted) {
const abortErr = new Error('aborted');
abortErr.name = 'AbortError';
throw abortErr;
}
// Usage lands as a promise on the result; awaiting after fullStream is
// drained is safe. AI SDK v6 names: `inputTokens` / `outputTokens`.
let promptTokens: number | null = null;
let completionTokens: number | null = null;
try {
const usage = await result.usage;
if (typeof usage.inputTokens === 'number') promptTokens = usage.inputTokens;
if (typeof usage.outputTokens === 'number') completionTokens = usage.outputTokens;
} catch {
// Some providers omit usage on partial streams; leave both null.
}
if (onUsage && (promptTokens !== null || completionTokens !== null)) {
onUsage(promptTokens, completionTokens);
}
if (reasoningAccumulated.length > 0) {
ctx.log.debug(
{ reasoningChars: reasoningAccumulated.length, model, elapsed_ms: Date.now() - startedAt },
'streamCompletion: captured reasoning',
);
}
return {
finishReason,
content,
toolCalls,
promptTokens,
completionTokens,
reasoning: reasoningAccumulated,
};
}
export async function executeStreamPhase(
ctx: InferenceContext,
args: TurnArgs,
session: Session,
messages: OpenAiMessage[],
state: StreamPhaseState,
agent: Agent | null,
// v1.11.8: when false, web_search and web_fetch are stripped from the
// tool list sent to the LLM, so the model can't even attempt them.
webToolsEnabled: boolean,
): Promise<StreamResult> {
const { sessionId, chatId, assistantMessageId, signal } = args;
const startedRow = await ctx.sql<{ started_at: string }[]>`
UPDATE messages
SET started_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING started_at
`;
state.startedAt = startedRow[0]?.started_at ?? null;
ctx.publish(sessionId, {
type: 'message_started',
message_id: assistantMessageId,
chat_id: chatId,
role: 'assistant',
});
let pendingFlushTimer: NodeJS.Timeout | null = null;
let flushPromise: Promise<unknown> = Promise.resolve();
const flushNow = () => {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
const snapshot = state.accumulated;
flushPromise = flushPromise.then(() =>
ctx.sql`UPDATE messages SET content = ${snapshot} WHERE id = ${assistantMessageId}`
);
};
const scheduleFlush = () => {
if (pendingFlushTimer) return;
pendingFlushTimer = setTimeout(() => {
pendingFlushTimer = null;
flushNow();
}, DB_FLUSH_INTERVAL_MS);
};
// Tool whitelist: if an agent is set, filter the global tool list to only the
// tool names it allows. Unknown names in agent.tools are dropped silently
// (handled here by intersection). When no agent: send all tools.
// v1.11.8: a second filter strips web_search + web_fetch unless the chat
// has them explicitly enabled. Counts as an opt-in security boundary: the
// model can't summon a tool that wasn't offered to it.
const WEB_TOOL_NAMES: ReadonlySet<string> = new Set(['web_search', 'web_fetch']);
const effectiveTools: ToolJsonSchema[] = (agent
? toolJsonSchemas().filter((t) => agent.tools.includes(t.function.name))
: toolJsonSchemas()
).filter((t) => webToolsEnabled || !WEB_TOOL_NAMES.has(t.function.name));
const effectiveTemperature = agent?.temperature;
// v1.12.2: ctx_max lookup is cached after the first hit per model, so this
// is a Map probe in steady state. We capture nCtx once at the top of the
// stream so the throttled usage publish doesn't refetch each tick.
const mctxForStream = await modelContext.getModelContext(session.model);
const nCtxForStream = mctxForStream?.n_ctx ?? null;
// v1.12.2 → v1.13.1-A: live usage publishes were throttled to ~500ms when
// the manual SSE parser saw `parsed.usage` per chunk. AI SDK v6 surfaces
// usage only at stream end (result.usage promise), so the throttle is
// effectively a single trailing publish. ChatThroughput will tick once at
// stream completion rather than mid-stream — known regression vs v1.12.2,
// recovered if a future dispatch interpolates from delta cadence.
const USAGE_THROTTLE_MS = 500;
let lastUsageAt = 0;
let pendingUsage: { p: number | null; c: number | null } | null = null;
let usageTimer: NodeJS.Timeout | null = null;
const flushUsage = () => {
if (!pendingUsage) return;
const { p, c } = pendingUsage;
pendingUsage = null;
lastUsageAt = Date.now();
ctx.publish(sessionId, {
type: 'usage',
message_id: assistantMessageId,
chat_id: chatId,
completion_tokens: c,
ctx_used: p,
ctx_max: nCtxForStream,
});
};
try {
return await streamCompletion(
ctx,
session.model,
messages,
{ tools: effectiveTools, temperature: effectiveTemperature },
(delta) => {
state.accumulated += delta;
ctx.publish(sessionId, {
type: 'delta',
message_id: assistantMessageId,
chat_id: chatId,
content: delta,
});
ctx.log.debug({ sessionId, delta }, 'inference delta');
scheduleFlush();
},
(prompt, completion) => {
pendingUsage = { p: prompt, c: completion };
const elapsed = Date.now() - lastUsageAt;
if (elapsed >= USAGE_THROTTLE_MS) {
flushUsage();
} else if (!usageTimer) {
usageTimer = setTimeout(() => {
usageTimer = null;
flushUsage();
}, USAGE_THROTTLE_MS - elapsed);
}
},
signal
);
} finally {
if (pendingFlushTimer) {
clearTimeout(pendingFlushTimer);
pendingFlushTimer = null;
}
if (usageTimer) {
clearTimeout(usageTimer);
usageTimer = null;
}
await flushPromise;
}
}

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import type { Session, ToolCall } from '../../types/api.js';
import * as modelContext from '../model-context.js';
import { PathScopeError } from '../path_guard.js';
import { TOOLS_BY_NAME } from '../tools.js';
import { maybeFlagForCompaction } from './payload.js';
import { insertParts, partsFromAssistantMessage, partsFromToolMessage } from './parts.js';
import type {
InferenceContext,
StreamResult,
TurnArgs,
} from './turn.js';
// v1.12.4: ESM value-import cycle. executeToolPhase recurses into
// runAssistantTurn which lives in inference.ts. The cycle is safe because
// the reference is read at call time (inside an async function body), not
// at module top-level. Node + tsc resolve this cleanly.
import { runAssistantTurn } from './turn.js';
async function executeToolCall(
projectRoot: string,
toolCall: ToolCall
): Promise<{ output: unknown; truncated: boolean; error?: string }> {
const tool = TOOLS_BY_NAME[toolCall.name];
if (!tool) {
return { output: null, truncated: false, error: `unknown tool: ${toolCall.name}` };
}
const parsed = tool.inputSchema.safeParse(toolCall.args);
if (!parsed.success) {
// v1.12 Track B.2: enrich the zod-reject path so the model sees a
// one-line, tool-named hint ("tool 'search_symbols' rejected — query:
// Required") instead of a JSON blob of flatten output. Higher recovery
// rate on the next turn; doom-loop guard still bounds infinite retries.
// The cast is because tool.inputSchema is ZodType<unknown>, so zod can't
// statically narrow flatten()'s fieldErrors key set — but the runtime
// shape is the standard { formErrors: string[]; fieldErrors: Record<...> }.
const flatten = parsed.error.flatten() as {
formErrors: string[];
fieldErrors: Record<string, string[] | undefined>;
};
const fieldErrors = Object.entries(flatten.fieldErrors)
.map(([field, errs]) => `${field}: ${errs?.[0] ?? 'invalid'}`)
.join('; ');
const formError = flatten.formErrors[0];
const hint = fieldErrors || formError || 'unknown validation error';
return {
output: null,
truncated: false,
error: `tool '${toolCall.name}' rejected — ${hint}`,
};
}
try {
const output = await tool.execute(parsed.data, projectRoot);
const truncated =
typeof output === 'object' && output !== null && 'truncated' in output
? Boolean((output as { truncated: unknown }).truncated)
: false;
return { output, truncated };
} catch (err) {
if (err instanceof PathScopeError) {
return { output: null, truncated: false, error: err.message };
}
return {
output: null,
truncated: false,
error: err instanceof Error ? err.message : String(err),
};
}
}
export async function executeToolPhase(
ctx: InferenceContext,
args: TurnArgs,
result: StreamResult,
startedAt: string | null,
session: Session,
projectRoot: string
): Promise<void> {
const { sessionId, chatId, assistantMessageId, toolsUsed, signal } = args;
const { content, toolCalls, promptTokens, completionTokens } = result;
// v1.11.3: ctx_max comes from llama-swap /upstream/<model>/props, not the
// streaming completion (which doesn't emit n_ctx). getModelContext caches
// the positive lookup for the process lifetime, so this is a single Map
// hit after the first invocation per model.
const mctx = await modelContext.getModelContext(session.model);
const nCtx = mctx?.n_ctx ?? null;
const [updated] = await ctx.sql<
{ tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
>`
UPDATE messages
SET content = ${content},
status = 'complete',
tool_calls = ${ctx.sql.json(toolCalls as never)},
tokens_used = ${completionTokens},
ctx_used = ${promptTokens},
ctx_max = ${nCtx},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING tokens_used, ctx_used, ctx_max, finished_at
`;
// v1.13.0: dual-write to message_parts. v1.13.1-B made parts authoritative
// for reads via the messages_with_parts view; the JSON column write above
// remains for v1.13.1 fallback compatibility (dropped in v1.13.2).
// v1.13.1-C: include result.reasoning so models with separate reasoning
// channels (qwen3.6) get a kind='reasoning' part at sequence 0.
// TODO(v1.13.1): wrap the UPDATE above and this insertParts in a single
// sql.begin before flipping read authority to message_parts. Without the
// transaction, a crash between the two leaves an orphan message that
// becomes invisible in the parts-authoritative read path.
await insertParts(
ctx.sql,
partsFromAssistantMessage({
content,
tool_calls: toolCalls,
reasoning: result.reasoning,
}).map((p) => ({
...p,
message_id: assistantMessageId,
})),
);
// v1.11: flag for compaction if this turn pushed us over the usable budget.
// We never compact mid-loop (the recursive runAssistantTurn keeps tools
// flowing); the flag fires on the NEXT turn's pre-fetch hook above.
await maybeFlagForCompaction(ctx, chatId, updated);
const [toolSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: toolSessRow!.project_id, name: toolSessRow!.name, updated_at: toolSessRow!.updated_at });
for (const tc of toolCalls) {
ctx.publish(sessionId, {
type: 'tool_call',
message_id: assistantMessageId,
chat_id: chatId,
tool_call: tc,
});
}
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
tokens_used: updated?.tokens_used ?? null,
ctx_used: updated?.ctx_used ?? null,
ctx_max: updated?.ctx_max ?? null,
started_at: startedAt,
finished_at: updated?.finished_at ?? null,
model: session.model,
});
// Batch 9.7: ask_user_input pauses the loop. The tool row is still inserted
// (the answer endpoint needs a target row to UPDATE), but tool_results is
// pre-stamped with output=null as a "pending" sentinel and no tool_result
// frame goes out — the card renders from the tool_call frame alone. Mixed
// batches still execute the other tools normally.
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'tool_running', at: new Date().toISOString() });
let pausingForUserInput = false;
await Promise.all(
toolCalls.map(async (tc) => {
const [toolRow] = await ctx.sql<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
VALUES (${sessionId}, ${chatId}, 'tool', '', 'complete', clock_timestamp())
RETURNING id
`;
const toolMessageId = toolRow!.id;
if (tc.name === 'ask_user_input') {
pausingForUserInput = true;
const sentinel = { tool_call_id: tc.id, output: null, truncated: false };
await ctx.sql`
UPDATE messages
SET tool_results = ${ctx.sql.json(sentinel as never)}
WHERE id = ${toolMessageId}
`;
// v1.13.0: mirror the pending sentinel into message_parts. The
// answer-endpoint UPDATE later (messages.ts:576) will delete and
// re-insert this part when the user submits their answer.
// TODO(v1.13.1): wrap the INSERT + UPDATE + insertParts triple in
// a per-iteration sql.begin before flipping read authority.
await insertParts(
ctx.sql,
partsFromToolMessage({ tool_results: sentinel }).map((p) => ({
...p,
message_id: toolMessageId,
})),
);
return;
}
const tres = await executeToolCall(projectRoot, tc);
const stored = {
tool_call_id: tc.id,
output: tres.output,
truncated: tres.truncated,
...(tres.error ? { error: tres.error } : {}),
};
await ctx.sql`
UPDATE messages
SET tool_results = ${ctx.sql.json(stored as never)}
WHERE id = ${toolMessageId}
`;
// v1.13.0: dual-write the tool_result part.
// TODO(v1.13.1): wrap the INSERT + UPDATE + insertParts triple in a
// per-iteration sql.begin before flipping read authority.
await insertParts(
ctx.sql,
partsFromToolMessage({ tool_results: stored }).map((p) => ({
...p,
message_id: toolMessageId,
})),
);
ctx.publish(sessionId, {
type: 'tool_result',
tool_message_id: toolMessageId,
chat_id: chatId,
tool_call_id: tc.id,
output: tres.output,
truncated: tres.truncated,
...(tres.error ? { error: tres.error } : {}),
});
})
);
if (pausingForUserInput) {
ctx.publishUser({
type: 'chat_status',
chat_id: chatId,
status: 'waiting_for_input',
at: new Date().toISOString(),
});
ctx.log.info(
{ sessionId, chatId, assistantMessageId },
'inference paused awaiting user input',
);
return;
}
const [nextAssistant] = await ctx.sql<{ id: string }[]>`
INSERT INTO messages (session_id, chat_id, role, content, status, created_at)
VALUES (${sessionId}, ${chatId}, 'assistant', '', 'streaming', clock_timestamp())
RETURNING id
`;
await runAssistantTurn(ctx, {
sessionId,
chatId,
assistantMessageId: nextAssistant!.id,
// v1.8.2: charge this turn's actual tool invocations against the budget.
// One assistant message can emit multiple tool_calls, so we add the run
// count, not 1. The next turn's budget check sees the cumulative total.
toolsUsed: toolsUsed + result.toolCalls.length,
// v1.11.6: append the just-executed tool calls to the per-turn history
// so the next runAssistantTurn's doom-loop check can see them. We don't
// cap the array length here — per-turn budgets keep it bounded
// (typically <30 entries), and slicing happens inside detectDoomLoop.
recentToolCalls: [...args.recentToolCalls, ...result.toolCalls],
signal,
});
}

View File

@@ -0,0 +1,329 @@
import type { FastifyBaseLogger } from 'fastify';
import type { Sql } from '../../db.js';
import type { Config } from '../../config.js';
import type {
Agent,
ErrorReason,
Message,
MessageMetadata,
Project,
Session,
ToolCall,
UserStreamFrame,
} from '../../types/api.js';
import { ALL_TOOLS } from '../tools.js';
import { resolveProjectRoot } from '../path_guard.js';
import { maybeAutoNameChat } from '../auto_name.js';
import { getAgentById } from '../agents.js';
import * as compaction from '../compaction.js';
import * as modelContext from '../model-context.js';
import type { Broker } from '../broker.js';
import { resolveToolBudget } from './budget.js';
import {
DOOM_LOOP_THRESHOLD,
detectDoomLoop,
} from './sentinels.js';
import {
buildMessagesPayload,
loadContext,
} from './payload.js';
import {
finalizeCompletion,
handleAbortOrError,
} from './error-handler.js';
import {
executeStreamPhase,
streamCompletion,
} from './stream-phase.js';
import { executeToolPhase } from './tool-phase.js';
import { DB_FLUSH_INTERVAL_MS, type StreamPhaseState } from './types.js';
import {
runCapHitSummary,
runDoomLoopSummary,
} from './sentinel-summaries.js';
// v1.12.4: re-exported so external callers (tests, future consumers) keep
// importing from services/inference.js as the public surface.
export { detectDoomLoop, DOOM_LOOP_THRESHOLD } from './sentinels.js';
export { buildMessagesPayload } from './payload.js';
export interface InferenceFrame {
type:
| 'message_started'
| 'delta'
| 'tool_call'
| 'tool_result'
| 'message_complete'
| 'usage'
| 'messages_deleted'
| 'session_renamed'
| 'chat_renamed'
| 'error';
message_id?: string;
message_ids?: string[];
chat_id?: string;
tool_message_id?: string;
tool_call_id?: string;
// v1.8.2: 'system' added so cap-hit sentinel messages can announce themselves
// through the normal message_started → delta → message_complete sequence.
role?: 'assistant' | 'tool' | 'user' | 'system';
content?: string;
tool_call?: ToolCall;
output?: unknown;
truncated?: boolean;
error?: string;
// v1.8.2: structured error reason. Set on `type: 'error'` so the UI can
// surface a specific message; `error` stays the human-readable text.
reason?: ErrorReason;
// v1.8.2: piggybacks on `message_complete` so static or terminally-resolved
// messages can carry their persisted metadata to the live stream without a
// refetch (sentinels carry { kind: 'cap_hit', ... }; failed messages carry
// { kind: 'error', ... }).
metadata?: MessageMetadata | null;
tokens_used?: number | null;
ctx_used?: number | null;
ctx_max?: number | null;
completion_tokens?: number | null;
started_at?: string | null;
finished_at?: string | null;
model?: string;
session_id?: string;
name?: string;
}
export type FramePublisher = (sessionId: string, frame: InferenceFrame) => void;
export interface InferenceContext {
sql: Sql;
config: Config;
log: FastifyBaseLogger;
publish: FramePublisher;
publishUser: (frame: UserStreamFrame) => void;
// v1.11: passed through so compaction.process can publish 'compacted'
// frames on the same session WS channel useSessionStream subscribes to.
// Compaction is the only path that needs the raw broker handle (regular
// inference goes through `publish`); keeping a separate field avoids
// tempting other code paths into bypassing the session-id binding.
broker: Broker;
}
// v1.12.4: payload assembly extracted to ./inference/payload.ts (tests
// import buildMessagesPayload from this module, so a re-export below
// preserves the public surface). Stream + tool phases extracted to
// ./inference/stream-phase.ts and ./inference/tool-phase.ts.
export interface StreamResult {
finishReason: string | null;
content: string;
toolCalls: ToolCall[];
promptTokens: number | null;
completionTokens: number | null;
// v1.13.1-C: reasoning text accumulated across reasoning-delta parts.
// Empty string when the model doesn't emit reasoning (most cases).
reasoning: string;
}
export interface TurnArgs {
sessionId: string;
chatId: string;
assistantMessageId: string;
// v1.8.2: cumulative tool calls executed this run. Compared against the
// resolved budget at the top of each turn. Replaces the older `depth`
// counter (which counted iterations, not invocations).
toolsUsed: number;
// v1.11.6: ordered tool calls executed in this user-message turn (across
// recursive runAssistantTurn invocations). Reset to [] at user-message
// boundaries by runInference, same as toolsUsed. Doom-loop check at the
// top of runAssistantTurn slices the last DOOM_LOOP_THRESHOLD entries.
recentToolCalls: ToolCall[];
signal: AbortSignal | undefined;
}
export async function runAssistantTurn(
ctx: InferenceContext,
args: TurnArgs,
): Promise<void> {
const { sessionId, chatId } = args;
// v1.11: if the prior turn flagged this chat for compaction, run it first
// so loadContext below reads the post-compaction history. We swallow
// compaction failures (clearing the flag so we don't loop) and proceed
// with the un-compacted history — a slow turn that hits the model's
// hard limit is recoverable; a dead session is not.
const chatFlag = await ctx.sql<{ needs_compaction: boolean }[]>`
SELECT needs_compaction FROM chats WHERE id = ${chatId}
`;
if (chatFlag[0]?.needs_compaction) {
try {
await compaction.process({
sql: ctx.sql,
config: ctx.config,
log: ctx.log,
broker: ctx.broker,
chatId,
});
} catch (err) {
ctx.log.warn({ err, chatId }, 'auto-compaction failed; clearing flag and proceeding');
await ctx.sql`UPDATE chats SET needs_compaction = false WHERE id = ${chatId}`;
}
}
const loaded = await loadContext(ctx.sql, sessionId, chatId);
if (!loaded) {
ctx.log.warn({ sessionId }, 'inference: session or project missing');
return;
}
const { session, project, history } = loaded;
const projectRoot = await resolveProjectRoot(project.path);
// Agent resolution is per-turn so PATCH agent_id mid-conversation takes
// effect on the next message. Unknown agent_id returns null silently —
// session falls back to base prompt + all tools + default temperature.
const agent = session.agent_id
? await getAgentById(project.path, session.agent_id)
: null;
// v1.8.2: cap-hit replaces the older "tool loop depth exceeded" failure.
// When we've already burned the budget *before* this turn even runs, we
// skip straight to the summary flow — the in-flight assistant message slot
// gets reused for the wrap-up reply instead of being marked failed.
const budget = resolveToolBudget(agent);
if (args.toolsUsed >= budget) {
await runCapHitSummary(ctx, args, session, project, history, agent, budget);
return;
}
// v1.11.6: doom-loop guard. Detected BEFORE the budget cap (the model can
// burn through 3 identical calls long before the 15-call budget fires).
// Same in-flight-slot-reuse pattern as runCapHitSummary — wrap-up reply
// lands in args.assistantMessageId, then a doom_loop sentinel is inserted
// to make the abort visible in the chat history.
const loop = detectDoomLoop(args.recentToolCalls);
if (loop) {
await runDoomLoopSummary(ctx, args, session, project, history, agent, loop);
return;
}
const messages = await buildMessagesPayload(session, project, history, agent);
// v1.11.8: resolve per-chat web-tools opt-in. Tri-state on the wire:
// - session.web_search_enabled = null → inherit project default
// - session.web_search_enabled = true/false → explicit
// Both web_search and web_fetch are gated by this single flag (the UI
// label is "Enable web search and fetch" — same store, both tools).
// Default is false unless explicitly opted in, matching the v1.9
// plumbing intent ("inert until Batch 8 ships the actual tools").
const webToolsEnabled =
session.web_search_enabled ?? project.default_web_search_enabled ?? false;
const state: StreamPhaseState = { accumulated: '', startedAt: null };
let result: StreamResult;
try {
result = await executeStreamPhase(ctx, args, session, messages, state, agent, webToolsEnabled);
} catch (err) {
await handleAbortOrError(ctx, args, state.accumulated, err);
return;
}
if (result.toolCalls.length > 0) {
await executeToolPhase(ctx, args, result, state.startedAt, session, projectRoot);
return;
}
await finalizeCompletion(ctx, args, result, state.startedAt, session);
}
export async function runInference(
ctx: InferenceContext,
sessionId: string,
chatId: string,
assistantMessageId: string,
signal?: AbortSignal
): Promise<void> {
// v1.8.2: every fresh inference (initial send, regenerate, force_send,
// continue) starts with a clean budget. Tool-call accumulation across
// Continue invocations is what the hard ceiling guards against, not the
// per-call budget.
// v1.11.6: recentToolCalls also resets — doom-loop detection is scoped
// to a single user-message turn, so a Continue starts with no history.
return runAssistantTurn(ctx, {
sessionId,
chatId,
assistantMessageId,
toolsUsed: 0,
recentToolCalls: [],
signal,
});
}
// v1.8.2: cap-hit summary flow. Called instead of erroring when the loop
// hits its budget. Reuses the in-flight assistant message slot to stream a
// short wrap-up reply with the synthetic note prepended and tools disabled,
// then always inserts a cap_hit sentinel afterward (regardless of summary
// outcome) so the UI can show a Continue affordance.
interface InferenceRegistration {
controller: AbortController;
completed: Promise<void>;
}
export function createInferenceRunner(
ctx: Omit<InferenceContext, 'publishUser'>,
publishUserFn: (user: string, frame: UserStreamFrame) => void
) {
const registry = new Map<string, InferenceRegistration>();
return {
enqueue(sessionId: string, chatId: string, assistantMessageId: string, user: string) {
const callCtx: InferenceContext = {
...ctx,
publishUser: (frame) => publishUserFn(user, frame),
// v1.11: broker comes in via ctx (set at registration time). Repeated
// here so the destructure carries it onto the per-call ctx without
// having to add it to every enqueue/cancel signature individually.
broker: ctx.broker,
};
// v1.8 mobile-tabs: announce working before the async loop starts so
// every device subscribed to the user channel sees the amber dot.
callCtx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'streaming', at: new Date().toISOString() });
const controller = new AbortController();
let resolveCompleted!: () => void;
const completed = new Promise<void>((res) => { resolveCompleted = res; });
const registration: InferenceRegistration = { controller, completed };
registry.set(chatId, registration);
void (async () => {
try {
await runInference(callCtx, sessionId, chatId, assistantMessageId, controller.signal);
setImmediate(() => {
void maybeAutoNameChat(callCtx, chatId, sessionId).catch((err: Error) => {
callCtx.log.warn({ err, chatId }, 'auto-name failed');
});
});
} catch (err) {
callCtx.log.error({ err }, 'unhandled inference error');
} finally {
resolveCompleted();
// Only clear our own registration; a force-send may have replaced it.
if (registry.get(chatId) === registration) {
registry.delete(chatId);
}
}
})();
},
async cancel(_sessionId: string, chatId: string): Promise<boolean> {
const reg = registry.get(chatId);
if (!reg) return false;
reg.controller.abort();
// Swallow — we just need to wait for the catch/finally to persist state.
await reg.completed.catch(() => {});
return true;
},
hasActive(chatId: string): boolean {
return registry.has(chatId);
},
};
}
export const _toolNames = ALL_TOOLS.map((t) => t.name);

View File

@@ -0,0 +1,13 @@
// v1.12.4: shared inter-phase types/constants for the extracted phase files.
// Lives here so stream-phase, tool-phase, and the summary functions still in
// inference.ts can all reference the same definitions without circular imports.
export interface StreamPhaseState {
accumulated: string;
startedAt: string | null;
}
// 500ms keeps the DB UPDATE rate bounded under heavy streaming. Used by
// executeStreamPhase, runCapHitSummary, and runDoomLoopSummary — every site
// that does a debounced content flush during streaming.
export const DB_FLUSH_INTERVAL_MS = 500;

View File

@@ -0,0 +1,53 @@
// v1.10.5: XML-tag tool-call fallback. Some models emit
// <tool_call><function=foo><parameter=key>value</parameter></function></tool_call>
// in plain content instead of using the OpenAI tool_calls JSON channel.
// The streaming loop in inference.ts extracts these blocks via these helpers.
export const XML_TOOL_OPEN = '<tool_call>';
export const XML_TOOL_CLOSE = '</tool_call>';
export function parseXmlToolCall(
block: string,
): { name: string; args: Record<string, unknown> } | null {
const nameMatch = block.match(/<function=([^>]+)>/);
if (!nameMatch || !nameMatch[1]) return null;
const name = nameMatch[1].trim();
if (!name) return null;
const args: Record<string, unknown> = {};
// Non-greedy body so each <parameter=…>…</parameter> pair is matched
// independently even when multiple appear in the same block.
const paramRe = /<parameter=([^>]+)>([\s\S]*?)<\/parameter>/g;
for (const m of block.matchAll(paramRe)) {
const key = (m[1] ?? '').trim();
if (!key) continue;
const raw = (m[2] ?? '').trim();
try {
args[key] = JSON.parse(raw);
} catch {
args[key] = raw;
}
}
return { name, args };
}
// Locate the first character that begins (or completely contains) an
// unfinished <tool_call> opener in `s`. Returns -1 when `s` can be flushed
// to the client in full without risking a partial tag leak.
// Case 1: a full `<tool_call>` opener with no matching closer — caller
// must keep everything from that index forward until the next
// chunk arrives with the closer.
// Case 2: `s` ends with a strict prefix of `<tool_call>` (e.g. `<tool_c`).
// Caller must keep just that suffix in the buffer.
// Note: case 1 assumes the calling loop already extracted every complete
// <tool_call>…</tool_call> pair before reaching this check.
export function partialXmlOpenerStart(s: string): number {
const fullOpener = s.indexOf(XML_TOOL_OPEN);
if (fullOpener !== -1) return fullOpener;
const lastLt = s.lastIndexOf('<');
if (lastLt === -1) return -1;
const suffix = s.slice(lastLt);
if (XML_TOOL_OPEN.startsWith(suffix) && suffix.length < XML_TOOL_OPEN.length) {
return lastLt;
}
return -1;
}

View File

@@ -62,6 +62,39 @@ function stripHtml(html: string): { text: string; title: string | undefined } {
return { text, title };
}
// v1.11.10: streaming body reader. Aborts the response stream the instant
// cumulative bytes cross maxBytes, so a server that lies about
// Content-Length (or omits it entirely) can't make us buffer gigabytes
// before the post-read check fires. reader.cancel() releases the
// underlying connection on the spot.
async function readBodyCapped(
res: Response,
maxBytes: number,
): Promise<{ ok: true; body: string } | { ok: false; bytesRead: number }> {
if (!res.body) return { ok: true, body: '' };
const reader = res.body.getReader();
const chunks: Uint8Array[] = [];
let total = 0;
try {
while (true) {
const { done, value } = await reader.read();
if (done) break;
total += value.byteLength;
if (total > maxBytes) {
// Best-effort cancel — surfaces on the server side as a closed
// connection and (in our tests) fires the ReadableStream's
// cancel() callback so we can assert the abort happened.
await reader.cancel();
return { ok: false, bytesRead: total };
}
chunks.push(value);
}
} finally {
try { reader.releaseLock(); } catch { /* already released by cancel() */ }
}
return { ok: true, body: Buffer.concat(chunks).toString('utf8') };
}
function truncate(text: string, max: number): { content: string; truncated: boolean } {
if (text.length <= max) return { content: text, truncated: false };
const omitted = text.length - max;
@@ -159,19 +192,20 @@ export async function executeWebFetch(
}
}
const contentType = (res.headers.get('content-type') ?? '').toLowerCase();
// Read body. We rely on the 5MB cap by checking length after consumption
// — most malicious or accidental large responses also exceed it via the
// Content-Length pre-flight above. A truly hostile server that lies
// about length AND streams gigabytes would defeat that; the per-hop
// 15s timeout is the secondary fence.
const body = await res.text();
// v1.11.8 review: byte-count, not char-count. A 5MB cap on body.length
// (UTF-16 code units) lets a multi-byte payload (emoji, CJK) pass when
// its wire size already exceeded MAX_BYTES.
const bodyBytes = Buffer.byteLength(body, 'utf8');
if (bodyBytes > MAX_BYTES) {
return { error: 'response_too_large', reason: `body ${bodyBytes} bytes > ${MAX_BYTES}` };
// v1.11.10: stream the body with a hard byte cap. Previously we read
// res.text() in one shot and then byte-length-checked — a server that
// lies about Content-Length (or omits it) could make us buffer
// gigabytes before the post-check fired. readBodyCapped aborts the
// stream the instant total bytes cross MAX_BYTES. The Content-Length
// pre-flight above stays as a cheap early reject for honest servers.
const read = await readBodyCapped(res, MAX_BYTES);
if (!read.ok) {
return {
error: 'body_too_large',
reason: `Response body exceeded ${MAX_BYTES} bytes (read ${read.bytesRead} before abort)`,
};
}
const body = read.body;
let textRaw: string;
let title: string | undefined;

View File

@@ -39,6 +39,19 @@ export interface Session {
// project.default_web_search_enabled. Plumbed but inert in v1.9 — the
// actual web_search tool ships in Batch 8.
web_search_enabled: boolean | null;
// v1.12.1: server-side workspace pane layout. Replaces per-device
// localStorage so all devices viewing the session see the same panes.
workspace_panes: WorkspacePane[];
}
export type WorkspacePaneKind = 'chat' | 'terminal' | 'agent' | 'empty' | 'settings';
export interface WorkspacePane {
id: string;
kind: WorkspacePaneKind;
chatId?: string;
chatIds: string[];
activeChatIdx: number;
}
// v1.8.1: agents come from two sources. 'global' = /data/AGENTS.md (always
@@ -173,6 +186,11 @@ export interface Message {
// v1.8.2: per-message metadata. See MessageMetadata for the discriminated
// shapes currently in use.
metadata: MessageMetadata | null;
// v1.13.1-C: reasoning content captured from the model's reasoning stream
// (qwen3.6 etc.). Populated from message_parts via the messages_with_parts
// view's reasoning_parts column. Optional — most rows have no reasoning
// and the API may omit the field on legacy responses.
reasoning_parts?: Array<{ text: string }> | null;
// v1.11: anchored rolling compaction. Optional so consumers that SELECT
// the pre-v1.11 column set still type-check. See compaction.ts +
// schema.sql for semantics.
@@ -273,6 +291,11 @@ export interface SessionRenamedFrame {
session_id: string;
name: string;
}
export interface SessionWorkspaceUpdatedFrame {
type: 'session_workspace_updated';
session_id: string;
workspace_panes: WorkspacePane[];
}
export interface SessionArchivedFrame {
type: 'session_archived';
session_id: string;
@@ -324,7 +347,7 @@ export interface ProjectUpdatedFrame {
export interface ChatStatusFrame {
type: 'chat_status';
chat_id: string;
status: 'working' | 'idle' | 'error';
status: 'streaming' | 'tool_running' | 'waiting_for_input' | 'idle' | 'error';
at: string;
reason?: ErrorReason;
}
@@ -335,6 +358,7 @@ export type UserStreamFrame =
| SessionDeletedFrame
| SessionUpdatedFrame
| SessionRenamedFrame
| SessionWorkspaceUpdatedFrame
| SessionArchivedFrame
| ChatCreatedFrame
| ChatUpdatedFrame

View File

@@ -143,6 +143,11 @@ export const api = {
),
openChatsCount: (id: string) =>
request<{ count: number }>(`/api/sessions/${id}/chats/open-count`),
updateWorkspacePanes: (id: string, panes: Session['workspace_panes']) =>
request<Session>(`/api/sessions/${id}/workspace`, {
method: 'PATCH',
body: JSON.stringify({ workspace_panes: panes }),
}),
},
chats: {
@@ -175,6 +180,11 @@ export const api = {
request<{ ok: true }>(`/api/chats/${chatId}/compact`, { method: 'POST' }),
stop: (chatId: string) =>
request<{ stopped: boolean }>(`/api/chats/${chatId}/stop`, { method: 'POST' }),
discardStale: (chatId: string, messageId: string) =>
request<Message>(`/api/chats/${chatId}/discard_stale`, {
method: 'POST',
body: JSON.stringify({ message_id: messageId }),
}),
forceSend: (chatId: string, content: string) =>
request<{ user_message_id: string; assistant_message_id: string }>(
`/api/chats/${chatId}/force_send`,

View File

@@ -34,6 +34,8 @@ export interface Session {
agent_id: string | null;
// v1.9: null = inherit from project.default_web_search_enabled.
web_search_enabled: boolean | null;
// v1.12.1: server-authoritative pane layout, replaces localStorage.
workspace_panes: WorkspacePane[];
}
// v1.8.1: 'global' = /data/AGENTS.md (always-on), 'project' = per-project
@@ -159,6 +161,11 @@ export interface Message {
// v1.8.2: per-message metadata; see MessageMetadata. null for the vast
// majority of messages.
metadata: MessageMetadata | null;
// v1.13.1-C: reasoning content captured from models that stream reasoning
// tokens separately (qwen3.6 etc.). Backend populates from message_parts;
// optional on the wire — frontend doesn't render this yet (reserved for
// a v1.14 UI surface).
reasoning_parts?: Array<{ text: string }> | null;
// v1.11: anchored rolling compaction fields. Optional on the wire so that
// older API responses (or test fixtures) parse without explicit nulls.
// summary — true on the assistant row that holds the active
@@ -330,6 +337,17 @@ export type WsFrame =
// to the client without a refetch.
metadata?: MessageMetadata | null;
}
// v1.12.2: live throughput frame, published mid-stream every ~500ms with
// the latest token + ctx counts so ChatThroughput can render tok/s and
// ctx_used while the model is still generating.
| {
type: 'usage';
message_id: string;
chat_id?: string;
completion_tokens: number | null;
ctx_used: number | null;
ctx_max: number | null;
}
| { type: 'messages_deleted'; message_ids: string[]; chat_id?: string }
| { type: 'chat_renamed'; chat_id: string; name: string }
// v1.11: published by services/compaction.ts after the new anchored

View File

@@ -2,6 +2,7 @@ import { useState } from 'react';
import { Bot, History, MessageSquare, Plus, Terminal, X } from 'lucide-react';
import type { Chat, WorkspacePane } from '@/api/types';
import { StatusDot } from '@/components/StatusDot';
import { ChatThroughput } from '@/components/ChatThroughput';
import {
ContextMenu,
ContextMenuContent,
@@ -99,6 +100,7 @@ export function ChatTabBar({
>
<MessageSquare size={12} className="shrink-0" />
<StatusDot chatId={chat.id} />
<ChatThroughput chatId={chat.id} />
{renamingId === chat.id ? (
<input
autoFocus

View File

@@ -0,0 +1,28 @@
import { useChatStatus } from '@/hooks/useChatStatus';
import { useChatThroughput } from '@/hooks/useChatThroughput';
import { cn } from '@/lib/utils';
interface Props {
chatId: string | null | undefined;
className?: string;
}
// v1.12.2: inline throughput readout. Renders next to StatusDot while the
// chat is streaming or running a tool. Hidden in idle/error/waiting states
// — the dot already communicates those.
export function ChatThroughput({ chatId, className }: Props) {
const status = useChatStatus(chatId);
const t = useChatThroughput(chatId);
if (!chatId || !t) return null;
if (status !== 'streaming' && status !== 'tool_running') return null;
const tps = t.tps != null && t.tps > 0 ? Math.round(t.tps) : null;
const showCtx = t.ctx_used != null && t.ctx_max != null;
if (tps === null && !showCtx) return null;
return (
<span className={cn('text-xs text-muted-foreground tabular-nums', className)}>
{tps !== null && `${tps} tok/s`}
{tps !== null && showCtx && ' · '}
{showCtx && `${t.ctx_used!.toLocaleString()}/${t.ctx_max!.toLocaleString()}`}
</span>
);
}

View File

@@ -13,6 +13,7 @@ import { toast } from 'sonner';
import type { Chat, WorkspacePane } from '@/api/types';
import { BottomSheet } from '@/components/BottomSheet';
import { StatusDot } from '@/components/StatusDot';
import { ChatThroughput } from '@/components/ChatThroughput';
import {
DropdownMenu,
DropdownMenuContent,
@@ -206,6 +207,7 @@ export function MobileTabSwitcher({
>
<span className="shrink-0 text-muted-foreground">{paneIcon(active?.kind ?? 'chat')}</span>
<StatusDot chatId={activeChatId} />
<ChatThroughput chatId={activeChatId} />
<span className="truncate flex-1 text-left">{activeLabel}</span>
<ChevronDown size={14} className="opacity-60 shrink-0" />
</button>
@@ -237,6 +239,7 @@ export function MobileTabSwitcher({
>
<span className="shrink-0 text-muted-foreground">{paneIcon(pane.kind)}</span>
<StatusDot chatId={cid ?? null} />
<ChatThroughput chatId={cid ?? null} />
{renamingChatId === cid && cid ? (
<input
autoFocus

View File

@@ -0,0 +1,34 @@
interface Props {
onRetry: () => void;
onDiscard: () => void;
}
// v1.12.3: shown when an assistant message has been 'streaming' for 60+
// seconds without new tokens. Lives above ChatInput in ChatPane. Retry
// discards the stuck row then resends the last user message; Discard just
// clears the row and drops the dot to idle.
export function StaleStreamBanner({ onRetry, onDiscard }: Props) {
return (
<div className="border border-amber-500/30 bg-amber-500/5 rounded-md p-3 mb-2 mx-4 flex items-center justify-between gap-2">
<span className="text-sm text-muted-foreground">
Previous response didn't complete.
</span>
<div className="flex gap-2">
<button
type="button"
onClick={onRetry}
className="text-xs px-2 py-1 rounded border border-border hover:bg-accent max-md:min-h-[44px] max-md:px-3"
>
Retry
</button>
<button
type="button"
onClick={onDiscard}
className="text-xs px-2 py-1 rounded border border-border hover:bg-accent max-md:min-h-[44px] max-md:px-3"
>
Discard
</button>
</div>
</div>
);
}

View File

@@ -6,15 +6,10 @@ interface Props {
className?: string;
}
const STATUS_CLASS: Record<DerivedStatus, string> = {
working: 'bg-amber-500 animate-pulse',
idle_warm: 'bg-emerald-500',
idle_cold: 'bg-muted-foreground/40',
error: 'bg-destructive',
};
const STATUS_LABEL: Record<DerivedStatus, string> = {
working: 'working',
streaming: 'streaming',
tool_running: 'running tool',
waiting_for_input: 'waiting for input',
idle_warm: 'idle',
idle_cold: 'idle',
error: 'error',
@@ -22,15 +17,58 @@ const STATUS_LABEL: Record<DerivedStatus, string> = {
export function StatusDot({ chatId, className }: Props) {
const status = useChatStatus(chatId);
if (status === 'streaming') {
return (
<span
aria-label="Status: streaming"
title="streaming"
className={cn('inline-block relative w-3 h-3 shrink-0', className)}
>
<span className="absolute inset-0 animate-spin-slow">
<span className="absolute top-0 left-1/2 -translate-x-1/2 w-1 h-1 rounded-full bg-amber-500" />
<span className="absolute bottom-0 left-1/2 -translate-x-1/2 w-1 h-1 rounded-full bg-amber-500/60" />
</span>
</span>
);
}
if (status === 'tool_running') {
return (
<span
aria-label="Status: running tool"
title="running tool"
className={cn(
'inline-block w-3 h-3 rounded-full border-2 border-sky-500 border-t-transparent animate-spin shrink-0',
className,
)}
/>
);
}
if (status === 'waiting_for_input') {
return (
<span
aria-label="Status: waiting for input"
title="waiting for input"
className={cn(
'inline-block w-1.5 h-1.5 rounded-full shrink-0 bg-violet-500',
className,
)}
/>
);
}
const bg =
status === 'idle_warm' ? 'bg-emerald-500'
: status === 'error' ? 'bg-destructive'
: 'bg-muted-foreground/40';
return (
<span
aria-label={`Status: ${STATUS_LABEL[status]}`}
title={STATUS_LABEL[status]}
className={cn(
'inline-block w-1.5 h-1.5 rounded-full shrink-0',
STATUS_CLASS[status],
className,
)}
className={cn('inline-block w-1.5 h-1.5 rounded-full shrink-0', bg, className)}
/>
);
}

View File

@@ -5,6 +5,7 @@ import { api } from '@/api/client';
import { useSessionStream } from '@/hooks/useSessionStream';
import { MessageList } from '@/components/MessageList';
import { ChatInput } from '@/components/ChatInput';
import { StaleStreamBanner } from '@/components/StaleStreamBanner';
import {
DropdownMenu,
DropdownMenuContent,
@@ -44,6 +45,38 @@ export function ChatPane({ sessionId, chatId, projectId, agentId, onAgentChange,
const chatMessages = stream.messages.filter((m) => m.chat_id === chatId);
const streaming = chatMessages.some((m) => m.status === 'streaming');
// v1.12.3: stale-stream detection. Watches the (at most one) streaming
// assistant row. If its content length doesn't grow for STALE_THRESHOLD_MS,
// assume the upstream call is dead and surface the recovery banner. We use
// content length as the activity signal because every token delta extends
// it; last_seq isn't currently bumped per delta.
const STALE_THRESHOLD_MS = 60_000;
const streamingMsg = chatMessages.find((m) => m.status === 'streaming' && m.role === 'assistant');
const streamingId = streamingMsg?.id ?? null;
const streamingLen = streamingMsg?.content.length ?? 0;
const lastActivityRef = useRef<{ id: string; len: number; at: number } | null>(null);
const [stale, setStale] = useState(false);
useEffect(() => {
if (!streamingId) {
lastActivityRef.current = null;
setStale(false);
return;
}
const prev = lastActivityRef.current;
if (!prev || prev.id !== streamingId || prev.len !== streamingLen) {
lastActivityRef.current = { id: streamingId, len: streamingLen, at: Date.now() };
setStale(false);
}
const interval = setInterval(() => {
const a = lastActivityRef.current;
if (!a) return;
if (Date.now() - a.at >= STALE_THRESHOLD_MS) {
setStale(true);
}
}, 5_000);
return () => clearInterval(interval);
}, [streamingId, streamingLen]);
// v1.11.5: per-chat model context limit comes from chat.model_context_limit
// populated by GET /api/sessions/:id/chats. Threaded into ChatInput so
// ContextBar can render a zero-state before the first assistant message.
@@ -87,6 +120,45 @@ export function ChatPane({ sessionId, chatId, projectId, agentId, onAgentChange,
}
}
const handleDiscardStale = useCallback(async () => {
if (!streamingId) return;
try {
await api.chats.discardStale(chatId, streamingId);
setStale(false);
lastActivityRef.current = null;
} catch (err) {
// 409 (race) is benign — the row already terminated some other way.
const msg = err instanceof Error ? err.message : 'discard failed';
if (!msg.includes('409')) toast.error(msg);
setStale(false);
}
}, [chatId, streamingId]);
const handleRetryStale = useCallback(async () => {
if (!streamingId) return;
const lastUser = [...chatMessages].reverse().find((m) => m.role === 'user' && m.kind === 'message');
if (!lastUser) {
toast.error('no prior user message to retry');
return;
}
try {
await api.chats.discardStale(chatId, streamingId);
} catch (err) {
const msg = err instanceof Error ? err.message : 'discard failed';
if (!msg.includes('409')) {
toast.error(msg);
return;
}
}
setStale(false);
lastActivityRef.current = null;
try {
await api.messages.send(chatId, lastUser.content);
} catch (err) {
toast.error(err instanceof Error ? err.message : 'retry send failed');
}
}, [chatId, streamingId, chatMessages]);
const handleForceSend = useCallback(async (content: string) => {
const trimmed = content.trim();
if (!trimmed) return;
@@ -187,6 +259,13 @@ export function ChatPane({ sessionId, chatId, projectId, agentId, onAgentChange,
</div>
)}
{stale && streamingId && (
<StaleStreamBanner
onRetry={() => void handleRetryStale()}
onDiscard={() => void handleDiscardStale()}
/>
)}
<ChatInput
disabled={false}
projectId={projectId}

View File

@@ -41,6 +41,12 @@ export interface SessionUpdatedEvent {
updated_at: string;
}
export interface SessionWorkspaceUpdatedEvent {
type: 'session_workspace_updated';
session_id: string;
workspace_panes: import('@/api/types').WorkspacePane[];
}
export interface SessionLoadedEvent {
type: 'session_loaded';
session_id: string;
@@ -131,7 +137,7 @@ export interface ProjectUpdatedEvent {
export interface ChatStatusEvent {
type: 'chat_status';
chat_id: string;
status: 'working' | 'idle' | 'error';
status: 'streaming' | 'tool_running' | 'waiting_for_input' | 'idle' | 'error';
at: string;
reason?: ErrorReason;
}
@@ -143,6 +149,7 @@ export type SessionEvent =
| SessionCreatedEvent
| SessionDeletedEvent
| SessionUpdatedEvent
| SessionWorkspaceUpdatedEvent
| SessionLoadedEvent
| OpenFileInBrowserEvent
| AttachChatFileEvent

View File

@@ -1,8 +1,14 @@
import { useEffect, useState } from 'react';
import { sessionEvents } from './sessionEvents';
export type RawStatus = 'working' | 'idle' | 'error';
export type DerivedStatus = 'working' | 'idle_warm' | 'idle_cold' | 'error';
export type RawStatus = 'streaming' | 'tool_running' | 'waiting_for_input' | 'idle' | 'error';
export type DerivedStatus =
| 'streaming'
| 'tool_running'
| 'waiting_for_input'
| 'idle_warm'
| 'idle_cold'
| 'error';
// Window during which an idle dot stays green; after this, it fades to gray.
const WARM_WINDOW_MS = 30_000;
@@ -53,7 +59,9 @@ if (!G.__boocode_chat_status_subscribed) {
function derive(entry: Entry | undefined): DerivedStatus {
if (!entry) return 'idle_cold';
if (entry.status === 'working') return 'working';
if (entry.status === 'streaming') return 'streaming';
if (entry.status === 'tool_running') return 'tool_running';
if (entry.status === 'waiting_for_input') return 'waiting_for_input';
if (entry.status === 'error') return 'error';
const age = Date.now() - new Date(entry.at).getTime();
return age < WARM_WINDOW_MS ? 'idle_warm' : 'idle_cold';

View File

@@ -0,0 +1,106 @@
import { useEffect, useState } from 'react';
// v1.12.2: live throughput stream consumer. Fed by useSessionStream when a
// 'usage' WS frame lands. Renders next to StatusDot via ChatThroughput.
//
// Singleton + Set<setState> pattern mirrors useChatStatus so any component
// can subscribe to any chatId without prop drilling.
export interface ThroughputSample {
tps: number | null;
ctx_used: number | null;
ctx_max: number | null;
}
interface Entry {
ctx_used: number | null;
ctx_max: number | null;
completion_tokens: number | null;
recorded_at: number;
prev_completion_tokens: number | null;
prev_recorded_at: number | null;
tps: number | null;
}
// Stale window. After this, useChatThroughput returns null — clears the
// indicator after the stream ends without the next inference turn.
const STALE_MS = 10_000;
const entries = new Map<string, Entry>();
const subscribers = new Set<() => void>();
function notify(): void {
for (const s of subscribers) {
try { s(); } catch { /* swallow */ }
}
}
// v1.12.2: imported by useSessionStream's WS handler. Computes tps from the
// gap between successive completion_tokens samples; first sample yields null
// (we need two points). Skips zero-progress samples so a duplicate usage
// frame doesn't push tps to 0.
export function recordUsage(
chatId: string,
data: { completion_tokens: number | null; ctx_used: number | null; ctx_max: number | null },
): void {
const now = Date.now();
const prev = entries.get(chatId);
let tps: number | null = prev?.tps ?? null;
if (
prev &&
data.completion_tokens != null &&
prev.completion_tokens != null &&
data.completion_tokens > prev.completion_tokens &&
now > prev.recorded_at
) {
const dTokens = data.completion_tokens - prev.completion_tokens;
const dSeconds = (now - prev.recorded_at) / 1000;
tps = dTokens / dSeconds;
}
entries.set(chatId, {
ctx_used: data.ctx_used,
ctx_max: data.ctx_max,
completion_tokens: data.completion_tokens,
recorded_at: now,
prev_completion_tokens: prev?.completion_tokens ?? null,
prev_recorded_at: prev?.recorded_at ?? null,
tps,
});
notify();
}
export function clearThroughput(chatId: string): void {
if (entries.delete(chatId)) notify();
}
// Periodic sweep: re-notify so stale entries fall off the UI when the
// stream ends without a follow-up frame. Light — one timer for the whole app.
const G = globalThis as Record<string, unknown>;
if (!G.__boocode_throughput_ticker) {
G.__boocode_throughput_ticker = true;
setInterval(() => {
const now = Date.now();
let touched = false;
for (const [k, v] of entries) {
if (now - v.recorded_at > STALE_MS) {
entries.delete(k);
touched = true;
}
}
if (touched) notify();
}, 2_000);
}
export function useChatThroughput(chatId: string | null | undefined): ThroughputSample | null {
const [, force] = useState({});
useEffect(() => {
const sub = () => force({});
subscribers.add(sub);
return () => { subscribers.delete(sub); };
}, []);
if (!chatId) return null;
const entry = entries.get(chatId);
if (!entry) return null;
if (Date.now() - entry.recorded_at > STALE_MS) return null;
return { tps: entry.tps, ctx_used: entry.ctx_used, ctx_max: entry.ctx_max };
}

View File

@@ -12,6 +12,7 @@ export interface UseSessionChatsOpts {
// about pane indexing.
openChatInActivePane: (chatId: string) => void;
initializeFirstChatIfEmpty: (chatId: string) => void;
validatePanes: (validChatIds: Set<string>) => void;
}
export interface UseSessionChatsResult {
@@ -44,12 +45,15 @@ export function useSessionChats(
openChatInActivePaneRef.current = opts.openChatInActivePane;
const initializeFirstChatIfEmptyRef = useRef(opts.initializeFirstChatIfEmpty);
initializeFirstChatIfEmptyRef.current = opts.initializeFirstChatIfEmpty;
const validatePanesRef = useRef(opts.validatePanes);
validatePanesRef.current = opts.validatePanes;
useEffect(() => {
let cancelled = false;
api.chats.listForSession(sessionId).then((list) => {
if (cancelled) return;
setChats(list);
validatePanesRef.current(new Set(list.map((c) => c.id)));
const openChat = list.find((c) => c.status === 'open');
if (openChat) {
initializeFirstChatIfEmptyRef.current(openChat.id);

View File

@@ -3,6 +3,7 @@ import { toast } from 'sonner';
import type { Message, WsFrame } from '@/api/types';
import { api } from '@/api/client';
import { sessionEvents } from './sessionEvents';
import { recordUsage } from './useChatThroughput';
// session_renamed frame removed from WsFrame — it was declared but never
// published on the per-session WS channel (server publishes via broker.publishUser
@@ -125,6 +126,19 @@ function applyFrame(state: State, frame: WsFrame): State {
);
return { ...state, messages: next };
}
case 'usage': {
// v1.12.2: live throughput. Side-effects into the module-level
// singleton consumed by ChatThroughput; no message-state mutation.
// chat_id is the optional ws-frame field; usage frames always include it.
if (frame.chat_id) {
recordUsage(frame.chat_id, {
completion_tokens: frame.completion_tokens,
ctx_used: frame.ctx_used,
ctx_max: frame.ctx_max,
});
}
return state;
}
case 'messages_deleted': {
const removeSet = new Set(frame.message_ids);
return {

View File

@@ -143,6 +143,9 @@ function applyEvent(prev: SidebarResponse, event: import('./sessionEvents').Sess
case 'session_loaded':
// activeSessionProjectId is updated in the subscribe callback; no data change here.
return prev;
case 'session_workspace_updated':
// Pane layout is consumed by useWorkspacePanes; sidebar has no stake.
return prev;
case 'open_file_in_browser':
// Consumed by Workspace (T7); no sidebar state change needed.
return prev;

View File

@@ -4,9 +4,14 @@ import { toast } from 'sonner';
import { api } from '@/api/client';
import type { WorkspacePane } from '@/api/types';
import { setActivePaneInfo, clearActivePane } from '@/hooks/useActivePane';
import { sessionEvents } from '@/hooks/sessionEvents';
export const MAX_PANES = 5;
const STORAGE_KEY = 'boocode.workspace.panes';
// v1.12.1: legacy localStorage key. Read once on mount to seed the server
// for sessions still on per-device state, then deleted. Server is now
// authoritative via sessions.workspace_panes.
const LEGACY_STORAGE_KEY = 'boocode.workspace.panes';
const SAVE_DEBOUNCE_MS = 300;
function generateId(): string {
return crypto.randomUUID();
@@ -51,9 +56,11 @@ function nonSettingsCount(panes: WorkspacePane[]): number {
return panes.reduce((n, p) => n + (p.kind === 'settings' ? 0 : 1), 0);
}
function loadPanes(sessionId: string): WorkspacePane[] | null {
// v1.12.1: read legacy per-device localStorage. If present, the caller seeds
// the server then deletes the key. One-time migration per session.
function readLegacyPanes(sessionId: string): WorkspacePane[] | null {
try {
const raw = localStorage.getItem(`${STORAGE_KEY}.${sessionId}`);
const raw = localStorage.getItem(`${LEGACY_STORAGE_KEY}.${sessionId}`);
if (!raw) return null;
const parsed = JSON.parse(raw) as WorkspacePane[];
if (!Array.isArray(parsed) || parsed.length === 0) return null;
@@ -63,15 +70,6 @@ function loadPanes(sessionId: string): WorkspacePane[] | null {
}
}
function savePanes(sessionId: string, panes: WorkspacePane[]): void {
try {
localStorage.setItem(
`${STORAGE_KEY}.${sessionId}`,
JSON.stringify(persistablePanes(panes)),
);
} catch { /* quota or disabled */ }
}
export interface UseWorkspacePanesResult {
panes: WorkspacePane[];
activePaneIdx: number;
@@ -96,6 +94,7 @@ export interface UseWorkspacePanesResult {
removePane: (idx: number) => void;
removeChatFromPanes: (chatId: string) => void;
initializeFirstChatIfEmpty: (chatId: string) => void;
validatePanes: (validChatIds: Set<string>) => void;
handlePaneDragStart: (idx: number) => (e: DragEvent<HTMLDivElement>) => void;
handlePaneDragOver: (idx: number) => (e: DragEvent<HTMLDivElement>) => void;
handlePaneDragLeave: () => void;
@@ -106,15 +105,85 @@ export interface UseWorkspacePanesResult {
}
export function useWorkspacePanes(sessionId: string): UseWorkspacePanesResult {
const [panes, setPanes] = useState<WorkspacePane[]>(() => {
return loadPanes(sessionId) ?? [emptyPane()];
});
const [panes, setPanes] = useState<WorkspacePane[]>(() => [emptyPane()]);
const [activePaneIdx, setActivePaneIdx] = useState(0);
const draggingIdxRef = useRef<number | null>(null);
const [dragOverIdx, setDragOverIdx] = useState<number | null>(null);
// v1.12.1: skip PATCH while hydrating from the server. Without this, the
// initial [emptyPane()] would be saved over the server's real state before
// the GET resolves.
const hydratedRef = useRef(false);
// Tracks the last value broadcast by another device (or this one's own
// round-trip). If a PATCH would echo this exact payload, we skip the call.
const lastRemoteJsonRef = useRef<string>('[]');
// v1.12.1: hydrate from server on mount, then subscribe to remote updates.
useEffect(() => {
savePanes(sessionId, panes);
hydratedRef.current = false;
let cancelled = false;
void (async () => {
try {
const session = await api.sessions.get(sessionId);
if (cancelled) return;
let initial: WorkspacePane[] = Array.isArray(session.workspace_panes)
? session.workspace_panes
: [];
// One-time migration: if server is empty but legacy localStorage has
// a layout, seed the server and delete the local key.
if (initial.length === 0) {
const legacy = readLegacyPanes(sessionId);
if (legacy && legacy.length > 0) {
try {
const updated = await api.sessions.updateWorkspacePanes(sessionId, legacy);
if (cancelled) return;
initial = updated.workspace_panes;
localStorage.removeItem(`${LEGACY_STORAGE_KEY}.${sessionId}`);
} catch {
initial = legacy;
}
}
}
const next = initial.length > 0 ? initial : [emptyPane()];
lastRemoteJsonRef.current = JSON.stringify(persistablePanes(next));
setPanes(next);
setActivePaneIdx(0);
} finally {
if (!cancelled) hydratedRef.current = true;
}
})();
return () => { cancelled = true; };
}, [sessionId]);
// v1.12.1: live cross-device sync. Replace local state when another device
// (or our own write echo) lands a session_workspace_updated frame.
useEffect(() => {
return sessionEvents.subscribe((ev) => {
if (ev.type !== 'session_workspace_updated') return;
if (ev.session_id !== sessionId) return;
const incoming = Array.isArray(ev.workspace_panes) ? ev.workspace_panes : [];
const json = JSON.stringify(incoming);
if (json === lastRemoteJsonRef.current) return;
lastRemoteJsonRef.current = json;
setPanes(incoming.length > 0 ? incoming : [emptyPane()]);
setActivePaneIdx((prev) => Math.min(prev, Math.max(0, incoming.length - 1)));
});
}, [sessionId]);
// v1.12.1: debounced PATCH on every change. Settings panes are stripped
// before saving (ephemeral per v1.9).
useEffect(() => {
if (!hydratedRef.current) return;
const payload = persistablePanes(panes);
const json = JSON.stringify(payload);
if (json === lastRemoteJsonRef.current) return;
const timer = setTimeout(() => {
lastRemoteJsonRef.current = json;
api.sessions.updateWorkspacePanes(sessionId, payload).catch(() => {
// Non-fatal: next change retries. Persistent failures surface via
// the network layer's existing reconnect toast.
});
}, SAVE_DEBOUNCE_MS);
return () => clearTimeout(timer);
}, [sessionId, panes]);
useEffect(() => {
@@ -328,6 +397,23 @@ export function useWorkspacePanes(sessionId: string): UseWorkspacePanesResult {
});
}, []);
const validatePanes = useCallback((validChatIds: Set<string>) => {
setPanes((prev) => {
const cleaned = prev.map((pane) => {
if (pane.kind !== 'chat' || pane.chatIds.length === 0) return pane;
const nextIds = pane.chatIds.filter((id) => validChatIds.has(id));
if (nextIds.length === pane.chatIds.length) return pane;
if (nextIds.length === 0) {
return { ...pane, kind: 'empty' as const, chatId: undefined, chatIds: [], activeChatIdx: -1 };
}
const nextActiveIdx = Math.min(pane.activeChatIdx, nextIds.length - 1);
return { ...pane, chatIds: nextIds, activeChatIdx: nextActiveIdx, chatId: nextIds[nextActiveIdx] };
});
const unchanged = cleaned.every((p, i) => p === prev[i]);
return unchanged ? prev : cleaned;
});
}, []);
const removeChatFromPanes = useCallback((chatId: string) => {
setPanes((prev) => prev.map((p) => {
const idx = p.chatIds.indexOf(chatId);
@@ -411,6 +497,7 @@ export function useWorkspacePanes(sessionId: string): UseWorkspacePanesResult {
removePane,
removeChatFromPanes,
initializeFirstChatIfEmpty,
validatePanes,
handlePaneDragStart,
handlePaneDragOver,
handlePaneDragLeave,

View File

@@ -59,6 +59,7 @@ function SessionInner({ sessionId }: { sessionId: string }) {
removePane,
removeChatFromPanes,
initializeFirstChatIfEmpty,
validatePanes,
} = panesHook;
const openChatInActivePane = useCallback(
@@ -70,6 +71,7 @@ function SessionInner({ sessionId }: { sessionId: string }) {
openChatInPane,
openChatInActivePane,
initializeFirstChatIfEmpty,
validatePanes,
});
const { chats, renameChat } = chatsHook;

View File

@@ -138,6 +138,7 @@
--radius-xl: calc(var(--radius) + 4px);
--font-sans: "Inter Variable", "Inter", system-ui, sans-serif;
--font-mono: "JetBrains Mono Variable", ui-monospace, SFMono-Regular, monospace;
--animate-spin-slow: spin 1.2s linear infinite;
}
@layer base {

View File

@@ -1,6 +1,6 @@
# BooCode v1.x — Roadmap
Last updated: 2026-05-20
Last updated: 2026-05-21
## Overview
@@ -10,7 +10,7 @@ Live at `https://code.indifferentketchup.com` (Caddy → Authelia → Tailscale
**Architectural commitments:**
- No embeddings. The model uses file-view tools (`view_file`, `list_dir`, `grep`, `find_files`) + sidecar analyzers (codecontext, codesight). Walked away from the RAG pipeline May 2026.
- No embeddings. Model uses file-view tools (`view_file`, `list_dir`, `grep`, `find_files`) + sidecar analyzers (codecontext, codesight) + codecontext MCP tools. Walked away from the RAG pipeline May 2026.
- Read-only in v1.x. Write tools land in BooCoder (separate container, post-v1.x).
- One Postgres (`boocode_db`), one frontend SPA, container-per-service for new capabilities.
@@ -18,136 +18,87 @@ External code lifted from / referenced in: see `boocode_code_review.md` for full
-----
## Shipped (status as of 2026-05-20)
## Shipped (status as of 2026-05-21)
| Version | Theme | Notes |
| Version | Theme | Tag |
|---|---|---|
| v1.0 | Initial scaffold | live |
| Batches 14.4 | Markdown, sidebar, panes, chats-inside-sessions, archive, fork/delete, header polish, settings drawer | merged |
| v1.5 | resolveProjectPath, BOOTSTRAP_ROOT, vitest pin | merged |
| v1.6, v1.6.1, v1.6.2 | Mobile pass + RightRail mobile drawer | merged |
| v1.7 | Drag-drop file + paste-as-attachment | merged |
| v1.8, v1.8.1, v1.8.2 | Settings drawer, git_status tool, WS reconnect, **per-turn budget reset + Continue affordance + CapHitSentinel** | merged |
| v1.9.1 | Skills system (`/opt/skills/` + `skill_find`/`skill_use`/`skill_resource` tools + `/skill` slash command) | merged |
| v1.9.7 | `ask_user_input` elicitation tool | merged |
| **Batch 9 (Agents Tier 2)** | `AGENTS.md` + 6 builtin agents + AgentPicker in ChatInput toolbar + `sessions.agent_id` | **merged in `92bd3b1`**, included in v1.9.1/v1.9.7/v1.10.x tags |
| v1.10.0 | BooTerm: separate container, xterm.js + node-pty + tmux | merged |
| v1.10.1 | BooTerm-user (spawn as samkintop, login bash, Claude Code/opencode PATH) | merged |
| v1.10.4, v1.10.5 | Mobile terminal + XML tool-call fallback parser | merged |
| **v1.11.0** | **opencode-style compaction port** (auto-overflow, anchored summary, tail preservation) | merged |
| v1.11.1 | Compaction follow-up (working indicator during compaction, unit tests, .bak cleanup) | merged |
| v1.11.2 | ContextBar (persistent context-usage indicator) | merged |
| v1.11.3 | `ctx_max` capture via `/upstream/<model>/props` (replaces dead `timings.n_ctx` read) | merged |
| v1.0 | Initial scaffold | |
| Batches 14.4 | Markdown, sidebar, panes, chats-inside-sessions, archive, fork/delete, header polish, settings drawer | |
| v1.5 | resolveProjectPath, BOOTSTRAP_ROOT, vitest pin | |
| v1.6, v1.6.1, v1.6.2 | Mobile pass + RightRail mobile drawer | |
| v1.7 | Drag-drop file + paste-as-attachment | |
| v1.8, v1.8.1, v1.8.2 | Settings drawer, git_status tool, WS reconnect, per-turn budget reset + Continue affordance + CapHitSentinel | — |
| v1.9.1 | Skills system (`/opt/skills/` + `skill_find` / `skill_use` / `skill_resource` + `/skill` slash command) | `v1.9.1` |
| v1.9.7 | `ask_user_input` elicitation tool | `v1.9.7` |
| Batch 9 (Agents Tier 2) | `AGENTS.md` + 6 builtin agents + AgentPicker in ChatInput toolbar + `sessions.agent_id` | folded into `v1.9.1`/`v1.9.7` |
| v1.10.0 | BooTerm: separate container, xterm.js + node-pty + tmux | `v1.10.0` |
| v1.10.1 | BooTerm-user (spawn as samkintop, login bash, Claude Code/opencode PATH) | `v1.10.1` |
| v1.10.4, v1.10.5 | Mobile terminal + XML tool-call fallback parser | |
| v1.11.0 | opencode-style compaction port (auto-overflow, anchored summary, tail preservation) | |
| v1.11.1 | Compaction follow-up (working indicator during compaction, unit tests, .bak cleanup) | |
| v1.11.2 | ContextBar (persistent context-usage indicator above MessageList) | — |
| v1.11.3 | `ctx_max` capture via `/upstream/<model>/props` (replaces dead `timings.n_ctx` read) | `v1.11.3` |
| v1.11.5 | ContextBar inline next to agent picker; remove ChatContextPopover; default new sessions to no agent | — |
| v1.11.6 | Doom-loop guard from opencode (3 identical tool calls → sentinel, abort recursion) | — |
| v1.11.7 | pathGuard secrets filter (continue.dev `DEFAULT_SECURITY_IGNORE_FILETYPES`) | — |
| v1.11.8 | web_search + web_fetch tools via SearXNG | — |
| v1.11.9 | Manual redirect handling — re-run URL guard on each hop (SSRF hardening) | — |
| v1.11.10 | Stream-cap response body at 5MB, abort on overflow | `v1.11.x` |
| **v1.12.0** | **codecontext sidecar (Go HTTP shim, NDJSON MCP framing, child.Wait supervisor) + container guidance (BOOCHAT.md/BOOCODER.md) + 7 vendored skills + system-prompt.ts extraction + mtime-watch cache + 8 codecontext tool wrappers + per-agent tool whitelists + .codecontextignore template + agents.ts ALL_TOOL_NAMES single-source-of-truth fix** | `v1.12.0` |
-----
## In flight / queued
## In flight (uncommitted on disk, 2026-05-21)
| Version | Theme | Status |
v1.12.1 work — landed today, not yet committed:
| Item | Status | Notes |
|---|---|---|
| ~~v1.11.4~~ | ~~Per-turn budget + Continue affordance~~ | **CANCELLED** — already shipped in v1.8.2 |
| **v1.11.5** | ContextBar relocate (above agent-picker row), thicker, always-visible, remove ChatContextPopover | **dispatched** |
| v1.11.6 | Doom-loop guard from opencode (3 identical tool calls → sentinel, abort recursion) | drafted |
| v1.11.7 | pathGuard secrets filter (continue.dev's `DEFAULT_SECURITY_IGNORE_FILETYPES`) | drafted |
| v1.11.x | Tag consolidation point (everything since v1.11.0) | queued |
| Server-side workspace pane sync | Done | `sessions.workspace_panes jsonb` column; PATCH endpoint; `session_workspace_updated` WS frame; localStorage migration on first load; deprecated `session_panes` table dropped |
| Richer status indicators | Done | Five states (`streaming` / `tool_running` / `waiting_for_input` / `idle` / `error`) with distinct visuals: amber orbiting dots for streaming, amber spinning ring for tool execution, blue static for waiting on user, emerald/gray/red for idle/error |
| Startup hung-row sweep | Done | `UPDATE messages SET status='failed' WHERE status='streaming' AND created_at < NOW() - INTERVAL '5 minutes'` on server boot |
| One stuck row from v1.12.0 smoke | Cleared | Manual UPDATE (`d63c25b1`) |
| `detectSameNameLoop` code path | Added, never fired | Candidate for revert in next batch — dead code |
| Diagnostic logging in inference.ts | Added for debugging | Must come out before commit |
-----
## Major work after v1.11.x
## v1.12.x cleanup (NEXT — small, immediate)
| Version | Theme | LoC est. |
|---|---|---|
| **v1.12** | codecontext sidecar + tool output truncation + repair tool call (Integration 1 + 3 from May review, fused) | ~600 |
| v1.13 | Phase B groundwork — parts table + AI SDK adoption + per-tool `read_only`/`write` tagging | ~1500 |
| v1.14 | Phase C — outer agent loop (multi-step until non-tool finish, AGENTS.md `steps` field, reasoning as part type) | ~800 |
| v1.15 | Phase D — permission ruleset + MCP client (lays foundation for BooCoder) | ~600 |
| v1.16 | Batch 11b — codesight repo_health (call graph, circular deps, dead code) | ~400 |
| **v2.0** | Batch 14 — BooCoder pending changes (new container, write tools, plandex pattern) | ~1200 |
| v2.1 | Batch 15 — BooCoder runtime isolation (per-session Docker sandbox, OpenHands pattern) | ~600 |
| v2.x | Batch 16/17 — Multi-provider LLM (optional, pi-ai) and Workflow graphs (far future, agent-framework concepts) | tbd |
Five items. Group them or split them — your call.
-----
### v1.12.1 — commit consolidation
## Roadmap doc deviations and corrections
**Action items, in order:**
This roadmap was significantly out of sync with reality until 2026-05-20. Key corrections folded in:
1. **Remove diagnostic logging** from `apps/server/src/services/inference.ts`. The 12 `ctx.log.info` calls added today proved the inference loop was functioning correctly; the prompts were just slow. Verbose for production. Strip them, keep the file clean.
1. **Batch 9 (Agents Tier 2) is done**, not "next up." Shipped as commit `92bd3b1`, included in v1.9.1 forward. The original "Track A: Batch 9 next" recommendation was correct but the doc never got updated.
2. **v1.6.2 merged.** No longer "in flight."
3. **Batch 5 (fork/delete), Batch 6 (drag-drop), Batch 7 (settings drawer), Batch 8 (web search), Batch 10 (BooTerm) all shipped**, scattered across the v1.6v1.10 version line. Original "Track A polish then agents" plan was abandoned; work happened opportunistically.
4. **v1.11.0 was a major unplanned addition** — opencode-style compaction (auto-overflow detection + anchored rolling summary + tail preservation). This is NOT a batch from the old roadmap. It opened a new patch line (v1.11.x) of small follow-ups in front of the original Batches 1117.
5. **Batch 11 (codecontext sidecar) moves to v1.12.** Bundles with truncation and repair-tool-call lift (both from opencode) since they share concerns and the `tool_choice='required'` confirmation makes repair-tool-call viable.
6. **Phase B (parts table + AI SDK + tool-call lifecycle) becomes v1.13.** This absorbs the old Batch 13 (append-only event log) — same outcome (typed message parts), different mental framing.
7. **Phase C and Phase D are new** (numbered v1.14/v1.15). They originate from the opencode integration analysis, not from the original 17-batch plan. Phase C delivers the outer agent loop with explicit step boundaries. Phase D delivers the permission ruleset + MCP client needed for codecontext to be useful and for BooCoder to gate writes.
8. **BooCoder (v2.0/v2.1)** is the second-major-version line. New container, new safety story (pending changes + per-session Docker sandbox). Maps to original Batches 14/15.
2. **Revert `detectSameNameLoop`.** Three additions in inference.ts:
- `DOOM_LOOP_SAME_NAME_THRESHOLD = 5` constant
- `detectSameNameLoop()` function
- Call site in `runAssistantTurn` immediately after the existing `detectDoomLoop` check
Never fired in any real run today. Dead code. The existing `detectDoomLoop` (identical args, threshold 3) is sufficient.
-----
3. **Drop the stale `messages_status_check` CHECK constraint** in `apps/server/src/schema.sql`. Two constraints exist on the table:
- `messages_status_check` allows `streaming|complete|failed` (old, stale)
- `messages_status_chk` allows `streaming|complete|failed|cancelled` (new)
The old one prevents `cancelled` from being written. Drop it with `ALTER TABLE messages DROP CONSTRAINT IF EXISTS messages_status_check;`.
## v1.11.x patches in detail
4. **Stop-handler writes terminal status.** When user clicks stop mid-stream, the abort path must `UPDATE messages SET status='cancelled' WHERE id = $assistantMessageId AND status='streaming'`. Currently rows just sit `streaming` forever. The startup sweep catches them on restart, but they should be written immediately. Edit `apps/server/src/services/inference.ts` `handleAbortOrError` to add the UPDATE.
### v1.11.0 — opencode-style compaction port ✅
5. **Commit + tag v1.12.1.** Include the workspace pane sync, status indicator overhaul, startup sweep, and items 14 above. Single commit per item is fine; tag at end.
**What shipped:** Auto-detection of context overflow (`isOverflow(usage, model)`) triggers compaction on the *next* user turn. Compaction preserves the last 2 turns verbatim and produces an anchored Markdown summary (8-section template lifted verbatim from opencode `compaction.ts`) that replaces older head messages. Summary is rolling — each new compaction updates the prior summary, not stacks. Schema additions: `messages.compacted_at`, `messages.summary`, `messages.tail_start_id`, `chats.needs_compaction`. WS `compacted` frame fires sonner toast on completion.
**Estimated:** ~150 LoC net (deletions dominate).
**Key divergences from opencode:** Per-chat (not per-session) compaction state because BooCode history is per-chat. UUID `tail_start_id` not BIGINT. No `parent_id` on messages. Context limit comes from `messages.ctx_max` (last-known `n_ctx`), not a `model.context_limit` field.
### v1.12.2 — live throughput display (small UX win)
### v1.11.1 — Compaction follow-up ✅
Surface `tokens_per_second` and `ctx_used` next to the status indicator while streaming. Backend already emits these in the `usage` frame; just consume them in the StatusDot wrapper or a sibling component. ~80 LoC, frontend-only.
Working-state `chat_status: working/idle` frames around the LLM call inside `compaction.process()`. 24 new vitest cases for the six pure functions (`usable`, `isOverflow`, `estimate`, `turns`, `select`, `buildPrompt`). 7 `.bak-v1.11` files deleted.
### v1.12.3 — stale-stream frontend banner
### v1.11.2 — ContextBar ✅
New `ContextBar.tsx` rendering above MessageList. Shows `{used} / {max} ({pct}%)` with color tiers computed against `max - 20k` reserve (matches `compaction.usable()`): muted <60%, amber 60-80%, orange 80-95%, red ≥95%. Tooltip shows "Auto-compaction at ~N%". Mobile breakpoints: `< 380px` shows "Ctx" + numbers; `380-639px` adds parenthetical %; `≥ 640px` shows full "Context" label.
### v1.11.3 — ctx_max capture fix ✅
Discovered the dead code at `inference.ts:479-481` and `compaction.ts:300` reading `parsed.timings.n_ctx` never fired — llama-server emits `prompt_n / predicted_n / *_ms / *_per_second` in timings but NOT `n_ctx`. New `model-context.ts` module fetches `GET /upstream/<model>/props` with 3s timeout, positive cache (no TTL), 60s negative cache. Wired into all 4 ctx_max write sites (3 in inference.ts, 1 in compaction.ts). 12 new vitest cases. 7 historical rows backfilled to `ctx_max = 262144` (single-day backfill, only qwen3.6-35b-a3b-mxfp4 in use).
### v1.11.4 — CANCELLED
Original scope: per-turn budget reset + Continue affordance + CapHitSentinel card. Recon revealed all three are already shipped (v1.8.2 timestamps in inference.ts comments). Dead version slot.
### v1.11.5 — ContextBar relocate (DISPATCHED)
Relocate ContextBar from above MessageList to above the agent-picker row. Bump height from ~4px bar to ~10-12px. Always-visible (zero-state when no assistant messages + use `model_context_limit` from v1.11.3 cache). Remove `ChatContextPopover` entirely (redundant signal; mobile-hostile).
### v1.11.6 — Doom-loop guard (QUEUED)
Detect 3 identical tool calls in a row within one turn (same name + same args via JSON.stringify). On detection: abort tool-call recursion, insert `metadata.kind='doom_loop'` sentinel, trigger summary turn via existing `runCapHitSummary` path. New `DoomLoopSentinel.tsx` component (no Continue button — looping shouldn't be retried with same tools). Per-turn sliding window, scoped to current turn's tool-call accumulator.
**Lift source:** opencode `processor.ts`, `DOOM_LOOP_THRESHOLD = 3` constant.
### v1.11.7 — pathGuard secrets filter (QUEUED)
Extend pathGuard with `DEFAULT_SECURITY_IGNORE_FILETYPES` from continue.dev `core/indexing/ignore.ts`. Three-tier matcher: exact basenames (`credentials`, `secrets.yml`), extensions (`.env`, `.pem`, `.key`, `.crt`, etc.), prefix patterns (`id_rsa`, `id_dsa`, `id_ecdsa`, `id_ed25519`). Blocked files appear in `list_dir` and `find_files` results with `(blocked)` annotation. `view_file` returns `{ error: 'blocked_secret_file', ... }`. `grep` cannot read blocked file contents. No override mechanism in v1.x (use host shell).
**Why it matters:** `/opt:/opt:ro` mount currently exposes `boolab/.env`, `dubdrive/users.json`, `authelia/state`, every other service's secrets to any tool past path validation. Cheap close on that surface area.
-----
## v1.12 — codecontext sidecar + truncation + repair tool call
Three lifts fused because they share concerns:
1. **codecontext sidecar** — new container, single-instance, path-addressed multi-project. Mount `/opt/projects:/workspace:ro`. 8 tools wired as static `ToolDef` wrappers in `apps/server/src/services/tools/codecontext/` (one file per tool). HTTP client to `http://codecontext:8765`. New module `apps/server/src/services/codecontext_bridge.ts` translates `project_id``/workspace/<relative>/` paths.
2. **Tool output truncation** — opencode `truncate.ts` pattern. Cap at 2000 lines / 50KB. Larger outputs: write full content server-side, return preview + opaque `id`. New tool `view_truncated_output(id)` retrieves full content by server-mapped id. **No pathGuard exception** for `/tmp` directory — the opaque-id approach avoids exposing a writable filesystem location to the model. Only codecontext outputs need truncation; native tools (view_file 200 lines, grep 200 results, list_dir 500 entries, find_files 200 results) already cap reasonably.
3. **`experimental_repairToolCall` equivalent** — when model emits malformed tool call (JSON parse fails or Zod validation fails), return a synthetic tool result instead of an error: `{ error, raw_args, tool_name, hint: 'Retry with valid JSON arguments.' }`. Model self-corrects on next step. Add one line to system prompt instructing self-correction on malformed-args results. Confirmed working precondition: `tool_choice: "required"` accepted by llama-swap (verified 2026-05-20 against qwen3.6-35b-a3b-mxfp4).
**Hand-roll, not AI SDK adoption.** AI SDK migration deferred to v1.13.
**AGENTS.md updates:** Each of the 6 builtin agents gets a curated codecontext tool whitelist:
- Architect: all 8
- Debugger: `search_symbols`, `get_dependencies`
- Code Reviewer: `get_file_analysis`
- Refactorer: `get_semantic_neighborhoods`, `get_dependencies`
- Security Auditor: `get_file_analysis`, `search_symbols`, `get_dependencies`
- Prompt Builder: none (no structural reasoning relevance)
**Dependencies:** v1.11.x merged. No others.
**Estimated:** 600 LoC across 3-4 dispatches under the v1.12 umbrella.
When a chat has a `streaming` row older than ~60s with no new tokens, the UI should surface a "Previous response didn't complete. [Retry] [Discard]" banner instead of silently queueing new sends. Today's debugging spent four hours misreading slow streams as dead; this is the UX fix that prevents that. ~150 LoC, frontend + small backend endpoint for the discard action.
-----
@@ -162,11 +113,15 @@ Three lifts fused because they share concerns:
3. Tool registry: `ToolDef<T>` gains `category: 'read_only' | 'write'` field. BooCode v1.x rejects any `write` tool at registry time (defense in depth for the BooCoder split). Alpha-sort tool list before sending to model (prompt-cache stability).
4. Reasoning content (`reasoning_content` from Qwen3.6) captured as its own part type instead of dropped or inlined.
**Migration risk:** non-trivial. inference.ts is ~1400 lines with custom XML fallback, SSE parsing, compaction integration. Plan dedicated cutover window. Compaction.ts must update to assemble head from parts.
**Migration risk:** non-trivial. `inference.ts` is ~1700 lines with custom XML fallback, SSE parsing, compaction integration. Plan dedicated cutover window. `compaction.ts` must update to assemble head from parts.
**Replaces:** Original Batch 13 (append-only event log) — same outcome, different vocabulary.
**Dependencies:** v1.12 merged.
**Today's debugging spike validates this work.** Four hours of confusion came from JSON-blob `tool_calls` / `tool_results` columns hiding state from logs and from the inference state machine being invisible. Typed parts + per-part status would have shown the slow-stream-vs-dead distinction in seconds.
**Dependencies:** v1.12.x cleanup merged.
**Estimated:** ~1500 LoC.
-----
@@ -179,10 +134,12 @@ Three lifts fused because they share concerns:
1. Outer loop continues until model returns non-tool finish OR step cap hit. Step ≠ tool call: one step can contain multiple tool calls in parallel.
2. `agent.steps ?? Infinity` per-agent step cap. AGENTS.md gains `steps:` field. Refactorer `steps: 5`, Architect `steps: 20`, etc.
3. Step-boundary events (`step_start`, `step_finish`) explicit in the parts stream. Per-step snapshot for revert (planned for BooCoder; backend-only in v1.14).
4. Doom-loop guard (v1.11.6) migrates from "abort recursion" to "raise within loop iteration." Same predicate, different control flow.
4. Doom-loop guards (v1.11.6) migrate from "abort recursion" to "raise within loop iteration." Same predicate, different control flow.
**Dependencies:** v1.13 merged.
**Estimated:** ~800 LoC.
-----
## v1.15 — Phase D: permission ruleset + MCP client
@@ -200,6 +157,8 @@ Three lifts fused because they share concerns:
**Dependencies:** v1.13 merged (parts table for permission events). Independent of v1.14.
**Estimated:** ~600 LoC.
-----
## v1.16 — Batch 11b: codesight repo_health
@@ -208,6 +167,8 @@ Call graph, circular dependency detection, dead code flagging. Port `analyze.mjs
**Dependencies:** v1.12 merged (can reuse codecontext parse output where overlapping).
**Estimated:** ~400 LoC.
-----
## v2.0 — BooCoder pending changes
@@ -218,6 +179,8 @@ New container `boocoder` at `100.114.205.53:9502`. Owns write tools (`edit_file`
**Dependencies:** v1.13 (parts) + v1.15 (permissions).
**Estimated:** ~1200 LoC.
-----
## v2.1 — BooCoder runtime isolation
@@ -228,6 +191,8 @@ Per-session Docker sandbox spawned by BooCoder on first write. Only project path
**Dependencies:** v2.0.
**Estimated:** ~600 LoC.
-----
## v2.x — Optional / far future
@@ -243,17 +208,18 @@ Per-session Docker sandbox spawned by BooCoder on first write. Only project path
| Container | Port | Mount | Purpose | Status |
|---|---|---|---|---|
| `boocode` | `100.114.205.53:9500` | `/opt:/opt:ro` | Chat + read-only tools + SPA | Live |
| `boocode` | `100.114.205.53:9500` | `/opt:/opt` | Chat + read-only tools + SPA | Live |
| `boocode_db` | `127.0.0.1:5500` | `boocode_pgdata` volume | Postgres 16-alpine | Live |
| `booterm` | `100.114.205.53:9501` | `/opt/repos:/opt/repos:rw` | Terminals (tmux + node-pty) | Live (v1.10.0) |
| `codecontext` | `:8765` (internal) | `/opt/projects:/workspace:ro` | MCP server for architect tools | v1.12 |
| **`codecontext`** | **`:8765` (internal)** | **`/opt/projects:/workspace:ro`** | **MCP server for architect tools** | **Live (v1.12.0)** |
| `boocoder` | `100.114.205.53:9502` | per-session sandbox | Write tools | v2.0 |
### Schema additions by version
- **v1.11.0:** `messages.compacted_at`, `messages.summary`, `messages.tail_start_id`, `chats.needs_compaction`
- **v1.11.7:** none (pathGuard logic, no DB)
- **v1.12:** none (codecontext is stateless on disk; truncation uses in-memory id→path map with TTL cleanup)
- **v1.12.0:** none (codecontext stateless; truncation in-memory id-map with TTL cleanup)
- **v1.12.1:** `sessions.workspace_panes jsonb` (workspace sync); drop deprecated `session_panes` table; drop stale `messages_status_check` constraint
- **v1.13:** `message_parts` table; `messages` becomes header-only
- **v1.14:** `agents.steps` column (or AGENTS.md parser extension; no DB if file-only)
- **v1.15:** `permissions` table, `agent_permissions` join, `session_permissions` join
@@ -268,11 +234,11 @@ Full inventory in `boocode_code_review.md`. Headline items:
| Source | Used for | Where |
|---|---|---|
| **`sst/opencode`** (MIT, TS) | **Compaction algorithms** | **v1.11.0 (shipped)** |
| `sst/opencode` (MIT, TS) | Doom-loop guard | v1.11.6 |
| `sst/opencode` (MIT, TS) | `repairToolCall`, truncate.ts, MCP client, permission evaluate, runLoop | v1.12/v1.13/v1.14/v1.15 |
| `continuedev/continue` (Apache-2.0) | `DEFAULT_SECURITY_IGNORE_FILETYPES` | v1.11.7 |
| `nmakod/codecontext` (MIT, Go) | Architect: codebase map sidecar | v1.12 |
| `sst/opencode` (MIT, TS) | Compaction algorithms | v1.11.0 (shipped) |
| `sst/opencode` (MIT, TS) | Doom-loop guard | v1.11.6 (shipped) |
| `sst/opencode` (MIT, TS) | `repairToolCall`, truncate.ts, MCP client, permission evaluate, runLoop | v1.12 (shipped) / v1.13 / v1.14 / v1.15 |
| `continuedev/continue` (Apache-2.0) | `DEFAULT_SECURITY_IGNORE_FILETYPES` | v1.11.7 (shipped) |
| `nmakod/codecontext` (MIT, Go) | Architect: codebase map sidecar | v1.12.0 (shipped) |
| `spirituslab/codesight` (MIT-ish, TS) | Architect: repo health analyzer | v1.16 |
| `Aider-AI/aider` (Apache-2.0) | Fallback `.scm` grammars | v1.12 (fallback) |
| `cline/cline` (Apache-2.0) | Plan/Act pattern (absorbed into v1.15 permissions) | v1.15 |
@@ -281,8 +247,6 @@ Full inventory in `boocode_code_review.md`. Headline items:
| `aimasteracc/tree-sitter-analyzer` (MIT) | Outline-first patterns | v1.12 (alt) |
| `earendil-works/pi` (MIT) | Multi-provider LLM | v2.x (optional) |
**Original Batch 13 (event log from OpenHands) replaced** by v1.13 (parts table). Same outcome, different framing.
-----
## Decisions log
@@ -293,10 +257,15 @@ Full inventory in `boocode_code_review.md`. Headline items:
- **Globstar parked** — not an architect tool. Future verify-before-commit candidate only.
- **codeprysm rejected** — embedding-based. Node/edge taxonomy noted as reference if we ever build our own graph.
- **Batch 9 decoupled from Batch 7 (2026-05-16); shipped in `92bd3b1`.** Builtin defaults: six agents (Code Reviewer, Debugger, Refactorer, Architect, Security Auditor, Prompt Builder) with no `model` field. Session model wins by default.
- **opencode lift opened** (2026-05-20). Started with compaction (v1.11.0). Continuing through v1.15. Five distinct algorithms: compaction, doom-loop guard, repairToolCall, runLoop, permission evaluate. Plus `truncate.ts` and `MCP client`. Each lifts the algorithm, not the Effect-TS plumbing.
- **AI SDK adoption deferred to v1.13.** Hand-roll repairToolCall in v1.12 first. Migrate everything together when parts table lands.
- **`tool_choice='required'` confirmed supported** by llama-swap (qwen3.6-35b-a3b-mxfp4, 2026-05-20). Unblocks repair tool call viability.
- **v1.11.4 cancelled** (2026-05-20). Per-turn budget reset + Continue affordance + CapHitSentinel were already shipped in v1.8.2. Roadmap was 14 versions stale at time of recon.
- **opencode lift opened** (2026-05-20). Started with compaction (v1.11.0). Continuing through v1.15. Five distinct algorithms: compaction, doom-loop guard, repairToolCall, runLoop, permission evaluate. Plus `truncate.ts` and MCP client. Each lifts the algorithm, not the Effect-TS plumbing.
- **AI SDK adoption deferred to v1.13.** Hand-roll repairToolCall in v1.12 — not actually done in v1.12.0; truncation also deferred. v1.12.0 shipped codecontext + container guidance + skills only.
- **`tool_choice='required'` confirmed supported** by llama-swap (qwen3.6-35b-a3b-mxfp4, 2026-05-20).
- **v1.11.4 cancelled** (2026-05-20). Per-turn budget reset + Continue affordance + CapHitSentinel were already shipped in v1.8.2.
- **v1.12.0 shipped** (2026-05-21). codecontext sidecar Track B + container guidance Track A. v1.12 truncation and repairToolCall were deferred into v1.13's AI SDK migration where they get for-free.
- **v1.12.1 workspace pane sync** (2026-05-21). Moved pane state from per-device localStorage to `sessions.workspace_panes jsonb` with WS broadcast for cross-device sync. Deprecated `session_panes` table dropped. Legacy localStorage migrates on first load.
- **v1.12.1 status indicator overhaul** (2026-05-21). ChatStatusFrame expanded from `working|idle|error` to `streaming|tool_running|waiting_for_input|idle|error`. StatusDot rewritten with distinct animations per state. Added `executeToolPhase`-entry `tool_running` publish.
- **detectSameNameLoop reverted** (planned v1.12.1). Added during the 2026-05-21 debugging spike to catch same-tool-name-with-different-args loops. Never fired in any real run because the existing `detectDoomLoop` covers the actual failure modes. Dead code, reverting.
- **The 2026-05-21 "freeze" debugging spike taught one lesson**: BooCode has no UI signal for the difference between a slow stream and a dead stream. Diagnostic logging (added today, reverted in v1.12.1) revealed the inference loop was working correctly throughout — what looked like four hours of deterministic hang was multiple instances of qwen3.6 generating 8k tokens of self-doubt at temperature 0.2 on a "find the bug" prompt with no real bug. v1.12.2 (live tok/s display) and v1.12.3 (stale-stream banner) directly address this gap.
-----

88
pnpm-lock.yaml generated
View File

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