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3 Commits
| Author | SHA1 | Message | Date | |
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Nan Gao
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aafd5077b2
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feat(subagents): show effective model and token usage on task cards (#4049)
* feat(subagents): show runtime metadata on task cards * fix(subagents): stop task-card render loop and dedupe model fetches Address code review on the runtime-metadata cards: - P1 render loop: the terminal ToolMessage is re-parsed on every MessageList render and always carries modelName/usage, so the presence-based setTasks condition fired a fresh state object each render -> "Maximum update depth exceeded". computeNextSubtask now returns a value-compared `changed` flag and a pure subtaskNotification() routes terminal transitions through the deferred after-render path while skipping no-op re-parses. - Per-card useModels refetch: add staleTime: Infinity to the ["models"] query so every subtask card shares one /api/models fetch instead of refetching on each mount. * make format * refactor(subagents): dedupe token-usage validators + tidy event narrowing Address PR review follow-ups: - DRY: extract one shared token-usage validator per side. Backend status_contract.normalize_token_usage() now backs both the terminal ToolMessage metadata and the subagent.step/.end run events (step_events.py), and frontend messages/usage.normalizeTokenUsage() backs both the live task_running event (lifecycle.ts) and the terminal ToolMessage metadata (subtask-result.ts). Prevents the input/output/ total_tokens validation from drifting across the four former copies. - Nit: onCustomEvent narrows event.type once instead of re-checking the object shape per branch; the redundant task_started early-return (already validated by taskEventToSubtaskUpdate) is dropped. |
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hataa
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266883b3dd
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fix(subagents): inherit summarization middleware and harden step capture (#3875 Phase 3) (#4009)
Phase 3 of #3875 — subagents previously inherited none of the lead's context-compaction, so a deep-research subagent (max_turns up to 150) could accumulate >1M cumulative input before max_turns/timeout/token_budget engaged, even after Phase 2's budget capped the pathological tail. - Gate the subagent runtime chain on the SAME ``app_config.summarization.enabled`` switch the lead reads (per maintainer guidance in #3875), via the shared ``create_summarization_middleware`` factory. One config covers both chains; no separate ``subagents.summarization`` field. No-op when summarization is off (factory returns None). - ``skip_memory_flush=True`` on the subagent path: the factory otherwise attaches ``memory_flush_hook`` (when memory.enabled), which flushes pre-compaction messages into durable memory keyed by thread_id. Subagents share the parent's thread_id, so without skipping the hook a subagent's internal turns would pollute the PARENT thread's durable memory (#3875 Phase 3 review point). - Harden ``capture_new_step_messages`` to tolerate history contraction: summarization rewrites the messages channel via ``RemoveMessage(id=REMOVE_ALL_MESSAGES)``, shrinking len(messages) below the step-capture cursor. Without a reset, every step appended after the compaction point was dropped until length overtook the stale cursor (#3845 interaction, maintainer validation point (a)). Cursor now resets to the new tail; id/content dedup prevents re-emitting pre-compaction steps. - Couple the DEFAULT token-budget ceiling to ``summarization.enabled`` (#3875 Phase 3 review point): 1M when compaction is on, 2M when off (preserves Phase 2's deliberate headroom for summarization-off deep-research runs that can exceed 1M). A user-set budget (global or per-agent) always wins regardless of the switch. Flagged tunable. The summarization middleware does not implement ``consume_stop_reason``, so the Phase 2 guard-cap stop-reason channel is unaffected. Refs: https://github.com/bytedance/deer-flow/issues/3875 |
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Nan Gao
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4fcb4bc366
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feat(subagents): persist and display subagent step history (#3779) (#3845)
* feat(subagents): persist and display subagent step history (#3779) Capture both assistant turns and tool outputs during subagent execution, stream them in task_running events, and persist them as subagent.* run events so the subtask card's step timeline survives a reload. Backend: - step_events.py: pure layer (capture_step_message, build_subagent_step, subagent_run_event) shared by streaming and persistence - executor.py: capture ToolMessage outputs, not just AIMessage turns - worker.py: persist task_* custom events to RunEventStore (category "subagent" keeps them out of the thread feed; list_events backfills) Frontend: - core/tasks/steps.ts + api.ts: SubtaskStep model, messageToStep, eventsToSteps, mergeSteps, fetchSubtaskSteps - subtask card accumulates live steps and backfills on expand - carry run_id onto history content messages for the events endpoint * fix(subagents): show AI turns in subtask card + paginate step backfill (#3779) Two follow-ups to the subagent step-history feature: Problem 1 — reload backfill could silently truncate the step timeline because list_events capped at 500 events (seq-ASC) across the whole run. Add task_id filtering + an after_seq forward cursor to list_events (all three stores + abstract base + the /events route), and make fetchSubtaskSteps page through one task's subagent.step events until a short page. No schema migration: the DB filter rides the existing run-scoped index via event_metadata["task_id"]. Problem 2 — the card only rendered tool steps, so persisted AI turns were never shown. Replace toolStepsForDisplay with stepsForDisplay: interleave AI reasoning turns (with text) and tool steps by message_index, drop blank-text AI turns, and drop the trailing final-answer AI turn when completed (already shown as result). Card renders AI steps as muted clamped markdown with a sparkles icon. Tests: store task_id/after_seq filtering + pagination across memory/db/jsonl, the /events route forwarding, stepsForDisplay rules, and fetchSubtaskSteps pagination. Docs updated in both AGENTS.md. * make format * fix(subagents): capture full multi-tool step tail, batch step persistence, cap tool-call args (#3779) Address PR review findings on the subagent step-history feature: 1. executor.py streamed on stream_mode="values" and captured only messages[-1] per chunk, so a multi-tool-call turn (ToolNode appends one ToolMessage per call in a single super-step) lost all but the last tool output in both the live task_running stream and the persisted history. Replace with capture_new_step_messages, which walks the newly-appended tail (and still re-checks the trailing message on no-growth chunks so id-less in-place replacements survive). 2. worker.py persisted each step with the store's low-frequency put() (a per-thread advisory lock per call); a deep subagent (max_turns=150) emits hundreds of steps on the hot stream loop. Replace with _SubagentEventBuffer, which batches via put_batch (flush on terminal subagent.end, at FLUSH_THRESHOLD, and in the worker finally). 3. build_subagent_step capped only text; tool_calls[].args were copied verbatim, so a large write_file/bash payload produced an unbounded subagent.step row. Cap each call's serialized args at SUBAGENT_STEP_MAX_CHARS, flagged args_truncated. Tests updated/added for all three; AGENTS.md refreshed. * fix(subagents): merge backfill into latest subtask state; reuse message_content_to_text (#3779) Address the remaining two PR review findings: 4. subtask-card's fetchSubtaskSteps().then(updateSubtask) closed over a stale tasks snapshot: a late-resolving backfill wrote setTasks({...stale}), clobbering SSE steps/status and sibling subtasks that arrived during the fetch. useUpdateSubtask now reads/writes through a tasksRef mirroring the latest state (ref-to-latest), and the pure per-subtask transition is extracted to core/tasks/subtask-update.ts::computeNextSubtask (unit-tested). 5. step_events._content_to_text duplicated deerflow.utils.messages. message_content_to_text; call the shared helper instead (guarding None content with 'or ""' so a tool-call-only turn still renders as ""). Tests added for computeNextSubtask and the None-content case; AGENTS.md docs updated. |