* 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.
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
* 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.
