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* 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.
176 lines
5.8 KiB
Python
176 lines
5.8 KiB
Python
"""Worker-side persistence of subagent step events (issue #3779).
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The worker streams ``task_*`` custom events to the SSE bridge for live display.
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``_SubagentEventBuffer`` additionally writes them to the RunEventStore so the
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subtask card's full step history survives a reload. This module tests that glue:
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recognized events are buffered and flushed via ``put_batch`` (not per-event
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``put``, which the store documents as a low-frequency path), unknown chunks are
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skipped, a missing store is a no-op, terminal events flush eagerly, and store
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failures never bubble into the stream loop.
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"""
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from __future__ import annotations
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import os
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import subprocess
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import sys
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import pytest
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from deerflow.runtime.runs.worker import _SubagentEventBuffer
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def test_worker_imports_first_without_circular_import():
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"""Gateway startup imports worker early; importing it first must not trigger
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a circular import through deerflow.subagents (regression for the #3779 fix).
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pytest preloads many modules, so the cycle only reproduces when worker is the
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first deerflow import — hence a clean subprocess.
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"""
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repo_backend = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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env = {**os.environ, "PYTHONPATH": repo_backend}
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result = subprocess.run(
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[sys.executable, "-c", "import deerflow.runtime.runs.worker"],
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capture_output=True,
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text=True,
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env=env,
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)
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assert result.returncode == 0, result.stderr
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class _FakeStore:
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def __init__(self):
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self.puts: list[dict] = []
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self.batches: list[list[dict]] = []
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async def put(self, **kwargs):
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self.puts.append(kwargs)
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return kwargs
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async def put_batch(self, events):
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# Copy so later buffer reuse can't mutate what we recorded.
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self.batches.append([dict(e) for e in events])
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return list(events)
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class _BoomStore:
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async def put_batch(self, events):
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raise RuntimeError("db down")
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def _running_step(task_id="call_1", message_index=1):
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return {
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"type": "task_running",
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"task_id": task_id,
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"message": {"type": "tool", "name": "web_search", "content": "results"},
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"message_index": message_index,
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}
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@pytest.mark.asyncio
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async def test_steps_are_buffered_not_put_per_event():
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# Steps must not hit the low-frequency put() path; they accumulate until flush.
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store = _FakeStore()
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buffer = _SubagentEventBuffer(store, "thread_1", "run_1")
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await buffer.add(_running_step(message_index=1))
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await buffer.add(_running_step(message_index=2))
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assert store.puts == [] # never uses the per-event put path
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assert store.batches == [] # nothing flushed yet
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await buffer.flush()
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assert len(store.batches) == 1
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batch = store.batches[0]
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assert [e["metadata"]["message_index"] for e in batch] == [1, 2]
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assert all(e["thread_id"] == "thread_1" and e["run_id"] == "run_1" for e in batch)
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assert all(e["event_type"] == "subagent.step" and e["category"] == "subagent" for e in batch)
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@pytest.mark.asyncio
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async def test_flush_is_idempotent_when_empty():
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store = _FakeStore()
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buffer = _SubagentEventBuffer(store, "t", "r")
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await buffer.flush() # nothing buffered
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await buffer.flush()
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assert store.batches == []
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@pytest.mark.asyncio
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async def test_terminal_event_flushes_eagerly():
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# A completed subagent's steps should be durable promptly, not stuck in the
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# buffer until the whole run ends.
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store = _FakeStore()
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buffer = _SubagentEventBuffer(store, "thread_1", "run_1")
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await buffer.add(_running_step(message_index=1))
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await buffer.add({"type": "task_completed", "task_id": "call_1", "result": "done"})
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assert len(store.batches) == 1
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batch = store.batches[0]
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assert [e["event_type"] for e in batch] == ["subagent.step", "subagent.end"]
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@pytest.mark.asyncio
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async def test_size_threshold_triggers_flush():
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store = _FakeStore()
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buffer = _SubagentEventBuffer(store, "thread_1", "run_1")
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for i in range(_SubagentEventBuffer.FLUSH_THRESHOLD):
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await buffer.add(_running_step(message_index=i + 1))
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# Reaching the threshold flushes without waiting for the run to end.
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assert len(store.batches) == 1
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assert len(store.batches[0]) == _SubagentEventBuffer.FLUSH_THRESHOLD
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@pytest.mark.asyncio
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async def test_skips_non_task_chunk():
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store = _FakeStore()
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buffer = _SubagentEventBuffer(store, "t", "r")
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await buffer.add({"type": "messages"})
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await buffer.flush()
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assert store.batches == []
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@pytest.mark.asyncio
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async def test_missing_store_is_noop():
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# Must not raise when run_events is not configured.
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buffer = _SubagentEventBuffer(None, "t", "r")
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await buffer.add({"type": "task_started", "task_id": "c1"})
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await buffer.flush()
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@pytest.mark.asyncio
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async def test_store_errors_do_not_propagate():
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# A persistence failure must never break the live stream loop.
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buffer = _SubagentEventBuffer(_BoomStore(), "t", "r")
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await buffer.add(_running_step())
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await buffer.flush() # BoomStore raises inside; must be swallowed
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@pytest.mark.asyncio
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async def test_roundtrip_step_is_listable_but_not_in_message_feed():
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# End-to-end against the real in-memory store: a persisted subagent step is
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# retrievable via list_events (fetch-on-expand) yet never leaks into the
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# thread message feed (list_messages), which filters category == "message".
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from deerflow.runtime.events.store.memory import MemoryRunEventStore
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store = MemoryRunEventStore()
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buffer = _SubagentEventBuffer(store, "thread_1", "run_1")
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await buffer.add(_running_step(message_index=1))
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await buffer.flush()
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events = await store.list_events("thread_1", "run_1", event_types=["subagent.step"])
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assert len(events) == 1
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assert events[0]["metadata"]["task_id"] == "call_1"
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messages = await store.list_messages("thread_1")
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assert messages == []
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