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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.
294 lines
10 KiB
Python
294 lines
10 KiB
Python
"""Tests for the pure subagent step-payload builder (issue #3779).
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``build_subagent_step`` turns a captured subagent message dict (the
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``model_dump()`` of an AIMessage or ToolMessage) into the compact,
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serializable step payload that is both streamed (``task_running``) and
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persisted (``subagent.step`` run events). It is a pure function so it can
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be unit-tested without the executor/graph.
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"""
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from __future__ import annotations
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from deerflow.subagents.step_events import (
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SUBAGENT_EVENT_CATEGORY,
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SUBAGENT_STEP_MAX_CHARS,
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build_subagent_step,
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capture_new_step_messages,
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capture_step_message,
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subagent_run_event,
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truncate_step_text,
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)
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def test_ai_message_becomes_ai_step_with_tool_calls():
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message = {
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"type": "ai",
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"id": "ai-1",
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"content": "Let me search the web.",
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"tool_calls": [
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{"name": "web_search", "args": {"query": "deerflow"}, "id": "call_1", "type": "tool_call"},
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],
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}
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step = build_subagent_step(message, task_id="call_task", message_index=1)
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assert step["task_id"] == "call_task"
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assert step["message_index"] == 1
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assert step["kind"] == "ai"
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assert step["text"] == "Let me search the web."
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assert step["truncated"] is False
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assert step["tool_calls"] == [{"name": "web_search", "args": {"query": "deerflow"}}]
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assert "tool_name" not in step
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def test_tool_message_becomes_tool_step_with_output():
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message = {
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"type": "tool",
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"id": "tool-1",
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"name": "web_search",
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"tool_call_id": "call_1",
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"content": "Result: DeerFlow is a LangGraph super-agent.",
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}
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step = build_subagent_step(message, task_id="call_task", message_index=2)
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assert step["kind"] == "tool"
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assert step["tool_name"] == "web_search"
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assert step["text"] == "Result: DeerFlow is a LangGraph super-agent."
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assert step["truncated"] is False
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assert "tool_calls" not in step
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def test_long_tool_output_is_truncated_and_flagged():
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big = "x" * (SUBAGENT_STEP_MAX_CHARS + 500)
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message = {"type": "tool", "name": "read_file", "content": big}
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step = build_subagent_step(message, task_id="t", message_index=3, max_chars=SUBAGENT_STEP_MAX_CHARS)
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assert step["truncated"] is True
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assert len(step["text"]) == SUBAGENT_STEP_MAX_CHARS
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def test_list_content_blocks_are_flattened_to_text():
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message = {
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"type": "ai",
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"content": [
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{"type": "text", "text": "first"},
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{"type": "text", "text": "second"},
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],
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"tool_calls": [],
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}
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step = build_subagent_step(message, task_id="t", message_index=1)
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assert "first" in step["text"]
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assert "second" in step["text"]
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assert step["tool_calls"] == []
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def test_ai_text_is_also_truncated():
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big = "y" * (SUBAGENT_STEP_MAX_CHARS + 10)
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message = {"type": "ai", "content": big, "tool_calls": []}
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step = build_subagent_step(message, task_id="t", message_index=1, max_chars=SUBAGENT_STEP_MAX_CHARS)
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assert step["truncated"] is True
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assert len(step["text"]) == SUBAGENT_STEP_MAX_CHARS
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def test_truncate_step_text_helper():
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assert truncate_step_text("abc", 10) == ("abc", False)
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assert truncate_step_text("abcdef", 3) == ("abc", True)
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def test_capture_ai_message_appends_dict():
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captured: list[dict] = []
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seen: set[str] = set()
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appended = capture_step_message(AIMessage(content="hi", id="ai-1"), captured, seen)
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assert appended is True
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assert len(captured) == 1
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assert captured[0]["type"] == "ai"
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def test_capture_tool_message_is_now_captured():
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# Regression for #3779: tool outputs (ToolMessage) used to be dropped,
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# so "what each step produced" never reached the UI/store.
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captured: list[dict] = []
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seen: set[str] = set()
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appended = capture_step_message(
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ToolMessage(content="search results", tool_call_id="call_1", name="web_search", id="tool-1"),
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captured,
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seen,
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)
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assert appended is True
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assert captured[0]["type"] == "tool"
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assert captured[0]["name"] == "web_search"
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def test_capture_dedupes_by_id():
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captured: list[dict] = []
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seen: set[str] = set()
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msg = AIMessage(content="hi", id="ai-1")
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assert capture_step_message(msg, captured, seen) is True
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assert capture_step_message(msg, captured, seen) is False
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assert len(captured) == 1
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def test_capture_ignores_human_message():
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captured: list[dict] = []
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seen: set[str] = set()
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appended = capture_step_message(HumanMessage(content="user input", id="h-1"), captured, seen)
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assert appended is False
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assert captured == []
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def test_none_content_flattens_to_empty_string():
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# A tool-call-only AI turn can carry content=None; it must render as "" (not
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# the literal "None"), matching the shared message_content_to_text guard.
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message = {"type": "ai", "content": None, "tool_calls": []}
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step = build_subagent_step(message, task_id="t", message_index=1)
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assert step["text"] == ""
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def test_ai_step_caps_large_tool_call_args():
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# Regression for #3779: build_subagent_step capped `text` but copied
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# `tool_calls[].args` verbatim, so a write_file/bash call carrying a big
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# payload produced an unbounded persisted row. Args must now be capped too.
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big_payload = "F" * (SUBAGENT_STEP_MAX_CHARS + 4096)
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message = {
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"type": "ai",
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"content": "writing the file",
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"tool_calls": [
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{"name": "write_file", "args": {"path": "/mnt/out.txt", "content": big_payload}},
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],
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}
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step = build_subagent_step(message, task_id="t", message_index=1, max_chars=SUBAGENT_STEP_MAX_CHARS)
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call = step["tool_calls"][0]
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assert call["name"] == "write_file"
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assert call["args_truncated"] is True
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# The serialized args are bounded by the same cap the text field uses.
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assert isinstance(call["args"], str)
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assert len(call["args"]) == SUBAGENT_STEP_MAX_CHARS
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def test_ai_step_keeps_small_tool_call_args_structured():
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message = {
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"type": "ai",
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"content": "searching",
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"tool_calls": [{"name": "web_search", "args": {"query": "deerflow"}}],
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}
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step = build_subagent_step(message, task_id="t", message_index=1)
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call = step["tool_calls"][0]
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assert call["args"] == {"query": "deerflow"}
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assert "args_truncated" not in call
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def test_capture_new_step_messages_captures_full_multi_tool_tail():
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# Regression for #3779: a single super-step can append several ToolMessages
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# (one per tool call in a multi-tool turn). Capturing only messages[-1]
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# dropped all but the last; the tail walk must capture every new message.
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captured: list[dict] = []
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seen: set[str] = set()
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# Chunk 1: human + one AIMessage requesting 3 tool calls.
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chunk1 = [
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HumanMessage(content="do work", id="h-1"),
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AIMessage(content="running tools", id="ai-1"),
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]
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processed = capture_new_step_messages(chunk1, captured, seen, 0)
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assert processed == 2
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assert [c["id"] for c in captured] == ["ai-1"]
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# Chunk 2: values-mode re-yields the whole history plus 3 new ToolMessages
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# appended in one super-step.
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chunk2 = chunk1 + [
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ToolMessage(content="r1", tool_call_id="c1", name="web_search", id="tool-1"),
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ToolMessage(content="r2", tool_call_id="c2", name="read_file", id="tool-2"),
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ToolMessage(content="r3", tool_call_id="c3", name="web_search", id="tool-3"),
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]
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processed = capture_new_step_messages(chunk2, captured, seen, processed)
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assert processed == 5
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# All three tool outputs survive, not just the last.
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assert [c["id"] for c in captured] == ["ai-1", "tool-1", "tool-2", "tool-3"]
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def test_capture_new_step_messages_is_noop_on_values_reyield():
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# stream_mode="values" re-yields the same trailing message with unchanged
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# length; re-processing must not duplicate captures.
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captured: list[dict] = []
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seen: set[str] = set()
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messages = [AIMessage(content="hi", id="ai-1")]
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processed = capture_new_step_messages(messages, captured, seen, 0)
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assert processed == 1
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# Same list handed back (no growth) — cursor already at the end.
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processed = capture_new_step_messages(messages, captured, seen, processed)
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assert processed == 1
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assert len(captured) == 1
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def test_run_event_for_task_started():
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record = subagent_run_event({"type": "task_started", "task_id": "call_1", "description": "research X"})
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assert record["event_type"] == "subagent.start"
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assert record["category"] == SUBAGENT_EVENT_CATEGORY
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assert record["metadata"]["task_id"] == "call_1"
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assert record["content"]["description"] == "research X"
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def test_run_event_for_task_running_carries_step_payload():
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chunk = {
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"type": "task_running",
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"task_id": "call_1",
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"message": {"type": "tool", "name": "web_search", "content": "results"},
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"message_index": 2,
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}
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record = subagent_run_event(chunk)
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assert record["event_type"] == "subagent.step"
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assert record["category"] == SUBAGENT_EVENT_CATEGORY
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assert record["metadata"] == {"task_id": "call_1", "message_index": 2}
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assert record["content"] == build_subagent_step(chunk["message"], task_id="call_1", message_index=2)
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def test_run_event_for_terminal_status():
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record = subagent_run_event({"type": "task_completed", "task_id": "call_1", "result": "done"})
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assert record["event_type"] == "subagent.end"
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assert record["content"]["status"] == "completed"
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assert record["content"]["result"] == "done"
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failed = subagent_run_event({"type": "task_failed", "task_id": "call_1", "error": "boom"})
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assert failed["content"]["status"] == "failed"
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assert failed["content"]["error"] == "boom"
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def test_run_event_terminal_result_is_truncated():
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big = "z" * (SUBAGENT_STEP_MAX_CHARS + 100)
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record = subagent_run_event({"type": "task_completed", "task_id": "c1", "result": big})
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assert len(record["content"]["result"]) == SUBAGENT_STEP_MAX_CHARS
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assert record["content"]["result_truncated"] is True
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def test_run_event_ignores_non_task_chunks():
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assert subagent_run_event({"type": "something_else"}) is None
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assert subagent_run_event({"no_type": True}) is None
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assert subagent_run_event("not-a-dict") is None
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