deer-flow/backend/tests/test_run_journal.py

"""Tests for RunJournal callback handler.

Uses MemoryRunEventStore as the backend for direct event inspection.
"""

import asyncio
import weakref
from concurrent.futures import ThreadPoolExecutor
from threading import Barrier
from unittest.mock import MagicMock
from uuid import uuid4

import pytest
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, LLMResult

from deerflow.runtime.events.store.memory import MemoryRunEventStore
from deerflow.runtime.journal import RunJournal
from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY


def test_run_journal_is_marked_as_loop_bound():
    assert RunJournal.deerflow_loop_bound is True


def test_tool_promotion_claim_is_atomic_across_parallel_sync_wrappers():
    journal = RunJournal("r-claim", "t-claim", MemoryRunEventStore())
    barrier = Barrier(16)

    def claim():
        barrier.wait()
        return journal.claim_tool_promotions(["mcp_a"])

    with ThreadPoolExecutor(max_workers=16) as pool:
        results = list(pool.map(lambda _: claim(), range(16)))

    assert sum((result for result in results), []) == ["mcp_a"]


@pytest.mark.anyio
async def test_close_flushes_and_detaches_runtime_dependencies():
    class ProgressReporter:
        async def __call__(self, snapshot):
            del snapshot

    store = MemoryRunEventStore()
    reporter = ProgressReporter()
    store_ref = weakref.ref(store)
    reporter_ref = weakref.ref(reporter)
    journal = RunJournal(
        "r-close",
        "t-close",
        store,
        progress_reporter=reporter,
        flush_threshold=100,
    )
    journal.record_middleware("test", name="test", hook="after", action="record", changes={})

    await journal.close()

    assert journal._closed is True
    assert journal._store is None
    assert journal._progress_reporter is None
    assert journal._buffer == []
    assert journal._pending_flush_tasks == set()
    del store, reporter
    await asyncio.sleep(0)
    assert store_ref() is None
    assert reporter_ref() is None


@pytest.mark.anyio
async def test_closed_on_llm_end_returns_before_touching_response_or_state():
    store = MemoryRunEventStore()
    journal = RunJournal("r-closed-callback", "t-closed-callback", store)
    await journal.close()
    completion_before = journal.get_completion_data()

    # A plain object has no generations attribute, so this also pins the
    # early return ahead of response inspection.
    journal.on_llm_end(object(), run_id=uuid4(), tags=["lead_agent"])

    assert journal.get_completion_data() == completion_before
    assert journal._pending_llm_response is None
    assert journal._buffer == []
    assert journal._counted_message_llm_run_ids == set()
    assert journal._counted_llm_run_ids == set()


@pytest.mark.anyio
async def test_close_preserves_buffer_and_dependencies_when_flush_fails():
    class FailOnceRunEventStore(MemoryRunEventStore):
        def __init__(self) -> None:
            super().__init__()
            self.put_batch_calls = 0

        async def put_batch(self, events):
            self.put_batch_calls += 1
            if self.put_batch_calls == 1:
                raise RuntimeError("transient store failure")
            return await super().put_batch(events)

    store = FailOnceRunEventStore()
    journal = RunJournal("r-close-retry", "t-close-retry", store, flush_threshold=100)
    journal.record_middleware("test", name="test", hook="after", action="record", changes={})

    with pytest.raises(RuntimeError, match="transient store failure"):
        await journal.close()

    assert journal._closed is False
    assert journal._store is store
    assert len(journal._buffer) == 1

    await journal.close()

    assert journal._closed is True
    assert journal._store is None
    assert journal._buffer == []
    events = await store.list_events("t-close-retry", "r-close-retry")
    assert [event["event_type"] for event in events] == ["middleware:test"]


@pytest.mark.anyio
async def test_close_retries_pending_no_usage_response_without_duplication():
    class FailOnceRunEventStore(MemoryRunEventStore):
        def __init__(self) -> None:
            super().__init__()
            self.put_batch_calls = 0

        async def put_batch(self, events):
            self.put_batch_calls += 1
            if self.put_batch_calls == 1:
                raise RuntimeError("transient store failure")
            return await super().put_batch(events)

    async def progress_reporter(snapshot):
        del snapshot

    store = FailOnceRunEventStore()
    journal = RunJournal(
        "r-close-pending-retry",
        "t-close-pending-retry",
        store,
        flush_threshold=100,
        progress_reporter=progress_reporter,
    )
    journal.record_middleware("before", name="test", hook="after", action="record", changes={})
    journal.on_llm_end(
        _make_llm_response("Canonical without usage"),
        run_id=uuid4(),
        parent_run_id=None,
        tags=["lead_agent"],
    )

    assert journal._pending_llm_response is not None
    assert journal.get_completion_data()["message_count"] == 0

    with pytest.raises(RuntimeError, match="transient store failure"):
        await journal.close()

    assert journal._closed is False
    assert journal._store is store
    assert journal._progress_reporter is progress_reporter
    assert journal._pending_llm_response is None
    assert [event["event_type"] for event in journal._buffer] == [
        "middleware:before",
        "llm.ai.response",
    ]
    assert journal.get_completion_data()["message_count"] == 1
    assert journal.get_completion_data()["last_ai_message"] == "Canonical without usage"

    await journal.close()

    events = await store.list_events("t-close-pending-retry", "r-close-pending-retry")
    assert [event["event_type"] for event in events] == [
        "middleware:before",
        "llm.ai.response",
    ]
    responses = [event for event in events if event["event_type"] == "llm.ai.response"]
    assert len(responses) == 1
    assert responses[0]["content"]["content"] == "Canonical without usage"
    assert responses[0]["content"]["usage_metadata"] is None
    assert responses[0]["metadata"]["usage"] == {}
    assert journal.get_completion_data()["message_count"] == 1
    assert journal._closed is True
    assert journal._store is None
    assert journal._progress_reporter is None


@pytest.mark.anyio
async def test_close_without_flush_discards_buffer_and_detaches_runtime_dependencies():
    class TrackingRunEventStore(MemoryRunEventStore):
        def __init__(self) -> None:
            super().__init__()
            self.put_batch_calls = 0

        async def put_batch(self, events):
            self.put_batch_calls += 1
            return await super().put_batch(events)

    store = TrackingRunEventStore()
    journal = RunJournal("r-close-discard", "t-close-discard", store, flush_threshold=100)
    journal.record_middleware("test", name="test", hook="after", action="record", changes={})

    await journal.close(flush=False)

    assert store.put_batch_calls == 0
    assert journal._closed is True
    assert journal._store is None
    assert journal._buffer == []


@pytest.mark.anyio
async def test_close_without_flush_detaches_when_cancellation_interrupts_pending_task_cleanup():
    store = MemoryRunEventStore()
    journal = RunJournal("r-close-cancelled", "t-close-cancelled", store, flush_threshold=100)
    journal.record_middleware("test", name="test", hook="after", action="record", changes={})
    first_cancellation_seen = asyncio.Event()

    async def stubborn_pending_flush() -> None:
        try:
            await asyncio.Event().wait()
        except asyncio.CancelledError:
            first_cancellation_seen.set()
            await asyncio.Event().wait()

    pending_flush = asyncio.create_task(stubborn_pending_flush())
    journal._pending_flush_tasks.add(pending_flush)
    close_task = asyncio.create_task(journal.close(flush=False))
    await asyncio.wait_for(first_cancellation_seen.wait(), timeout=1)

    close_task.cancel()
    with pytest.raises(asyncio.CancelledError):
        await close_task

    assert pending_flush.done()
    assert journal._closed is True
    assert journal._store is None
    assert journal._buffer == []
    assert journal._pending_flush_tasks == set()


@pytest.fixture
def journal_setup():
    store = MemoryRunEventStore()
    j = RunJournal("r1", "t1", store, flush_threshold=100)
    return j, store


def _make_llm_response(content="Hello", usage=None, tool_calls=None, additional_kwargs=None):
    """Create a mock LLM response with a message.

    model_dump() returns checkpoint-aligned format matching real AIMessage.
    """
    msg = MagicMock()
    msg.type = "ai"
    msg.content = content
    msg.id = f"msg-{id(msg)}"
    msg.tool_calls = tool_calls or []
    msg.invalid_tool_calls = []
    msg.response_metadata = {"model_name": "test-model"}
    msg.usage_metadata = usage
    msg.additional_kwargs = additional_kwargs or {}
    msg.name = None
    # model_dump returns checkpoint-aligned format
    msg.model_dump.return_value = {
        "content": content,
        "additional_kwargs": additional_kwargs or {},
        "response_metadata": {"model_name": "test-model"},
        "type": "ai",
        "name": None,
        "id": msg.id,
        "tool_calls": tool_calls or [],
        "invalid_tool_calls": [],
        "usage_metadata": usage,
    }

    gen = MagicMock()
    gen.message = msg

    response = MagicMock()
    response.generations = [[gen]]
    return response


def _combine_llm_responses(*responses):
    response = MagicMock()
    response.generations = [generation for item in responses for generation in item.generations]
    return response


class TestLlmCallbacks:
    @pytest.mark.anyio
    async def test_on_chat_model_start_persists_original_user_input_without_mutating_model_message(self, journal_setup):
        j, store = journal_setup
        wrapped_content = "--- BEGIN USER INPUT ---\nShow revenue\n--- END USER INPUT ---"
        model_message = HumanMessage(
            content=wrapped_content,
            id="human-1",
            additional_kwargs={ORIGINAL_USER_CONTENT_KEY: "Show revenue", "channel": "web"},
        )

        j.on_chat_model_start({}, [[model_message]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == "Show revenue"
        events = await store.list_events("t1", "r1")
        human_event = next(event for event in events if event["event_type"] == "llm.human.input")
        assert human_event["content"]["content"] == "Show revenue"
        assert human_event["content"]["id"] == "human-1"
        assert human_event["content"]["additional_kwargs"] == {"channel": "web"}
        assert model_message.content == wrapped_content
        assert model_message.additional_kwargs[ORIGINAL_USER_CONTENT_KEY] == "Show revenue"

    @pytest.mark.anyio
    async def test_on_llm_end_produces_trace_event(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_start({}, [], run_id=run_id, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Hi"), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()
        events = await store.list_events("t1", "r1")
        trace_events = [e for e in events if e["event_type"] == "llm.ai.response"]
        assert len(trace_events) == 1
        assert trace_events[0]["category"] == "message"

    @pytest.mark.anyio
    async def test_on_llm_end_lead_agent_produces_ai_message(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_start({}, [], run_id=run_id, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Answer"), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()
        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["event_type"] == "llm.ai.response"
        # Content is checkpoint-aligned model_dump format
        assert messages[0]["content"]["type"] == "ai"
        assert messages[0]["content"]["content"] == "Answer"

    @pytest.mark.anyio
    async def test_on_llm_end_with_tool_calls_produces_ai_tool_call(self, journal_setup):
        """LLM response with pending tool_calls emits llm.ai.response with tool_calls in content."""
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_end(
            _make_llm_response("Let me search", tool_calls=[{"id": "call_1", "name": "search", "args": {}}]),
            run_id=run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await j.flush()
        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["event_type"] == "llm.ai.response"
        assert len(messages[0]["content"]["tool_calls"]) == 1

    @pytest.mark.anyio
    async def test_on_llm_end_subagent_no_ai_message(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_start({}, [], run_id=run_id, tags=["subagent:research"])
        j.on_llm_end(_make_llm_response("Sub answer"), run_id=run_id, parent_run_id=None, tags=["subagent:research"])
        await j.flush()
        messages = await store.list_messages("t1")
        # subagent responses still emit llm.ai.response with category="message"
        assert len(messages) == 1

    @pytest.mark.anyio
    async def test_token_accumulation(self, journal_setup):
        j, store = journal_setup
        usage1 = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        usage2 = {"input_tokens": 20, "output_tokens": 10, "total_tokens": 30}
        j.on_llm_end(_make_llm_response("A", usage=usage1), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("B", usage=usage2), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        assert j._total_input_tokens == 30
        assert j._total_output_tokens == 15
        assert j._total_tokens == 45
        assert j._llm_call_count == 2

    @pytest.mark.anyio
    async def test_total_tokens_computed_from_input_output(self, journal_setup):
        """If total_tokens is 0, it should be computed from input + output."""
        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("Hi", usage={"input_tokens": 100, "output_tokens": 50, "total_tokens": 0}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        assert j._total_tokens == 150

    @pytest.mark.anyio
    async def test_caller_token_classification(self, journal_setup):
        j, store = journal_setup
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("B", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["subagent:research"])
        j.on_llm_end(_make_llm_response("C", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["middleware:summarization"])
        # token tracking not broken by caller type
        assert j._total_tokens == 45
        assert j._llm_call_count == 3

    @pytest.mark.anyio
    async def test_usage_metadata_none_no_crash(self, journal_setup):
        j, store = journal_setup
        j.on_llm_end(_make_llm_response("No usage", usage=None), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await j.flush()

    @pytest.mark.anyio
    async def test_latency_tracking(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_start({}, [], run_id=run_id, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Fast"), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()
        events = await store.list_events("t1", "r1")
        llm_resp = [e for e in events if e["event_type"] == "llm.ai.response"][0]
        assert "latency_ms" in llm_resp["metadata"]
        assert llm_resp["metadata"]["latency_ms"] is not None


class TestLifecycleCallbacks:
    @pytest.mark.anyio
    async def test_chain_start_end_produce_trace_events(self, journal_setup):
        j, store = journal_setup
        j.on_chain_start({}, {}, run_id=uuid4(), parent_run_id=None)
        j.on_chain_end({}, run_id=uuid4())
        await asyncio.sleep(0.05)
        await j.flush()
        events = await store.list_events("t1", "r1")
        types = {e["event_type"] for e in events}
        assert "run.start" in types
        assert "run.end" in types

    @pytest.mark.anyio
    async def test_nested_chain_no_run_lifecycle_events(self, journal_setup):
        """Nested chains (parent_run_id set) should NOT produce root run lifecycle events."""
        j, store = journal_setup
        parent_id = uuid4()
        j.on_chain_start({}, {}, run_id=uuid4(), parent_run_id=parent_id)
        j.on_chain_end({}, run_id=uuid4(), parent_run_id=parent_id)
        await j.flush()
        events = await store.list_events("t1", "r1")
        assert not any(e["event_type"] == "run.start" for e in events)
        assert not any(e["event_type"] == "run.end" for e in events)


class TestToolCallbacks:
    @pytest.mark.anyio
    async def test_tool_end_with_tool_message(self, journal_setup):
        """on_tool_end with a ToolMessage stores it as llm.tool.result."""
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        tool_msg = ToolMessage(content="results", tool_call_id="call_1", name="web_search")
        j.on_tool_end(tool_msg, run_id=uuid4())
        await j.flush()
        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["event_type"] == "llm.tool.result"
        assert messages[0]["content"]["type"] == "tool"

    @pytest.mark.anyio
    async def test_tool_end_with_command_unwraps_tool_message(self, journal_setup):
        """on_tool_end with Command(update={'messages':[ToolMessage]}) unwraps inner message."""
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        inner = ToolMessage(content="file list", tool_call_id="call_2", name="present_files")
        cmd = Command(update={"messages": [inner]})
        j.on_tool_end(cmd, run_id=uuid4())
        await j.flush()
        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["event_type"] == "llm.tool.result"
        assert messages[0]["content"]["content"] == "file list"

    @pytest.mark.anyio
    async def test_on_tool_error_no_crash(self, journal_setup):
        """on_tool_error should not crash (no event emitted by default)."""
        j, store = journal_setup
        j.on_tool_error(TimeoutError("timeout"), run_id=uuid4(), name="web_fetch")
        await j.flush()
        # Base implementation does not emit tool_error — just verify no crash
        events = await store.list_events("t1", "r1")
        assert isinstance(events, list)


class TestFinalToolMessageReconciliation:
    @pytest.mark.anyio
    async def test_root_chain_end_reconciles_missing_ask_clarification_tool_message(self, journal_setup):
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_clarify", "name": "ask_clarification", "args": {"question": "Which format?"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        tool_msg = ToolMessage(
            content="Which format?",
            tool_call_id="call_clarify",
            name="ask_clarification",
            artifact={"human_input": {"kind": "human_input_request", "request_id": "clarification:call_clarify"}},
        )

        j.on_chain_end({"messages": [tool_msg]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        tool_results = [m for m in messages if m["event_type"] == "llm.tool.result"]
        assert len(tool_results) == 1
        assert tool_results[0]["content"]["name"] == "ask_clarification"
        assert tool_results[0]["content"]["artifact"]["human_input"]["request_id"] == "clarification:call_clarify"

    @pytest.mark.anyio
    async def test_root_chain_end_does_not_duplicate_tool_message_captured_by_on_tool_end(self, journal_setup):
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_clarify", "name": "ask_clarification", "args": {"question": "Which format?"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        tool_msg = ToolMessage(content="Which format?", tool_call_id="call_clarify", name="ask_clarification")

        j.on_tool_end(tool_msg, run_id=uuid4())
        j.on_chain_end({"messages": [tool_msg]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        tool_results = [m for m in messages if m["event_type"] == "llm.tool.result"]
        assert len(tool_results) == 1

    @pytest.mark.anyio
    async def test_root_chain_end_ignores_retained_old_tool_message_from_previous_run(self, journal_setup):
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_current", "name": "ask_clarification", "args": {"question": "Current?"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        retained_old_tool_msg = ToolMessage(content="Old question", tool_call_id="call_old", name="ask_clarification")

        j.on_chain_end({"messages": [retained_old_tool_msg]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        assert not any(m["event_type"] == "llm.tool.result" for m in messages)

    @pytest.mark.anyio
    async def test_root_chain_end_ignores_subagent_tool_message(self, journal_setup):
        """Reconciliation covers the lead agent's own calls only.

        A subagent's internal tool results belong to its own step feed
        (``subagent.step``), not to the thread's message feed;
        ``_remember_current_run_tool_calls`` records lead-agent calls only.
        This is the boundary that keeps reconciliation safe now that it is no
        longer narrowed to an ``ask_clarification`` allowlist.
        """
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_search", "name": "web_search", "args": {"query": "deerflow"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["subagent:general-purpose"],
        )
        tool_msg = ToolMessage(content="Search result", tool_call_id="call_search", name="web_search")

        j.on_chain_end({"messages": [tool_msg]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        assert not any(m["event_type"] == "llm.tool.result" for m in messages)

    @pytest.mark.anyio
    async def test_root_chain_end_ignores_hidden_ask_clarification_tool_message(self, journal_setup):
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_clarify", "name": "ask_clarification", "args": {"question": "Hidden?"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        tool_msg = ToolMessage(
            content="Hidden?",
            tool_call_id="call_clarify",
            name="ask_clarification",
            additional_kwargs={"hide_from_ui": True},
        )

        j.on_chain_end({"messages": [tool_msg]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        assert not any(m["event_type"] == "llm.tool.result" for m in messages)

    @pytest.mark.anyio
    async def test_root_chain_end_reconciles_any_middleware_short_circuited_tool_message(self, journal_setup):
        """A middleware that blocks a tool call still returns a user-visible result.

        ReadBeforeWriteMiddleware answers a blocked ``write_file`` with an error
        ToolMessage instead of running the tool, so LangChain never emits
        ``on_tool_end`` and the message never reached the event store. The user
        saw it during the run and it vanished on reload (#4666). Reconciliation
        is not specific to ``ask_clarification``: any visible tool result the
        model asked for in this run belongs in the thread feed.
        """
        from langchain_core.messages import ToolMessage

        j, store = journal_setup
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_write", "name": "write_file", "args": {"path": "/mnt/user-data/outputs/a.txt"}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        blocked = ToolMessage(
            content="Error: write_file blocked — read the file before writing to it",
            tool_call_id="call_write",
            name="write_file",
        )

        j.on_chain_end({"messages": [blocked]}, run_id=uuid4())
        await j.flush()

        messages = await store.list_messages("t1")
        tool_results = [m for m in messages if m["event_type"] == "llm.tool.result"]
        assert len(tool_results) == 1
        assert tool_results[0]["content"]["name"] == "write_file"


class TestCustomEvents:
    @pytest.mark.anyio
    async def test_on_custom_event_not_implemented(self, journal_setup):
        """RunJournal does not implement on_custom_event — no crash expected."""
        j, store = journal_setup
        # BaseCallbackHandler.on_custom_event is a no-op by default
        j.on_custom_event("task_running", {"task_id": "t1"}, run_id=uuid4())
        await j.flush()
        events = await store.list_events("t1", "r1")
        assert isinstance(events, list)


class TestBufferFlush:
    @pytest.mark.anyio
    async def test_flush_threshold(self, journal_setup):
        j, store = journal_setup
        j._flush_threshold = 2
        # Each on_llm_end emits 1 event
        usage = {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        assert len(j._buffer) == 1
        j.on_llm_end(_make_llm_response("B", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        # At threshold the buffer should have been flushed asynchronously
        await asyncio.sleep(0.1)
        events = await store.list_events("t1", "r1")
        assert len(events) >= 2

    @pytest.mark.anyio
    async def test_pending_response_counts_toward_flush_threshold(self, journal_setup):
        j, store = journal_setup
        j._flush_threshold = 2
        j.record_middleware("before", name="BeforeMiddleware", hook="after_model", action="record", changes={})

        j.on_llm_end(_make_llm_response("Pending"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await asyncio.sleep(0.1)

        assert j._pending_llm_response is None
        events = await store.list_events("t1", "r1")
        assert [event["event_type"] for event in events] == ["middleware:before", "llm.ai.response"]

    @pytest.mark.anyio
    async def test_events_retained_when_no_loop(self, journal_setup):
        """Events buffered in a sync (no-loop) context should survive
        until the async flush() in the finally block."""
        j, store = journal_setup
        j._flush_threshold = 1

        original = asyncio.get_running_loop

        def no_loop():
            raise RuntimeError("no running event loop")

        asyncio.get_running_loop = no_loop
        try:
            j._put(event_type="llm.ai.response", category="message", content="test")
        finally:
            asyncio.get_running_loop = original

        assert len(j._buffer) == 1
        await j.flush()
        events = await store.list_events("t1", "r1")
        assert any(e["event_type"] == "llm.ai.response" for e in events)


class TestFeedGeneration:
    """The counter that tells a cached feed lookup when to re-ask.

    A message this run produces is not in the feed while it is only buffered,
    so a reader looking it up legitimately misses. Bumping this on every write
    lets that reader retry exactly when retrying could answer differently,
    rather than either polling the store or caching the miss for the whole run
    (#4696 review).
    """

    @pytest.mark.anyio
    async def test_pending_response_alone_does_not_advance_it(self, journal_setup):
        j, _store = journal_setup
        j.on_llm_end(_make_llm_response("A"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])

        assert j._buffer == []
        assert j._pending_llm_response is not None
        assert j.feed_generation == 0

    @pytest.mark.anyio
    async def test_a_threshold_flush_advances_it(self, journal_setup):
        j, _store = journal_setup
        j._flush_threshold = 1

        usage = {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await asyncio.sleep(0.1)

        assert j.feed_generation == 1

    @pytest.mark.anyio
    async def test_a_terminal_flush_advances_it(self, journal_setup):
        j, _store = journal_setup
        j.on_llm_end(_make_llm_response("A"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])

        await j.flush()

        assert j.feed_generation == 1

    @pytest.mark.anyio
    async def test_a_failed_write_leaves_it_alone(self):
        """Nothing became readable, so a cached miss must not be re-asked."""

        class FailingStore(MemoryRunEventStore):
            async def put_batch(self, events):
                raise RuntimeError("store unavailable")

        j = RunJournal("r-gen", "t-gen", FailingStore(), flush_threshold=1)
        j.on_llm_end(_make_llm_response("A"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await asyncio.sleep(0.1)

        assert j.feed_generation == 0


class TestIdentifyCaller:
    def test_lead_agent_tag(self, journal_setup):
        j, _ = journal_setup
        assert j._identify_caller(["lead_agent"]) == "lead_agent"

    def test_subagent_tag(self, journal_setup):
        j, _ = journal_setup
        assert j._identify_caller(["subagent:research"]) == "subagent:research"

    def test_middleware_tag(self, journal_setup):
        j, _ = journal_setup
        assert j._identify_caller(["middleware:summarization"]) == "middleware:summarization"

    def test_no_tags_returns_lead_agent(self, journal_setup):
        j, _ = journal_setup
        assert j._identify_caller([]) == "lead_agent"
        assert j._identify_caller(None) == "lead_agent"


class TestChainErrorCallback:
    @pytest.mark.anyio
    async def test_on_chain_error_writes_run_error(self, journal_setup):
        j, store = journal_setup
        j.on_chain_error(ValueError("boom"), run_id=uuid4())
        await asyncio.sleep(0.05)
        await j.flush()
        events = await store.list_events("t1", "r1")
        error_events = [e for e in events if e["event_type"] == "run.error"]
        assert len(error_events) == 1
        assert "boom" in error_events[0]["content"]
        assert error_events[0]["metadata"]["error_type"] == "ValueError"


class TestTokenTrackingDisabled:
    @pytest.mark.anyio
    async def test_track_token_usage_false(self):
        store = MemoryRunEventStore()
        j = RunJournal("r1", "t1", store, track_token_usage=False, flush_threshold=100)
        j.on_llm_end(
            _make_llm_response("X", usage={"input_tokens": 50, "output_tokens": 50, "total_tokens": 100}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        data = j.get_completion_data()
        assert data["total_tokens"] == 0
        assert data["llm_call_count"] == 0


class TestConvenienceFields:
    @pytest.mark.anyio
    async def test_first_human_message_via_set(self, journal_setup):
        j, _ = journal_setup
        j.set_first_human_message("What is AI?")
        data = j.get_completion_data()
        assert data["first_human_message"] == "What is AI?"

    @pytest.mark.anyio
    async def test_completion_data_counts_human_ai_and_tool_messages(self, journal_setup):
        from langchain_core.messages import HumanMessage, ToolMessage

        j, _ = journal_setup
        j.on_chat_model_start({}, [[HumanMessage(content="Question")]], run_id=uuid4(), tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Answer"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_tool_end(ToolMessage(content="Tool result", tool_call_id="call_1", name="search"), run_id=uuid4())

        data = j.get_completion_data()

        assert data["message_count"] == 3
        assert data["first_human_message"] == "Question"
        assert data["last_ai_message"] == "Answer"

    @pytest.mark.anyio
    async def test_tool_call_only_ai_does_not_clear_last_ai_message(self, journal_setup):
        j, _ = journal_setup
        j.on_llm_end(_make_llm_response("Useful answer"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(
            _make_llm_response("", tool_calls=[{"id": "call_1", "name": "search", "args": {}}]),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await j.flush()

        data = j.get_completion_data()

        assert data["message_count"] == 2
        assert data["last_ai_message"] == "Useful answer"

    @pytest.mark.anyio
    async def test_last_ai_message_extracts_mixed_content_without_extra_newlines(self, journal_setup):
        j, _ = journal_setup
        j.on_llm_end(
            _make_llm_response(
                [
                    {"type": "text", "text": "First "},
                    {"type": "text", "content": "second"},
                    " third",
                    {"type": "image", "url": "ignored"},
                ]
            ),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await j.flush()

        data = j.get_completion_data()

        assert data["message_count"] == 1
        assert data["last_ai_message"] == "First second third"

    @pytest.mark.anyio
    async def test_last_ai_message_extracts_mapping_content(self, journal_setup):
        j, _ = journal_setup
        j.on_llm_end(_make_llm_response({"content": "Nested answer"}), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await j.flush()

        data = j.get_completion_data()

        assert data["message_count"] == 1
        assert data["last_ai_message"] == "Nested answer"

    @pytest.mark.anyio
    async def test_duplicate_llm_run_id_does_not_double_count_message_summary(self, journal_setup):
        j, _ = journal_setup
        run_id = uuid4()

        j.on_llm_end(_make_llm_response("Answer", usage=None), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(
            _make_llm_response("Answer", usage={"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}),
            run_id=run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )

        data = j.get_completion_data()

        assert data["message_count"] == 1
        assert data["last_ai_message"] == "Answer"
        assert data["total_tokens"] == 15

    @pytest.mark.anyio
    async def test_subagent_ai_does_not_overwrite_lead_last_ai_message(self, journal_setup):
        j, _ = journal_setup
        j.on_llm_end(_make_llm_response("Lead answer"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Subagent detail"), run_id=uuid4(), parent_run_id=None, tags=["subagent:research"])
        await j.flush()

        data = j.get_completion_data()

        assert data["message_count"] == 2
        assert data["last_ai_message"] == "Lead answer"

    @pytest.mark.anyio
    async def test_get_completion_data(self, journal_setup):
        j, _ = journal_setup
        j._total_tokens = 100
        j._msg_count = 5
        data = j.get_completion_data()
        assert data["total_tokens"] == 100
        assert data["message_count"] == 5


class TestMiddlewareEvents:
    @pytest.mark.anyio
    async def test_record_middleware_uses_middleware_category(self, journal_setup):
        j, store = journal_setup
        j.record_middleware(
            "title",
            name="TitleMiddleware",
            hook="after_model",
            action="generate_title",
            changes={"title": "Test Title", "thread_id": "t1"},
        )
        await j.flush()
        events = await store.list_events("t1", "r1")
        mw_events = [e for e in events if e["event_type"] == "middleware:title"]
        assert len(mw_events) == 1
        assert mw_events[0]["category"] == "middleware"
        assert mw_events[0]["content"]["name"] == "TitleMiddleware"
        assert mw_events[0]["content"]["hook"] == "after_model"
        assert mw_events[0]["content"]["action"] == "generate_title"
        assert mw_events[0]["content"]["changes"]["title"] == "Test Title"

    @pytest.mark.anyio
    async def test_middleware_tag_variants(self, journal_setup):
        """Different middleware tags produce distinct event_types."""
        j, store = journal_setup
        j.record_middleware("title", name="TitleMiddleware", hook="after_model", action="generate_title", changes={})
        j.record_middleware("guardrail", name="GuardrailMiddleware", hook="before_tool", action="deny", changes={})
        await j.flush()
        events = await store.list_events("t1", "r1")
        event_types = {e["event_type"] for e in events}
        assert "middleware:title" in event_types
        assert "middleware:guardrail" in event_types


class TestContextEvents:
    @pytest.mark.anyio
    async def test_record_memory_context_is_readable_from_public_store_contract(self, journal_setup):
        j, store = journal_setup

        j.record_memory_context(
            content_sha256="a" * 64,
        )
        # Goal continuations may enter the graph more than once under the same
        # run-scoped journal; the effective frozen memory event stays singular.
        j.record_memory_context(
            content_sha256="a" * 64,
        )
        await j.flush()

        events = await store.list_events("t1", "r1", event_types=["context:memory"])
        assert len(events) == 1
        assert events[0]["category"] == "context"
        assert events[0]["content"] == {"content_sha256": "a" * 64}

    @pytest.mark.anyio
    async def test_record_memory_context_can_retry_after_buffer_failure(self, journal_setup, monkeypatch):
        j, store = journal_setup
        original_put = j._put
        attempts = 0

        def fail_once(**kwargs):
            nonlocal attempts
            attempts += 1
            if attempts == 1:
                raise RuntimeError("buffer unavailable")
            return original_put(**kwargs)

        monkeypatch.setattr(j, "_put", fail_once)

        with pytest.raises(RuntimeError, match="buffer unavailable"):
            j.record_memory_context(content_sha256="a" * 64)
        j.record_memory_context(content_sha256="a" * 64)
        await j.flush()

        events = await store.list_events("t1", "r1", event_types=["context:memory"])
        assert len(events) == 1
        assert events[0]["content"] == {"content_sha256": "a" * 64}


class TestCallerBucketing:
    """Tests for caller-bucketed token accumulation (lead_agent / subagent / middleware)."""

    def test_lead_agent_bucketing(self, journal_setup):
        j, _ = journal_setup
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        assert j._lead_agent_tokens == 15
        assert j._subagent_tokens == 0
        assert j._middleware_tokens == 0

    def test_subagent_bucketing(self, journal_setup):
        j, _ = journal_setup
        usage = {"input_tokens": 20, "output_tokens": 10, "total_tokens": 30}
        j.on_llm_end(_make_llm_response("B", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["subagent:research"])
        assert j._subagent_tokens == 30
        assert j._lead_agent_tokens == 0
        assert j._middleware_tokens == 0

    def test_middleware_bucketing(self, journal_setup):
        j, _ = journal_setup
        usage = {"input_tokens": 5, "output_tokens": 2, "total_tokens": 7}
        j.on_llm_end(_make_llm_response("C", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["middleware:summarize"])
        assert j._middleware_tokens == 7
        assert j._lead_agent_tokens == 0
        assert j._subagent_tokens == 0

    def test_mixed_callers_sum_independently(self, journal_setup):
        j, _ = journal_setup
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("B", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["subagent:bash"])
        j.on_llm_end(_make_llm_response("C", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["middleware:title"])
        assert j._lead_agent_tokens == 15
        assert j._subagent_tokens == 15
        assert j._middleware_tokens == 15
        assert j._total_tokens == 45

    def test_get_completion_data_includes_buckets(self, journal_setup):
        j, _ = journal_setup
        j._lead_agent_tokens = 100
        j._subagent_tokens = 200
        j._middleware_tokens = 50
        data = j.get_completion_data()
        assert data["lead_agent_tokens"] == 100
        assert data["subagent_tokens"] == 200
        assert data["middleware_tokens"] == 50

    def test_dedup_same_run_id(self, journal_setup):
        """Same langchain run_id in on_llm_end must not double-count."""
        j, _ = journal_setup
        run_id = uuid4()
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        assert j._total_tokens == 15
        assert j._lead_agent_tokens == 15
        assert j._llm_call_count == 1

    @pytest.mark.anyio
    async def test_dedup_same_run_id_persists_single_message(self, journal_setup):
        """A re-fired on_llm_end for one run_id must persist the message once.

        LangChain can deliver on_llm_end more than once for the same run_id.
        Token accounting already dedups on that; the durable llm.ai.response
        row must be deduped on the same premise, or count_messages and message
        pagination (which read append-only rows without dedup) inflate.
        """
        j, store = journal_setup
        run_id = uuid4()
        response = _make_llm_response("Answer")
        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()
        messages = await store.list_messages("t1")
        assert [m["event_type"] for m in messages] == ["llm.ai.response"]
        assert await store.count_messages("t1") == 1
        # The run summary counts the message exactly once as well.
        assert j._msg_count == 1

    @pytest.mark.anyio
    async def test_adjacent_late_usage_enriches_canonical_response_only(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        original_tool_calls = [{"id": "call-original", "name": "search", "args": {}}]
        replay_tool_calls = [{"id": "call-replay", "name": "write_file", "args": {}}]

        j.on_llm_end(
            _make_llm_response(
                "Canonical",
                tool_calls=original_tool_calls,
                additional_kwargs={
                    "deerflow_error_fallback": True,
                    "error_detail": "canonical fallback",
                },
            ),
            run_id=run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        j.on_llm_end(
            _make_llm_response(
                "Replay",
                usage=usage,
                tool_calls=replay_tool_calls,
                additional_kwargs={
                    "deerflow_error_fallback": True,
                    "error_detail": "replay fallback",
                },
            ),
            run_id=run_id,
            parent_run_id=None,
            tags=["subagent:research"],
        )
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == "Canonical"
        assert messages[0]["content"]["tool_calls"] == original_tool_calls
        assert messages[0]["content"]["additional_kwargs"]["error_detail"] == "canonical fallback"
        assert messages[0]["content"]["usage_metadata"] == usage
        assert messages[0]["metadata"]["caller"] == "lead_agent"
        assert messages[0]["metadata"]["usage"] == usage
        assert j._current_run_tool_call_names == {"call-original": "search"}
        assert j.had_llm_error_fallback is True
        assert j.llm_error_fallback_message == "canonical fallback"
        assert j.get_completion_data()["last_ai_message"] == "Canonical"
        assert j.get_completion_data()["lead_agent_tokens"] == 15
        assert j.get_completion_data()["subagent_tokens"] == 0

    @pytest.mark.anyio
    async def test_same_message_object_replay_cannot_mutate_canonical_summary(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        message = AIMessage(content="Canonical answer")
        response = LLMResult(generations=[[ChatGeneration(message=message)]])

        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        message.content = "Replay answer"
        message.usage_metadata = {
            "input_tokens": 10,
            "output_tokens": 5,
            "total_tokens": 15,
            "input_token_details": {"cache_read": 3},
        }
        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        message.usage_metadata["input_token_details"]["cache_read"] = 999
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == "Canonical answer"
        expected_usage = {
            "input_tokens": 10,
            "output_tokens": 5,
            "total_tokens": 15,
            "input_token_details": {"cache_read": 3},
        }
        assert messages[0]["metadata"]["usage"] == expected_usage
        assert messages[0]["content"]["usage_metadata"] == expected_usage
        assert j.get_completion_data()["message_count"] == 1
        assert j.get_completion_data()["last_ai_message"] == "Canonical answer"

    @pytest.mark.anyio
    async def test_positive_usage_event_does_not_retain_nested_provider_metadata(self, journal_setup):
        j, store = journal_setup
        usage = {
            "input_tokens": 8,
            "output_tokens": 3,
            "total_tokens": 11,
            "output_token_details": {"reasoning": 2},
        }
        message = AIMessage(content="Canonical with usage", usage_metadata=usage)
        response = LLMResult(generations=[[ChatGeneration(message=message)]])

        j.on_llm_end(response, run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        message.usage_metadata["output_token_details"]["reasoning"] = 999
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["metadata"]["usage"]["output_token_details"] == {"reasoning": 2}
        assert messages[0]["content"]["usage_metadata"]["output_token_details"] == {"reasoning": 2}

    @pytest.mark.anyio
    async def test_mutating_staged_message_before_flush_cannot_mutate_canonical_summary(self, journal_setup):
        j, store = journal_setup
        message = AIMessage(content="Canonical before flush")
        response = LLMResult(generations=[[ChatGeneration(message=message)]])

        j.on_llm_end(response, run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        message.content = "Mutation before flush"
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == "Canonical before flush"
        assert j.get_completion_data()["message_count"] == 1
        assert j.get_completion_data()["last_ai_message"] == "Canonical before flush"

    @pytest.mark.anyio
    async def test_nested_same_message_object_replay_cannot_mutate_canonical_summary(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        message = AIMessage(content=[{"type": "text", "text": "Canonical nested answer"}])
        response = LLMResult(generations=[[ChatGeneration(message=message)]])

        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        message.content[0]["text"] = "Replay nested answer"
        message.usage_metadata = {"input_tokens": 8, "output_tokens": 3, "total_tokens": 11}
        j.on_llm_end(response, run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == [{"type": "text", "text": "Canonical nested answer"}]
        assert messages[0]["content"]["usage_metadata"] == message.usage_metadata
        assert j.get_completion_data()["message_count"] == 1
        assert j.get_completion_data()["last_ai_message"] == "Canonical nested answer"

    @pytest.mark.anyio
    async def test_all_zero_usage_remains_pending_and_positive_usage_enriches_it(self, journal_setup):
        j, store = journal_setup
        run_id = uuid4()
        zero_usage = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
        positive_usage = {"input_tokens": 4, "output_tokens": 2, "total_tokens": 6}

        j.on_llm_end(_make_llm_response("Zero usage", usage=zero_usage), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        assert j._buffer == []
        assert j._pending_llm_response is not None
        assert j.get_completion_data()["message_count"] == 0

        j.on_llm_end(_make_llm_response("Replay payload", usage=positive_usage), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == "Zero usage"
        assert messages[0]["content"]["usage_metadata"] == positive_usage
        assert messages[0]["metadata"]["usage"] == positive_usage
        assert j.get_completion_data()["message_count"] == 1
        assert j.get_completion_data()["last_ai_message"] == "Zero usage"

    @pytest.mark.anyio
    async def test_replay_generation_length_cannot_change_canonical_set(self, journal_setup):
        j, store = journal_setup
        short_usage = {"input_tokens": 8, "output_tokens": 3, "total_tokens": 11}
        extra_usage = {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}
        first_run_id = uuid4()
        second_run_id = uuid4()

        j.on_llm_end(
            _combine_llm_responses(_make_llm_response("Canonical one"), _make_llm_response("Canonical two")),
            run_id=first_run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        j.on_llm_end(
            _make_llm_response("Short replay", usage=short_usage),
            run_id=first_run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        j.on_llm_end(
            _make_llm_response("Single canonical"),
            run_id=second_run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        j.on_llm_end(
            _combine_llm_responses(
                _make_llm_response("Long replay one", usage=extra_usage),
                _make_llm_response("Long replay two"),
            ),
            run_id=second_run_id,
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await j.flush()

        messages = await store.list_messages("t1")
        assert [message["content"]["content"] for message in messages] == [
            "Canonical one",
            "Canonical two",
            "Single canonical",
        ]
        assert messages[0]["metadata"]["usage"] == short_usage
        assert messages[0]["content"]["usage_metadata"] == short_usage
        assert messages[1]["metadata"]["usage"] == {}
        assert messages[1]["content"]["usage_metadata"] is None
        assert messages[2]["metadata"]["usage"] == extra_usage
        assert messages[2]["content"]["usage_metadata"] == extra_usage
        assert j.get_completion_data()["message_count"] == 3
        assert j.get_completion_data()["last_ai_message"] == "Single canonical"

    @pytest.mark.anyio
    async def test_interleaved_late_usage_updates_summary_only(self, journal_setup):
        j, store = journal_setup
        first_run_id = uuid4()
        second_run_id = uuid4()
        usage = {"input_tokens": 9, "output_tokens": 4, "total_tokens": 13}

        j.on_llm_end(_make_llm_response("First canonical"), run_id=first_run_id, parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Second canonical"), run_id=second_run_id, parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(
            _make_llm_response(
                "Late replay",
                usage=usage,
                tool_calls=[{"id": "late-call", "name": "write_file", "args": {}}],
                additional_kwargs={"deerflow_error_fallback": True, "error_detail": "late fallback"},
            ),
            run_id=first_run_id,
            parent_run_id=None,
            tags=["subagent:research"],
        )
        await j.flush()

        messages = await store.list_messages("t1")
        assert [message["content"]["content"] for message in messages] == ["First canonical", "Second canonical"]
        assert messages[0]["metadata"]["usage"] == {}
        assert messages[0]["content"]["usage_metadata"] is None
        assert j.get_completion_data()["total_tokens"] == 13
        assert j.get_completion_data()["lead_agent_tokens"] == 13
        assert j.get_completion_data()["subagent_tokens"] == 0
        assert j.get_completion_data()["message_count"] == 2
        assert j.get_completion_data()["last_ai_message"] == "Second canonical"
        assert "late-call" not in j._current_run_tool_call_names
        assert j.had_llm_error_fallback is False

    @pytest.mark.anyio
    async def test_single_no_usage_response_persists_once_at_flush(self, journal_setup):
        j, store = journal_setup

        j.on_llm_end(_make_llm_response("No usage"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        assert j._buffer == []
        assert j._pending_llm_response is not None

        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["content"]["content"] == "No usage"
        assert messages[0]["metadata"]["usage"] == {}

    @pytest.mark.anyio
    async def test_distinct_run_ids_each_persist_a_message(self, journal_setup):
        """The dedup guard is per run_id and must not drop distinct responses."""
        j, store = journal_setup
        j.on_llm_end(_make_llm_response("First"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.on_llm_end(_make_llm_response("Second"), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await j.flush()
        assert await store.count_messages("t1") == 2

    @pytest.mark.anyio
    async def test_first_no_usage_second_with_usage(self, journal_setup):
        """Late usage enriches the single canonical event and the run summary."""
        j, store = journal_setup
        run_id = uuid4()
        j.on_llm_end(_make_llm_response("A", usage=None), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=run_id, parent_run_id=None, tags=["lead_agent"])
        await j.flush()

        messages = await store.list_messages("t1")
        assert len(messages) == 1
        assert messages[0]["metadata"]["usage"] == usage
        assert messages[0]["content"]["usage_metadata"] == usage
        assert j.get_completion_data()["total_tokens"] == 15

    def test_track_token_usage_false_skips_buckets(self):
        """When token tracking is disabled, caller buckets stay at 0."""
        store = MemoryRunEventStore()
        j = RunJournal("r1", "t1", store, track_token_usage=False, flush_threshold=100)
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("X", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["subagent:research"])
        assert j._subagent_tokens == 0
        assert j._lead_agent_tokens == 0

    def test_default_no_tags_buckets_as_lead_agent(self, journal_setup):
        """LLM calls without explicit tags default to lead_agent bucket."""
        j, _ = journal_setup
        usage = {"input_tokens": 5, "output_tokens": 5, "total_tokens": 10}
        j.on_llm_end(_make_llm_response("Hi", usage=usage), run_id=uuid4(), parent_run_id=None)
        assert j._lead_agent_tokens == 10
        assert j._subagent_tokens == 0
        assert j._middleware_tokens == 0

    def test_unknown_tag_buckets_as_lead_agent(self, journal_setup):
        """Calls with unrecognized tags (not lead_agent/subagent:/middleware:) go to lead_agent."""
        j, _ = journal_setup
        usage = {"input_tokens": 5, "output_tokens": 5, "total_tokens": 10}
        j.on_llm_end(_make_llm_response("Hi", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["some_random_tag"])
        assert j._lead_agent_tokens == 10


class TestExternalUsageRecords:
    """Tests for record_external_llm_usage_records."""

    def test_records_added_to_subagent_bucket(self, journal_setup):
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "ext-1",
                "caller": "subagent:general-purpose",
                "input_tokens": 100,
                "output_tokens": 50,
                "total_tokens": 150,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._subagent_tokens == 150
        assert j._total_tokens == 150
        assert j._total_input_tokens == 100
        assert j._total_output_tokens == 50

    def test_records_added_to_middleware_bucket(self, journal_setup):
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "ext-2",
                "caller": "middleware:summarize",
                "input_tokens": 30,
                "output_tokens": 10,
                "total_tokens": 40,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._middleware_tokens == 40
        assert j._lead_agent_tokens == 0
        assert j._subagent_tokens == 0

    def test_records_added_to_lead_agent_bucket(self, journal_setup):
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "ext-3",
                "caller": "lead_agent",
                "input_tokens": 10,
                "output_tokens": 5,
                "total_tokens": 15,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._lead_agent_tokens == 15

    def test_dedup_same_source_run_id(self, journal_setup):
        """Same source_run_id must not be double-counted."""
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "dup-1",
                "caller": "subagent:research",
                "input_tokens": 50,
                "output_tokens": 25,
                "total_tokens": 75,
            }
        ]
        j.record_external_llm_usage_records(records)
        j.record_external_llm_usage_records(records)
        assert j._subagent_tokens == 75
        assert j._total_tokens == 75

    def test_total_tokens_missing_computed_from_input_output(self, journal_setup):
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "ext-4",
                "caller": "subagent:bash",
                "input_tokens": 200,
                "output_tokens": 100,
                "total_tokens": 0,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._subagent_tokens == 300
        assert j._total_tokens == 300

    def test_total_tokens_zero_no_count(self, journal_setup):
        """Records with zero total and zero input+output must not be counted."""
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "ext-5",
                "caller": "subagent:research",
                "input_tokens": 0,
                "output_tokens": 0,
                "total_tokens": 0,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._total_tokens == 0
        assert j._subagent_tokens == 0

    def test_empty_source_run_id_skipped(self, journal_setup):
        j, _ = journal_setup
        records = [
            {
                "source_run_id": "",
                "caller": "subagent:research",
                "input_tokens": 50,
                "output_tokens": 25,
                "total_tokens": 75,
            }
        ]
        j.record_external_llm_usage_records(records)
        assert j._total_tokens == 0

    def test_multiple_records_in_single_call(self, journal_setup):
        j, _ = journal_setup
        records = [
            {"source_run_id": "r1", "caller": "subagent:gp", "input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
            {"source_run_id": "r2", "caller": "subagent:bash", "input_tokens": 20, "output_tokens": 10, "total_tokens": 30},
        ]
        j.record_external_llm_usage_records(records)
        assert j._subagent_tokens == 45
        assert j._total_tokens == 45

    def test_external_records_coexist_with_inline_callbacks(self, journal_setup):
        """External records and inline on_llm_end must not interfere."""
        j, _ = journal_setup
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("A", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        j.record_external_llm_usage_records([{"source_run_id": "ext-6", "caller": "subagent:gp", "input_tokens": 100, "output_tokens": 50, "total_tokens": 150}])
        assert j._lead_agent_tokens == 15
        assert j._subagent_tokens == 150
        assert j._total_tokens == 165

    def test_track_token_usage_false_skips_external_records(self):
        """When token tracking is disabled, external records must not accumulate."""
        store = MemoryRunEventStore()
        j = RunJournal("r1", "t1", store, track_token_usage=False, flush_threshold=100)
        j.record_external_llm_usage_records([{"source_run_id": "ext-7", "caller": "subagent:gp", "input_tokens": 100, "output_tokens": 50, "total_tokens": 150}])
        assert j._total_tokens == 0
        assert j._subagent_tokens == 0


class TestProgressSnapshots:
    @pytest.mark.anyio
    async def test_on_llm_end_reports_progress_snapshot(self):
        snapshots: list[dict] = []

        async def reporter(snapshot: dict) -> None:
            snapshots.append(snapshot)

        store = MemoryRunEventStore()
        j = RunJournal(
            "r1",
            "t1",
            store,
            flush_threshold=100,
            progress_reporter=reporter,
            progress_flush_interval=0,
        )
        usage = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
        j.on_llm_end(_make_llm_response("Answer", usage=usage), run_id=uuid4(), parent_run_id=None, tags=["lead_agent"])
        await j.flush()

        assert snapshots
        assert snapshots[-1]["total_tokens"] == 15
        assert snapshots[-1]["llm_call_count"] == 1
        assert snapshots[-1]["message_count"] == 1
        assert snapshots[-1]["last_ai_message"] == "Answer"

    @pytest.mark.anyio
    async def test_throttled_progress_flush_emits_trailing_snapshot(self):
        snapshots: list[dict] = []
        trailing_seen = asyncio.Event()

        async def reporter(snapshot: dict) -> None:
            snapshots.append(snapshot)
            if snapshot["total_tokens"] == 45:
                trailing_seen.set()

        store = MemoryRunEventStore()
        j = RunJournal(
            "r1",
            "t1",
            store,
            flush_threshold=100,
            progress_reporter=reporter,
            progress_flush_interval=0.01,
        )
        j.on_llm_end(
            _make_llm_response("First", usage={"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        j.on_llm_end(
            _make_llm_response("Second", usage={"input_tokens": 20, "output_tokens": 10, "total_tokens": 30}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await asyncio.wait_for(trailing_seen.wait(), timeout=1.0)
        await j.flush()

        assert len(snapshots) >= 2
        assert snapshots[-1]["total_tokens"] == 45
        assert snapshots[-1]["llm_call_count"] == 2
        assert snapshots[-1]["last_ai_message"] == "Second"

    @pytest.mark.anyio
    async def test_flush_cancels_delayed_progress_without_final_progress_write(self):
        snapshots: list[dict] = []

        async def reporter(snapshot: dict) -> None:
            snapshots.append(snapshot)

        store = MemoryRunEventStore()
        j = RunJournal(
            "r1",
            "t1",
            store,
            flush_threshold=100,
            progress_reporter=reporter,
            progress_flush_interval=10.0,
        )
        j.on_llm_end(
            _make_llm_response("First", usage={"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        await asyncio.sleep(0)
        assert snapshots[-1]["total_tokens"] == 15
        j.on_llm_end(
            _make_llm_response("Second", usage={"input_tokens": 20, "output_tokens": 10, "total_tokens": 30}),
            run_id=uuid4(),
            parent_run_id=None,
            tags=["lead_agent"],
        )
        pending_task = j._pending_progress_task
        assert pending_task is not None
        pending_task_ref = weakref.ref(pending_task)

        await asyncio.wait_for(j.flush(), timeout=0.2)

        assert snapshots[-1]["total_tokens"] == 15
        assert snapshots[-1]["llm_call_count"] == 1
        assert snapshots[-1]["last_ai_message"] == "First"
        assert j._pending_progress_task is None

        # The journal must not keep the cancelled task (and its traceback
        # frame) alive until cyclic GC. Dropping this last local reference
        # should release it immediately.
        del pending_task
        await asyncio.sleep(0)
        assert pending_task_ref() is None


class TestChatModelStartHumanMessage:
    """Tests for on_chat_model_start extracting the first human message."""

    @staticmethod
    def _human_input_response(source: str = "ask_clarification") -> dict:
        return {
            "version": 1,
            "kind": "human_input_response",
            "source": source,
            "request_id": "clarification:call-abc",
            "response_kind": "option",
            "option_id": "option-2",
            "value": "staging",
        }

    @pytest.mark.anyio
    async def test_extracts_first_human_message(self, journal_setup):
        """on_chat_model_start captures the first HumanMessage from prompts."""
        from langchain_core.messages import AIMessage, HumanMessage

        j, store = journal_setup
        messages_batch = [
            [HumanMessage(content="What is AI?"), AIMessage(content="Hi there")],
        ]
        j.on_chat_model_start({}, messages_batch, run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == "What is AI?"
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["content"] == "What is AI?"

    @pytest.mark.anyio
    async def test_skips_hidden_human_messages(self, journal_setup):
        """HumanMessages hidden from the UI are internal context, not user input."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        messages_batch = [
            [
                HumanMessage(content="What is the weather today?"),
                HumanMessage(
                    content="Your todo list from earlier...",
                    name="todo_reminder",
                    additional_kwargs={"hide_from_ui": True},
                ),
            ],
        ]
        j.on_chat_model_start({}, messages_batch, run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == "What is the weather today?"
        assert j.get_completion_data()["message_count"] == 1
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["content"] == "What is the weather today?"

    @pytest.mark.anyio
    async def test_only_hidden_human_messages_are_not_captured(self, journal_setup):
        """A prompt containing only internal HumanMessages has no user input."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        hidden_message = HumanMessage(
            content="Internal context",
            additional_kwargs={"hide_from_ui": True},
        )
        j.on_chat_model_start({}, [[hidden_message]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg is None
        assert j.get_completion_data()["message_count"] == 0
        events = await store.list_events("t1", "r1")
        assert not any(e["event_type"] == "llm.human.input" for e in events)

    @pytest.mark.anyio
    @pytest.mark.parametrize("source", ["ask_clarification", "sandbox_network"])
    async def test_hidden_human_input_response_is_captured(self, journal_setup, source):
        """Hidden HumanInputCard replies are user-authored and must survive compaction."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        hidden_response = HumanMessage(
            content='For your clarification "Which environment?", my answer is: staging',
            additional_kwargs={
                "hide_from_ui": True,
                "human_input_response": self._human_input_response(source=source),
            },
        )
        j.on_chat_model_start({}, [[hidden_response]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == 'For your clarification "Which environment?", my answer is: staging'
        assert j.get_completion_data()["message_count"] == 1
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["additional_kwargs"]["hide_from_ui"] is True
        assert human_events[0]["content"]["additional_kwargs"]["human_input_response"]["request_id"] == "clarification:call-abc"

    @pytest.mark.anyio
    async def test_hidden_human_input_response_wins_over_older_visible_prompt(self, journal_setup):
        """The latest hidden card reply is the run input, not an older visible prompt."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        older_prompt = HumanMessage(content="Write a quicksort PDF")
        hidden_response = HumanMessage(
            content='For your clarification "Which format?", my answer is: tutorial',
            additional_kwargs={
                "hide_from_ui": True,
                "human_input_response": self._human_input_response(),
            },
        )
        j.on_chat_model_start({}, [[older_prompt, hidden_response]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == 'For your clarification "Which format?", my answer is: tutorial'
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["content"] == 'For your clarification "Which format?", my answer is: tutorial'

    @pytest.mark.anyio
    async def test_hidden_human_input_response_ignores_non_allowlisted_source(self, journal_setup):
        """Only explicit HumanInputCard sources are persisted while hidden."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        hidden_response = HumanMessage(
            content="Internal approval response",
            additional_kwargs={
                "hide_from_ui": True,
                "human_input_response": self._human_input_response(source="future_approval"),
            },
        )
        j.on_chat_model_start({}, [[hidden_response]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg is None
        assert j.get_completion_data()["message_count"] == 0
        events = await store.list_events("t1", "r1")
        assert not any(e["event_type"] == "llm.human.input" for e in events)

    @pytest.mark.anyio
    async def test_legacy_summary_message_is_not_captured_as_user_input(self, journal_setup):
        """Legacy synthetic summaries are internal context even if hide_from_ui is absent."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        legacy_summary = HumanMessage(content="Older compressed conversation state", name="summary")
        j.on_chat_model_start({}, [[legacy_summary]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg is None
        assert j.get_completion_data()["message_count"] == 0
        events = await store.list_events("t1", "r1")
        assert not any(e["event_type"] == "llm.human.input" for e in events)

    @pytest.mark.anyio
    async def test_visible_human_message_after_hidden_only_prompt_is_captured(self, journal_setup):
        """Skipping an internal-only prompt does not block later user input."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        hidden_message = HumanMessage(
            content="Internal context",
            additional_kwargs={"hide_from_ui": True},
        )
        j.on_chat_model_start({}, [[hidden_message]], run_id=uuid4(), tags=["lead_agent"])
        j.on_chat_model_start(
            {},
            [[HumanMessage(content="Real question")]],
            run_id=uuid4(),
            tags=["lead_agent"],
        )
        await j.flush()

        assert j._first_human_msg == "Real question"
        assert j.get_completion_data()["message_count"] == 1
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["content"] == "Real question"

    @pytest.mark.anyio
    async def test_summarization_prompt_does_not_capture_first_human_message(self, journal_setup):
        """Internal summarization prompts must not replace the run's real user input."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        summarization_prompt = HumanMessage(
            content="<role>\nContext Extraction Assistant\n</role>\n\n<primary_objective>\nExtract context...",
        )
        j.on_chat_model_start(
            {},
            [[summarization_prompt]],
            run_id=uuid4(),
            tags=["middleware:summarize"],
        )
        j.on_chat_model_start(
            {},
            [[HumanMessage(content="Real user follow-up")]],
            run_id=uuid4(),
            tags=["lead_agent"],
        )
        await j.flush()

        assert j._first_human_msg == "Real user follow-up"
        assert j.get_completion_data()["message_count"] == 1
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1
        assert human_events[0]["content"]["content"] == "Real user follow-up"
        assert human_events[0]["metadata"]["caller"] == "lead_agent"

    @pytest.mark.anyio
    @pytest.mark.parametrize("tags", [["middleware:summarize"], ["subagent:research"]])
    async def test_non_lead_human_prompts_are_not_captured_as_user_input(self, journal_setup, tags):
        """Only lead-agent LLM starts create UI-facing human input events."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        j.on_chat_model_start(
            {},
            [[HumanMessage(content="Internal prompt")]],
            run_id=uuid4(),
            tags=tags,
        )
        await j.flush()

        assert j._first_human_msg is None
        assert j.get_completion_data()["message_count"] == 0
        events = await store.list_events("t1", "r1")
        assert not any(e["event_type"] == "llm.human.input" for e in events)

    @pytest.mark.anyio
    async def test_only_first_human_message_captured(self, journal_setup):
        """Subsequent on_chat_model_start calls do not overwrite the first message."""
        from langchain_core.messages import HumanMessage

        j, store = journal_setup
        j.on_chat_model_start({}, [[HumanMessage(content="First question")]], run_id=uuid4(), tags=["lead_agent"])
        j.on_chat_model_start({}, [[HumanMessage(content="Second question")]], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()

        assert j._first_human_msg == "First question"
        events = await store.list_events("t1", "r1")
        human_events = [e for e in events if e["event_type"] == "llm.human.input"]
        assert len(human_events) == 1

    @pytest.mark.anyio
    async def test_empty_messages_no_crash(self, journal_setup):
        """on_chat_model_start with empty messages does not crash."""
        j, store = journal_setup
        j.on_chat_model_start({}, [], run_id=uuid4(), tags=["lead_agent"])
        await j.flush()
        assert j._first_human_msg is None


class TestDeliveryTracking:
    """Slice 1 (#4272): journal records artifact production for run.delivery."""

    @staticmethod
    def _register_tool_call(j: RunJournal, tool_call_id: str, name: str) -> None:
        from langchain_core.messages import AIMessage

        ai = AIMessage(content="", tool_calls=[{"id": tool_call_id, "name": name, "args": {}}])
        j._remember_current_run_tool_calls(ai, caller="lead_agent")

    def test_callbacks_run_inline_to_serialize_parallel_mutations(self, journal_setup):
        j, _ = journal_setup

        # LangChain dispatches synchronous handlers with run_inline=False via
        # run_in_executor, allowing parallel tool callbacks to mutate one
        # journal from different threads.
        assert j.run_inline is True

    @pytest.mark.anyio
    async def test_concurrent_callbacks_on_one_journal_are_serialized(self, journal_setup):
        from langchain_core.callbacks.manager import ahandle_event
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, _ = journal_setup
        commands = []
        for index, path in enumerate(("report.md", "report.md", "appendix.md"), start=1):
            tool_call_id = f"call_{index}"
            self._register_tool_call(j, tool_call_id, "present_files")
            commands.append(
                Command(
                    update={
                        "artifacts": [f"/mnt/user-data/outputs/{path}"],
                        "messages": [ToolMessage("Successfully presented files", tool_call_id=tool_call_id)],
                    }
                )
            )

        # This is the real LangChain async callback dispatcher. Because the
        # journal is run_inline, each synchronous mutation completes on the
        # event-loop thread instead of racing in executor threads.
        await asyncio.gather(
            *(
                ahandle_event(
                    [j],
                    "on_tool_end",
                    "ignore_agent",
                    command,
                    run_id=uuid4(),
                )
                for command in commands
            )
        )

        content = j.get_delivery_content()
        assert content["presented"] == 2
        assert set(content["paths"]) == {
            "/mnt/user-data/outputs/report.md",
            "/mnt/user-data/outputs/appendix.md",
        }
        assert set(content["by_tool"]["present_files"]) == set(content["paths"])

    @pytest.mark.anyio
    async def test_concurrent_runs_keep_delivery_accumulators_isolated(self):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        store = MemoryRunEventStore()
        journals = [RunJournal(run_id, "t1", store, flush_threshold=100) for run_id in ("r1", "r2")]

        async def finish_run(journal: RunJournal, index: int) -> None:
            tool_call_id = f"call_run_{index}"
            self._register_tool_call(journal, tool_call_id, "present_files")
            journal.on_tool_end(
                Command(
                    update={
                        "artifacts": [f"/mnt/user-data/outputs/report-{index}.md"],
                        "messages": [ToolMessage("Successfully presented files", tool_call_id=tool_call_id)],
                    }
                ),
                run_id=uuid4(),
            )
            await asyncio.sleep(0)
            journal.record_delivery()
            await journal.flush()

        await asyncio.gather(*(finish_run(journal, index) for index, journal in enumerate(journals, start=1)))

        for index in (1, 2):
            events = await store.list_events("t1", f"r{index}")
            content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
            assert content == {
                "presented": 1,
                "paths": [f"/mnt/user-data/outputs/report-{index}.md"],
                "by_tool": {"present_files": [f"/mnt/user-data/outputs/report-{index}.md"]},
            }

    @pytest.mark.anyio
    async def test_present_files_success_command_recorded_with_attribution(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_1", "present_files")
        cmd = Command(
            update={
                "artifacts": ["/mnt/user-data/outputs/report.md"],
                "messages": [ToolMessage("Successfully presented files", tool_call_id="call_1")],
            }
        )
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        delivery = [e for e in events if e["event_type"] == "run.delivery"]
        assert len(delivery) == 1
        content = delivery[0]["content"]
        assert content["presented"] == 1
        assert content["paths"] == ["/mnt/user-data/outputs/report.md"]
        assert content["by_tool"] == {"present_files": ["/mnt/user-data/outputs/report.md"]}
        assert delivery[0]["category"] == "outputs"

    @pytest.mark.anyio
    async def test_tool_callback_name_preserves_attribution_when_message_lookup_misses(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        tool_run_id = uuid4()
        j.on_tool_start(
            {"name": "present_files"},
            "",
            run_id=tool_run_id,
        )
        j.on_tool_end(
            Command(
                update={
                    "artifacts": ["/mnt/user-data/outputs/report.md"],
                    "messages": [ToolMessage("Successfully presented files", tool_call_id="call_missing")],
                }
            ),
            run_id=tool_run_id,
        )
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content["by_tool"] == {"present_files": ["/mnt/user-data/outputs/report.md"]}

    @pytest.mark.anyio
    async def test_command_with_multiple_messages_records_artifacts_once(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_multi", "present_files")
        cmd = Command(
            update={
                "artifacts": ["/mnt/user-data/outputs/report.md"],
                "messages": [
                    ToolMessage("Successfully presented files", tool_call_id="call_multi"),
                    HumanMessage("Additional command message"),
                ],
            }
        )
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content == {
            "presented": 1,
            "paths": ["/mnt/user-data/outputs/report.md"],
            "by_tool": {"present_files": ["/mnt/user-data/outputs/report.md"]},
        }

    @pytest.mark.anyio
    async def test_command_with_multiple_tool_names_leaves_artifacts_unattributed(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_present", "present_files")
        self._register_tool_call(j, "call_browser", "browser_screenshot")
        cmd = Command(
            update={
                "artifacts": [
                    "/mnt/user-data/outputs/report.md",
                    "/mnt/user-data/outputs/shot.png",
                ],
                "messages": [
                    ToolMessage("Successfully presented files", tool_call_id="call_present"),
                    ToolMessage("Saved browser screenshot", tool_call_id="call_browser"),
                ],
            }
        )
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content == {
            "presented": 2,
            "paths": [
                "/mnt/user-data/outputs/report.md",
                "/mnt/user-data/outputs/shot.png",
            ],
            "by_tool": {},
        }

    @pytest.mark.anyio
    async def test_error_command_without_artifacts_not_recorded(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_2", "present_files")
        cmd = Command(update={"messages": [ToolMessage("Error: Only files in /mnt/user-data/outputs can be presented", tool_call_id="call_2")]})
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        delivery = [e for e in events if e["event_type"] == "run.delivery"]
        assert len(delivery) == 1
        assert delivery[0]["content"] == {"presented": 0, "paths": [], "by_tool": {}}

    @pytest.mark.anyio
    async def test_browser_tool_artifacts_recorded_under_producing_tool(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_3", "browser_screenshot")
        cmd = Command(
            update={
                "artifacts": ["/mnt/user-data/outputs/shot.png"],
                "messages": [ToolMessage("Saved browser screenshot", tool_call_id="call_3")],
            }
        )
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content["presented"] == 1
        assert content["by_tool"] == {"browser_screenshot": ["/mnt/user-data/outputs/shot.png"]}

    @pytest.mark.anyio
    async def test_duplicate_path_tool_pair_recorded_once(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        self._register_tool_call(j, "call_4", "present_files")
        for _ in range(2):
            j.on_tool_end(
                Command(
                    update={
                        "artifacts": ["/mnt/user-data/outputs/report.md"],
                        "messages": [ToolMessage("Successfully presented files", tool_call_id="call_4")],
                    }
                ),
                run_id=uuid4(),
            )
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content["presented"] == 1
        assert content["paths"] == ["/mnt/user-data/outputs/report.md"]

    @pytest.mark.anyio
    async def test_unattributed_artifacts_counted_without_by_tool_entry(self, journal_setup):
        from langchain_core.messages import ToolMessage
        from langgraph.types import Command

        j, store = journal_setup
        # No _register_tool_call: attribution missing (e.g. tool_call names map miss).
        cmd = Command(
            update={
                "artifacts": ["/mnt/user-data/outputs/anon.txt"],
                "messages": [ToolMessage("ok", tool_call_id="call_unknown")],
            }
        )
        j.on_tool_end(cmd, run_id=uuid4())
        j.record_delivery()
        await j.flush()

        events = await store.list_events("t1", "r1")
        content = next(e for e in events if e["event_type"] == "run.delivery")["content"]
        assert content["presented"] == 1
        assert content["paths"] == ["/mnt/user-data/outputs/anon.txt"]
        assert content["by_tool"] == {}