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https://github.com/crewAIInc/crewAI.git
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1ca4762a80
Three findings from review, all confirmed against the code. Gating on suppress_flow_events was wrong. That flag asks for console quiet and is a public field, so a caller who set it on their own flow silently lost flow:completed, flow:failed and flow:method_failed. Deciding origin from the defining module was also wrong. Flow.from_declaration() returns a Flow typed in crewai.flow.flow, so a caller's declarative flow was reported as one of CrewAI's own - the inversion this split exists to prevent. Both had the same root cause: the discriminator was inferred. Flow now declares is_crewai_internal, set on the agent executor and the memory encoding/recall flows, and one helper serves both origin and the outcome gate. A failed conversational session was reported as completed. Its session closes with FlowFinishedEvent whatever happened, so a failed turn produced flow:conversation_turn_failed and flow:completed together. The turn failure is now recorded on the flow and read back when the session finishes. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH
539 lines
17 KiB
Python
539 lines
17 KiB
Python
"""Flow outcome and human-in-the-loop signals must reach telemetry.
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Driven through real ``Flow`` executions rather than by emitting events directly,
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so these fail if the event bus, the listener wiring, or the emitting call site
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changes - not just if the listener body does.
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Before this, a flow reported only that it *started*: ``FlowFinishedEvent``,
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``FlowFailedEvent``, ``MethodExecutionFailedEvent``, ``MethodExecutionPausedEvent``
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and ``FlowPausedEvent`` all reached the console formatter and stopped there, and
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the input and conversation-failure events had no listener at all.
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"""
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from __future__ import annotations
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import contextlib
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import time
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import pytest
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from crewai.flow.async_feedback import HumanFeedbackPending, PendingFeedbackContext
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from crewai.flow.flow import Flow, listen, start
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from crewai.flow.human_feedback import human_feedback
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from crewai.flow.input_provider import InputResponse
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def _reregister_listener() -> None:
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"""Re-subscribe the global listener to the event bus.
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The repo-wide ``cleanup_event_handlers`` fixture clears every handler after
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each test, so anything relying on the shared listener sees an empty bus
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unless it happens to run first.
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"""
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from crewai.events import event_listener as listener_module
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from crewai.events.event_bus import crewai_event_bus
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from crewai.events.types.flow_events import FlowStartedEvent
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# Only when the bus is empty: subscribing a second time registers a fresh
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# set of closures, and every handler then fires twice.
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if crewai_event_bus._sync_handlers.get(FlowStartedEvent):
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return
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listener_module.event_listener.setup_listeners(crewai_event_bus)
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@pytest.fixture
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def flow_spans(monkeypatch: pytest.MonkeyPatch) -> list[tuple[str, str]]:
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"""Record (flow_name, origin) for every Flow Execution span."""
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from crewai.events import event_listener as listener_module
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_reregister_listener()
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recorded: list[tuple[str, str]] = []
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monkeypatch.setattr(
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listener_module.event_listener._telemetry,
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"flow_execution_span",
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lambda flow_name, node_names, origin="user": recorded.append(
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(flow_name, origin)
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),
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)
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return recorded
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@pytest.fixture
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def durations(monkeypatch: pytest.MonkeyPatch) -> list[tuple[str, float, str]]:
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"""Record every (flow_name, duration_ms, outcome) the listener reports."""
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from crewai.events import event_listener as listener_module
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_reregister_listener()
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recorded: list[tuple[str, float, str]] = []
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monkeypatch.setattr(
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listener_module.event_listener._telemetry,
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"flow_completed_span",
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lambda flow_name, duration_ms, outcome, origin="user": recorded.append(
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(flow_name, duration_ms, outcome)
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),
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)
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return recorded
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@pytest.fixture
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def features(monkeypatch: pytest.MonkeyPatch) -> list[str]:
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"""Record every feature the listener reports for a real flow run.
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Observes the telemetry boundary rather than exported spans: the suite builds
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the Telemetry singleton with collection disabled, so it has no provider to
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export through, and replacing that singleton mid-session leaves the event
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bus without its handlers. That the recorded features become spans is covered
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by ``test_tracer_isolation``.
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"""
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from crewai.events import event_listener as listener_module
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_reregister_listener()
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recorded: list[str] = []
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monkeypatch.setattr(
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listener_module.event_listener._telemetry,
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"feature_usage_span",
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recorded.append,
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)
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return recorded
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def test_completed_flow_reports_its_outcome(features: list[str]) -> None:
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class OkFlow(Flow):
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@start()
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def go(self) -> str:
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return "ok"
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OkFlow().kickoff()
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assert "flow:completed" in features
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def test_failed_flow_reports_the_failure_and_the_method(features: list[str]) -> None:
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class BoomFlow(Flow):
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@start()
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def go(self) -> str:
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raise RuntimeError("boom")
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with pytest.raises(RuntimeError, match="boom"):
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BoomFlow().kickoff()
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emitted = features
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assert "flow:failed" in emitted
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assert "flow:method_failed" in emitted
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assert "flow:completed" not in emitted
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def test_a_failed_flow_is_still_counted_as_an_execution(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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"""The start-time span must survive, or aborted runs vanish from counts.
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``flow_executions_daily_target`` counts ``Flow Execution`` spans, emitted
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when the flow starts. Holding that span open until completion to measure
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duration - the obvious way to add duration - would drop every run that never
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finishes, so the outcome signals are reported separately instead.
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"""
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from crewai.events import event_listener as listener_module
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_reregister_listener()
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started: list[str] = []
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monkeypatch.setattr(
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listener_module.event_listener._telemetry,
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"flow_execution_span",
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lambda flow_name, node_names, origin="user": started.append(flow_name),
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)
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class BoomFlow(Flow):
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@start()
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def go(self) -> str:
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raise RuntimeError("boom")
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with pytest.raises(RuntimeError, match="boom"):
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BoomFlow().kickoff()
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assert "BoomFlow" in started
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def test_requesting_input_reports_both_sides(features: list[str]) -> None:
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class StubProvider:
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def request_input(self, message: str, flow: Flow, metadata=None):
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return InputResponse(value="typed answer")
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class AskFlow(Flow):
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@start()
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def go(self) -> str:
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return self.ask("What topic?")
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AskFlow(input_provider=StubProvider()).kickoff()
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emitted = features
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assert "flow:input_requested" in emitted
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assert "flow:input_received" in emitted
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def test_paused_flow_reports_the_pause(features: list[str]) -> None:
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"""An async feedback provider pauses the flow; both signals must land."""
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class AsyncProvider:
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def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
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raise HumanFeedbackPending(context=context)
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class PausingFlow(Flow):
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@start()
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@human_feedback(message="Review:", provider=AsyncProvider())
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def generate(self) -> str:
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return "content"
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@listen(generate)
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def process(self, result) -> str:
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return f"processed: {result.feedback}"
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# Whether the pause surfaces as an exception depends on the persistence
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# backend in use; the signals must land either way.
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with contextlib.suppress(BaseException):
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PausingFlow().kickoff()
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emitted = features
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assert "flow:hitl_paused" in emitted
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assert "flow:paused" in emitted
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def test_failed_conversation_turn_is_reported(features: list[str]) -> None:
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"""Only completed turns were tracked, so failure rate was unknowable."""
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class FailingChat(Flow):
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conversational = True
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@start()
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def begin(self) -> str:
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raise RuntimeError("turn exploded")
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with pytest.raises(RuntimeError, match="turn exploded"):
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FailingChat().handle_turn("hello")
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assert "flow:conversation_turn_failed" in features
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def test_no_user_authored_strings_are_recorded(features: list[str]) -> None:
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"""Method names, flow names and error text must not reach telemetry."""
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class SecretNamedFlow(Flow):
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@start()
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def my_secret_method_name(self) -> str:
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raise RuntimeError("secret error detail")
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with pytest.raises(RuntimeError, match="secret error detail"):
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SecretNamedFlow().kickoff()
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emitted = features
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assert emitted
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for feature in emitted:
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assert "my_secret_method_name" not in feature
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assert "secret error detail" not in feature
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assert "SecretNamedFlow" not in feature
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def test_completed_flow_reports_a_real_duration(
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durations: list[tuple[str, float, str]],
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) -> None:
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"""Elapsed time must be measured, not merely present."""
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class SlowFlow(Flow):
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@start()
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def go(self) -> str:
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time.sleep(0.05)
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return "ok"
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SlowFlow().kickoff()
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assert len(durations) == 1
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flow_name, duration_ms, outcome = durations[0]
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assert flow_name == "SlowFlow"
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assert outcome == "completed"
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assert duration_ms >= 50
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def test_failed_flow_reports_its_duration_and_outcome(
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durations: list[tuple[str, float, str]],
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) -> None:
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class SlowBoomFlow(Flow):
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@start()
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def go(self) -> str:
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time.sleep(0.05)
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raise RuntimeError("boom")
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with pytest.raises(RuntimeError, match="boom"):
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SlowBoomFlow().kickoff()
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assert len(durations) == 1
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flow_name, duration_ms, outcome = durations[0]
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assert flow_name == "SlowBoomFlow"
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assert outcome == "failed"
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assert duration_ms >= 50
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def test_no_duration_is_reported_without_a_recorded_start(
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durations: list[tuple[str, float, str]],
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) -> None:
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"""A completion with no observed start reports nothing, and does not raise.
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A conversational turn can re-emit completion for a restored run, so the
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stamp is genuinely absent rather than impossible.
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"""
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from crewai.events.event_bus import crewai_event_bus
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from crewai.events.types.flow_events import FlowFinishedEvent
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class NeverStartedFlow(Flow):
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@start()
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def go(self) -> str:
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return "ok"
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flow = NeverStartedFlow()
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crewai_event_bus.emit(
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flow,
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FlowFinishedEvent(flow_name="NeverStartedFlow", result="ok", state={}),
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)
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assert durations == []
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def test_duration_is_reported_once_per_run(
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durations: list[tuple[str, float, str]],
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) -> None:
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"""The stamp is cleared on use, so a repeated completion cannot double-count."""
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from crewai.events.event_bus import crewai_event_bus
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from crewai.events.types.flow_events import FlowFinishedEvent
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class OkFlow(Flow):
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@start()
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def go(self) -> str:
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return "ok"
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flow = OkFlow()
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flow.kickoff()
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crewai_event_bus.emit(
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flow, FlowFinishedEvent(flow_name="OkFlow", result="ok", state={})
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)
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assert len(durations) == 1
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def test_user_authored_flows_are_tagged_as_user(flow_spans) -> None:
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class MyOwnFlow(Flow):
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@start()
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def go(self) -> str:
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return "ok"
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MyOwnFlow().kickoff()
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assert ("MyOwnFlow", "user") in flow_spans
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def test_crewais_own_agent_executor_is_tagged_internal(flow_spans) -> None:
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"""The agent executor is a Flow and runs once per agent execution.
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Without an origin tag it is indistinguishable from a user's flows in the
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daily counts, and it dominates them.
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"""
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from crewai import Agent, Crew, Task
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from crewai.llms.base_llm import BaseLLM
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class StubLLM(BaseLLM):
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def __init__(self) -> None:
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super().__init__(model="stub-model")
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def call(self, messages, **kwargs) -> str:
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return "Final Answer: done"
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def supports_function_calling(self) -> bool:
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return False
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def supports_stop_words(self) -> bool:
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return False
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def get_context_window_size(self) -> int:
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return 8192
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agent = Agent(role="R", goal="G", backstory="B", llm=StubLLM())
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task = Task(description="Do it", expected_output="A result", agent=agent)
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Crew(agents=[agent], tasks=[task]).kickoff()
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origins = {name: origin for name, origin in flow_spans}
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assert origins.get("AgentExecutor") == "internal"
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def test_resumed_flow_is_reported(tmp_path, features: list[str]) -> None:
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"""A restored run is only visible here - there is no resume event.
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Without it, a paused flow that was abandoned cannot be told apart from one
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the user came back to.
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"""
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from pydantic import BaseModel
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from crewai.events.event_bus import crewai_event_bus
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from crewai.events.types.flow_events import FlowPausedEvent
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from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
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persistence = SQLiteFlowPersistence(str(tmp_path / "flows.db"))
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class State(BaseModel):
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id: str = "resume-test-1"
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class AsyncProvider:
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def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
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raise HumanFeedbackPending(context=context)
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class ReviewFlow(Flow[State]):
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@start()
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@human_feedback(message="Review:", provider=AsyncProvider())
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def draft(self) -> str:
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return "draft"
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@listen(draft)
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def finish(self, result) -> str:
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return f"final: {result.feedback}"
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paused: dict[str, str] = {}
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@crewai_event_bus.on(FlowPausedEvent)
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def _capture(source, event) -> None:
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paused["flow_id"] = event.flow_id
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with contextlib.suppress(BaseException):
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ReviewFlow(persistence=persistence).kickoff()
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assert "flow:paused" in features
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assert "flow:resumed" not in features
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flow = ReviewFlow.from_pending(paused["flow_id"], persistence)
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flow.resume("looks good")
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assert "flow:resumed" in features
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assert "flow:completed" in features
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def test_a_user_flow_that_suppresses_console_events_still_reports(
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features: list[str],
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) -> None:
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"""``suppress_flow_events`` asks for console quiet, not for no telemetry."""
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class QuietFlow(Flow):
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suppress_flow_events: bool = True
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@start()
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def go(self) -> str:
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return "ok"
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QuietFlow().kickoff()
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assert "flow:completed" in features
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def test_a_declarative_flow_is_not_treated_as_internal(
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flow_spans: list[tuple[str, str]],
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) -> None:
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"""``Flow.from_declaration()`` yields a ``Flow``, defined inside crewai.
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Deciding origin from the defining module would report a caller's
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declarative flow as one of CrewAI's own.
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"""
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flow = Flow.from_declaration(contents={"name": "MyDeclarativeFlow"})
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assert getattr(type(flow), "is_crewai_internal", False) is False
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def test_a_failed_conversation_session_is_not_reported_completed(
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features: list[str], durations: list[tuple[str, float, str]]
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) -> None:
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"""A conversational session closes with FlowFinishedEvent either way.
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Reading that event at face value counted a failed session as a success,
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alongside the turn-failure signal.
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"""
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class FailingChat(Flow):
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conversational = True
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@start()
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def begin(self) -> str:
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raise RuntimeError("turn exploded")
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chat = FailingChat()
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with pytest.raises(RuntimeError, match="turn exploded"):
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chat.handle_turn("hello")
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chat.finalize_session_traces()
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assert "flow:conversation_turn_failed" in features
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assert "flow:completed" not in features
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assert all(outcome != "completed" for _n, _d, outcome in durations)
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def test_infrastructure_flows_do_not_pollute_outcome_signals(
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features: list[str], durations: list[tuple[str, float, str]]
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) -> None:
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"""CrewAI's own flows must not be counted as user flow outcomes.
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The agent executor, memory encoding and memory recall are all Flows and run
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far more often than anything a user wrote. Counting their outcomes in the
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same feature would make ``flow:completed`` mostly bookkeeping. Their outcome
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is still recorded on the Flow Completed span, which carries ``origin``.
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"""
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from crewai import Agent, Crew, Task
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from crewai.llms.base_llm import BaseLLM
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class StubLLM(BaseLLM):
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def __init__(self) -> None:
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super().__init__(model="stub-model")
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def call(self, messages, **kwargs) -> str:
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return "Final Answer: done"
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def supports_function_calling(self) -> bool:
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return False
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def supports_stop_words(self) -> bool:
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return False
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def get_context_window_size(self) -> int:
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return 8192
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agent = Agent(role="R", goal="G", backstory="B", llm=StubLLM())
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task = Task(description="Do it", expected_output="A result", agent=agent)
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Crew(agents=[agent], tasks=[task]).kickoff()
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assert "flow:completed" not in features
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assert ("AgentExecutor", "completed") in [
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(name, outcome) for name, _duration, outcome in durations
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]
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def test_a_checkpoint_restore_is_not_counted_as_a_resume(
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features: list[str],
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) -> None:
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"""Only a run restored from a human pause counts as resumed.
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``_is_execution_resuming`` is also set by checkpoint restores that never
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paused for anyone. Counting those would push resumes above pauses and make
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the abandonment rate meaningless.
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"""
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from crewai.events.event_bus import crewai_event_bus
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from crewai.events.types.flow_events import FlowStartedEvent
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|
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class RestoredFlow(Flow):
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@start()
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def go(self) -> str:
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return "ok"
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|
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flow = RestoredFlow()
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flow._is_execution_resuming = True
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assert flow._pending_feedback_context is None
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|
|
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crewai_event_bus.emit(flow, FlowStartedEvent(flow_name="RestoredFlow"))
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|
|
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assert "flow:resumed" not in features
|