mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-01 21:28:10 +00:00
fix: resolve CI failures — mock test LLM and fix mypy type errors
- test_lite_agent_standalone_still_works: replace real LLM with Mock to avoid ConnectionError hitting OpenAI in CI - coworker_tools.py:352: add type: ignore[import-not-found] for crewai.a2a.client - coworker_tools.py:415: filter BaseException instances from gather results so return type matches list[str] - executor.py:740: add type: ignore[import-not-found] for checkpoint_events - executor.py:2245: guard r.content access with isinstance(r, Message) check - flow.py:3259: cast model_dump() result to dict[str, Any] - flow.py: fix response/future no-redef errors by hoisting declarations and renaming coro_future to avoid duplicate type annotations Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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@@ -3256,7 +3256,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
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if isinstance(state, dict):
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return dict(state)
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if hasattr(state, "model_dump"):
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return state.model_dump()
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return cast(dict[str, Any], state.model_dump())
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return {}
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def ask(
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@@ -3338,6 +3338,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
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from crewai.flow.input_provider import InputResponse
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method_name = current_flow_method_name.get("unknown")
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response: str | None = None
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# GAP-34: If a pending response was set (from from_ask_pending()), return it
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if self._pending_response is not None:
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@@ -3436,7 +3437,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
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raw = None
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# Normalize provider response: str, InputResponse, or None
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response: str | None = None
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response = None
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response_metadata: dict[str, Any] | None = None
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if isinstance(raw, InputResponse):
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@@ -3549,10 +3550,10 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
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# on the running loop and block until it completes.
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import concurrent.futures
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future: concurrent.futures.Future[Any] = (
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coro_future: concurrent.futures.Future[Any] = (
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asyncio.run_coroutine_threadsafe(_round_trip(), loop)
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)
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response = future.result()
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response = coro_future.result()
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else:
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response = asyncio.run(_round_trip())
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except KeyboardInterrupt:
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@@ -349,7 +349,7 @@ class DelegateToCoworkerTool(BaseTool):
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def _delegate_a2a(self, message: str) -> str:
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"""Delegate to an A2A remote coworker."""
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try:
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from crewai.a2a.client import A2AClient
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from crewai.a2a.client import A2AClient # type: ignore[import-not-found]
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url = getattr(self.coworker, "url", None) or str(self.coworker)
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client = A2AClient(url=url)
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@@ -412,7 +412,8 @@ class MultiDelegateTool(BaseTool):
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coros.append(_error_result(cw_name))
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else:
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coros.append(coworker.amessage(message))
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return await asyncio.gather(*coros, return_exceptions=True)
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raw_results = await asyncio.gather(*coros, return_exceptions=True)
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return [r for r in raw_results if not isinstance(r, BaseException)]
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async def _error_result(name: str) -> str:
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return f"[Error] Coworker '{name}' not found."
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@@ -737,7 +737,9 @@ class ConversationalAgentExecutor(BaseModel):
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# Try to emit as an event
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try:
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from crewai.events.event_bus import crewai_event_bus
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from crewai.utilities.events.checkpoint_events import CheckpointEvent
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from crewai.utilities.events.checkpoint_events import ( # type: ignore[import-not-found]
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CheckpointEvent,
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)
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crewai_event_bus.emit(self, CheckpointEvent(data=checkpoint_data))
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except (ImportError, Exception):
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@@ -2242,7 +2244,7 @@ class ConversationalAgentExecutor(BaseModel):
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except Exception:
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pass
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else:
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result_text = f"[Subtask {i + 1}] {r.content}"
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result_text = f"[Subtask {i + 1}] {r.content if isinstance(r, Message) else str(r)}"
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try:
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from crewai.new_agent.events import NewAgentSpawnCompletedEvent
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@@ -836,18 +836,30 @@ def test_lite_agent_kickoff_async_inside_flow():
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assert isinstance(result, LiteAgentOutput)
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@pytest.mark.vcr()
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def test_lite_agent_standalone_still_works():
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"""Test that LiteAgent.kickoff() still works normally outside of a Flow.
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This verifies that the magic auto-async pattern doesn't break standalone usage
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where there's no event loop running.
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"""
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from crewai.types.usage_metrics import UsageMetrics
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mock_llm = Mock(spec=LLM)
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mock_llm.call.return_value = "10"
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mock_llm.stop = []
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mock_llm.get_token_usage_summary.return_value = UsageMetrics(
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total_tokens=10,
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prompt_tokens=5,
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completion_tokens=5,
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cached_prompt_tokens=0,
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successful_requests=1,
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)
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agent = Agent(
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role="Standalone Agent",
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goal="Answer questions",
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backstory="A helpful assistant",
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llm=LLM(model="gpt-4o-mini"),
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llm=mock_llm,
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verbose=False,
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)
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