fix: remove trailing whitespace from docstring

Co-Authored-By: João <joao@crewai.com>

fastapi_streaming_example.py

@@ -0,0 +1,177 @@
+"""
+FastAPI Streaming Integration Example for CrewAI
+
+This example demonstrates how to integrate CrewAI with FastAPI to stream
+crew execution events in real-time using Server-Sent Events (SSE).
+
+Installation:
+    pip install crewai fastapi uvicorn
+
+Usage:
+    python fastapi_streaming_example.py
+
+Then visit:
+    http://localhost:8000/docs for the API documentation
+    http://localhost:8000/stream?topic=AI to see streaming in action
+"""
+
+import json
+from typing import AsyncGenerator
+
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from crewai import Agent, Crew, Task
+
+app = FastAPI(title="CrewAI Streaming API")
+
+
+class ResearchRequest(BaseModel):
+    topic: str
+
+
+def create_research_crew(topic: str) -> Crew:
+    """Create a research crew for the given topic."""
+    researcher = Agent(
+        role="Researcher",
+        goal=f"Research and analyze information about {topic}",
+        backstory="You're an expert researcher with deep knowledge in various fields.",
+        verbose=True,
+    )
+
+    task = Task(
+        description=f"Research and provide a comprehensive summary about {topic}",
+        expected_output="A detailed summary with key insights",
+        agent=researcher,
+    )
+
+    return Crew(agents=[researcher], tasks=[task], verbose=True)
+
+
+@app.get("/")
+async def root():
+    """Root endpoint with API information."""
+    return {
+        "message": "CrewAI Streaming API",
+        "endpoints": {
+            "/stream": "GET - Stream crew execution events (query param: topic)",
+            "/research": "POST - Execute crew and return final result",
+        },
+    }
+
+
+@app.get("/stream")
+async def stream_crew_execution(topic: str = "artificial intelligence"):
+    """
+    Stream crew execution events in real-time using Server-Sent Events.
+
+    Args:
+        topic: The research topic (default: "artificial intelligence")
+
+    Returns:
+        StreamingResponse with text/event-stream content type
+    """
+
+    async def event_generator() -> AsyncGenerator[str, None]:
+        """Generate Server-Sent Events from crew execution."""
+        crew = create_research_crew(topic)
+
+        try:
+            for event in crew.kickoff_stream(inputs={"topic": topic}):
+                event_data = json.dumps(event)
+                yield f"data: {event_data}\n\n"
+
+            yield "data: {\"type\": \"done\"}\n\n"
+
+        except Exception as e:
+            error_event = {"type": "error", "data": {"message": str(e)}}
+            yield f"data: {json.dumps(error_event)}\n\n"
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@app.post("/research")
+async def research_topic(request: ResearchRequest):
+    """
+    Execute crew research and return the final result.
+
+    Args:
+        request: ResearchRequest with topic field
+
+    Returns:
+        JSON response with the research result
+    """
+    crew = create_research_crew(request.topic)
+
+    try:
+        result = crew.kickoff(inputs={"topic": request.topic})
+        return {
+            "success": True,
+            "topic": request.topic,
+            "result": result.raw,
+            "usage_metrics": (
+                result.token_usage.model_dump() if result.token_usage else None
+            ),
+        }
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+@app.get("/stream-filtered")
+async def stream_filtered_events(
+    topic: str = "artificial intelligence", event_types: str = "llm_stream_chunk"
+):
+    """
+    Stream only specific event types.
+
+    Args:
+        topic: The research topic
+        event_types: Comma-separated list of event types to include
+
+    Returns:
+        StreamingResponse with filtered events
+    """
+    allowed_types = set(event_types.split(","))
+
+    async def event_generator() -> AsyncGenerator[str, None]:
+        crew = create_research_crew(topic)
+
+        try:
+            for event in crew.kickoff_stream(inputs={"topic": topic}):
+                if event["type"] in allowed_types:
+                    event_data = json.dumps(event)
+                    yield f"data: {event_data}\n\n"
+
+            yield "data: {\"type\": \"done\"}\n\n"
+
+        except Exception as e:
+            error_event = {"type": "error", "data": {"message": str(e)}}
+            yield f"data: {json.dumps(error_event)}\n\n"
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+        },
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    print("Starting CrewAI Streaming API...")
+    print("Visit http://localhost:8000/docs for API documentation")
+    print("Try: http://localhost:8000/stream?topic=quantum%20computing")
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

src/crewai/crew.py

@@ -766,6 +766,118 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
+    def kickoff_stream(self, inputs: dict[str, Any] | None = None):
+        """
+        Stream crew execution events in real-time.
+
+        This method yields events as they occur during crew execution, making it
+        easy to integrate with streaming frameworks like FastAPI's StreamingResponse.
+
+        Args:
+            inputs: Optional dictionary of inputs for the crew execution
+
+        Yields:
+            dict: Event dictionaries containing event type and data
+
+        Example:
+            ```python
+            from fastapi import FastAPI
+            from fastapi.responses import StreamingResponse
+            import json
+            
+            app = FastAPI()
+            
+            @app.get("/stream")
+            async def stream_crew():
+                def event_generator():
+                    for event in crew.kickoff_stream(inputs={"topic": "AI"}):
+                        yield f"data: {json.dumps(event)}\\n\\n"
+
+                return StreamingResponse(
+                    event_generator(),
+                    media_type="text/event-stream"
+                )
+            ```
+        """
+        import queue
+        import threading
+        from crewai.events.base_events import BaseEvent
+        
+        event_queue: queue.Queue = queue.Queue()
+        completion_event = threading.Event()
+        exception_holder = {"exception": None}
+        
+        def event_handler(source: Any, event: BaseEvent):
+            event_dict = {
+                "type": event.type,
+                "data": event.model_dump(exclude={"from_task", "from_agent"}),
+            }
+            event_queue.put(event_dict)
+        
+        from crewai.events.types.crew_events import (
+            CrewKickoffStartedEvent,
+            CrewKickoffCompletedEvent,
+            CrewKickoffFailedEvent,
+        )
+        from crewai.events.types.task_events import (
+            TaskStartedEvent,
+            TaskCompletedEvent,
+            TaskFailedEvent,
+        )
+        from crewai.events.types.agent_events import (
+            AgentExecutionStartedEvent,
+            AgentExecutionCompletedEvent,
+        )
+        from crewai.events.types.llm_events import (
+            LLMStreamChunkEvent,
+            LLMCallStartedEvent,
+            LLMCallCompletedEvent,
+        )
+        from crewai.events.types.tool_usage_events import (
+            ToolUsageStartedEvent,
+            ToolUsageFinishedEvent,
+            ToolUsageErrorEvent,
+        )
+        
+        crewai_event_bus.register_handler(CrewKickoffStartedEvent, event_handler)
+        crewai_event_bus.register_handler(CrewKickoffCompletedEvent, event_handler)
+        crewai_event_bus.register_handler(CrewKickoffFailedEvent, event_handler)
+        crewai_event_bus.register_handler(TaskStartedEvent, event_handler)
+        crewai_event_bus.register_handler(TaskCompletedEvent, event_handler)
+        crewai_event_bus.register_handler(TaskFailedEvent, event_handler)
+        crewai_event_bus.register_handler(AgentExecutionStartedEvent, event_handler)
+        crewai_event_bus.register_handler(AgentExecutionCompletedEvent, event_handler)
+        crewai_event_bus.register_handler(LLMStreamChunkEvent, event_handler)
+        crewai_event_bus.register_handler(LLMCallStartedEvent, event_handler)
+        crewai_event_bus.register_handler(LLMCallCompletedEvent, event_handler)
+        crewai_event_bus.register_handler(ToolUsageStartedEvent, event_handler)
+        crewai_event_bus.register_handler(ToolUsageFinishedEvent, event_handler)
+        crewai_event_bus.register_handler(ToolUsageErrorEvent, event_handler)
+        
+        def run_kickoff():
+            try:
+                result = self.kickoff(inputs=inputs)
+                event_queue.put({"type": "final_output", "data": {"output": result.raw}})
+            except Exception as e:
+                exception_holder["exception"] = e
+            finally:
+                completion_event.set()
+        
+        thread = threading.Thread(target=run_kickoff, daemon=True)
+        thread.start()
+        
+        try:
+            while not completion_event.is_set() or not event_queue.empty():
+                event = event_queue.get(timeout=0.1) if not event_queue.empty() else None
+                if event is not None:
+                    yield event
+
+            if exception_holder["exception"]:
+                raise exception_holder["exception"]
+
+        finally:
+            thread.join(timeout=1)
+
     def _handle_crew_planning(self):
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")

tests/test_crew.py

@@ -4744,3 +4744,81 @@ def test_ensure_exchanged_messages_are_propagated_to_external_memory():
     assert "Researcher" in messages[0]["content"]
     assert messages[1]["role"] == "user"
     assert "Research a topic to teach a kid aged 6 about math" in messages[1]["content"]
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_kickoff_stream(researcher):
+    """Test that crew.kickoff_stream() yields events during execution."""
+    task = Task(
+        description="Research a topic about AI",
+        expected_output="A brief summary about AI",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    events = list(crew.kickoff_stream())
+
+    assert len(events) > 0
+
+    event_types = [event["type"] for event in events]
+    assert "crew_kickoff_started" in event_types
+    assert "final_output" in event_types
+
+    final_output_event = next(e for e in events if e["type"] == "final_output")
+    assert "output" in final_output_event["data"]
+    assert isinstance(final_output_event["data"]["output"], str)
+    assert len(final_output_event["data"]["output"]) > 0
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_kickoff_stream_with_inputs(researcher):
+    """Test that crew.kickoff_stream() works with inputs."""
+    task = Task(
+        description="Research about {topic}",
+        expected_output="A brief summary about {topic}",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    events = list(crew.kickoff_stream(inputs={"topic": "machine learning"}))
+
+    assert len(events) > 0
+
+    event_types = [event["type"] for event in events]
+    assert "crew_kickoff_started" in event_types
+    assert "final_output" in event_types
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_kickoff_stream_includes_llm_chunks(researcher):
+    """Test that crew.kickoff_stream() includes LLM stream chunks."""
+    task = Task(
+        description="Write a short poem about AI",
+        expected_output="A 2-line poem",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    events = list(crew.kickoff_stream())
+
+    event_types = [event["type"] for event in events]
+
+    assert "task_started" in event_types or "agent_execution_started" in event_types
+
+
+def test_crew_kickoff_stream_handles_errors(researcher):
+    """Test that crew.kickoff_stream() properly handles errors."""
+    task = Task(
+        description="This task will fail",
+        expected_output="Should not complete",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    with patch("crewai.crew.Crew.kickoff", side_effect=Exception("Test error")):
+        with pytest.raises(Exception, match="Test error"):
+            list(crew.kickoff_stream())