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58fb717ab2
- Add ExecutionStep and ExecutionTrace models to track crew execution steps - Add ExecutionTraceCollector to capture events and build execution traces - Add trace_execution parameter to Crew class (disabled by default) - Add execution_trace field to CrewOutput to return trace data - Integrate trace collection into crew.kickoff() method - Add comprehensive tests covering execution tracing functionality - Add example demonstrating how to use execution tracing - Export new classes in __init__.py Addresses issue #3268: Users can now track the sequence of steps/actions that a crew takes to complete a goal, including agent thoughts, tool calls, and intermediate results, similar to LangGraph's conversation state. Co-Authored-By: João <joao@crewai.com>
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
from crewai import Agent, Crew, Task, Process, LLM
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researcher = Agent(
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role="Researcher",
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goal="Research and analyze information",
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backstory="You are an expert researcher with years of experience.",
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llm=LLM(model="gpt-4o-mini")
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)
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writer = Agent(
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role="Writer",
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goal="Write compelling content",
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backstory="You are a skilled writer who creates engaging content.",
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llm=LLM(model="gpt-4o-mini")
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)
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research_task = Task(
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description="Research the latest trends in AI",
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expected_output="A comprehensive report on AI trends",
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agent=researcher
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)
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writing_task = Task(
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description="Write an article based on the research",
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expected_output="A well-written article about AI trends",
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agent=writer
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)
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crew = Crew(
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agents=[researcher, writer],
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tasks=[research_task, writing_task],
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process=Process.sequential,
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trace_execution=True
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)
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result = crew.kickoff(inputs={"topic": "artificial intelligence"})
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if result.execution_trace:
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print(f"Total execution steps: {result.execution_trace.total_steps}")
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print(f"Execution duration: {result.execution_trace.end_time - result.execution_trace.start_time}")
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thoughts = result.execution_trace.get_steps_by_type("agent_thought")
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print(f"Agent thoughts captured: {len(thoughts)}")
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tool_calls = result.execution_trace.get_steps_by_type("tool_call_started")
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print(f"Tool calls made: {len(tool_calls)}")
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for step in result.execution_trace.steps:
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print(f"{step.timestamp}: {step.step_type} - {step.agent_role or 'System'}")
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