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b3b2b1e25f
- Add streaming events: CrewStreamChunkEvent, TaskStreamChunkEvent, AgentStreamChunkEvent - Extend Crew.kickoff() with stream parameter and callback support - Propagate streaming through task and agent execution chains - Integrate with existing LLM streaming infrastructure - Add comprehensive tests and examples - Maintain backward compatibility Fixes #2950 Co-Authored-By: João <joao@crewai.com>
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
from crewai import Agent, Task, Crew
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from crewai.llm import LLM
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def stream_callback(chunk, agent_role, task_description, step_type):
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"""Callback function to handle streaming chunks from multiple agents."""
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print(f"[{agent_role}] {step_type}: {chunk}", end="", flush=True)
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llm = LLM(model="gpt-4o-mini", stream=True)
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researcher = Agent(
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role="Research Analyst",
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goal="Research and analyze topics thoroughly",
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backstory="You are an experienced research analyst who excels at gathering and analyzing information.",
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llm=llm,
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verbose=False
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)
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writer = Agent(
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role="Content Writer",
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goal="Write engaging content based on research",
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backstory="You are a skilled content writer who creates compelling narratives from research data.",
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llm=llm,
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verbose=False
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)
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research_task = Task(
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description="Research the latest trends in artificial intelligence and machine learning",
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expected_output="A comprehensive research summary of AI/ML 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 engaging blog post about AI trends based on the research",
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expected_output="A well-written blog post about AI trends",
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agent=writer,
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context=[research_task]
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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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verbose=False
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)
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print("Starting multi-agent crew execution with streaming...")
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result = crew.kickoff(
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stream=True,
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stream_callback=stream_callback
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)
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print(f"\n\nFinal result:\n{result}")
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