--- title: crewAI Procedures description: Understanding and utilizing procedures in the crewAI framework for sequential execution of multiple crews. --- ## What is a Procedure? A procedure in crewAI represents a sequence of crews that are executed one after another. It allows for the chaining of multiple crews, where the output of one crew becomes the input for the next, enabling complex, multi-stage workflows. ## Procedure Attributes | Attribute | Parameters | Description | | :-------- | :--------- | :------------------------------------------ | | **Crews** | `crews` | A list of crews to be executed in sequence. | ## Working with Procedures The following example demonstrates how to create, execute, and work with Procedures: ```python import asyncio from crewai import Agent, Task, Crew, Procedure from crewai.crews.crew_output import CrewOutput # Define agents researcher = Agent( role='Senior Research Analyst', goal='Discover innovative AI technologies', backstory="You're a senior research analyst specializing in AI trends.", ) writer = Agent( role='Content Writer', goal='Write engaging articles on AI discoveries', backstory="You're a senior writer specializing in AI content.", ) # Define tasks for each crew research_task = Task( description='Identify breakthrough AI technologies', agent=researcher ) write_task = Task( description='Draft an article on the latest AI technologies', agent=writer ) # Create crews research_crew = Crew( agents=[researcher], tasks=[research_task], verbose=True ) writing_crew = Crew( agents=[writer], tasks=[write_task], verbose=True ) # Create a procedure procedure = research_crew >> writing_crew # Alternative way to create a procedure # procedure = Procedure(crews=[research_crew, writing_crew]) # Function to run the procedure async def run_procedure(): inputs = [ {"topic": "AI in healthcare"}, {"topic": "AI in finance"} ] results = await procedure.kickoff(inputs) return results # Execute the procedure and process results async def main(): results = await run_procedure() for i, result in enumerate(results): print(f"\nResult {i + 1}:") # Access raw output print("Raw output:", result.raw) # Access JSON output (if available) if result.json_dict: print("JSON output:", result.json_dict) # Access Pydantic model output (if available) if result.pydantic: print("Pydantic output:", result.pydantic) # Access individual task outputs for j, task_output in enumerate(result.tasks_output): print(f"Task {j + 1} output:", task_output.raw) # Access token usage print("Token usage:", result.token_usage) # Convert result to dictionary result_dict = result.to_dict() print("Result as dictionary:", result_dict) # String representation of the result print("String representation:", str(result)) # Run the main function if __name__ == "__main__": asyncio.run(main()) ```