Design and run role-based agent teams using CrewAI.
Quick Start
- Define agents with clear roles and goals.
- Define tasks with explicit expected outputs.
- Choose a process (sequential vs. hierarchical).
- Run the crew and inspect outputs.
Minimal Example
from crewai import Agent, Task, Crew, Process
researcher = Agent(role="Researcher", goal="Find 5 key trends")
writer = Agent(role="Writer", goal="Summarize findings")
research = Task(description="Research AI agents", expected_output="5 bullets", agent=researcher)
write = Task(description="Write a summary", expected_output="Short memo", agent=writer, context=[research])
crew = Crew(agents=[researcher, writer], tasks=[research, write], process=Process.sequential) result = crew.kickoff(inputs={"topic": "AI agents"}) print(result.raw)
## Design Guidance
- Keep roles narrow and outputs explicit.
- Use context chaining to pass outputs between tasks.
- Prefer sequential for reliability; hierarchical for delegation-heavy workflows.
## Use Alternatives When
- You need complex graph cycles → consider LangGraph.
- You’re focused on document retrieval → consider LlamaIndex.
## References
- Extended examples: `references/examples.md`