| name | agents-crewai |
| description | Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution. |
CrewAI
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
