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