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agents-crewai

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.

Categoryai
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Install

One-line setup

Copy and run this in your terminal to install the skill. Re-run to reinstall and update an existing install.

npx codex-skills-registry@latest --skill=ai/agents-crewai --yes

CrewAI

Design and run role-based agent teams using CrewAI.

Quick Start

1) Define agents with clear roles and goals.

2) Define tasks with explicit expected outputs.

3) Choose a process (sequential vs. hierarchical).

4) 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