CrewAI
BuildersAgent frameworkCrewAI
Python framework for orchestrating role-based teams of agents.
- Who it's for: people building software and AI agents
- Pricing: Open source framework; paid enterprise platform (verify on the official site)
- Official site: https://www.crewai.com
- Source / changelog: https://github.com/crewAIInc/crewAI
Best for / Not for
Best for
- Developers prototyping role-based multi-agent teams
- Content and research pipelines
- People who like YAML-configured agents and tasks
Not for
- Simple single-agent tasks
- No-code users
3 concrete use cases
- Research crew: Researcher, writer and editor agents produce a cited brief.
- Lead qualification: An analyst agent scores inbound leads against criteria.
- Report generation: Agents gather data and draft a weekly report.
Pros and cons
Pros
- Intuitive roles/tasks model
- Open source
- Quick scaffolding CLI
Cons
- Multi-agent runs can be slow and costly
- Less low-level control than graph frameworks
- Debugging agent conversations takes effort
How to get started with CrewAI in 10 minutes
- Install with uv tool install crewai
- Create a project with the crewai create command from the docs
- Set your model API key
- Edit the agents and tasks config
- Run the crew and read the output
Comparisons
CrewAI alternatives
- OpenAI Agents SDK: Lightweight framework for building multi-agent workflows with tools, handoffs and tracing.
- LangGraph: Library for building stateful, long-running agents as graphs, with persistence and human-in-the-loop.
- LangChain: Open-source framework for building LLM apps and agents with integrations.
- LlamaIndex: Framework for building agents and RAG apps over your data; LlamaCloud for parsing.
- AutoGen: Open-source framework from Microsoft for multi-agent applications.
- Pydantic AI: Python agent framework built by the Pydantic team with type-safe outputs.
Recent changes
Weekly updates for CrewAI will be logged here as they appear in issues.