CrewAI models a team. You describe each agent as a role with a goal, hand the crew a list of tasks, and let them work through it. The abstraction is people-shaped rather than graph-shaped, which makes it fast to describe a workflow out loud.
Reach for it when
- The work splits naturally across roles like researcher, writer, reviewer
- You want a readable description of who does what
- You are prototyping a multi agent idea quickly
Look elsewhere when
- You need precise control over branching and loops
- You need to pause mid run for human approval
What the lessons will cover
- 01Install and setup
- 02Agents, roles and goals
- 03Tasks and expected output
- 04Crews and process types
- 05Tools for agents
- 06Sequential and hierarchical flows
- 07Memory
- 08A full research crew
Others in multi agent
AutoGenAgents that solve problems by talking to each other.
MetaGPTAgents assigned software company roles.
CAMEL-AIStudying how large agent societies cooperate.Common questions
Is CrewAI free to use?
CrewAI is open source and free to run yourself. You still pay whichever model provider you point it at, and this tutorial is free with no signup.
Do I need to know Python to use CrewAI?
Basic Python is enough. Functions, dictionaries and imports cover most of what CrewAI asks of you.
When should I not use CrewAI?
You need precise control over branching and loops. You need to pause mid run for human approval.
Lessons for CrewAI are being written. Meanwhile the LangGraph tutorial covers the same ground: state, tools, loops, memory and human approval. Most of it carries straight over.
Start the LangGraph tutorial →