Crew AI vs AutoGen vs LangChain

If you want to build a team of AI agents, three names come up first. CrewAI. AutoGen. LangChain, or really LangGraph, which is the agent part of it.

People treat them as the same thing. They are not. They solve the same problem in very different ways. Pick the wrong one and you fight the tool for weeks.

Here is the plain version, with no hype.

The short answer

• CrewAI is built for crews of agents with roles. One is a researcher, one a writer, one a reviewer. They work together.
• AutoGen frames the work as a conversation between agents. They talk, and the talking gets the job done.
• LangGraph models the work as a graph you control step by step. More effort, more control.

If you want the fastest start with clear roles, look at CrewAI. If you want tight control over every step, look at LangGraph. AutoGen we cover with a caveat below.

CrewAI: agents with roles

CrewAI is a lean Python framework built just for running autonomous agents. It is standalone. It does not sit on top of LangChain, which is a mix-up I hear all the time. You can read that straight from the CrewAI repo.

It has two modes. Crews, where agents work together on their own. And Flows, where you drive the steps with events. That split is the thing to understand before you start.

Good fit: you have a job with clear roles and you want agents working as a small team, fast.

AutoGen: agents that talk

Microsoft's AutoGen treats multi-agent work as a chat. Agents send messages to each other in a group, and the back-and-forth solves the problem. It is built in three layers, from a low-level core up to ready-made chat patterns. You can see the structure in the AutoGen repo.

One big thing to know. AutoGen is now in maintenance mode. The repo tells new users to start with the Microsoft Agent Framework instead. So if you are choosing today, go in with eyes open. Building on a tool that is winding down is a risk you take on purpose, not by accident.

LangGraph: agents as a graph

LangGraph is the agent orchestration layer from the LangChain team. It is low-level on purpose. You model your agents as a graph with state, which gives you fine control over what happens and when. Its own repo calls it a framework for "long-running, stateful agents" and notes it runs without the rest of LangChain. See the LangGraph repo.

It is used by a lot of real projects. The repo lists names like Klarna, Replit, and Elastic running it in production.

Good fit: you have a developer, the job is complex, and you need to control every step.

The honest trade-off

All three are popular. As of June 2026, AutoGen, CrewAI, and LangGraph each have tens of thousands of stars on GitHub, so none of them is a fringe choice.

But here is the thing. Picking the framework is the easy 20%. The hard part is what comes after. Wiring it into your real tools. Giving it memory. Adding a check so no agent marks its own homework. Putting a human on anything that ships.

That is orchestration, and it is where most builds fall over. The tool does not decide whether you win. The system around it does. If you are still deciding whether you need agents at all, start with do you actually need an AI agent.

So which one?

• New, want speed and clear roles: CrewAI.
• Need tight, step-by-step control with a developer on hand: LangGraph.
• Looking at AutoGen: check the Microsoft Agent Framework first, since AutoGen is winding down.

For the wider build decision beyond the framework, our studio DevWiz has a good primer on how to pick your AI stack.

And if this is core to your business, the real question is not which framework. It is who orchestrates the whole thing and owns the result. That is the part we build with you. See how we run AI as an operating system, or read the AI agent orchestration guide for the patterns.