Field notes, protocols and teardowns on agentic AI, cloud architecture, LLM economics and DevOps practice — written by the crew of Sector 7G.
Every agent framework claims to do the same thing, and none of them do. This is a map of what each one is actually for — sorted by the shape of your problem, not by the vendor's pitch.
AutoGen is in maintenance mode and Microsoft points new users at Agent Framework. The underlying question still stands — so here is CrewAI against MAF, and the distinction that decides it.
MCP turns the N-by-M integration problem into N plus M. Write a server once and any client that speaks the protocol can use your tools — which is why it spread so quickly.
A definition narrow enough to be useful: a model that decides its own next action, in a loop, against real tools. If the sequence is fixed, it is a pipeline — and that is usually better.
If you are building an AI feature into a TypeScript web application, these are the two obvious candidates — and they were designed for quite different jobs.
One is the validation library half of Python already depends on. The other is an agent framework built on it by the same team. The confusion is understandable and the answer is short.
OpenAI's own agent framework is notable for how little it does. Four concepts, a short learning curve, and a very direct relationship with one provider.