AI AGENT FRAMEWORKS COMPARED: HOW TO CHOOSE
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.
There is no best agent framework, and the question is usually the wrong one. These libraries solve genuinely different problems that happen to share a vocabulary. Pick by the shape of your problem: how much control you need over what runs next, whether you are modelling one worker or several, and how much of your stack you are willing to hand over.
Four questions that decide it#
Answer these before you read a single feature comparison. They eliminate most of the field on their own.
- Do you need to see and control the control flow? If a wrong step is expensive — money moves, records change, a customer is emailed — you want the graph explicit and inspectable, not emergent.
- Is this one agent or several? Multi-agent is a real architecture with real costs. Most problems that look like a team of specialists are one agent with several tools.
- Python or TypeScript? This eliminates more options than anything else, and pretending otherwise wastes a fortnight.
- How much do you want to own? A framework is a bet on someone else's abstractions surviving contact with your problem.
The rough map#
Explicit control flow. LangGraph models an agent as a state machine you draw yourself. You get durability, inspectable state and deliberate loops, at the cost of writing more of it out. This is the right shape when the cost of a wrong step is high.
Teams of roles. CrewAI is built around agents with roles, goals and tasks — a metaphor that maps well onto content pipelines and research workflows, and badly onto anything needing tight transactional control. Its Flows layer covers the cases where you need a step to happen exactly there. Microsoft's Agent Framework (MAF) occupies neighbouring ground with graph-based orchestration — sequential, concurrent, handoff and group collaboration — and runs on .NET as well as Python. The two are worth comparing directly.
If you are looking for AutoGen: it is in maintenance mode, and Microsoft directs new users to Agent Framework. Microsoft's own overview calls it the direct successor, created by the same teams, and says it combines AutoGen's simple agent abstractions with Semantic Kernel's enterprise features and adds graph-based workflows.
Type-safe, unglamorous. Pydantic AI brings the validation discipline Python developers already use to model outputs and tool arguments. If your pain is "the model returned something almost right", start here.
Vendor-native. The OpenAI Agents SDK, Google's ADK and Genkit, and Microsoft's Agent Framework are the first-party routes, and they are the shortest path to something working. The lock-in is subtler than it used to be: the OpenAI Agents SDK is now provider-agnostic — its README describes support for the Responses and Chat Completions APIs plus a long tail of other models — and MAF is multi-provider by design and speaks MCP and A2A. The coupling that remains is to a framework and its conventions rather than to one model vendor, which is a much cheaper thing to unwind. Worth making consciously all the same.
TypeScript. The field narrows sharply. See Vercel AI SDK vs LangChain.
The option everyone skips#
You may not need a framework. A loop, a switch statement and a well-typed tool schema get further than the discourse suggests, and they leave you with code you can debug. We make that case properly in Do You Need an Agent Framework at All? — read it before you commit, not after.
Start from the definitions#
If the word "agent" is doing unclear work in your team's conversations, the frameworks will not fix that. What Is an AI Agent? sets out a definition narrow enough to be useful, and What Is MCP? covers the protocol that increasingly sits underneath all of them.