LangGraph

LangGraph models AI agents as state machines — nodes process data, edges define transitions, and the graph orchestrates multi-step reasoning with durable execution. If your AI agent needs to branch, loop, retry, or maintain state across steps, LangGraph is the most production-ready framework for it.

For teams building complex AI workflows — multi-agent systems, tool-calling chains, human-in-the-loop approvals — LangGraph is the serious option. CrewAI is easier for quick role-based agent prototypes. AutoGen (Microsoft) handles conversational multi-agent patterns but is shifting to maintenance mode. OpenAI's Agents SDK is simpler but less flexible.

The catch: The learning curve is steep — you need to think in graphs, nodes, edges, and state schemas. LangGraph inherits LangChain's "abstraction over abstraction" problem. It doesn't natively support MCP or A2A protocols. And the tight coupling to the LangChain ecosystem means if you're not already in that world, you're adopting a lot of opinions. For simple single-agent tasks, this is overkill.