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LangGraph vs AutoGen

LangGraphAutoGen

Bottom line: LangGraph for engineering teams building production agents; AutoGen for teams already running AutoGen in production.

	LangGraph Graph-based orchestration for stateful, controllable LLM agents. Visit	AutoGen Microsoft's multi-agent conversation framework (now in maintenance mode). Visit
Votes	0	0
Pricing	Freemium	Free
Category	Agent Frameworks	Agent Frameworks
Tags	agentsorchestrationllm-frameworkopen-sourceworkflow	agentsmulti-agentopen-sourcemicrosoftorchestration
Best for	Engineering teams building production agents Developers needing controllable, resumable workflows Teams already invested in LangChain	Teams already running AutoGen in production Researchers studying multi-agent systems Developers prototyping agent collaboration
Pros	Explicit, debuggable control over agent state and flow MIT-licensed core, free to self-host Model-agnostic, not tied to one LLM vendor Strong support for cycles, checkpointing, and human-in-the-loop Backed by the widely used LangChain ecosystem	Pioneered accessible multi-agent conversation patterns Free and open source Backed by Microsoft Research with strong documentation 0.4 architecture is asynchronous and more observable Works with many model providers
Cons	Steeper learning curve than high-level agent builders Requires comfort with graph and state concepts Paid platform and observability are separate products Documentation and APIs have evolved quickly Can be overkill for simple single-shot LLM tasks	In maintenance mode as of 2026, no new feature focus Microsoft steers new projects to the Agent Framework Multiple version lines (0.2 vs 0.4/0.7) cause confusion Multi-agent loops can be hard to control and cost-predict Less enterprise tooling than the successor framework

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