AutoGen vs LangGraph

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	AutoGen Agents	LangGraph Agents
Tagline	Microsoft's open-source framework for multi-agent AI applications	Stateful, graph-based agent orchestration from LangChain.
Category	Agents	Agents
Pricing	Free· Free and open-source (MIT/CC-BY-4.0). You pay only for the underlying model providers (OpenAI, Azure OpenAI, Anthropic, local models, etc.) you plug into it.	Freemium· Developer: $0 / seat per month · Plus: $39 / seat per month · Enterprise: Custom pricing
Model	Model-agnostic: OpenAI (GPT-4o, GPT-4.1, o-series), Azure OpenAI, Anthropic Claude, Google Gemini, Ollama, and any OpenAI-compatible local model (vLLM, llama.cpp)	BYO (Claude / GPT / open)
Editorial score	—	8.8 / 10
Use cases	Coder and critic pair programming loopsMulti-agent research and report writingMagentic-One style generalist web+file+code agent teamsRAG assistants with tool-callingBack-office task routing across specialist agentsDistributed cross-language agent systems (Python + .NET)No-code agent prototyping in AutoGen StudioAcademic benchmarks for multi-agent LLM research	stateful agentshuman-in-loopproduction
Pros	Genuinely multi-agent by design — group chat, selector, swarm, and Magentic-One patterns are first-class, not bolted on Clean layered architecture (Core / AgentChat / Extensions) lets you start high-level and drop down when you need custom orchestration Distributed, event-driven runtime with gRPC means agents can run across processes, machines, and even .NET/Python boundaries AutoGen Studio provides a no-code UI for prototyping and demoing agent teams to non-engineers Model-agnostic client layer covers OpenAI, Azure OpenAI, Anthropic, Gemini, Ollama, and any OpenAI-compatible endpoint Strong human-in-the-loop and code-execution primitives (Docker sandbox) for safe agent-written code Backed by Microsoft Research with an active community, frequent releases, and extensive documentation and samples	Reliable, debuggable agent graphs Built-in persistence + HITL Production-grade Tight LangSmith integration
Cons	The v0.2 to v0.4 rewrite broke APIs and much of the older tutorial content on the internet is now out of date Steeper learning curve than single-agent frameworks — you must think about message routing, termination conditions, and turn selection Multi-agent loops can be expensive and slow; without careful termination logic, agents happily burn tokens talking to each other Python-first (with a partial .NET port); no first-class TypeScript/JS SDK Observability and eval tooling are thinner than in competitors like LangGraph or CrewAI unless you bolt on external tracing	Steeper learning curve than CrewAI Verbose to set up
Website	microsoft.github.io	www.langchain.com

Pick AutoGen if

✅ Genuinely multi-agent by design — group chat, selector, swarm, and Magentic-One patterns are first-class, not bolted on
✅ Clean layered architecture (Core / AgentChat / Extensions) lets you start high-level and drop down when you need custom orchestration
✅ Distributed, event-driven runtime with gRPC means agents can run across processes, machines, and even .NET/Python boundaries
✅ AutoGen Studio provides a no-code UI for prototyping and demoing agent teams to non-engineers

Pick LangGraph if

✅ Reliable, debuggable agent graphs
✅ Built-in persistence + HITL
✅ Production-grade
✅ Tight LangSmith integration

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