AutoGen vs LangGraph
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose AutoGen if you want microsoft framework for multi-agent conversational ai applications (Supervised agent, free); choose LangGraph if you want low-level framework for stateful, durable, graph-based llm agents (Supervised agent, freemium).
| AutoGen | LangGraph | |
|---|---|---|
| What it is | Microsoft framework for multi-agent conversational AI applications | Low-level framework for stateful, durable, graph-based LLM agents |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | free · Free (open source) | freemium · Framework free (MIT); LangGraph Platform via LangSmith (free Developer tier) |
| Best for | developers, enterprise | developers, enterprise, mid-market |
| Deployment | self-hosted, api | self-hosted, api, saas |
| Modalities | text, code, api | text, code, api |
| Models | model-agnostic, gpt, claude, open-source | model-agnostic, gpt, claude, gemini, open-source |
| Protocols | function-calling, rest-api | function-calling, mcp, rest-api |
| Integrations | OpenAI, Azure OpenAI, Anthropic, Ollama | OpenAI, Anthropic, Google, AWS Bedrock, LangSmith |
| Capabilities | 4 documented | 4 documented |
AutoGen
- +Strong, well-known abstraction for multi-agent conversation (two-agent and group-chat patterns) from Microsoft Research
- +v0.4 rewrite brings an asynchronous, event-driven architecture with better observability and control
- +Open source, model-agnostic, and supports humans as first-class participants in agent conversations
- -Framework, not a product: autonomy and reliability depend entirely on what the developer builds
- -Now community-managed and described as in maintenance mode, with the original team's active work continuing under the renamed AG2 project
LangGraph
- +Explicit graph model makes complex agent control flow (loops, branching, multi-agent routing) inspectable and controllable
- +Production-grade primitives: durable execution, checkpointing/time-travel, and first-class human-in-the-loop interrupts
- +Open source and model-agnostic, with a hosted LangGraph Platform and LangSmith observability for deployment
- -Lower-level and more verbose than higher-level agent libraries; a steeper learning curve
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
Which should you choose?
AutoGen is microsoft framework for multi-agent conversational ai applications, best for developers, enterprise. LangGraph is low-level framework for stateful, durable, graph-based llm agents, best for developers, enterprise, mid-market. The right choice depends on the autonomy level you want, your existing integrations, and your budget, all compared above.