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.
This comparison is generated from the sourced AutoGen and LangGraph profiles. Open either profile to review its evidence and last-reviewed date.