LangChain vs LangGraph
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose LangChain if you want open-source framework and platform for building and deploying llm agents (Supervised agent, freemium); choose LangGraph if you want low-level framework for stateful, durable, graph-based llm agents (Supervised agent, freemium).
| LangChain | LangGraph | |
|---|---|---|
| What it is | Open-source framework and platform for building and deploying LLM agents | Low-level framework for stateful, durable, graph-based LLM agents |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | freemium · Framework free (MIT); LangSmith free Developer tier | freemium · Framework free (MIT); LangGraph Platform via LangSmith (free Developer tier) |
| Best for | developers, enterprise, mid-market | developers, enterprise, mid-market |
| Deployment | self-hosted, api, saas | self-hosted, api, saas |
| Modalities | text, code, api | text, code, api |
| Models | model-agnostic, gpt, claude, gemini, llama, open-source | model-agnostic, gpt, claude, gemini, open-source |
| Protocols | function-calling, mcp, rest-api | function-calling, mcp, rest-api |
| Integrations | OpenAI, Anthropic, Google, AWS Bedrock, Pinecone, Hugging Face | OpenAI, Anthropic, Google, AWS Bedrock, LangSmith |
| Capabilities | 4 documented | 4 documented |
LangChain
- +Largest open-source LLM/agent framework community with very broad integration coverage
- +Model-agnostic design future-proofs apps against LLM churn
- +LangGraph adds production-grade primitives (durability, checkpointing, human-in-the-loop) that bare API calls lack
- -Frequently criticized for heavy abstractions and churn between API versions; debugging deep chains can be painful
- -Most production value (observability, deploy) lives in the paid LangSmith platform
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?
LangChain is open-source framework and platform for building and deploying llm agents, best for developers, enterprise, mid-market. 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 LangChain and LangGraph profiles. Open either profile to review its evidence and last-reviewed date.