LangGraph vs LangChain

A side-by-side comparison to help you choose the right tool.

LangGraph scores higher overall (87/100)

But the best choice depends on your specific needs. Compare below.

Pricing
Open-source project with no core license fee.
Free plan
Yes
Best for
Teams building serious agent workflows, Developers who need state and branching control, Builders who outgrew simple chains
Platforms
mac, windows, linux, api
API
Yes
Languages
en
Pricing
Open-source framework; no license fee for the core project.
Free plan
Yes
Best for
Developers prototyping or shipping LLM apps quickly, Teams that want a big ecosystem of examples and integrations, Builders creating RAG or tool-using workflows
Platforms
mac, windows, linux, api
API
Yes
Languages
en

Choose LangGraph if:

  • You are Teams building serious agent workflows
  • You are Developers who need state and branching control
  • You are Builders who outgrew simple chains
  • You want to start free
Read LangGraph review →

Choose LangChain if:

  • You are Developers prototyping or shipping LLM apps quickly
  • You are Teams that want a big ecosystem of examples and integrations
  • You are Builders creating RAG or tool-using workflows
  • You want to start free
Read LangChain review →

FAQ

What is the difference between LangGraph and LangChain?
LangGraph is a graph-based framework for building stateful, multi-step agent workflows with more explicit control than plain prompt chaining. LangChain is a widely used open-source framework for building llm apps with tools, chains, retrieval, and agent workflows.
Which is cheaper, LangGraph or LangChain?
LangGraph: Open-source project with no core license fee.. LangChain: Open-source framework; no license fee for the core project.. LangGraph has a free plan. LangChain has a free plan.
Who is LangGraph best for?
LangGraph is best for Teams building serious agent workflows, Developers who need state and branching control, Builders who outgrew simple chains.
Who is LangChain best for?
LangChain is best for Developers prototyping or shipping LLM apps quickly, Teams that want a big ecosystem of examples and integrations, Builders creating RAG or tool-using workflows.