Compare

LangGraph vs LangChain: Which One Should You Use in 2025?

LangGraph and LangChain come from the same company, but they are built for different problems. Here's how to choose.

Comparing: LangGraph LangChain

Quick Verdict

Choose LangGraph if

You're building multi-step agent workflows with branching logic, parallel execution, or human-in-the-loop patterns

Choose LangChain if

You're building RAG pipelines, chatbots, or applications where the main value is in integrating many data sources and LLM providers

Avoid both if

You need a simple LLM wrapper — use the Anthropic or OpenAI SDK directly

They Are Not Competing Products

The most important thing to understand: LangGraph and LangChain are complementary, not competing. LangGraph uses LangChain's integration ecosystem. You can (and often should) use both.

The question is which one is your primary abstraction.

LangChain's Sweet Spot

LangChain shines when your primary challenge is integrations. You need to query Postgres, Pinecone, and a custom API, then route the results to one of three LLM providers depending on the content type. LangChain has pre-built connectors for all of this.

For RAG applications — retrieval-augmented generation — LangChain is still the default choice. The retriever abstractions, document loaders, and vector store integrations are mature and well-tested.

LangGraph's Sweet Spot

LangGraph is for when your workflow has structure. Multi-step research agents, approval workflows, parallel processing pipelines — any scenario where you need to model what happens when step A succeeds vs fails, or where multiple agents need to coordinate on a shared task.

The graph model makes these patterns explicit rather than implicit. You can visualize the workflow, set checkpoints, interrupt for human review, and resume from any node.

The Migration Question

If you have existing LangChain code, you don't need to rewrite it. LangGraph runs on top of LangChain. You can keep your retrievers, document loaders, and integrations and add graph-based orchestration on top.

Key Differences

  • Mental model: Sequential chains (LangChain) vs Directed graphs (LangGraph)
  • Best for: RAG and integrations (LangChain) vs Complex agent workflows (LangGraph)
  • State management: Implicit (LangChain) vs Explicit and typed (LangGraph)
  • Observability: Good with LangSmith (both)
  • Learning curve: Moderate (LangChain) vs Steeper (LangGraph)

      • Bottom Line

      New project, needs multi-step agent orchestration → Start with LangGraph. New project, needs RAG or multi-provider LLM routing → Start with LangChain. New project that needs both agent orchestration and rich data integrations → LangGraph as the primary execution layer, LangChain for retrievers and connectors. Complex existing LangChain project → Add LangGraph for the agent orchestration layer, keep LangChain for integrations.

      Related Radar Items

      Radar LangGraph Radar LangChain