LangGraph vs LangChain: What Is the Difference?
By Karthick Selvam · Published
LangGraph is a library from the LangChain team for building stateful, multi-step agent workflows as graphs.
LangChain
Focuses on components and simple chains: prompts, models, retrievers, tools and their integrations.
LangGraph
Models an application as a graph of nodes (steps) and edges (transitions) with shared state. It supports loops, branching, persistence, human-in-the-loop approval and multi-agent designs.
When to use which
Use LangChain components for straightforward pipelines like RAG. Use LangGraph when you need controllable agent behaviour with cycles, retries and checkpoints.
Together
They are often used together: LangChain components inside LangGraph nodes.
Frequently asked questions
Is LangGraph only for agents?
It is designed for agentic and stateful workflows, but any multi-step process with branching can benefit.
Related programmes
- Agentic AI with LangChain & LangGraph
- Building AI Agents with LangGraph & CrewAI
- Multi-Agent Systems: Design & Orchestration
Turn this into a team capability
Our corporate programmes cover topics like this hands-on, mapped to your stack.
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