LangChain vs LangGraph

LangChain for simple linear pipelines. LangGraph for stateful, multi-step, multi-agent workflows.

LangGraph is built on top of LangChain and made by the same team. It is not a replacement — it is an extension for when you need real state management and non-linear flows.

	LangChain (Chains / LCEL)	LangGraph
Architecture	Linear chains or DAGs via LCEL (LangChain Expression Language).	Explicit state graph: nodes, edges, conditional transitions.
State management	Limited. State lives in memory for the duration of a chain.	First-class. Persistent checkpoints, resumable runs, human-in-the-loop.
Multi-agent	Possible but awkward to express.	Native. Each agent is a subgraph.
Debugging	LangSmith traces.	LangSmith traces + explicit state inspection.
Learning curve	Moderate. LCEL has good docs.	Steeper. Graph mental model takes time.
Best for	Retrieval chains, simple tool-calling pipelines, chatbots.	Complex agents, workflows with approval steps, multi-agent systems.

Pick LangChain (Chains / LCEL) when

Use LangChain chains when: your pipeline is linear (retrieve → generate → return) and does not need to loop back or wait for external input.

Pick LangGraph when

Use LangGraph when: you need retry logic, human approval gates, multiple agents that share state, or long-running workflows that need to pause and resume.

Bottom line

Start with LangChain. The moment you find yourself fighting the linear model to add branching, loops, or persistent state, switch to LangGraph. That is what it was built for.

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Glossary