LangChain and LangGraph come from the same team and are often mentioned together, which leads people to assume they’re interchangeable. They solve genuinely different problems.
LangChain is a general-purpose framework for chaining together model calls, tools, and data sources, well suited to linear or moderately branching workflows: retrieval-augmented generation, straightforward multi-step chains.
LangGraph is built specifically for agent workflows that need to loop, branch conditionally, and maintain complex state across many steps, the kind of graph-shaped logic a simple linear chain can’t represent cleanly.
Sketch your actual workflow on paper first. Basically a straight line, or a simple branch? LangChain’s simpler abstraction is usually enough. Looks like a genuine graph with loops and conditional paths? LangGraph’s explicit state management saves you from working around LangChain’s more linear assumptions.
You can also use both together, LangGraph works alongside LangChain’s components rather than replacing them. Most simple agents don’t need LangGraph’s added complexity, genuinely stateful, looping agents usually do. See LangChain’s own documentation for both frameworks.
