LangChain vs LangGraph: 4 Key Differences and When to Use Each
| Source: Towards Data Science
Tags: LangChain, LangGraph, AI agents, orchestration, agentic AI
LangGraph is not a LangChain replacement but a stateful, loop-capable extension—use LangChain for forward pipelines and LangGraph when your agent needs to route backward, retry, or maintain typed state across turns.
Details
Soner Yıldırım's Towards Data Science breakdown of LangChain vs. LangGraph clarifies four key architectural differences. First, LangChain is a pipeline: data flows forward through chained components, and loops require external Python code. LangGraph treats loops as first-class citizens via node-edge graphs with conditional routing—agents can return to earlier nodes without workarounds. Second, LangChain is stateless, passing state as a dictionary between steps; LangGraph uses a typed schema (TypedDict) as a persistent state object shared across all nodes. Third, routing in LangChain follows fixed sequences; LangGraph's conditional edges let agents branch based on current state. Fourth, LangGraph enables richer orchestration patterns like supervisor agents that are awkward in pure LangChain. A customer service agent with booking and pricing engine nodes is used as a concrete example. Both tools are actively maintained within the same LangChain ecosystem—LangGraph does not replace LangChain.