Towards Data Science· 2 days ago
LangChain vs LangGraph: 4 Key Differences and When to Use Each
The article contrasts LangChain’s modular, prompt‑centric design with LangGraph’s graph‑oriented, stateful agent framework, highlighting four core distinctions: (1) LangChain focuses on linear chains of LLM calls using components like LLMChain and PromptTemplate, while LangGraph models workflows as directed graphs with nodes and edges that maintain state across steps; (2) LangChain is lightweight and ideal for simple pipelines, whereas LangGraph supports complex multi‑step reasoning and long‑term memory via its built‑in state machine; (3) LangGraph’s scheduler introduces higher latency but enables dynamic branching based on intermediate results; (4) Integration patterns differ—LangChain uses a Chain API, whereas LangGraph exposes a Graph API with explicit node definitions. The piece advises choosing LangChain for quick prototyping and LangGraph when building production‑grade, multi‑
