LangChain vs LangGraph: When to Use Which (2026)

LangChain and LangGraph are from the same team but built for different problems. Picking wrong adds weeks of refactoring later.

The core difference

LangChain is a Swiss-army-knife library for chaining LLM calls — prompts, retrievers, tools, output parsers. Linear or simple branching flows.

LangGraph is a stateful, graph-based agent runtime built on top of LangChain primitives. Cyclic flows, human-in-the-loop checkpoints, durable state. Built for agents that need to backtrack, ask the user a clarifying question, or run for hours.

Code complexity for a 3-tool agent

LangChain agent: ~25 lines, hidden retry logic via AgentExecutor, debugging is mostly print statements.

LangGraph agent: ~80 lines, explicit state schema (TypedDict), nodes for each step, debuggable as a literal graph in LangSmith.

For a one-shot "research and summarize" agent, LangChain's brevity wins. For anything that might run multiple turns or fail mid-flight, LangGraph's explicitness saves you.

State persistence

LangChain: nothing built-in. You bring your own Postgres or Redis.

LangGraph: checkpoint sqlite/Postgres adapter is first-class. Resume mid-flow, replay from any checkpoint, time-travel debugging.

When to pick which

Pick LangChain when: - Single-shot task (RAG query, summarization, classification) - Prototyping a concept in <100 lines - Simple linear chain with optional branching

Pick LangGraph when: - Multi-turn agent that might loop - Need human approval steps - Long-running workflows (>2 minutes) - Need to resume from failure - You're going to production

Honest opinion

We start every new agent on Nexora with LangGraph. The 50-line tax is negligible compared to the cost of refactoring from LangChain when complexity grows. LangChain is now mostly used as the underlying primitive library.

Hire a LangChain or LangGraph engineer →