TL;DR
LangChain and LangGraph come from the same team and solve different problems. LangChain is a broad framework for building applications from LLM components: models, prompts, retrievers, and tools. LangGraph is a library for orchestrating stateful, multi-step agent workflows as a graph, with explicit control over loops, branching, and state. Most complex agents use both.
Why the two get confused
They share a name, a maintainer, and a lot of documentation, so they read like versions of one thing. They are not. LangChain gives you the building blocks; LangGraph gives you a way to wire those blocks into a controllable, stateful flow.
What is LangChain?
LangChain is a framework and ecosystem for building applications on top of language models. It provides standard interfaces and integrations for the pieces you keep needing: model wrappers, prompt templates, output parsers, retrievers for RAG, memory, and a large library of tool integrations. Its strength is breadth. It is the glue that lets you swap a model or a vector store without rewriting your app.
LangChain shines for straightforward flows: a chain of steps, a retrieval-augmented answer, a single tool-using agent. When control flow is mostly linear, LangChain on its own is often enough.
What is LangGraph?
LangGraph is a lower-level library, from the same team, for building agent workflows as a directed graph. Each node is a step, such as a model call, a tool call, or a function; edges decide what runs next, including loops and conditional branches. State is explicit and passed between nodes, so the workflow can remember, retry, branch, and resume.
LangGraph shines when control flow is not linear: multi-agent systems, human-in-the-loop steps, retries, cycles, or anything where you need precise control over what happens next and what state carries forward. It uses LangChain components inside its nodes; it does not replace them.
Picture a support agent that reads a ticket, looks up the account, drafts a reply, and pauses for a human to approve before sending. That pause, the branch when the human edits the draft, and the retry when the lookup fails are all control flow. In LangChain you would bolt that logic on by hand; in LangGraph the pause, the branch, and the retry are nodes and edges in the graph, with the ticket state carried through. That is the kind of job LangGraph exists for.
LangGraph vs LangChain: the core difference
LangChain is the component library; LangGraph is the orchestration layer. One gives you the parts, the other gives you controlled, stateful flow between them.
| LangChain | LangGraph | |
|---|---|---|
| What it is | A framework of LLM building blocks | A library for stateful agent graphs |
| Level | Higher-level components | Lower-level orchestration |
| Control flow | Chains, mostly linear | Graphs with loops and branches |
| State | Basic memory helpers | Explicit, passed between nodes |
| Best for | RAG, single agents, straightforward chains | Multi-agent, human-in-the-loop, retries, cycles |
| Relationship | Provides the components | Orchestrates those components |
How they fit together
They are designed to be used together, not chosen between. The parts come from one, the flow from the other:
LangChain components (models, tools, retrievers) -> used inside -> LangGraph nodes -> wired into a stateful graph -> the running agent
You reach for LangChain to assemble the parts, and for LangGraph when the flow between those parts needs real control.
Which should you use?
Start with LangChain for linear or lightly-branching apps: a RAG pipeline, a simple tool-using agent. Move to LangGraph when you need stateful, cyclic, or multi-agent control that a chain cannot express cleanly. Because they are built to work together, it is rarely either-or: LangChain for the pieces, LangGraph when the wiring gets hard.
Whichever you pick, the framework runs the agent but does not tell you whether the agent is any good. That is evaluation, scoring each run for correctness, cost, and latency, and it is framework-agnostic: Prefactor measures agents built on LangChain, LangGraph, or neither.