LangChain and LangGraph aren't competitors — LangGraph is built on top of LangChain. LangChain is the broad framework of building blocks for LLM applications, and it's excellent at linear flows (chains). LangGraph is a library that adds control for stateful, multi-step workflows that loop, branch, and maintain state — the kind of orchestration real AI agents need. The rule of thumb: use LangChain's components for everything, and reach for LangGraph when your workflow stops being a straight line. Most serious agent projects use both.
"LangGraph vs LangChain" is a slightly misleading way to frame the question, because it implies you have to choose one. You don't. Understanding how they relate — and specifically when a project graduates from one to the other — is what actually helps you build. This guide explains what each does, why LangGraph exists, when to use which, and how they work together, for anyone building or overseeing AI agent development.
Start with what LangChain is
To understand LangGraph, start with LangChain: an open-source framework that provides ready-made building blocks for LLM applications — model connections, document loading, retrieval, memory, and orchestration. Its core orchestration concept is the chain: a sequence of steps run one after another. Chains are perfect for linear workflows — "load the document, retrieve the relevant parts, generate an answer." For a large share of applications, that linear model is exactly right, and LangChain alone is all you need.
Why LangGraph exists
The trouble starts when workflows stop being linear. Real AI agents don't run a fixed sequence and stop — they loop (keep working until a task is done), branch (take different paths depending on what they find), maintain state across many steps, sometimes pause for human input, and may coordinate multiple agents. Expressing that kind of control with simple linear chains gets awkward fast. LangGraph was built by the LangChain team to solve exactly this: it's a library, on top of LangChain, for building stateful, multi-step workflows as graphs, giving you precise control over complex, non-linear agent behaviour.
A chain is a straight road. A graph is a road network with junctions, loops, and roundabouts. Simple journeys need a road; real agents need the network.
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How they compare
| LangChain | LangGraph | |
|---|---|---|
| What it is | Broad framework of building blocks | Library for stateful workflows, built on LangChain |
| Best at | Linear sequences (chains) | Loops, branching, persistent state |
| Core concept | Chains — steps in sequence | Graphs — nodes, edges, and state |
| Use it for | Components, RAG, simple flows | Complex agents, multi-step control |
| Relationship | The foundation | Extends the foundation |
| Learning curve | Approachable basics | More concepts (graphs, state) |
The key row is the last-but-one: they operate at different levels. LangChain gives you the components; LangGraph gives you a way to orchestrate them when the orchestration is complex.
When to use which
Use LangChain alone when your application is a straightforward sequence — a RAG system that retrieves and answers, a chatbot with memory, a document-processing pipeline. If you can describe the flow as "do A, then B, then C," chains handle it cleanly and there's no need for the extra machinery.
Reach for LangGraph when the flow isn't a straight line: an agent that keeps looping until it has solved a problem, a workflow that branches based on intermediate results, a process that must maintain state across many steps, a system that pauses for human approval before continuing, or several agents that need to coordinate. The signal is non-linearity — the moment "then" becomes "if this, loop back and try again," you're in LangGraph territory.
They work together, not against each other
The framing that matters: these are complementary. A typical real-world agent uses LangChain's components — model connections, retrieval, memory — for the building blocks, and LangGraph to orchestrate them into reliable, stateful behaviour. You don't pick a side; you use LangChain for what it's good at and add LangGraph when your control flow demands it. Teams that understand both can start simple and scale up smoothly, introducing LangGraph exactly when a growing agent needs it rather than rewriting from scratch.
A concrete progression
Picture building a research assistant. Version one just retrieves from your documents and answers — a clean linear chain, pure LangChain. Then you want it to keep searching until it has enough information, deciding after each search whether to continue or stop. That's a loop with state — and now LangChain alone is fighting you. You introduce LangGraph: the search becomes a node, the "do I have enough?" decision becomes a branch, and the accumulated findings become state that persists across iterations. Nothing you built is thrown away; the LangChain components stay, and LangGraph wraps them in the control structure the new behaviour needs. That progression — linear first, graph when the agent grows — is the natural path most teams follow.
Common misconceptions
- "LangGraph replaces LangChain." It's built on top of it and extends it. They're designed to be used together, not as alternatives.
- "You should always start with LangGraph." If your workflow is linear, plain LangChain is simpler and sufficient. Reach for the graph only when you need non-linear control.
- "LangGraph is a different framework to learn from scratch." Because it sits on LangChain, much of what you know carries over; LangGraph adds the graph-and-state layer on top.
The multi-agent angle
One of the clearest reasons teams graduate to LangGraph is multi-agent systems — several specialised agents working together, each handling part of a larger task and handing off to the others. A researcher agent gathers information, a writer agent drafts, a reviewer agent checks the work, and a coordinator decides what happens next. Orchestrating that hand-off, shared state, and decision-making is exactly what graphs are for, and it's very hard to express cleanly as linear chains. As enterprise AI moves from single assistants toward these coordinated agent teams, the LangGraph layer becomes less of an optional extra and more of a core skill — which is why understanding when and how to use it is increasingly part of building production AI rather than a niche concern.
Learning both effectively
Because they're layered, the sensible learning path mirrors the tools themselves: get comfortable with LangChain's components and chains first, then add LangGraph's graphs, nodes, and state once you understand what problem they solve. LangGraph's concepts are more involved, but they exist for a reason — they're how you build agents that are reliable rather than brittle. Structured, hands-on training that moves from the LangChain foundations through to LangGraph orchestration is the fastest way through, which is exactly the arc of our Agentic AI with LangChain and LangGraph programme, part of our enterprise AI training solutions.
- LangGraph is built on top of LangChain — they're complementary, not competing frameworks.
- LangChain excels at linear flows (chains); LangGraph adds control for stateful, looping, branching agent workflows.
- Use LangChain alone for straightforward sequences; add LangGraph when your workflow becomes non-linear.
- Real agents typically use both — LangChain for components, LangGraph for orchestration.
- Learn LangChain first, then LangGraph; the layering means your knowledge carries forward as complexity grows.
Glossary
- LangChain: a framework of building blocks for LLM applications.
- LangGraph: a library on LangChain for stateful, multi-step workflows.
- Chain: a linear sequence of steps — LangChain's core concept.
- Graph: nodes and edges expressing non-linear flow — LangGraph's core concept.
- State: information a workflow carries across steps.
- Node / edge: a step and a connection between steps in a LangGraph graph.
Frequently asked questions
What is the difference between LangChain and LangGraph?
LangChain is a broad framework of building blocks for LLM applications, best at linear sequences of steps (chains). LangGraph is a library built on top of it for stateful, multi-step workflows that loop, branch, and maintain state — the kind of control real agents need. In short: LangChain for the components and simple flows, LangGraph for complex agentic control.
Do I need both LangChain and LangGraph?
Often, yes. They're complementary, not competitors. Most teams use LangChain's components (model connections, retrieval, memory) and reach for LangGraph when their workflow becomes non-linear — an agent that loops until a task is done, branches on conditions, or must be paused and resumed. Simple applications may need only LangChain.
Is LangGraph a replacement for LangChain?
No. LangGraph is built on top of LangChain and extends it; it doesn't replace it. Think of LangChain as the toolbox and LangGraph as a specialised tool within it for orchestrating complex, stateful agent behaviour. They're designed to work together.
When should I use LangGraph instead of just LangChain?
Use LangGraph when your workflow isn't a straight line — when it needs loops, branching based on results, persistent state across steps, human-in-the-loop pauses, or multiple agents coordinating. If your application is a simple sequence (retrieve, then answer), plain LangChain is enough.
Is LangGraph harder to learn than LangChain?
LangGraph introduces concepts — graphs, nodes, edges, and state — that are more involved than LangChain's linear chains, so there's an additional learning curve. But it exists precisely because those concepts are what complex agents require; learning it is learning how to build reliable agentic systems, which structured training makes far more approachable.
Are LangChain and LangGraph free and open-source?
Yes, both are open-source and free to use. The company behind them offers paid products for observability and deployment, but the core LangChain and LangGraph libraries carry no licence cost. You pay only for the models and infrastructure you run them on.
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