LangGraph vs LangChain

Two layers from the same team

LangGraph and LangChain are built by the same team and are designed to sit at different layers of the stack rather than to compete. LangChain is the high-level framework: chains, tools, agents, memory, prompt templates, and the largest integration ecosystem in the category, aimed at getting an LLM application working quickly. LangGraph is the low-level orchestration library: it models an agent as a graph of nodes and edges over a shared, persisted state, exposing the control-flow primitives — loops, branches, human-in-the-loop pauses, checkpointing — that higher-level frameworks abstract away.

The practical mental model: reach for LangChain when you want breadth and speed, and reach for LangGraph when you need explicit control over how an agent moves through its steps and how its state persists across them. Because both are code-first, Python-and-TypeScript, MIT-licensed, and model-agnostic through the same adapter layer, moving between them is a matter of how much control you need, not a platform migration.

Graph orchestration vs framework abstractions

LangChain gives you agent executors and chains that hide the control loop. You describe the tools and the model, and the framework runs the reason-act loop for you. This is the fastest way to a working agent, and for many single-agent, tool-calling use cases it is all you need. The cost is that when the default loop does not match what you want — a custom retry policy, a branch that depends on intermediate state, a pause for human approval — you are working against an abstraction that was designed to hide exactly that machinery.

LangGraph inverts the trade. You define the nodes, the edges, and the shared state explicitly, and the runtime executes the graph you drew. Loops, conditional branches, and human-in-the-loop interrupts are first-class because you wired them yourself. For long-horizon agents that must be controllable and debuggable step by step, that explicitness is the whole point; for a two-step tool call, it is overhead.

State and persistence

This is the sharpest technical difference. LangChain is stateless by default — memory is an add-on component you attach to a chain or agent, and it is oriented toward conversational history rather than arbitrary workflow state. LangGraph is built around a typed, shared state object that every node reads and writes, with checkpointing that persists that state between steps and across runs.

The consequence shows up in reliability. LangGraph checkpointing lets an agent pause, wait for a human decision or an external event, and resume from exactly where it stopped without re-running earlier steps. Rebuilding that behavior on top of LangChain is possible but manual. When durability of in-flight agent state matters, LangGraph is the layer that provides it natively.

Ecosystem and learning curve

LangChain has the ecosystem advantage. It is the default abstraction most teams start with, which means the largest set of integrations across models, vector stores, and data sources, the most examples, and the most community answers when something breaks. For RAG, prompt orchestration, and broad tool coverage, that breadth shortens the path from idea to prototype.

LangGraph is newer and narrower by design. Its surface is orchestration, not integrations, so the learning curve is steeper — you have to think in nodes, edges, and state — but the concepts you learn are exactly the ones that matter for controllable agents. Teams already inside the LangChain ecosystem adopt LangGraph incrementally, keeping LangChain tools and integrations while moving the control flow into a graph.

Who should choose LangGraph

LangGraph is the right call for engineering teams that need precise control over agent control flow and state: long-horizon agents with loops and branches, human-in-the-loop approval steps, and workflows where resuming from a checkpoint without redoing work is a requirement. If you are already in the LangChain ecosystem and have hit the ceiling of what the built-in agent loop can express, LangGraph is the layer to drop into. Its MIT license, code-first design, and model-agnostic adapters mean adopting it does not lock you into anything you were not already using.

Who should choose LangChain

LangChain is the right call when breadth and speed matter more than low-level control. Teams building RAG applications, wiring up the largest set of model and vector-store integrations, or standing up a tool-calling agent prototype in an afternoon get the most from its batteries-included abstractions and mature ecosystem. It is also the more approachable starting point: you can ship a working chain before you need to understand graphs and shared state at all, and adopt LangGraph later for the specific flows that outgrow the framework.