LangGraph vs LangChain: which should you use?

Teams often ask whether to build on LangChain or LangGraph as if they had to pick a side. They do not. The two are designed to work together, and the real question is how much control your workflow needs.

The short version

LangChain gives you the building blocks: model wrappers, prompt templates, tool integrations, retrievers.
LangGraph gives you control flow: it models your application as a graph of steps with state, branching, loops and checkpoints.

You typically use LangChain components inside a LangGraph graph. It is not either-or.

When a simple chain is enough

If your flow is linear (take input, retrieve, prompt, return output), a straightforward LangChain pipeline is perfect. Adding a graph would be overengineering. Most single-purpose features live here.

When you need LangGraph

Reach for LangGraph when your application has real control-flow needs:

Branching: route requests to different agents or paths based on the input.
Loops: let an agent reason, act, observe and try again (the ReAct pattern).
State that persists: conversations and workflows that must survive across turns and restarts.
Human-in-the-loop: pause for a person to approve before continuing.

On a healthcare product we used a LangGraph orchestrator to route each clinician request to the right specialized agent, with persistent per-user memory that survived restarts. That branching, statefulness and durability is exactly what a plain chain cannot give you.

LangGraph vs LangChain for agents

If you are specifically building agents, this is where the distinction matters most.

LangChain historically offered agent abstractions (the older AgentExecutor), but they hid the control flow inside a loop you could not easily inspect or steer. Beyond a toy, that becomes hard to debug and harder to make reliable.

LangGraph is the modern answer for agents. It models the agent as an explicit graph: nodes for reasoning and tool calls, edges for the decisions between them, and shared state that persists across steps. That gives you what production agents actually need:

Inspectable control flow instead of a black-box loop
Durable state and memory across turns and restarts
Human-in-the-loop checkpoints where an agent pauses for approval
Multi-agent routing, where an orchestrator hands work to specialized sub-agents

You still use LangChain’s building blocks (models, tools, retrievers) inside the LangGraph nodes. So for agents it is not “either/or”: build the agent’s structure in LangGraph, using LangChain components for the pieces. A single tool call can stay a plain LangChain call; a real agent that reasons, acts and remembers belongs in LangGraph.

How to decide

Start simple. If your feature is a straight line, use a LangChain chain. The moment you need branching, loops, durable state, a human checkpoint, or a genuine agent, move to LangGraph, and keep using LangChain’s components within it.

EpochC builds production agents on LangGraph and LangChain. Start a project or hire on Upwork.