What Is LangGraph? Stateful AI Agents, Explained (2026)

LangGraph is one of the most-used ways to build a serious AI agent in 2026 — and one of the most confused, mostly because people can’t tell where LangChain ends and LangGraph begins. Here’s a plain-English, independent answer: what LangGraph is, how it works, how it differs from LangChain, and when to reach for each — with every framework we mention verified live.

What is LangGraph?

LangGraph is an open-source, MIT-licensed framework — built by LangChain, for Python and JavaScript — that lets you build AI agents as a directed graph of nodes and edges sharing one typed state. You model the agent’s logic as nodes (discrete steps — call a model, run a tool, validate output) connected by edges (which decide what happens next), all sharing one typed state object. Because it’s a graph rather than a straight line, the agent can loop back, branch down different paths, pause for a human, and pick up exactly where it left off.

• What it is: open-source framework for stateful AI agents (a graph of nodes + edges)
• Built by: LangChain, Inc.
• License: MIT — free to self-host
• Languages: Python and JavaScript / TypeScript (LangGraph.js)
• Merged into LangChain’s agent engine: October 2025

It’s built and maintained by LangChain, and it’s the framework you reach for when a simple prompt-and-response isn’t enough — when the agent needs to reason across multiple steps and stay coherent while doing it. That makes LangGraph a practical foundation for the kind of agentic AI that actually completes multi-step work.

How LangGraph works: state, nodes, and edges

LangGraph organizes every agent around three primitives:

• State — a single, typed object (usually a Python TypedDict, though a Pydantic model or dataclass works too) that every node can read and update. It’s the agent’s shared memory, and it’s explicit — you can see exactly what the agent knows at each step.
• Nodes — plain functions that do one thing: call an LLM, run a tool, check a result. Each node takes the state, does its work, and returns an update to the state.
• Edges — the wiring that decides which node runs next. A conditional edge lets the agent branch (“if the model asked for a tool, go to the Tools node; otherwise finish”), which is what makes loops and decisions possible.

Crucially, LangGraph checkpoints the state after every node (to memory, SQLite, or Postgres). That single design choice is what unlocks the hard stuff: an agent can pause for human approval before a risky action, resume exactly where it stopped after a crash, and keep a full trace of what it did. It can also stream its progress — tokens, state updates, and tool calls — as it runs, so a UI can show the agent working instead of hanging on a spinner. That’s the kind of control that separates a demo from a production agent.

LangGraph vs LangChain: what’s the difference?

This is the question everyone asks, so here’s the clean version: LangChain and LangGraph aren’t competitors — they’re different layers of the same stack. LangChain is the high-level toolkit (model abstractions, 600+ integrations, and simple chain composition). LangGraph is the low-level runtime that actually executes a stateful, multi-step agent.

A LangChain chain is a conveyor belt — one direction, no going back. LangGraph is a flowchart — you can loop, branch, pause, and hand off to a human.

And as of October 2025 the two are formally joined: LangChain’s own agents now run on LangGraph’s execution engine internally (its create_agent replaced the old AgentExecutor), so learning LangGraph is learning how modern LangChain agents actually work under the hood.

When to use LangGraph (vs LangChain)

• Use LangChain when the workflow is linear: a retrieval-augmented (RAG) pipeline, a single-turn question-answer, or a quick prototype where you just need models and integrations wired together fast.
• Use LangGraph when the agent needs to loop (reason, act, observe, repeat), branch on a decision, pause for human approval, or recover from a failed step without starting over.
• In production, use both. LangChain supplies the model and tool integrations; LangGraph runs the agent loop. That’s the standard 2026 pattern, not an either/or.

A simple LangGraph example

The canonical LangGraph agent is a two-node loop: an Agent node (the LLM decides what to do) and a Tools node (it runs the tool the model asked for). A conditional edge checks the model’s output:

• If the model requested a tool → go to the Tools node, run it, and loop back to the Agent with the result.
• If the model produced a final answer → go to end.

In code, that skeleton is about a dozen lines:

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

class State(TypedDict):
    messages: list

def agent(state: State):      # the LLM reasons about the next step
    return {"messages": call_model(state["messages"])}

def tools(state: State):      # run whatever tool the model asked for
    return {"messages": run_tool(state["messages"])}

def next_step(state: State):  # branch: loop again, or finish
    return "tools" if needs_tool(state["messages"]) else END

builder = StateGraph(State)
builder.add_node("agent", agent)
builder.add_node("tools", tools)
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", next_step)
builder.add_edge("tools", "agent")   # loop back with the result

graph = builder.compile()
graph.invoke({"messages": ["Book me a flight under $400"]})

That loop — reason, act, observe, repeat until done — is the core pattern behind almost every capable agent, and LangGraph makes it explicit and inspectable instead of hidden inside a black box. Add a checkpoint and a “pause before this tool” step, and you have a human-in-the-loop agent that’s safe to point at real systems.

LangGraph alternatives

LangGraph is powerful but low-level — it trades convenience for control. If you want a different trade-off, the main alternatives are:

• CrewAI — role-based “crews” of agents (a planner, a researcher, a writer) that collaborate; higher-level and quicker to start.
• Microsoft AutoGen — conversational multi-agent systems where agents talk to each other to solve a task.
• LlamaIndex — data- and RAG-centric agents, strongest when the job is reasoning over your documents.
• OpenAI Agents SDK — a lighter, OpenAI-native way to build tool-using agents.

How to get started with LangGraph

Start small: install LangGraph, define a state schema, and build the two-node reason/act loop above before adding branches. Wire in your model and tools through LangChain, connect any external systems via MCP servers, and add a checkpoint so you can pause and resume. Keep the agent’s autonomy narrow at first — one well-scoped job — and widen it as you trust it. For the bigger picture of what you’re building, see our guides to AI agents and agentic AI.