langgraph-agents

name	langgraph-agents
description	LangGraph stateful AI agents with graph-based workflows. Use when creating state-machine agents with checkpoints, human-in-the-loop, streaming execution, or subgraph composition.
user-invocable	false
allowed-tools	Bash(python *), Bash(uv *), BashOutput, Read, Write, Edit, Grep, Glob, TodoWrite
created	"2026-01-08T00:00:00.000Z"
modified	"2026-05-09T00:00:00.000Z"
reviewed	"2026-04-25T00:00:00.000Z"

LangGraph Agents

When to Use This Skill

Use this skill when...	Use a sibling skill instead when...
Building stateful agents as graphs of nodes/edges with checkpointing	Writing simple LCEL chains without state — use langchain-development
Adding human-in-the-loop approval, streaming, or time-travel debugging	Doing basic tool binding without a graph — use langchain-development
Composing multi-agent systems as subgraphs	Needing hierarchical planning + file-system context — use deep-agents
Wiring graphs into an initialised project	Scaffolding a brand-new project — use langchain-init (/langchain:init)

Core Expertise

LangGraph is a low-level orchestration framework for stateful agents:

• Graph-based workflow definition (nodes and edges)
• Durable execution with checkpointing
• Human-in-the-loop interactions
• Short-term and long-term memory
• Streaming and time-travel debugging
• LangSmith observability integration

Installation

# Core LangGraph package
npm install @langchain/langgraph

# Required dependencies
npm install @langchain/core
npm install @langchain/openai  # or your preferred model provider

# Optional: Checkpointing backends
npm install @langchain/langgraph-checkpoint-sqlite

Graph Fundamentals

State Definition

import { Annotation, StateGraph } from "@langchain/langgraph";

// Define state schema using Annotation
const StateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (prev, next) => [...prev, ...next],
    default: () => [],
  }),
  currentStep: Annotation<string>({
    reducer: (_, next) => next,
    default: () => "start",
  }),
});

type State = typeof StateAnnotation.State;

Basic Graph

import { StateGraph, START, END } from "@langchain/langgraph";

const graph = new StateGraph(StateAnnotation)
  .addNode("agent", agentNode)
  .addNode("tools", toolsNode)
  .addEdge(START, "agent")
  .addConditionalEdges("agent", routeAgent)
  .addEdge("tools", "agent")
  .compile();

Nodes

// Nodes are async functions that receive and return state
async function agentNode(state: State): Promise<Partial<State>> {
  const response = await model.invoke(state.messages);
  return {
    messages: [response],
  };
}

async function toolsNode(state: State): Promise<Partial<State>> {
  const lastMessage = state.messages[state.messages.length - 1];
  const toolCalls = lastMessage.tool_calls || [];

  const results = await Promise.all(
    toolCalls.map(tc => tools[tc.name].invoke(tc.args))
  );

  return {
    messages: results.map((r, i) =>
      new ToolMessage({ content: r, tool_call_id: toolCalls[i].id })
    ),
  };
}

Conditional Edges

function routeAgent(state: State): string {
  const lastMessage = state.messages[state.messages.length - 1];

  if (lastMessage.tool_calls?.length) {
    return "tools";
  }
  return END;
}

// Add conditional routing
graph.addConditionalEdges("agent", routeAgent, {
  tools: "tools",
  [END]: END,
});

Prebuilt Agents

ReAct Agent

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({ model: "gpt-4o" });

const agent = createReactAgent({
  llm: model,
  tools: [searchTool, calculatorTool],
});

// Run the agent
const result = await agent.invoke({
  messages: [{ role: "user", content: "What's the weather in NYC?" }],
});

With System Prompt

const agent = createReactAgent({
  llm: model,
  tools: [searchTool],
  stateModifier: "You are a helpful research assistant.",
});

Checkpointing (Persistence)

Memory Checkpointer

import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const graph = new StateGraph(StateAnnotation)
  .addNode("agent", agentNode)
  .compile({ checkpointer });

// Invoke with thread_id for persistence
const config = { configurable: { thread_id: "user-123" } };

await graph.invoke({ messages: [userMessage] }, config);

// Continue conversation in same thread
await graph.invoke({ messages: [anotherMessage] }, config);

SQLite Checkpointer

import { SqliteSaver } from "@langchain/langgraph-checkpoint-sqlite";

const checkpointer = SqliteSaver.fromConnString("./checkpoints.db");

const graph = workflow.compile({ checkpointer });

Get State History

// Get current state
const state = await graph.getState(config);

// Get state history (time travel)
const history = await graph.getStateHistory(config);
for await (const snapshot of history) {
  console.log(snapshot.values, snapshot.next);
}

Human-in-the-Loop

Interrupt Before Node

const graph = new StateGraph(StateAnnotation)
  .addNode("agent", agentNode)
  .addNode("tools", toolsNode)
  .compile({
    checkpointer,
    interruptBefore: ["tools"],  // Pause before running tools
  });

// First invocation stops before tools
const result1 = await graph.invoke(input, config);
// result1.next === ["tools"]

// User reviews, then continue
const result2 = await graph.invoke(null, config);

Interrupt After Node

const graph = workflow.compile({
  checkpointer,
  interruptAfter: ["agent"],  // Pause after agent responds
});

Update State

// Modify state during interrupt
await graph.updateState(config, {
  messages: [new HumanMessage("Actually, do X instead")],
});

// Continue with modified state
await graph.invoke(null, config);

Streaming

Stream Events

const stream = await graph.stream(
  { messages: [userMessage] },
  { streamMode: "values" }
);

for await (const state of stream) {
  console.log(state.messages[state.messages.length - 1]);
}

Stream Updates

const stream = await graph.stream(
  { messages: [userMessage] },
  { streamMode: "updates" }
);

for await (const update of stream) {
  // { nodeId: { ...stateUpdate } }
  console.log(update);
}

Stream Messages

const stream = await graph.stream(
  { messages: [userMessage] },
  { streamMode: "messages" }
);

for await (const [message, metadata] of stream) {
  if (message.content) {
    process.stdout.write(message.content);
  }
}

Subgraphs

Define Subgraph

const researchGraph = new StateGraph(ResearchState)
  .addNode("search", searchNode)
  .addNode("summarize", summarizeNode)
  .addEdge(START, "search")
  .addEdge("search", "summarize")
  .addEdge("summarize", END)
  .compile();

// Use as node in parent graph
const parentGraph = new StateGraph(ParentState)
  .addNode("research", researchGraph)
  .addNode("write", writeNode)
  .addEdge(START, "research")
  .addEdge("research", "write")
  .addEdge("write", END)
  .compile();

Long-Term Memory (Store)

import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const graph = workflow.compile({
  checkpointer,
  store,
});

// In nodes, access store via config
async function agentNode(
  state: State,
  config: RunnableConfig
): Promise<Partial<State>> {
  const store = config.store;

  // Get memories for user
  const memories = await store.search(["user", userId]);

  // Save new memory
  await store.put(["user", userId], memoryId, { content: "..." });

  return { ... };
}

Common Patterns

Tool Execution Loop

const graph = new StateGraph(StateAnnotation)
  .addNode("agent", agentNode)
  .addNode("tools", toolsNode)
  .addEdge(START, "agent")
  .addConditionalEdges("agent", (state) => {
    const last = state.messages[state.messages.length - 1];
    return last.tool_calls?.length ? "tools" : END;
  })
  .addEdge("tools", "agent")
  .compile();

Multi-Agent Workflow

const graph = new StateGraph(StateAnnotation)
  .addNode("researcher", researcherAgent)
  .addNode("writer", writerAgent)
  .addNode("reviewer", reviewerAgent)
  .addEdge(START, "researcher")
  .addEdge("researcher", "writer")
  .addEdge("writer", "reviewer")
  .addConditionalEdges("reviewer", (state) => {
    return state.approved ? END : "writer";
  })
  .compile();

Agentic Optimizations

Context	Pattern
Quick iteration	Use MemorySaver for development
Production	Use SqliteSaver or external DB
Debug state	graph.getState(config)
Time travel	graph.getStateHistory(config)
Trace execution	Enable LANGCHAIN_TRACING_V2
Reduce tokens	Stream updates, not full state
Human approval	interruptBefore: ["dangerous_node"]

Quick Reference

Core Imports

Import	Package
StateGraph	@langchain/langgraph
Annotation	@langchain/langgraph
START, END	@langchain/langgraph
MemorySaver	@langchain/langgraph
createReactAgent	@langchain/langgraph/prebuilt

Graph Methods

Method	Description
.addNode(id, fn)	Add a node
.addEdge(from, to)	Add unconditional edge
.addConditionalEdges(from, fn)	Add conditional routing
.compile()	Build executable graph
.invoke(input, config)	Run to completion
.stream(input, config)	Stream execution
.getState(config)	Get current state
.updateState(config, update)	Modify state

Stream Modes

Mode	Output
"values"	Full state after each step
"updates"	Only changed values
"messages"	Message chunks for streaming UI
"debug"	Detailed execution info

Config Options

Option	Description
thread_id	Conversation/session ID
checkpoint_id	Specific checkpoint to resume
recursion_limit	Max graph iterations (default: 25)