Alpha Notice: These docs cover the v1-alpha release. Content is incomplete and subject to change.For the latest stable version, see the current LangGraph Python or LangGraph JavaScript docs.
When working with non-deterministic systems that make model-based decisions (e.g., agents powered by LLMs), it can be useful to examine their decision-making process in detail:
  1. Understand reasoning: Analyze the steps that led to a successful result.
  2. Debug mistakes: Identify where and why errors occurred.
  3. Explore alternatives: Test different paths to uncover better solutions.
LangGraph provides time travel functionality to support these use cases. Specifically, you can resume execution from a prior checkpoint — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a new fork in the history. To use time-travel in LangGraph:
  1. Run the graph with initial inputs using invoke or stream methods.
  2. Identify a checkpoint in an existing thread: Use the getStateHistory() method to retrieve the execution history for a specific thread_id and locate the desired checkpoint_id. Alternatively, set a breakpoint before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
  3. Update the graph state (optional): Use the updateState method to modify the graph’s state at the checkpoint and resume execution from alternative state.
  4. Resume execution from the checkpoint: Use the invoke or stream methods with an input of null and a configuration containing the appropriate thread_id and checkpoint_id.
For a conceptual overview of time-travel, see Time travel.

​
In a workflow

This example builds a simple LangGraph workflow that generates a joke topic and writes a joke using an LLM. It demonstrates how to run the graph, retrieve past execution checkpoints, optionally modify the state, and resume execution from a chosen checkpoint to explore alternate outcomes.

​
Setup

First we need to install the packages required 
npm install @langchain/langgraph @langchain/anthropic
Next, we need to set API keys for Anthropic (the LLM we will use) 
process.env.ANTHROPIC_API_KEY = "YOUR_API_KEY";
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import { v4 as uuidv4 } from "uuid";
import * as z from "zod";
import { StateGraph, START, END } from "@langchain/langgraph";
import { ChatAnthropic } from "@langchain/anthropic";
import { MemorySaver } from "@langchain/langgraph";

const State = z.object({
  topic: z.string().optional(),
  joke: z.string().optional(),
});

const llm = new ChatAnthropic({
  model: "claude-3-5-sonnet-latest",
  temperature: 0,
});

// Build workflow
const workflow = new StateGraph(State)
  // Add nodes
  .addNode("generateTopic", async (state) => {
    // LLM call to generate a topic for the joke
    const msg = await llm.invoke("Give me a funny topic for a joke");
    return { topic: msg.content };
  })
  .addNode("writeJoke", async (state) => {
    // LLM call to write a joke based on the topic
    const msg = await llm.invoke(`Write a short joke about ${state.topic}`);
    return { joke: msg.content };
  })
  // Add edges to connect nodes
  .addEdge(START, "generateTopic")
  .addEdge("generateTopic", "writeJoke")
  .addEdge("writeJoke", END);

// Compile
const checkpointer = new MemorySaver();
const graph = workflow.compile({ checkpointer });

​
1. Run the graph

const config = {
  configurable: {
    thread_id: uuidv4(),
  },
};

const state = await graph.invoke({}, config);

console.log(state.topic);
console.log();
console.log(state.joke);
Output: 
How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don't know about? There's a lot of comedic potential in the everyday mystery that unites us all!

# The Secret Life of Socks in the Dryer

I finally discovered where all my missing socks go after the dryer. Turns out they're not missing at all—they've just eloped with someone else's socks from the laundromat to start new lives together.

My blue argyle is now living in Bermuda with a red polka dot, posting vacation photos on Sockstagram and sending me lint as alimony.

​
2. Identify a checkpoint

// The states are returned in reverse chronological order.
const states = [];
for await (const state of graph.getStateHistory(config)) {
  states.push(state);
}

for (const state of states) {
  console.log(state.next);
  console.log(state.config.configurable?.checkpoint_id);
  console.log();
}
Output: 
[]
1f02ac4a-ec9f-6524-8002-8f7b0bbeed0e

['writeJoke']
1f02ac4a-ce2a-6494-8001-cb2e2d651227

['generateTopic']
1f02ac4a-a4e0-630d-8000-b73c254ba748

['__start__']
1f02ac4a-a4dd-665e-bfff-e6c8c44315d9

// This is the state before last (states are listed in chronological order)
const selectedState = states[1];
console.log(selectedState.next);
console.log(selectedState.values);
Output: 
['writeJoke']
{'topic': 'How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don\\'t know about? There\\'s a lot of comedic potential in the everyday mystery that unites us all!'}

​
3. Update the state

updateState will create a new checkpoint. The new checkpoint will be associated with the same thread, but a new checkpoint ID. 
const newConfig = await graph.updateState(selectedState.config, {
  topic: "chickens",
});
console.log(newConfig);
Output: 
{'configurable': {'thread_id': 'c62e2e03-c27b-4cb6-8cea-ea9bfedae006', 'checkpoint_ns': '', 'checkpoint_id': '1f02ac4a-ecee-600b-8002-a1d21df32e4c'}}

​
4. Resume execution from the checkpoint

await graph.invoke(null, newConfig);
Output: 
{
  'topic': 'chickens',
  'joke': 'Why did the chicken join a band?\n\nBecause it had excellent drumsticks!'
}