Use time-travel

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.

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

To build this workflow in this example you need to set up the Anthropic LLM and install the required dependencies:

1. Install dependencies

npm

npm install @langchain/langgraph @langchain/core

2. Initialize the LLM:

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
  model: "claude-sonnet-4-5-20250929",
  apiKey: "<your_anthropic_key>"
});

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3. Implement the workflow The implementation of the workflow is a simple graph with two nodes, one for generating a joke topic, another for writing the joke itself and a state to storing the intermediate values.

import { v4 as uuidv4 } from "uuid";
import * as z from "zod";
import { StateGraph, StateSchema, GraphNode, START, END, MemorySaver } from "@langchain/langgraph";
import { ChatAnthropic } from "@langchain/anthropic";

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

const model = new ChatAnthropic({
  model: "claude-sonnet-4-5-20250929",
  temperature: 0,
});

const generateTopic: GraphNode<typeof State> = async (state) => {
  // LLM call to generate a topic for the joke
  const msg = await model.invoke("Give me a funny topic for a joke");
  return { topic: msg.content };
};

const writeJoke: GraphNode<typeof State> = async (state) => {
  // LLM call to write a joke based on the topic
  const msg = await model.invoke(`Write a short joke about ${state.topic}`);
  return { joke: msg.content };
};

// Build workflow
const workflow = new StateGraph(State)
  // Add nodes
  .addNode("generateTopic", generateTopic)
  .addNode("writeJoke", writeJoke)
  // 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

To start the workflow, invoke is called without any inputs. Note the thread_id to track this execution and retrieve its checkpoints later.

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

To continue from a previous point in the graphs run, use get_state_history to retrieve all the states and select the one where you want to resume execution.

// 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 (optional)

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

For resumings execution from the selected checkpoint, call invoke with the config that points to the new checkpoint.

await graph.invoke(null, newConfig);

Output:

{
  'topic': 'chickens',
  'joke': 'Why did the chicken join a band?\n\nBecause it had excellent drumsticks!'
}

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