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时间旅行

在典型的聊天机器人工作流中,用户与机器人交互一次或多次以完成任务。记忆和人机协同可以在图状态中启用检查点并控制未来的响应。

如果你希望用户能够从先前的响应开始并探索不同的结果怎么办?或者,如果你希望用户能够倒回聊天机器人的工作以修复错误或尝试不同的策略(这在自主软件工程师等应用中很常见)怎么办?

你可以使用 LangGraph 的内置**时间旅行**功能创建这些类型的体验。

Note

本教程基于自定义状态。

1. 倒回你的图

通过使用图的 get_state_history 方法获取检查点来倒回你的图。然后你可以在这个先前的时间点恢复执行。

通过使用图的 getStateHistory 方法获取检查点来倒回你的图。然后你可以在这个先前的时间点恢复执行。

聊天模型配置

查看支持的聊天模型配置方法：聊天模型选项卡

支持的模型包括：OpenAI、Anthropic、Azure、Google Gemini 和 AWS Bedrock。

from typing import Annotated

from langchain_tavily import TavilySearch
from langchain_core.messages import BaseMessage
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

class State(TypedDict):
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)

tool = TavilySearch(max_results=2)
tools = [tool]
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")

memory = InMemorySaver()
graph = graph_builder.compile(checkpointer=memory)

import {
  StateGraph,
  START,
  END,
  MessagesZodState,
  MemorySaver,
} from "@langchain/langgraph";
import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
import { TavilySearch } from "@langchain/tavily";
import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

const State = z.object({ messages: MessagesZodState.shape.messages });

const tools = [new TavilySearch({ maxResults: 2 })];
const llmWithTools = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
const memory = new MemorySaver();

const graph = new StateGraph(State)
  .addNode("chatbot", async (state) => ({
    messages: [await llmWithTools.invoke(state.messages)],
  }))
  .addNode("tools", new ToolNode(tools))
  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
  .addEdge("tools", "chatbot")
  .addEdge(START, "chatbot")
  .compile({ checkpointer: memory });

2. Add steps

向你的图添加步骤。每个步骤都将在其状态历史中设置检查点：

config = {"configurable": {"thread_id": "1"}}
events = graph.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": (
                    "I'm learning LangGraph. "
                    "Could you do some research on it for me?"
                ),
            },
        ],
    },
    config,
    stream_mode="values",
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

I'm learning LangGraph. Could you do some research on it for me?
================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01BscbfJJB9EWJFqGrN6E54e', 'input': {'query': 'LangGraph latest information and features'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01BscbfJJB9EWJFqGrN6E54e)
 Call ID: toolu_01BscbfJJB9EWJFqGrN6E54e
  Args:
    query: LangGraph latest information and features
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://blockchain.news/news/langchain-new-features-upcoming-events-update", "content": "LangChain, a leading platform in the AI development space, has released its latest updates, showcasing new use cases and enhancements across its ecosystem. According to the LangChain Blog, the updates cover advancements in LangGraph Platform, LangSmith's self-improving evaluators, and revamped documentation for LangGraph."}, {"url": "https://blog.langchain.dev/langgraph-platform-announce/", "content": "With these learnings under our belt, we decided to couple some of our latest offerings under LangGraph Platform. LangGraph Platform today includes LangGraph Server, LangGraph Studio, plus the CLI and SDK. ... we added features in LangGraph Server to deliver on a few key value areas. Below, we'll focus on these aspects of LangGraph Platform."}]
================================== Ai Message ==================================

Thank you for your patience. I've found some recent information about LangGraph for you. Let me summarize the key points:

1. LangGraph is part of the LangChain ecosystem, which is a leading platform in AI development.

2. Recent updates and features of LangGraph include:

   a. LangGraph Platform: This seems to be a cloud-based version of LangGraph, though specific details weren't provided in the search results.
...
3. Keep an eye on LangGraph Platform developments, as cloud-based solutions often provide an easier starting point for learners.
4. Consider how LangGraph fits into the broader LangChain ecosystem, especially its interaction with tools like LangSmith.

Is there any specific aspect of LangGraph you'd like to know more about? I'd be happy to do a more focused search on particular features or use cases.
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

events = graph.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": (
                    "Ya that's helpful. Maybe I'll "
                    "build an autonomous agent with it!"
                ),
            },
        ],
    },
    config,
    stream_mode="values",
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

Ya that's helpful. Maybe I'll build an autonomous agent with it!
================================== Ai Message ==================================

[{'text': "That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)
 Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo
  Args:
    query: Building autonomous agents with LangGraph examples and tutorials
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
================================== Ai Message ==================================

Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:

1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.

2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the "brain" of your agent, making decisions and generating responses.

3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.
...
6. Pay attention to how you structure the agent's decision-making process and workflow.
7. Don't forget to implement proper error handling and safety measures, especially if your agent will be interacting with external systems or making important decisions.

Building an autonomous agent is an iterative process, so be prepared to refine and improve your agent over time. Good luck with your project! If you need any more specific information as you progress, feel free to ask.
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

import { randomUUID } from "node:crypto";
const threadId = randomUUID();

let iter = 0;

for (const userInput of [
  "I'm learning LangGraph. Could you do some research on it for me?",
  "Ya that's helpful. Maybe I'll build an autonomous agent with it!",
]) {
  iter += 1;

  console.log(`\n--- Conversation Turn ${iter} ---\n`);
  const events = await graph.stream(
    { messages: [{ role: "user", content: userInput }] },
    { configurable: { thread_id: threadId }, streamMode: "values" }
  );

  for await (const event of events) {
    if ("messages" in event) {
      const lastMessage = event.messages.at(-1);

      console.log(
        "=".repeat(32),
        `${lastMessage?.getType()} Message`,
        "=".repeat(32)
      );
      console.log(lastMessage?.text);
    }
  }
}

--- Conversation Turn 1 ---

================================ human Message ================================
I'm learning LangGraph.js. Could you do some research on it for me?
================================ ai Message ================================
I'll search for information about LangGraph.js for you.
================================ tool Message ================================
{
  "query": "LangGraph.js framework TypeScript langchain what is it tutorial guide",
  "follow_up_questions": null,
  "answer": null,
  "images": [],
  "results": [
    {
      "url": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/an-absolute-beginners-guide-to-langgraph-js/4212496",
      "title": "An Absolute Beginner's Guide to LangGraph.js",
      "content": "(...)",
      "score": 0.79369855,
      "raw_content": null
    },
    {
      "url": "https://langchain-ai.github.io/langgraphjs/",
      "title": "LangGraph.js",
      "content": "(...)",
      "score": 0.78154784,
      "raw_content": null
    }
  ],
  "response_time": 2.37
}
================================ ai Message ================================
Let me provide you with an overview of LangGraph.js based on the search results:

LangGraph.js 是 LangChain 生态系统的一部分，是一个 JavaScript/TypeScript 库，专为创建和管理基于 LLM（大型语言模型）的复杂工作流而设计。以下是关于 LangGraph.js 的关键点：

1. 目的：
- 它是一个用于构建可控智能体的低级编排框架
- 特别适用于创建智能体工作流，其中 LLM 根据当前状态决定行动方案
- 帮助将工作流建模为带有节点和边的图

(...)

--- Conversation Turn 2 ---

================================ human Message ================================
Ya that's helpful. Maybe I'll build an autonomous agent with it!
================================ ai Message ================================
Let me search for specific information about building autonomous agents with LangGraph.js.
================================ tool Message ================================
{
  "query": "how to build autonomous agents with LangGraph.js examples tutorial react agent",
  "follow_up_questions": null,
  "answer": null,
  "images": [],
  "results": [
    {
      "url": "https://ai.google.dev/gemini-api/docs/langgraph-example",
      "title": "ReAct agent from scratch with Gemini 2.5 and LangGraph",
      "content": "(...)",
      "score": 0.7602419,
      "raw_content": null
    },
    {
      "url": "https://www.youtube.com/watch?v=ZfjaIshGkmk",
      "title": "Build Autonomous AI Agents with ReAct and LangGraph Tools",
      "content": "(...)",
      "score": 0.7471924,
      "raw_content": null
    }
  ],
  "response_time": 1.98
}
================================ ai Message ================================
根据搜索结果，我可以为你提供有关如何使用 LangGraph.js 构建自主智能体的实用概述。以下是你需要了解的内容：

1. 构建智能体的基本结构：
- LangGraph.js 提供了 ReAct（推理 + 行动）模式实现
- 基本组件包括：
  - State management for conversation history
  - Nodes for different actions
  - Edges for decision-making flow
  - Tools for specific functionalities

(...)

3. Replay the full state history

现在你已经向聊天机器人添加了步骤，你可以 replay（重放）完整的状态历史以查看发生的所有事情。

to_replay = None
for state in graph.get_state_history(config):
    print("Num Messages: ", len(state.values["messages"]), "Next: ", state.next)
    print("-" * 80)
    if len(state.values["messages"]) == 6:
        # We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
        to_replay = state

Num Messages:  8 Next:  ()
--------------------------------------------------------------------------------
Num Messages:  7 Next:  ('chatbot',)
--------------------------------------------------------------------------------
Num Messages:  6 Next:  ('tools',)
--------------------------------------------------------------------------------
Num Messages:  5 Next:  ('chatbot',)
--------------------------------------------------------------------------------
Num Messages:  4 Next:  ('__start__',)
--------------------------------------------------------------------------------
Num Messages:  4 Next:  ()
--------------------------------------------------------------------------------
Num Messages:  3 Next:  ('chatbot',)
--------------------------------------------------------------------------------
Num Messages:  2 Next:  ('tools',)
--------------------------------------------------------------------------------
Num Messages:  1 Next:  ('chatbot',)
--------------------------------------------------------------------------------
Num Messages:  0 Next:  ('__start__',)
--------------------------------------------------------------------------------

import type { StateSnapshot } from "@langchain/langgraph";

let toReplay: StateSnapshot | undefined;
for await (const state of graph.getStateHistory({
  configurable: { thread_id: threadId },
})) {
  console.log(
    `Num Messages: ${state.values.messages.length}, Next: ${JSON.stringify(
      state.next
    )}`
  );
  console.log("-".repeat(80));
  if (state.values.messages.length === 6) {
    // We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
    toReplay = state;
  }
}

Num Messages: 8 Next:  []
--------------------------------------------------------------------------------
Num Messages: 7 Next:  ["chatbot"]
--------------------------------------------------------------------------------
Num Messages: 6 Next:  ["tools"]
--------------------------------------------------------------------------------
Num Messages: 7, Next: ["chatbot"]
--------------------------------------------------------------------------------
Num Messages: 6, Next: ["tools"]
--------------------------------------------------------------------------------
Num Messages: 5, Next: ["chatbot"]
--------------------------------------------------------------------------------
Num Messages: 4, Next: ["__start__"]
--------------------------------------------------------------------------------
Num Messages: 4, Next: []
--------------------------------------------------------------------------------
Num Messages: 3, Next: ["chatbot"]
--------------------------------------------------------------------------------
Num Messages: 2, Next: ["tools"]
--------------------------------------------------------------------------------
Num Messages: 1, Next: ["chatbot"]
--------------------------------------------------------------------------------
Num Messages: 0, Next: ["__start__"]
--------------------------------------------------------------------------------

检查点在图的每一步都会保存。这**跨越调用**，因此你可以在整个线程的历史中回退。

Resume from a checkpoint

从 to_replay 状态恢复，该状态在第二次图调用中的 chatbot 节点之后。从这一点恢复将接下来调用 action 节点。

从 toReplay 状态恢复，该状态在其中一次图调用中特定节点之后。从这一点恢复将调用下一个计划的节点。

print(to_replay.next)
print(to_replay.config)

('tools',)
{'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}

Resume from the toReplay state, which is after the chatbot node in one of the graph invocations. Resuming from this point will call the next scheduled node.

console.log(toReplay.next);
console.log(toReplay.config);

["tools"]
{
  configurable: {
    thread_id: "007708b8-ea9b-4ff7-a7ad-3843364dbf75",
    checkpoint_ns: "",
    checkpoint_id: "1efd43e3-0c1f-6c4e-8006-891877d65740"
  }
}

4. 从某个时间点加载状态

检查点的 to_replay.config 包含一个 checkpoint_id 时间戳。提供这个 checkpoint_id 值告诉 LangGraph 的检查点保存器从那个时间点**加载**状态。

# The `checkpoint_id` in the `to_replay.config` corresponds to a state we've persisted to our checkpointer.
for event in graph.stream(None, to_replay.config, stream_mode="values"):
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================== Ai Message ==================================

[{'text': "That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)
 Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo
  Args:
    query: Building autonomous agents with LangGraph examples and tutorials
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
================================== Ai Message ==================================

Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:

1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.

2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the "brain" of your agent, making decisions and generating responses.

3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.
...

Remember, building an autonomous agent is an iterative process. Start simple and gradually increase complexity as you become more comfortable with LangGraph and its capabilities.

Would you like more information on any specific aspect of building your autonomous agent with LangGraph?
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

图从 tools 节点恢复执行。你可以看出这一点，因为上面打印的第一个值是我们搜索引擎工具的响应。

检查点的 toReplay.config 包含一个 checkpoint_id 时间戳。提供这个 checkpoint_id 值告诉 LangGraph 的检查点保存器从那个时间点**加载**状态。

// The `checkpoint_id` in the `toReplay.config` corresponds to a state we've persisted to our checkpointer.
for await (const event of await graph.stream(null, {
  ...toReplay?.config,
  streamMode: "values",
})) {
  if ("messages" in event) {
    const lastMessage = event.messages.at(-1);

    console.log(
      "=".repeat(32),
      `${lastMessage?.getType()} Message`,
      "=".repeat(32)
    );
    console.log(lastMessage?.text);
  }
}

================================ ai Message ================================
Let me search for specific information about building autonomous agents with LangGraph.js.
================================ tool Message ================================
{
  "query": "how to build autonomous agents with LangGraph.js examples tutorial",
  "follow_up_questions": null,
  "answer": null,
  "images": [],
  "results": [
    {
      "url": "https://www.mongodb.com/developer/languages/typescript/build-javascript-ai-agent-langgraphjs-mongodb/",
      "title": "Build a JavaScript AI Agent With LangGraph.js and MongoDB",
      "content": "(...)",
      "score": 0.7672197,
      "raw_content": null
    },
    {
      "url": "https://medium.com/@lorevanoudenhove/how-to-build-ai-agents-with-langgraph-a-step-by-step-guide-5d84d9c7e832",
      "title": "How to Build AI Agents with LangGraph: A Step-by-Step Guide",
      "content": "(...)",
      "score": 0.7407191,
      "raw_content": null
    }
  ],
  "response_time": 0.82
}
================================ ai Message ================================
根据搜索结果，我可以分享一些关于使用 LangGraph.js 构建自主智能体的实用信息。以下是一些具体的示例和方法：

1. HR 助理智能体示例：
- 可以使用员工信息处理与 HR 相关的查询
- 功能包括：
  - 启动和继续对话
  - 使用向量搜索查找信息
  - 使用检查点持久化对话状态
  - 管理线程对话

2. 能源节省计算器智能体：
- 作为太阳能电池板销售的潜在客户生成工具
- 能力包括：
  - 计算潜在的能源节省
  - 处理多步骤对话
  - 处理用户输入以获得个性化估算
  - 管理对话状态

(...)

图从 tools 节点恢复执行。你可以看出这一点，因为上面打印的第一个值是我们搜索引擎工具的响应。

恭喜！ 你现在已经在 LangGraph 中使用了时间旅行检查点遍历。能够回退并探索替代路径为调试、实验和交互式应用程序打开了无限可能。

Learn more

通过探索部署和高级功能，进一步深化你的 LangGraph 之旅：