ユーザが以前の応答から開始して異なる結果を探究できるようにしたい場合はどうすれば良いでしょうか？LangGraph の組み込みタイムトラベル機能を使用して、このタイプのエクスペリエンスを作成できます。

LangGraph : Get started : 基本 – タイムトラベル

作成 : クラスキャット・セールスインフォメーション
作成日時 : 06/05/2025

* 本記事は langchain-ai.github.io の以下のページを独自に翻訳した上で、補足説明を加えてまとめ直しています :

Get started : LangGraph basics : Time travel

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LangGraph : Get started : 基本 – タイムトラベル

典型的なチャットボット・ワークフローでは、ユーザはタスクを完了するためにボットと 1 回以上相互作用します。メモリと human-in-the-loop はグラフ状態内にチェックポイントを有効にし、将来のレスポンスを制御します。

ユーザが以前の応答から開始して異なる結果を探究できるようにしたい場合はどうすれば良いでしょうか？あるいは、(自律型ソフトウェアエンジニアのようなアプリケーションでは一般的ですが) ユーザがチャットボットのワークを巻き戻して間違いを修正したり異なる戦力を試すことができるようにしたい場合はどうすれば良いでしょう？

LangGraph の組み込み タイムトラベル機能 を使用して、このタイプのエクスペリエンスを作成できます。

1. グラフを巻き戻す

グラフの get_state_history メソッドを使用してチェックポイントを取得することでグラフを巻き戻します。それからこの前の時点で実行を再開できます。

OpenAI

pip install -U "langchain[openai]"

import os
from langchain.chat_models import init_chat_model

os.environ["OPENAI_API_KEY"] = "sk-..."

llm = init_chat_model("openai:gpt-4.1")

Anthropic

pip install -U "langchain[anthropic]"

import os
from langchain.chat_models import init_chat_model

os.environ["ANTHROPIC_API_KEY"] = "sk-..."

llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")

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

2. ステップの追加

グラフにステップを追加します。すべてのステップは状態履歴にチェックポイントされます。

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...

3. 完全な状態履歴を再生する

チャットボットにステップを追加したので、完全な状態履歴を再生して、発生したすべてを確認できます。

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__',)
--------------------------------------------------------------------------------

チェックポイントはグラフのすべてのステップについて保存されます。これは 呼び出し全体に渡る ので、完全なスレッド履歴に渡り巻き戻すことができます。

チェックポイントから再開する

to_replay 状態から再開します、これは 2 回目のグラフ呼び出しでチャットボットノードの後です。このポイントから再開すると次の アクション ノードを呼び出します。

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

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

4. 特定の時点から状態をロードする

チェックポイントの to_replay.config は checkpoint_id タイムスタンプを含みます。この checkpoint_id 値を提供すると LangGraph のチェックポインターはその時点から状態を ロードします。

# 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 indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:

1. Multi-Tool Agents:
   LangGraph is well-suited for building autonomous agents that can use multiple tools. This allows your agent to have a variety of capabilities and choose the appropriate tool based on the task at hand.

2. Integration with Large Language Models (LLMs):
   There's a tutorial that specifically mentions using Gemini 2.0 (Google's LLM) with LangGraph to build autonomous agents. This suggests that LangGraph can be integrated with various LLMs, giving you flexibility in choosing the language model that best fits your needs.

3. Practical Tutorials:
   There are tutorials available that provide full code examples for building and running multi-tool agents. These can be invaluable as you start your project, giving you a concrete starting point and demonstrating best practices.
...

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...

グラフはアクションノードから実行を再開しました。上記で出力された最初の値が検索エンジンツールからの応答ですので、これが事実であるとわかります。

Congratulations! LangGraph でタイムトラベル・チェックポイント走査 (traversal) を使用しました。巻き戻して代替のパスを探索できることは、デバッグ、実験とインタラクティブなアプリケーション可能性の世界を広げます。

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