在使用做出基于模型的决策的非确定性系统（例如，由 LLM 驱动的智能体）时，详细检查其决策过程可能很有用：
  1. 理解推理：分析导致成功结果的步骤。
  2. 调试错误：识别错误发生的位置和原因。
  3. 探索替代方案：测试不同的路径以发现更好的解决方案。
LangGraph 提供时光旅行功能来支持这些用例。具体来说，您可以从先前的检查点恢复执行 — 要么重放相同的状态，要么修改它以探索替代方案。在所有情况下，恢复过去的执行都会在历史中产生新的分叉。 要在 LangGraph 中使用时光旅行：
  1. 运行图，使用 invoke 或 stream 方法和初始输入。
  2. 识别现有线程中的检查点：使用 get_state_history 方法检索特定 thread_id 的执行历史并找到所需的 checkpoint_id。 或者，在您希望执行暂停的节点之前设置中断。然后您可以找到记录到该中断的最新检查点。
  3. 更新图状态（可选）：使用 update_state 方法在检查点修改图的状态并从替代状态恢复执行。
  4. 从检查点恢复执行：使用 invoke 或 stream 方法，输入为 None，配置包含适当的 thread_id 和 checkpoint_id。
有关时光旅行的概念概述，请参阅时光旅行。

​
在工作流程中

此示例构建了一个简单的 LangGraph 工作流程，该工作流程生成一个笑话主题并使用 LLM 编写笑话。它演示如何运行图、检索过去的执行检查点、可选地修改状态以及从选择的检查点恢复执行以探索替代结果。

​
设置

首先我们需要安装所需的包 
%%capture --no-stderr
pip install --quiet -U langgraph langchain_anthropic
接下来，我们需要为 Anthropic（我们将使用的 LLM）设置 API 密钥 
import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")
注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 让您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序。
import uuid

from typing_extensions import TypedDict, NotRequired
from langgraph.graph import StateGraph, START, END
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.memory import InMemorySaver


class State(TypedDict):
    topic: NotRequired[str]
    joke: NotRequired[str]


model = init_chat_model(
    "claude-sonnet-4-5-20250929",
    temperature=0,
)


def generate_topic(state: State):
    """LLM call to generate a topic for the joke"""
    msg = model.invoke("Give me a funny topic for a joke")
    return {"topic": msg.content}


def write_joke(state: State):
    """LLM call to write a joke based on the topic"""
    msg = model.invoke(f"Write a short joke about {state['topic']}")
    return {"joke": msg.content}


# Build workflow
workflow = StateGraph(State)

# Add nodes
workflow.add_node("generate_topic", generate_topic)
workflow.add_node("write_joke", write_joke)

# Add edges to connect nodes
workflow.add_edge(START, "generate_topic")
workflow.add_edge("generate_topic", "write_joke")
workflow.add_edge("write_joke", END)

# Compile
checkpointer = InMemorySaver()
graph = workflow.compile(checkpointer=checkpointer)
graph

​
1. Run the graph

config = {
    "configurable": {
        "thread_id": uuid.uuid4(),
    }
}
state = graph.invoke({}, config)

print(state["topic"])
print()
print(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.
states = list(graph.get_state_history(config))

for state in states:
    print(state.next)
    print(state.config["configurable"]["checkpoint_id"])
    print()
Output: 
()
1f02ac4a-ec9f-6524-8002-8f7b0bbeed0e

('write_joke',)
1f02ac4a-ce2a-6494-8001-cb2e2d651227

('generate_topic',)
1f02ac4a-a4e0-630d-8000-b73c254ba748

('__start__',)
1f02ac4a-a4dd-665e-bfff-e6c8c44315d9

# This is the state before last (states are listed in chronological order)
selected_state = states[1]
print(selected_state.next)
print(selected_state.values)
Output: 
('write_joke',)
{'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

update_state will create a new checkpoint. The new checkpoint will be associated with the same thread, but a new checkpoint ID. 
new_config = graph.update_state(selected_state.config, values={"topic": "chickens"})
print(new_config)
Output: 
{'configurable': {'thread_id': 'c62e2e03-c27b-4cb6-8cea-ea9bfedae006', 'checkpoint_ns': '', 'checkpoint_id': '1f02ac4a-ecee-600b-8002-a1d21df32e4c'}}

​
4. Resume execution from the checkpoint

graph.invoke(None, new_config)
Output: 
{'topic': 'chickens',
 'joke': 'Why did the chicken join a band?\n\nBecause it had excellent drumsticks!'}
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