添加人工在环控制¶
代理可能不可靠，可能需要人工输入才能成功完成任务。同样，对于某些操作，您可能需要在运行前要求人工批准，以确保一切按预期运行。
LangGraph 的 持久化 层支持人工在环工作流，允许根据用户反馈暂停和恢复执行。此功能的主要接口是 interrupt 函数。在节点内部调用 interrupt 将暂停执行。可以通过传入一个 Command 对象来恢复执行，并伴随来自人工的新输入。interrupt 在人体工程学上类似于 Python 的内置 input() 函数，但有一些注意事项。
注意
本教程基于 添加记忆 的内容。
1. 添加 human_assistance 工具¶
从 为聊天机器人添加记忆 教程的现有代码开始，为聊天机器人添加 human_assistance 工具。此工具使用 interrupt 来接收来自人工的信息。
我们首先选择一个聊天模型
import os
from langchain.chat_models import init_chat_model
os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
llm = init_chat_model(
"azure_openai:gpt-4.1",
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
现在我们可以将它与一个额外的工具集成到我们的 StateGraph 中
from typing import Annotated
from langchain_tavily import TavilySearch
from langchain_core.tools import tool
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
from langgraph.types import Command, interrupt
class State(TypedDict):
messages: Annotated[list, add_messages]
graph_builder = StateGraph(State)
@tool
def human_assistance(query: str) -> str:
"""Request assistance from a human."""
human_response = interrupt({"query": query})
return human_response["data"]
tool = TavilySearch(max_results=2)
tools = [tool, human_assistance]
llm_with_tools = llm.bind_tools(tools)
def chatbot(state: State):
message = llm_with_tools.invoke(state["messages"])
# Because we will be interrupting during tool execution,
# we disable parallel tool calling to avoid repeating any
# tool invocations when we resume.
assert len(message.tool_calls) <= 1
return {"messages": [message]}
graph_builder.add_node("chatbot", chatbot)
tool_node = ToolNode(tools=tools)
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")
2. 编译图¶
我们像以前一样使用检查点器编译图
3. 可视化图（可选）¶
可视化图后，您会得到与之前相同的布局——只是多了一个工具！
from IPython.display import Image, display
try:
display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
pass
4. 提示聊天机器人¶
现在，向聊天机器人提问一个将使用新的 human_assistance 工具的问题
user_input = "I need some expert guidance for building an AI agent. Could you request assistance for me?"
config = {"configurable": {"thread_id": "1"}}
events = graph.stream(
{"messages": [{"role": "user", "content": user_input}]},
config,
stream_mode="values",
)
for event in events:
if "messages" in event:
event["messages"][-1].pretty_print()
================================ Human Message =================================
I need some expert guidance for building an AI agent. Could you request assistance for me?
================================== Ai Message ==================================
[{'text': "Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]
Tool Calls:
human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)
Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW
Args:
query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
聊天机器人生成了一个工具调用，但随后执行被中断。如果您检查图状态，您会看到它停止在工具节点。
信息
仔细查看 human_assistance 工具
@tool
def human_assistance(query: str) -> str:
"""Request assistance from a human."""
human_response = interrupt({"query": query})
return human_response["data"]
与 Python 的内置 input() 函数类似，在工具内部调用 interrupt 将暂停执行。进度会根据 检查点器 进行持久化；因此，如果使用 Postgres 进行持久化，只要数据库处于活动状态，它就可以随时恢复。在此示例中，它使用内存检查点器进行持久化，并且只要 Python 内核正在运行，就可以随时恢复。
5. 恢复执行¶
要恢复执行，请传入一个包含工具所需数据的 Command 对象。此数据的格式可以根据需要进行定制。在此示例中，使用一个带有键 "data" 的字典。
human_response = (
"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."
" It's much more reliable and extensible than simple autonomous agents."
)
human_command = Command(resume={"data": human_response})
events = graph.stream(human_command, config, stream_mode="values")
for event in events:
if "messages" in event:
event["messages"][-1].pretty_print()
================================== Ai Message ==================================
[{'text': "Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]
Tool Calls:
human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)
Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW
Args:
query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
================================= Tool Message =================================
Name: human_assistance
We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.
================================== Ai Message ==================================
Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:
The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.
LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:
1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.
2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.
3. Advanced capabilities: Given that it's recommended over "simple autonomous agents," LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.
...
2. Look for tutorials or guides specifically focused on building AI agents with LangGraph.
3. Check if there are any community forums or discussion groups where you can ask questions and get support from other developers using LangGraph.
If you'd like more specific information about LangGraph or have any questions about this recommendation, please feel free to ask, and I can request further assistance from the experts.
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
输入已作为工具消息接收和处理。查看此调用的 LangSmith 追踪 以查看上述调用中完成的精确工作。请注意，状态在第一步中加载，以便我们的聊天机器人可以从中断的地方继续。
恭喜！您已使用 interrupt 为您的聊天机器人添加了人工在环执行功能，允许在需要时进行人工监督和干预。这开启了您可以用 AI 系统创建的潜在 UI。由于您已经添加了检查点器，只要底层持久化层正在运行，图就可以无限期地暂停并在任何时候恢复，就像什么都没发生过一样。
查看下面的代码片段以回顾本教程中的图
import os
from langchain.chat_models import init_chat_model
os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
llm = init_chat_model(
"azure_openai:gpt-4.1",
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
API 参考：TavilySearch | tool | MemorySaver | StateGraph | START | END | add_messages | ToolNode | tools_condition | Command | interrupt
from typing import Annotated
from langchain_tavily import TavilySearch
from langchain_core.tools import tool
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
from langgraph.types import Command, interrupt
class State(TypedDict):
messages: Annotated[list, add_messages]
graph_builder = StateGraph(State)
@tool
def human_assistance(query: str) -> str:
"""Request assistance from a human."""
human_response = interrupt({"query": query})
return human_response["data"]
tool = TavilySearch(max_results=2)
tools = [tool, human_assistance]
llm_with_tools = llm.bind_tools(tools)
def chatbot(state: State):
message = llm_with_tools.invoke(state["messages"])
assert(len(message.tool_calls) <= 1)
return {"messages": [message]}
graph_builder.add_node("chatbot", chatbot)
tool_node = ToolNode(tools=tools)
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)
后续步骤¶
到目前为止，教程示例都依赖于一个只包含一个条目的简单状态：消息列表。这种简单状态可以完成很多工作，但如果您想在不依赖消息列表的情况下定义复杂行为，可以向状态添加其他字段。