Add multiple LangGraph examples including basic agents, chatbots, conditional edges, checkpointers, tool calls, and human-in-the-loop approval processes. Update .gitignore to include local environment files.
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Eric Wang
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06_human_in_the_loop.py
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06_human_in_the_loop.py
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"""LangGraph 人工审批示例：普通工具直接执行，敏感工具先暂停审批。
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图结构：
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START -> model
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| 无 tool_calls ------------------------> END
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| 普通工具调用 -> tools ----------------> model
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| 敏感工具调用 -> approval
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| 批准 -> tools ----> model
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| 拒绝 -> model
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运行：
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uv run --with langgraph --with langchain-openai --with python-dotenv \
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python 06_human_in_the_loop.py
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说明：send_email 是模拟工具，不会真正发送邮件。
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"""
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import os
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import uuid
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from typing import Literal
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from dotenv import load_dotenv
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from langchain_core.messages import ToolMessage
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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from langgraph.types import Command, interrupt
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load_dotenv()
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# -----------------------------------------------------------------------------
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# 1. 定义工具
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# -----------------------------------------------------------------------------
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@tool
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def get_weather(city: str) -> str:
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"""查询指定城市当前的天气。城市名称使用中文，例如北京、上海。"""
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weather_data = {
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"北京": "晴，25°C，北风 2 级",
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"上海": "多云，28°C，东南风 3 级",
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"广州": "雷阵雨，30°C，南风 2 级",
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"深圳": "阴，29°C，东风 2 级",
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}
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return weather_data.get(city, f"暂时没有 {city} 的天气数据")
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@tool
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def calculator(
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a: float,
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b: float,
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operation: Literal["add", "subtract", "multiply", "divide"],
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) -> float:
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"""对两个数字进行加、减、乘、除运算。
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Args:
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a: 第一个数字。
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b: 第二个数字。
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operation: 运算类型：add、subtract、multiply 或 divide。
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"""
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if operation == "add":
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return a + b
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if operation == "subtract":
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return a - b
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if operation == "multiply":
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return a * b
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if b == 0:
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raise ValueError("除数不能为 0")
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return a / b
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@tool
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def send_email(recipient: str, subject: str, body: str) -> str:
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"""向指定邮箱发送邮件。这是有外部副作用的敏感操作，执行前必须审批。
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Args:
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recipient: 收件人的邮箱地址。
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subject: 邮件主题。
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body: 邮件正文。
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"""
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# 教学示例：这里只模拟发送，不连接真实邮件服务。
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print("\n[模拟发送邮件]")
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print(f"收件人：{recipient}")
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print(f"主题：{subject}")
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print(f"正文：{body}")
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return f"邮件已成功发送给 {recipient}"
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TOOLS = [get_weather, calculator, send_email]
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SENSITIVE_TOOL_NAMES = {"send_email"}
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# -----------------------------------------------------------------------------
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# 2. 创建模型并绑定工具
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# -----------------------------------------------------------------------------
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model = ChatOpenAI(
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model=os.getenv("OPENAI_MODEL", "openai/gpt-5.4"),
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
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temperature=0,
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max_retries=2,
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)
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model_with_tools = model.bind_tools(TOOLS)
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# -----------------------------------------------------------------------------
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# 3. 定义节点与路由
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# -----------------------------------------------------------------------------
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def call_model(state: MessagesState) -> dict:
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"""调用模型；模型可以直接回答，也可以生成一个或多个 tool_calls。"""
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response = model_with_tools.invoke(state["messages"])
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return {"messages": [response]}
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def route_after_model(
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state: MessagesState,
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) -> Literal["tools", "approval", "__end__"]:
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"""无工具调用则结束；敏感工具去审批；其余工具直接执行。"""
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tool_calls = state["messages"][-1].tool_calls
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if not tool_calls:
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return END
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if any(call["name"] in SENSITIVE_TOOL_NAMES for call in tool_calls):
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return "approval"
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return "tools"
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def approval_node(
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state: MessagesState,
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) -> Command[Literal["tools", "model"]]:
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"""暂停图执行，等待用户批准或拒绝当前批次的工具调用。"""
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tool_calls = state["messages"][-1].tool_calls
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# interrupt() 应放在副作用之前。恢复时节点可能从函数开头重新执行。
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decision = interrupt(
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{
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"type": "tool_approval",
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"message": "模型请求执行敏感工具，是否批准？",
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"tool_calls": tool_calls,
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}
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)
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approved = (
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bool(decision.get("approved"))
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if isinstance(decision, dict)
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else bool(decision)
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)
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if approved:
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# ToolNode 将执行这一批工具调用，并生成对应的 ToolMessage。
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return Command(goto="tools")
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# 每个 tool_call 都必须得到对应的 ToolMessage，否则消息协议不完整，
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# 部分模型服务会拒绝下一次请求。整个批次在审批拒绝后均不执行。
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rejection_messages = [
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ToolMessage(
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content="用户拒绝了本批次工具调用，工具未执行。请告知用户并询问下一步。",
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tool_call_id=call["id"],
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name=call["name"],
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)
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for call in tool_calls
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]
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return Command(
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update={"messages": rejection_messages},
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goto="model",
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)
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# ToolNode 自动查找并执行工具，再把结果包装为 ToolMessage。
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tool_node = ToolNode(TOOLS)
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# -----------------------------------------------------------------------------
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# 4. 构建图并启用 Checkpointer
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# -----------------------------------------------------------------------------
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builder = StateGraph(MessagesState)
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builder.add_node("model", call_model)
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builder.add_node("approval", approval_node)
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builder.add_node("tools", tool_node)
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builder.add_edge(START, "model")
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builder.add_conditional_edges(
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"model",
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route_after_model,
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{
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"tools": "tools",
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"approval": "approval",
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END: END,
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},
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)
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builder.add_edge("tools", "model")
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# interrupt() 必须配合 checkpointer 使用。InMemorySaver 只适合本地学习，
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# 进程退出后 checkpoint 会消失。
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checkpointer = InMemorySaver()
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graph = builder.compile(checkpointer=checkpointer)
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# -----------------------------------------------------------------------------
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# 5. 命令行交互
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# -----------------------------------------------------------------------------
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def print_update(event: dict) -> None:
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"""打印 model/tools 节点产生的消息更新。"""
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for node_name, update in event.items():
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if node_name == "__interrupt__" or not isinstance(update, dict):
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continue
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messages = update.get("messages", [])
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if not messages:
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continue
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print(f"\n[{node_name}]")
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for message in messages:
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message.pretty_print()
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def run_until_pause_or_end(graph_input: dict | Command, config: dict):
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"""运行图，返回本轮产生的 interrupt；正常结束则返回 None。"""
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pending_interrupt = None
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for event in graph.stream(
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graph_input,
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config=config,
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stream_mode="updates",
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):
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print_update(event)
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interrupts = event.get("__interrupt__", ())
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if interrupts:
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pending_interrupt = interrupts[0]
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return pending_interrupt
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def ask_for_approval(interrupt_value) -> bool:
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"""在命令行展示待执行工具，并读取审批结果。"""
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payload = interrupt_value.value
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print("\n=== 需要人工审批 ===")
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print(payload.get("message", "是否批准工具调用？"))
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for index, call in enumerate(payload.get("tool_calls", []), start=1):
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print(f"{index}. 工具：{call['name']}")
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print(f" 参数：{call['args']}")
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while True:
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answer = input("批准执行吗？[y/n]：").strip().lower()
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if answer in {"y", "yes"}:
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return True
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if answer in {"n", "no"}:
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return False
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print("请输入 y 或 n。")
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def run_turn(question: str, config: dict) -> None:
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"""执行一轮对话；如果暂停，则审批后使用 Command(resume=...) 恢复。"""
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pending = run_until_pause_or_end(
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{"messages": [{"role": "user", "content": question}]},
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config,
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)
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# 理论上一次模型响应可能导致多次暂停，因此使用循环处理。
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while pending is not None:
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approved = ask_for_approval(pending)
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pending = run_until_pause_or_end(
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Command(resume={"approved": approved}),
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config,
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)
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def main() -> None:
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print("=== LangGraph 工具助手（带人工审批）===")
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print("天气和计算器直接执行；发送邮件需要人工批准。")
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print("输入 /new 创建新会话，输入 quit 退出。")
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# 同一 thread_id 的多轮输入共享 checkpoint 和消息历史。
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thread_id = str(uuid.uuid4())
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while True:
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question = input(f"\n用户[{thread_id[:8]}]：").strip()
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if question.lower() in {"quit", "exit", "q"}:
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break
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if question == "/new":
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thread_id = str(uuid.uuid4())
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print(f"已创建新会话：{thread_id}")
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continue
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if not question:
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continue
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config = {
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"configurable": {"thread_id": thread_id},
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"recursion_limit": 20,
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}
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try:
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run_turn(question, config)
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except Exception as exc:
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print(f"\n执行失败：{type(exc).__name__}: {exc}")
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if __name__ == "__main__":
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main()
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