使用子代理构建个人助手

概述

监督者模式是一种多代理架构，其中中央监督者代理协调专门的worker代理。当任务需要不同类型的专业知识时，这种方法表现出色。与其构建一个跨领域管理工具选择的代理，不如创建一个由了解整体工作流程的监督者协调的专注专家。在本教程中，您将构建一个通过真实工作流程展示这些优势的个人助手系统。该系统将协调两个职责根本不同的专家：

一个日历代理，处理日程安排、可用性检查和事件管理。
一个电子邮件代理，管理通信、起草消息和发送通知。

我们还将整合人工审核，允许用户根据需要批准、编辑和拒绝操作（如外发电子邮件）。

为什么要使用监督者？

多代理架构允许您将工具分配到各个worker，每个worker都有自己的提示或指令。考虑一个直接访问所有日历和电子邮件API的代理：它必须从许多相似的工具中进行选择，了解每个API的确切格式，并同时处理多个领域。如果性能下降，将相关工具和关联提示分离到逻辑分组中可能会有所帮助（部分是为了管理迭代改进）。

概念

我们将涵盖以下概念：

设置

安装

本教程需要 langchain 包：

pip install langchain

有关更多详细信息，请参阅我们的安装指南。

LangSmith

设置 LangSmith 来检查代理内部发生的情况。然后设置以下环境变量：

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

组件

我们需要从 LangChain 的集成套件中选择一个聊天模型：

👉 Read the OpenAI chat model integration docs

pip install -U "langchain[openai]"

import os
from langchain.chat_models import init_chat_model

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

model = init_chat_model("gpt-5.2")

👉 Read the Anthropic chat model integration docs

pip install -U "langchain[anthropic]"

import os
from langchain.chat_models import init_chat_model

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

model = init_chat_model("claude-sonnet-4-6")

👉 Read the Azure chat model integration docs

pip install -U "langchain[openai]"

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"

model = init_chat_model(
    "azure_openai:gpt-5.2",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)

👉 Read the Google GenAI chat model integration docs

pip install -U "langchain[google-genai]"

import os
from langchain.chat_models import init_chat_model

os.environ["GOOGLE_API_KEY"] = "..."

model = init_chat_model("google_genai:gemini-2.5-flash-lite")

👉 Read the AWS Bedrock chat model integration docs

pip install -U "langchain[aws]"

from langchain.chat_models import init_chat_model

# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

model = init_chat_model(
    "anthropic.claude-3-5-sonnet-20240620-v1:0",
    model_provider="bedrock_converse",
)

👉 Read the HuggingFace chat model integration docs

pip install -U "langchain[huggingface]"

import os
from langchain.chat_models import init_chat_model

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..."

model = init_chat_model(
    "microsoft/Phi-3-mini-4k-instruct",
    model_provider="huggingface",
    temperature=0.7,
    max_tokens=1024,
)

👉 Read the OpenRouter chat model integration docs

pip install -U "langchain-openrouter"

import os
from langchain.chat_models import init_chat_model

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

model = init_chat_model(
    "auto",
    model_provider="openrouter",
)

1. 定义工具

首先定义需要结构化输入的工具。在实际应用中，这些会调用真实API（Google Calendar、SendGrid等）。对于本教程，您将使用桩来演示模式。

from langchain.tools import tool

@tool
def create_calendar_event(
    title: str,
    start_time: str,         # ISO 格式："2024-01-15T14:00:00"
    end_time: str,           # ISO 格式："2024-01-15T15:00:00"
    attendees: list[str],    # 电子邮件地址
    location: str = ""
) -> str:
    """Create a calendar event. Requires exact ISO datetime format."""
    # 桩：在实践中，这会调用 Google Calendar API、Outlook API 等
    return f"Event created: {title} from {start_time} to {end_time} with {len(attendees)} attendees"


@tool
def send_email(
    to: list[str],    # 电子邮件地址
    subject: str,
    body: str,
    cc: list[str] = []
) -> str:
    """Send an email via email API. Requires properly formatted addresses."""
    # 桩：在实践中，这会调用 SendGrid、Gmail API 等
    return f"Email sent to {', '.join(to)} - Subject: {subject}"


@tool
def get_available_time_slots(
    attendees: list[str],
    date: str,    # ISO 格式："2024-01-15"
    duration_minutes: int
) -> list[str]:
    """Check calendar availability for given attendees on a specific date."""
    # 桩：在实践中，这会查询日历API
    return ["09:00", "14:00", "16:00"]

2. 创建专门的子代理

接下来，我们将创建处理每个领域的专门子代理。

创建日历代理

日历代理理解自然语言日程请求并将其转换为精确的API调用。它处理日期解析、可用性检查和事件创建。

from langchain.agents import create_agent


CALENDAR_AGENT_PROMPT = (
    "You are a calendar scheduling assistant. "
    "Parse natural language scheduling requests (e.g., 'next Tuesday at 2pm') "
    "into proper ISO datetime formats. "
    "Use get_available_time_slots to check availability when needed. "
    "If there is no suitable time slot, stop and confirm unavailability in your response. "
    "Use create_calendar_event to schedule events. "
    "Always confirm what was scheduled in your final response."
)

calendar_agent = create_agent(
    model,
    tools=[create_calendar_event, get_available_time_slots],
    system_prompt=CALENDAR_AGENT_PROMPT,
)

测试日历代理以查看它如何处理自然语言日程安排：

query = "Schedule a team meeting next Tuesday at 2pm for 1 hour"

for step in calendar_agent.stream(
    {"messages": [{"role": "user", "content": query}]}
):
    for update in step.values():
        for message in update.get("messages", []):
            message.pretty_print()

================================== Ai Message ==================================
Tool Calls:
  get_available_time_slots (call_EIeoeIi1hE2VmwZSfHStGmXp)
 Call ID: call_EIeoeIi1hE2VmwZSfHStGmXp
  Args:
    attendees: []
    date: 2024-06-18
    duration_minutes: 60
================================= Tool Message =================================
Name: get_available_time_slots

["09:00", "14:00", "16:00"]
================================== Ai Message ==================================
Tool Calls:
  create_calendar_event (call_zgx3iJA66Ut0W8S3NpT93kEB)
 Call ID: call_zgx3iJA66Ut0W8S3NpT93kEB
  Args:
    title: Team Meeting
    start_time: 2024-06-18T14:00:00
    end_time: 2024-06-18T15:00:00
    attendees: []
================================= Tool Message =================================
Name: create_calendar_event

Event created: Team Meeting from 2024-06-18T14:00:00 to 2024-06-18T15:00:00 with 0 attendees
================================== Ai Message ==================================

The team meeting has been scheduled for next Tuesday, June 18th, at 2:00 PM and will last for 1 hour. If you need to add attendees or a location, please let me know!

代理将”下周二下午2点”解析为 ISO 格式（“2024-01-16T14:00:00”），计算结束时间，调用 create_calendar_event，并返回自然语言确认。

创建电子邮件代理

电子邮件代理处理消息撰写和发送。它专注于提取收件人信息、起草适当的主题行和正文，以及管理电子邮件通信。

EMAIL_AGENT_PROMPT = (
    "You are an email assistant. "
    "Compose professional emails based on natural language requests. "
    "Extract recipient information and craft appropriate subject lines and body text. "
    "Use send_email to send the message. "
    "Always confirm what was sent in your final response."
)

email_agent = create_agent(
    model,
    tools=[send_email],
    system_prompt=EMAIL_AGENT_PROMPT,
)

使用自然语言请求测试电子邮件代理：

query = "Send the design team a reminder about reviewing the new mockups"

for step in email_agent.stream(
    {"messages": [{"role": "user", "content": query}]}
):
    for update in step.values():
        for message in update.get("messages", []):
            message.pretty_print()

================================== Ai Message ==================================
Tool Calls:
  send_email (call_OMl51FziTVY6CRZvzYfjYOZr)
 Call ID: call_OMl51FziTVY6CRZvzYfjYOZr
  Args:
    to: ['design-team@example.com']
    subject: Reminder: Please Review the New Mockups
    body: Hi Design Team,

This is a friendly reminder to review the new mockups at your earliest convenience. Your feedback is important to ensure that we stay on track with our project timeline.

Please let me know if you have any questions or need additional information.

Thank you!

Best regards,
================================= Tool Message =================================
Name: send_email

Email sent to design-team@example.com - Subject: Reminder: Please Review the New Mockups
================================== Ai Message ==================================

I've sent a reminder to the design team asking them to review the new mockups. If you need any further communication on this topic, just let me know!

代理从非正式请求中推断收件人，起草专业的主题行和正文，调用 send_email，并返回确认。每个子代理都有狭窄的关注点，具有特定领域的工具和提示，使其能够出色地完成特定任务。

3. 将子代理包装为工具

现在将每个子代理包装为监督者可以调用的工具。这是创建分层系统的关键架构步骤。监督者将看到高级工具如”schedule_event”，而不是低级工具如”create_calendar_event”。

@tool
def schedule_event(request: str) -> str:
    """Schedule calendar events using natural language.

    Use this when the user wants to create, modify, or check calendar appointments.
    Handles date/time parsing, availability checking, and event creation.

    Input: Natural language scheduling request (e.g., 'meeting with design team
    next Tuesday at 2pm')
    """
    result = calendar_agent.invoke({
        "messages": [{"role": "user", "content": request}]
    })
    return result["messages"][-1].text


@tool
def manage_email(request: str) -> str:
    """Send emails using natural language.

    Use this when the user wants to send notifications, reminders, or any email
    communication. Handles recipient extraction, subject generation, and email
    composition.

    Input: Natural language email request (e.g., 'send them a reminder about
    the meeting')
    """
    result = email_agent.invoke({
        "messages": [{"role": "user", "content": request}]
    })
    return result["messages"][-1].text

工具描述帮助监督者决定何时使用每个工具，因此请使它们清晰具体。我们只返回子代理的最终回复，因为监督者不需要看到中间推理或工具调用。

4. 创建监督者代理

现在创建编排子代理的监督者。监督者只看到高级工具，并在领域级别（而不是单个API级别）做出路由决策。

SUPERVISOR_PROMPT = (
    "You are a helpful personal assistant. "
    "You can schedule calendar events and send emails. "
    "Break down user requests into appropriate tool calls and coordinate the results. "
    "When a request involves multiple actions, use multiple tools in sequence."
)

supervisor_agent = create_agent(
    model,
    tools=[schedule_event, manage_email],
    system_prompt=SUPERVISOR_PROMPT,
)

5. 使用监督者

现在用需要跨多个领域协调的复杂请求测试您的完整系统：

示例 1：简单的单一领域请求

query = "Schedule a team standup for tomorrow at 9am"

for step in supervisor_agent.stream(
    {"messages": [{"role": "user", "content": query}]}
):
    for update in step.values():
        for message in update.get("messages", []):
            message.pretty_print()

================================== Ai Message ==================================
Tool Calls:
  schedule_event (call_mXFJJDU8bKZadNUZPaag8Lct)
 Call ID: call_mXFJJDU8bKZadNUZPaag8Lct
  Args:
    request: Schedule a team standup for tomorrow at 9am with Alice and Bob.
================================= Tool Message =================================
Name: schedule_event

The team standup has been scheduled for tomorrow at 9:00 AM with Alice and Bob. If you need to make any changes or add more details, just let me know!
================================== Ai Message ==================================

The team standup with Alice and Bob is scheduled for tomorrow at 9:00 AM. If you need any further arrangements or adjustments, please let me know!

监督者识别这是日历任务，调用 schedule_event，日历代理处理日期解析和事件创建。

要完全透明地了解信息流，包括每次聊天模型调用的提示和响应，请查看LangSmith 追踪了解上述运行。

示例 2：复杂的多领域请求

query = (
    "Schedule a meeting with the design team next Tuesday at 2pm for 1 hour, "
    "and send them an email reminder about reviewing the new mockups."
)

for step in supervisor_agent.stream(
    {"messages": [{"role": "user", "content": query}]}
):
    for update in step.values():
        for message in update.get("messages", []):
            message.pretty_print()

================================== Ai Message ==================================
Tool Calls:
  schedule_event (call_YA68mqF0koZItCFPx0kGQfZi)
 Call ID: call_YA68mqF0koZItCFPx0kGQfZi
  Args:
    request: meeting with the design team next Tuesday at 2pm for 1 hour
  manage_email (call_XxqcJBvVIuKuRK794ZIzlLxx)
 Call ID: call_XxqcJBvVIuKuRK794ZIzlLxx
  Args:
    request: send the design team an email reminder about reviewing the new mockups
================================= Tool Message =================================
Name: schedule_event

Your meeting with the design team is scheduled for next Tuesday, June 18th, from 2:00pm to 3:00pm. Let me know if you need to add more details or make any changes!
================================= Tool Message =================================
Name: manage_email

I've sent an email reminder to the design team requesting them to review the new mockups. If you need to include more information or recipients, just let me know!
================================== Ai Message ==================================

Your meeting with the design team is scheduled for next Tuesday, June 18th, from 2:00pm to 3:00pm.

I've also sent an email reminder to the design team, asking them to review the new mockups.

Let me know if you'd like to add more details to the meeting or include additional information in the email!

监督者识别这需要日历和电子邮件两个操作，为会议调用 schedule_event，然后为提醒调用 manage_email。每个子代理完成其任务，监督者将两个结果综合成一个连贯的回复。

请参阅LangSmith 追踪以查看上述运行的详细信息流，包括各个聊天模型的提示和响应。

完整的工作示例

以下是整合在一个可运行脚本中的所有内容：

Show 查看完整代码

"""
Personal Assistant Supervisor Example

This example demonstrates the tool calling pattern for multi-agent systems.
A supervisor agent coordinates specialized sub-agents (calendar and email)
that are wrapped as tools.
"""

from langchain.tools import tool
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

# ============================================================================
# 步骤 1：定义低级 API 工具（桩）
# ============================================================================

@tool
def create_calendar_event(
    title: str,
    start_time: str,    # ISO 格式："2024-01-15T14:00:00"
    end_time: str,      # ISO 格式："2024-01-15T15:00:00"
    attendees: list[str],    # 电子邮件地址
    location: str = ""
) -> str:
    """Create a calendar event. Requires exact ISO datetime format."""
    return f"Event created: {title} from {start_time} to {end_time} with {len(attendees)} attendees"


@tool
def send_email(
    to: list[str],        # 电子邮件地址
    subject: str,
    body: str,
    cc: list[str] = []
) -> str:
    """Send an email via email API. Requires properly formatted addresses."""
    return f"Email sent to {', '.join(to)} - Subject: {subject}"


@tool
def get_available_time_slots(
    attendees: list[str],
    date: str,    # ISO 格式："2024-01-15"
    duration_minutes: int
) -> list[str]:
    """Check calendar availability for given attendees on a specific date."""
    return ["09:00", "14:00", "16:00"]


# ============================================================================
# 步骤 2：创建专门的子代理
# ============================================================================

model = init_chat_model("gpt-5.4")    # 例如

calendar_agent = create_agent(
    model,
    tools=[create_calendar_event, get_available_time_slots],
    system_prompt=(
        "You are a calendar scheduling assistant. "
        "Parse natural language scheduling requests (e.g., 'next Tuesday at 2pm') "
        "into proper ISO datetime formats. "
        "Use get_available_time_slots to check availability when needed. "
        "If there is no suitable time slot, stop and confirm unavailability in your response. "
        "Use create_calendar_event to schedule events. "
        "Always confirm what was scheduled in your final response."
    )
)

email_agent = create_agent(
    model,
    tools=[send_email],
    system_prompt=(
        "You are an email assistant. "
        "Compose professional emails based on natural language requests. "
        "Extract recipient information and craft appropriate subject lines and body text. "
        "Use send_email to send the message. "
        "Always confirm what was sent in your final response."
    )
)

# 步骤 3：将子代理包装为监督者的工具
# ============================================================================
# ============================================================================

@tool
def schedule_event(request: str) -> str:
    """Schedule calendar events using natural language.

    Use this when the user wants to create, modify, or check calendar appointments.
    Handles date/time parsing, availability checking, and event creation.

    Input: Natural language scheduling request (e.g., 'meeting with design team
    next Tuesday at 2pm')
    """
    result = calendar_agent.invoke({
        "messages": [{"role": "user", "content": request}]
    })
    return result["messages"][-1].text


@tool
def manage_email(request: str) -> str:
    """Send emails using natural language.

    Use this when the user wants to send notifications, reminders, or any email
    communication. Handles recipient extraction, subject generation, and email
    composition.

    Input: Natural language email request (e.g., 'send them a reminder about
    the meeting')
    """
    result = email_agent.invoke({
        "messages": [{"role": "user", "content": request}]
    })
    return result["messages"][-1].text


# ============================================================================
# Step 4: Create the supervisor agent
# ============================================================================

supervisor_agent = create_agent(
    model,
    tools=[schedule_event, manage_email],
    system_prompt=(
        "You are a helpful personal assistant. "
        "You can schedule calendar events and send emails. "
        "Break down user requests into appropriate tool calls and coordinate the results. "
        "When a request involves multiple actions, use multiple tools in sequence."
    )
)

# ============================================================================
# 示例：需要日历和电子邮件协调的用户请求
# ============================================================================

if __name__ == "__main__":
    # Example: User request requiring both calendar and email coordination
    user_request = (
        "Schedule a meeting with the design team next Tuesday at 2pm for 1 hour, "
        "and send them an email reminder about reviewing the new mockups."
    )

    print("User Request:", user_request)
    print("\n" + "="*80 + "\n")

    for step in supervisor_agent.stream(
        {"messages": [{"role": "user", "content": user_request}]}
    ):
        for update in step.values():
            for message in update.get("messages", []):
                message.pretty_print()

理解架构

您的系统有三层。底层包含需要精确格式的严格API工具。中间层包含接受自然语言的子代理，将其翻译为结构化API调用，并返回自然语言确认。顶层包含监督者，路由到高级能力并综合结果。这种关注点分离提供了几个好处：每层都有专注的职责，您可以添加新领域而不影响现有领域，并且可以独立测试和迭代每层。

6. 添加人工审核

对于敏感操作，合并人工审核可能是谨慎的做法。LangChain 包含用于审核工具调用的内置中间件，在这种情况下是子代理调用的工具。让我们为两个子代理添加人工审核：

我们将 create_calendar_event 和 send_email 工具配置为中断，允许所有响应类型（approve、edit、reject）
我们仅为顶层代理添加检查点保存器。这是暂停和恢复执行所必需的。

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware   
from langgraph.checkpoint.memory import InMemorySaver   


calendar_agent = create_agent(
    model,
    tools=[create_calendar_event, get_available_time_slots],
    system_prompt=CALENDAR_AGENT_PROMPT,
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={"create_calendar_event": True},
            description_prefix="Calendar event pending approval",
        ),
    ],
)

email_agent = create_agent(
    model,
    tools=[send_email],
    system_prompt=EMAIL_AGENT_PROMPT,
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={"send_email": True},
            description_prefix="Outbound email pending approval",
        ),
    ],
)

supervisor_agent = create_agent(
    model,
    tools=[schedule_event, manage_email],
    system_prompt=SUPERVISOR_PROMPT,
    checkpointer=InMemorySaver(),
)

让我们重复该查询。请注意，我们将中断事件收集到一个列表中以访问下游：

query = (
    "Schedule a meeting with the design team next Tuesday at 2pm for 1 hour, "
    "and send them an email reminder about reviewing the new mockups."
)

config = {"configurable": {"thread_id": "6"}}

interrupts = []
for step in supervisor_agent.stream(
    {"messages": [{"role": "user", "content": query}]},
    config,
):
    for update in step.values():
        if isinstance(update, dict):
            for message in update.get("messages", []):
                message.pretty_print()
        else:
            interrupt_ = update[0]
            interrupts.append(interrupt_)
            print(f"\nINTERRUPTED: {interrupt_.id}")

================================== Ai Message ==================================
Tool Calls:
  schedule_event (call_t4Wyn32ohaShpEZKuzZbl83z)
 Call ID: call_t4Wyn32ohaShpEZKuzZbl83z
  Args:
    request: Schedule a meeting with the design team next Tuesday at 2pm for 1 hour.
  manage_email (call_JWj4vDJ5VMnvkySymhCBm4IR)
 Call ID: call_JWj4vDJ5VMnvkySymhCBm4IR
  Args:
    request: Send an email reminder to the design team about reviewing the new mockups before our meeting next Tuesday at 2pm.

INTERRUPTED: 4f994c9721682a292af303ec1a46abb7

INTERRUPTED: 2b56f299be313ad8bc689eff02973f16

这次我们中断了执行。让我们检查中断事件：

for interrupt_ in interrupts:
    for request in interrupt_.value["action_requests"]:
        print(f"INTERRUPTED: {interrupt_.id}")
        print(f"{request['description']}\n")

INTERRUPTED: 4f994c9721682a292af303ec1a46abb7
Calendar event pending approval

Tool: create_calendar_event
Args: {'title': 'Meeting with the Design Team', 'start_time': '2024-06-18T14:00:00', 'end_time': '2024-06-18T15:00:00', 'attendees': ['design team']}

INTERRUPTED: 2b56f299be313ad8bc689eff02973f16
Outbound email pending approval

Tool: send_email
Args: {'to': ['designteam@example.com'], 'subject': 'Reminder: Review New Mockups Before Meeting Next Tuesday at 2pm', 'body': "Hello Team,\n\nThis is a reminder to review the new mockups ahead of our meeting scheduled for next Tuesday at 2pm. Your feedback and insights will be valuable for our discussion and next steps.\n\nPlease ensure you've gone through the designs and are ready to share your thoughts during the meeting.\n\nThank you!\n\nBest regards,\n[Your Name]"}

我们可以通过引用其 ID 来指定每个中断的决策，使用 Command。请参阅人工审核指南了解更多详细信息。为演示目的，这里我们将接受日历事件，但编辑外发邮件的主题：

from langgraph.types import Command   

resume = {}
for interrupt_ in interrupts:
    if interrupt_.id == "2b56f299be313ad8bc689eff02973f16":
        # 编辑邮件
        edited_action = interrupt_.value["action_requests"][0].copy()
        edited_action["args"]["subject"] = "Mockups reminder"
        resume[interrupt_.id] = {
            "decisions": [{"type": "edit", "edited_action": edited_action}]
        }
    else:
        resume[interrupt_.id] = {"decisions": [{"type": "approve"}]}

interrupts = []
for step in supervisor_agent.stream(
    Command(resume=resume),
    config,
):
    for update in step.values():
        if isinstance(update, dict):
            for message in update.get("messages", []):
                message.pretty_print()
        else:
            interrupt_ = update[0]
            interrupts.append(interrupt_)
            print(f"\nINTERRUPTED: {interrupt_.id}")

================================= Tool Message =================================
Name: schedule_event

Your meeting with the design team has been scheduled for next Tuesday, June 18th, from 2:00 pm to 3:00 pm.
================================= Tool Message =================================
Name: manage_email

Your email reminder to the design team has been sent. Here’s what was sent:

- Recipient: designteam@example.com
- Subject: Mockups reminder
- Body: A reminder to review the new mockups before the meeting next Tuesday at 2pm, with a request for feedback and readiness for discussion.

Let me know if you need any further assistance!
================================== Ai Message ==================================

- Your meeting with the design team has been scheduled for next Tuesday, June 18th, from 2:00 pm to 3:00 pm.
- An email reminder has been sent to the design team about reviewing the new mockups before the meeting.

Let me know if you need any further assistance!

运行按照我们的输入继续。

7. 高级：控制信息流

默认情况下，子代理只接收来自监督者的请求字符串。您可能希望传递额外的上下文，例如对话历史或用户偏好。

向子代理传递额外的对话上下文

from langchain.tools import tool, ToolRuntime

@tool
def schedule_event(
    request: str,
    runtime: ToolRuntime
) -> str:
    """Schedule calendar events using natural language."""
    # 自定义子代理接收的上下文
    original_user_message = next(
        message for message in runtime.state["messages"]
        if message.type == "human"
    )
    prompt = (
        "You are assisting with the following user inquiry:\n\n"
        f"{original_user_message.text}\n\n"
        "You are tasked with the following sub-request:\n\n"
        f"{request}"
    )
    result = calendar_agent.invoke({
        "messages": [{"role": "user", "content": prompt}],
    })
    return result["messages"][-1].text

这允许子代理看到完整的对话上下文，这对于解析歧义很有用，例如”安排同一时间明天”（引用之前的对话）。

您可以在 LangSmith 追踪的聊天模型调用中看到子代理接收的完整上下文。

控制监督者接收的内容

您还可以自定义流回监督者的信息：

import json

@tool
def schedule_event(request: str) -> str:
    """Schedule calendar events using natural language."""
    result = calendar_agent.invoke({
        "messages": [{"role": "user", "content": request}]
    })

    # 选项 1：只返回确认消息
    return result["messages"][-1].text

    # 选项 2：返回结构化数据
    # return json.dumps({
    #     "status": "success",
    #     "event_id": "evt_123",
    #     "summary": result["messages"][-1].text
    # })

**重要：**确保子代理提示强调其最终消息应包含所有相关信息。一个常见的失败模式是执行工具调用但不在最终回复中包含结果的子代理。

8. 关键要点

监督者模式创建了抽象层，其中每层都有明确的职责。在设计监督者系统时，从清晰的领域边界开始，并为每个子代理提供专注的工具和提示。为监督者编写清晰的工具描述，在集成前独立测试每层，并根据您的特定需求控制信息流。

何时使用监督者模式当您有多个不同领域（日历、电子邮件、CRM、数据库）时使用监督者模式，每个领域有多个工具或复杂逻辑，您想要集中式工作流程控制，以及子代理不需要直接与用户对话。对于只有几个工具的更简单情况，请使用单个代理。当代理需要与用户对话时，请改用交接。对于代理之间的对等协作，请考虑其他多代理模式。

下一步

了解交接以实现代理到代理的对话，探索上下文工程以微调信息流，阅读多代理概述以比较不同模式，并使用 LangSmith 来调试和监控您的多代理系统。

在 GitHub 上编辑此页面或提交问题。

通过 MCP 将这些文档连接到 Claude、VSCode 等，获取实时答案。

Tutorials

Conceptual overviews

Additional resources

概述

为什么要使用监督者？

概念

设置

安装

LangSmith

组件

1. 定义工具

2. 创建专门的子代理

创建日历代理

创建电子邮件代理

3. 将子代理包装为工具

4. 创建监督者代理

5. 使用监督者

示例 1：简单的单一领域请求

示例 2：复杂的多领域请求

完整的工作示例

理解架构

6. 添加人工审核

7. 高级：控制信息流

向子代理传递额外的对话上下文

控制监督者接收的内容

8. 关键要点

下一步

Tutorials

Conceptual overviews

Additional resources

​概述

​为什么要使用监督者？

​概念

​设置

​安装

​LangSmith

​组件

​1. 定义工具

​2. 创建专门的子代理

​创建日历代理

​创建电子邮件代理

​3. 将子代理包装为工具

​4. 创建监督者代理

​5. 使用监督者

​示例 1：简单的单一领域请求

​示例 2：复杂的多领域请求

​完整的工作示例

​理解架构

​6. 添加人工审核

​7. 高级：控制信息流

​向子代理传递额外的对话上下文

​控制监督者接收的内容

​8. 关键要点

​下一步

概述

为什么要使用监督者？

概念

设置

安装

LangSmith

组件

1. 定义工具

2. 创建专门的子代理

创建日历代理

创建电子邮件代理

3. 将子代理包装为工具

4. 创建监督者代理

5. 使用监督者

示例 1：简单的单一领域请求

示例 2：复杂的多领域请求

完整的工作示例

理解架构

6. 添加人工审核

7. 高级：控制信息流

向子代理传递额外的对话上下文

控制监督者接收的内容

8. 关键要点

下一步