构建一个主管代理

概览

主管模式是一种多代理架构，其中一个中央主管代理协调专业的工人代理。当任务需要不同类型的专业知识时，这种方法表现出色。与其构建一个管理跨领域工具选择的代理，不如创建由了解整体工作流程的主管代理协调的专注专家。在本教程中，您将构建一个个人助理系统，通过一个真实的 workflow 来展示这些优点。该系统将协调两个具有根本不同职责的专家：

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

我们还将加入人工审核，以允许用户根据需要批准、编辑和拒绝操作（例如发送邮件）。

为什么要使用主管代理？

多代理架构允许您将工具分配给不同的工人，每个工人都有自己的独立提示或指令。考虑一个直接访问所有日历和电子邮件 API 的代理：它必须从许多类似的工具中进行选择，理解每个 API 的确切格式，并同时处理多个领域。如果性能下降，将相关工具和相关提示分离到逻辑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了什么。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

OpenAI
Anthropic
Azure
Google Gemini
AWS Bedrock

👉 阅读 OpenAI 聊天模型集成文档

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-4.1")

1. 定义工具

首先定义需要结构化输入的工具。在实际应用程序中，这些工具将调用实际的 API（Google Calendar、SendGrid 等）。在本教程中，您将使用存根来演示该模式。

from langchain_core.tools import tool

@tool
def create_calendar_event(
    title: str,
    start_time: str,       # ISO format: "2024-01-15T14:00:00"
    end_time: str,         # ISO format: "2024-01-15T15:00:00"
    attendees: list[str],  # email addresses
    location: str = ""
) -> str:
    """Create a calendar event. Requires exact ISO datetime format."""
    # Stub: In practice, this would call Google Calendar API, Outlook API, etc.
    return f"Event created: {title} from {start_time} to {end_time} with {len(attendees)} attendees"


@tool
def send_email(
    to: list[str],  # email addresses
    subject: str,
    body: str,
    cc: list[str] = []
) -> str:
    """Send an email via email API. Requires properly formatted addresses."""
    # Stub: In practice, this would call SendGrid, Gmail API, etc.
    return f"Email sent to {', '.join(to)} - Subject: {subject}"


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

2. 创建专业子代理

接下来，我们将创建处理每个领域的专业子代理。

创建日历代理

日历代理理解自然语言调度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. "
    "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，看看它如何处理自然语言调度

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

电子邮件代理处理邮件撰写和发送。它专注于提取收件人信息、撰写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，撰写专业的主题和正文，调用 `send_email`，并返回确认。每个子代理都有一个狭窄的焦点，具有特定领域的工具和提示，使其能够擅长其特定任务。

3. 将子代理包装为工具

现在将每个子代理包装成主管代理可以调用的工具。这是创建分层系统的关键架构步骤。主管代理将看到“schedule_event”等高级工具，而不是“create_calendar_event”等低级工具。

@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

工具描述有助于主管代理决定何时使用每个工具，因此请使其清晰明确。我们只返回子代理的最终响应，因为主管代理不需要查看中间推理或工具调用。

4. 创建主管代理

现在创建协调子代理的主管代理。主管代理只看到高级工具，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. 使用主管代理

现在，使用需要跨多个领域协调的复杂请求来测试您的完整系统

示例 1：简单的单领域请求

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!

主管代理将其识别为日历任务，调用 `schedule_event`，然后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，请查看上述运行的LangSmith 跟踪。

示例 2：复杂的跨领域请求

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!

主管代理识别这需要日历和电子邮件操作，调用 `schedule_event` 来安排会议，然后调用 `manage_email` 来发送提醒。每个子代理完成其任务，主管代理将两个结果综合成一个连贯的响应。

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

完整的工作示例

这是一个可运行的完整脚本

显示查看完整代码

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

# ============================================================================
# Step 1: Define low-level API tools (stubbed)
# ============================================================================

@tool
def create_calendar_event(
    title: str,
    start_time: str,  # ISO format: "2024-01-15T14:00:00"
    end_time: str,    # ISO format: "2024-01-15T15:00:00"
    attendees: list[str],  # email addresses
    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],      # email addresses
    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 format: "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"]


# ============================================================================
# Step 2: Create specialized sub-agents
# ============================================================================

model = init_chat_model("claude-haiku-4-5-20251001")  # for example

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. "
        "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."
    )
)

# ============================================================================
# Step 3: Wrap sub-agents as tools for the supervisor
# ============================================================================

@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."
    )
)

# ============================================================================
# Step 5: Use the supervisor
# ============================================================================

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、将其转换为结构化 API 调用并返回自然语言确认的子代理。顶层包含路由到高级功能并综合结果的主管代理。这种关注点分离提供了几个好处：每层都有明确的职责，您可以添加新领域而不会影响现有领域，并且您可以独立测试和迭代每层。

6. 添加人工审核

纳入对敏感操作的人工审核可能是审慎的。LangChain 包含内置中间件来审查工具调用，在本例中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`）
我们只向顶级代理添加一个检查点。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(), 
)

让我们重复查询。请注意，我们将中断事件收集到一个列表中以便下游访问

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]"}

我们可以通过使用`Command`引用其 ID 来为每个中断指定决策。有关其他详细信息，请参阅人工审核指南。出于演示目的，我们将在此处接受日历事件，但编辑发送邮件的主题

from langgraph.types import Command 

resume = {}
for interrupt_ in interrupts:
    if interrupt_.id == "2b56f299be313ad8bc689eff02973f16":
        # Edit email
        edited_action = interrupt_.value["action_requests"][0].copy()
        edited_action["arguments"]["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!

运行将继续使用我们的输入。

7. 高级：控制信息流

默认情况下，子代理只接收主管代理的请求字符串。您可能希望传递额外的上下文，例如对话历史记录或用户偏好。

将额外的对话上下文传递给子代理

from langchain.tools import tool, ToolRuntime

@tool
def schedule_event(
    request: str,
    runtime: ToolRuntime
) -> str:
    """Schedule calendar events using natural language."""
    # Customize context received by sub-agent
    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

这允许子代理查看完整的对话上下文，这对于解决诸如“明天同一时间安排”之类的歧义非常有用（引用之前的对话）。

您可以在 LangSmith 跟踪的聊天模型调用中查看子代理收到的完整上下文。

控制主管代理接收的内容

您还可以自定义流回主管代理的信息

import json

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

    # Option 1: Return just the confirmation message
    return result["messages"][-1].text

    # Option 2: Return structured data
    # return json.dumps({
    #     "status": "success",
    #     "event_id": "evt_123",
    #     "summary": result["messages"][-1].text
    # })

重要提示：确保子代理提示强调其最终消息应包含所有相关信息。常见的故障模式是子代理执行工具调用但不将其结果包含在其最终响应中。

8. 主要收获

主管模式创建了抽象层，其中每层都有明确的职责。在设计主管系统时，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。为主管代理编写清晰的工具描述，在集成之前独立测试每个层，并根据您的特定需求控制信息流。

何时使用主管模式当您有多个不同的领域（日历、电子邮件、CRM、数据库），每个领域有多个工具或复杂的逻辑，您希望进行集中式工作流控制，并且子代理不需要直接与用户对话时，请使用主管模式。对于只有少数工具的简单情况，请使用单个代理。当代理需要与用户进行对话时，请改用交接。对于代理之间的点对点协作，请考虑其他多代理模式。

后续步骤

了解有关代理间对话的交接，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的模式，并使用LangSmith调试和监控您的多代理系统。

在 GitHub 上编辑此页面源文件。

以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。

教程

概念概述

附加资源

概览

为什么要使用主管代理？

概念

设置

安装

LangSmith

组件

1. 定义工具

2. 创建专业子代理

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. 创建主管代理

5. 使用主管代理

示例 1：简单的单领域请求

示例 2：复杂的跨领域请求

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

将额外的对话上下文传递给子代理

控制主管代理接收的内容

8. 主要收获

后续步骤

教程

概念概述

附加资源

​概览

​为什么要使用主管代理？

​概念

​设置

​安装

​LangSmith

​组件

​1. 定义工具

​2. 创建专业子代理

​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. 创建主管代理

​5. 使用主管代理

​示例 1：简单的单领域请求

​示例 2：复杂的跨领域请求

​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

​将额外的对话上下文传递给子代理

​控制主管代理接收的内容

​8. 主要收获

​后续步骤

概览

为什么要使用主管代理？

概念

设置

安装

LangSmith

组件

1. 定义工具

2. 创建专业子代理

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. 创建主管代理

5. 使用主管代理

示例 1：简单的单领域请求

示例 2：复杂的跨领域请求

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

将额外的对话上下文传递给子代理

控制主管代理接收的内容

8. 主要收获

后续步骤