使用子智能体构建个人助手

概述

监督者模式是一种多智能体架构，其中一个中央监督者智能体协调专业的工作者智能体。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智能体，不如创建由理解整体工作流的监督者协调的专注型专家。

在本教程中，你将构建一个个人助手系统，通过一个现实的工作流来展示这些优势。系统将协调两个具有根本不同职责的专家：

• 日历智能体，处理日程安排、可用性检查和事件管理。
• 邮件智能体，管理通信、撰写消息和发送通知。

我们还将整合人机协作审查，允许用户根据需要批准、编辑和拒绝操作（如外发邮件）。

为什么使用监督者？

多智能体架构允许你将工具分配给各个工作者，每个工作者都有自己的提示或指令。考虑一个直接访问所有日历和邮件 API 的智能体：它必须从许多类似的工具中选择、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，将相关工具和关联提示分离到逻辑组中可能会有帮助（部分原因是为了管理迭代改进）。

概念

我们将涵盖以下概念：

• 多智能体系统
• 人机协作审查

设置

安装

This tutorial requires the langchain package:

npm

npm install langchain

For more details, see our Installation guide.

LangSmith

Set up LangSmith to inspect what is happening inside your agent. Then set the following environment variables:

bash

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

组件

We will need to select a chat model from LangChain’s suite of integrations:

OpenAI

👉 Read the OpenAI chat model integration docs

npm

npm install @langchain/openai

initChatModel

import { initChatModel } from "langchain";

process.env.OPENAI_API_KEY = "your-api-key";

const model = await initChatModel("gpt-5.4");

Anthropic

👉 Read the Anthropic chat model integration docs

npm

npm install @langchain/anthropic

initChatModel

import { initChatModel } from "langchain";

process.env.ANTHROPIC_API_KEY = "your-api-key";

const model = await initChatModel("claude-sonnet-4-6");

Azure

👉 Read the Azure chat model integration docs

npm

npm install @langchain/azure

initChatModel

import { initChatModel } from "langchain";

process.env.AZURE_OPENAI_API_KEY = "your-api-key";
process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint";
process.env.OPENAI_API_VERSION = "your-api-version";

const model = await initChatModel("azure_openai:gpt-5.4");

Google Gemini

👉 Read the Google GenAI chat model integration docs

npm

npm install @langchain/google-genai

initChatModel

import { initChatModel } from "langchain";

process.env.GOOGLE_API_KEY = "your-api-key";

const model = await initChatModel("google-genai:gemini-2.5-flash-lite");

Bedrock Converse

👉 Read the AWS Bedrock chat model integration docs

npm

npm install @langchain/aws

initChatModel

import { initChatModel } from "langchain";

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

const model = await initChatModel("bedrock:gpt-5.4");

1. 定义工具

Start by defining the tools that require structured inputs. In real applications, these would call actual APIs (Google Calendar, SendGrid, etc.). For this tutorial, you’ll use stubs to demonstrate the pattern.

import { tool } from "langchain";
import { z } from "zod";

const createCalendarEvent = tool(
  async ({ title, startTime, endTime, attendees, location }) => {
    // Stub: In practice, this would call Google Calendar API, Outlook API, etc.
    return `Event created: ${title} from ${startTime} to ${endTime} with ${attendees.length} attendees`;
  },
  {
    name: "create_calendar_event",
    description: "Create a calendar event. Requires exact ISO datetime format.",
    schema: z.object({
      title: z.string(),
      startTime: z.string().describe("ISO format: '2024-01-15T14:00:00'"),
      endTime: z.string().describe("ISO format: '2024-01-15T15:00:00'"),
      attendees: z.array(z.string()).describe("email addresses"),
      location: z.string().optional(),
    }),
  }
);

const sendEmail = tool(
  async ({ to, subject, body, cc }) => {
    // Stub: In practice, this would call SendGrid, Gmail API, etc.
    return `Email sent to ${to.join(', ')} - Subject: ${subject}`;
  },
  {
    name: "send_email",
    description: "Send an email via email API. Requires properly formatted addresses.",
    schema: z.object({
      to: z.array(z.string()).describe("email addresses"),
      subject: z.string(),
      body: z.string(),
      cc: z.array(z.string()).optional(),
    }),
  }
);

const getAvailableTimeSlots = tool(
  async ({ attendees, date, durationMinutes }) => {
    // Stub: In practice, this would query calendar APIs
    return ["09:00", "14:00", "16:00"];
  },
  {
    name: "get_available_time_slots",
    description: "Check calendar availability for given attendees on a specific date.",
    schema: z.object({
      attendees: z.array(z.string()),
      date: z.string().describe("ISO format: '2024-01-15'"),
      durationMinutes: z.number(),
    }),
  }
);

2. 创建专业子智能体

Next, we’ll create specialized sub-agents that handle each domain.

创建日历智能体

The calendar agent understands natural language scheduling requests and translates them into precise API calls. It handles date parsing, availability checking, and event creation.

import { createAgent } from "langchain";

const 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.
`.trim();

const calendarAgent = createAgent({
  model: llm,
  tools: [createCalendarEvent, getAvailableTimeSlots],
  systemPrompt: CALENDAR_AGENT_PROMPT,
});

Test the calendar agent to see how it handles natural language scheduling:

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

const stream = await calendarAgent.stream({
  messages: [{ role: "user", content: query }]
});

for await (const step of stream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

================================== 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!

The agent parses “next Tuesday at 2pm” into ISO format (“2024-01-16T14:00:00”), calculates the end time, calls create_calendar_event, and returns a natural language confirmation.

创建邮件智能体

The email agent handles message composition and sending. It focuses on extracting recipient information, crafting appropriate subject lines and body text, and managing email communication.

const 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.
`.trim();

const emailAgent = createAgent({
  model: llm,
  tools: [sendEmail],
  systemPrompt: EMAIL_AGENT_PROMPT,
});

Test the email agent with a natural language request:

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

const stream = await emailAgent.stream({
  messages: [{ role: "user", content: query }]
});

for await (const step of stream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

================================== Ai Message ==================================
Tool Calls:
  send_email (call_OMl51FziTVY6CRZvzYfjYOZr)
 Call ID: call_OMl51FziTVY6CRZvzYfjYOZr
  Args:
    to: ['[e-mail]']
    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 [e-mail] - 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!

The agent infers the recipient from the informal request, crafts a professional subject line and body, calls send_email, and returns a confirmation. Each sub-agent has a narrow focus with domain-specific tools and prompts, allowing it to excel at its specific task.

3. 将子智能体包装为工具

Now wrap each sub-agent as a tool that the supervisor can invoke. This is the key architectural step that creates the layered system. The supervisor will see high-level tools like “schedule_event”, not low-level tools like “create_calendar_event”.

const scheduleEvent = tool(
  async ({ request }) => {
    const result = await calendarAgent.invoke({
      messages: [{ role: "user", content: request }]
    });
    const lastMessage = result.messages[result.messages.length - 1];
    return lastMessage.text;
  },
  {
    name: "schedule_event",
    description: `
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')
    `.trim(),
    schema: z.object({
      request: z.string().describe("Natural language scheduling request"),
    }),
  }
);

const manageEmail = tool(
  async ({ request }) => {
    const result = await emailAgent.invoke({
      messages: [{ role: "user", content: request }]
    });
    const lastMessage = result.messages[result.messages.length - 1];
    return lastMessage.text;
  },
  {
    name: "manage_email",
    description: `
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')
    `.trim(),
    schema: z.object({
      request: z.string().describe("Natural language email request"),
    }),
  }
);

The tool descriptions help the supervisor decide when to use each tool, so make them clear and specific. We return only the sub-agent’s final response, as the supervisor doesn’t need to see intermediate reasoning or tool calls.

4. 创建监督者智能体

Now create the supervisor that orchestrates the sub-agents. The supervisor only sees high-level tools and makes routing decisions at the domain level, not the individual API level.

const 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.
`.trim();

const supervisorAgent = createAgent({
  model: llm,
  tools: [scheduleEvent, manageEmail],
  systemPrompt: SUPERVISOR_PROMPT,
});

5. 使用监督者

Now test your complete system with complex requests that require coordination across multiple domains:

示例 1：简单的单领域请求

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

const stream = await supervisorAgent.stream({
  messages: [{ role: "user", content: query }]
});

for await (const step of stream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

================================== 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!

The supervisor identifies this as a calendar task, calls schedule_event, and the calendar agent handles date parsing and event creation.

For full transparency into the information flow, including prompts and responses for each chat model call, check out the LangSmith trace for the above run.

示例 2：复杂的多领域请求

const 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.";

const stream = await supervisorAgent.stream({
  messages: [{ role: "user", content: query }]
});

for await (const step of stream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

================================== 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!

The supervisor recognizes this requires both calendar and email actions, calls schedule_event for the meeting, then calls manage_email for the reminder. Each sub-agent completes its task, and the supervisor synthesizes both results into a coherent response.

Refer to the LangSmith trace to see the detailed information flow for the above run, including individual chat model prompts and responses.

完整工作示例

Here’s everything together in a runnable script:

Show View complete code

/**
 * 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.
 */

import { tool, createAgent } from "langchain";
import { ChatAnthropic } from "@langchain/anthropic";
import { z } from "zod";

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

const createCalendarEvent = tool(
  async ({ title, startTime, endTime, attendees, location }) => {
    // Stub: In practice, this would call Google Calendar API, Outlook API, etc.
    return `Event created: ${title} from ${startTime} to ${endTime} with ${attendees.length} attendees`;
  },
  {
    name: "create_calendar_event",
    description: "Create a calendar event. Requires exact ISO datetime format.",
    schema: z.object({
      title: z.string(),
      startTime: z.string().describe("ISO format: '2024-01-15T14:00:00'"),
      endTime: z.string().describe("ISO format: '2024-01-15T15:00:00'"),
      attendees: z.array(z.string()).describe("email addresses"),
      location: z.string().optional().default(""),
    }),
  }
);

const sendEmail = tool(
  async ({ to, subject, body, cc }) => {
    // Stub: In practice, this would call SendGrid, Gmail API, etc.
    return `Email sent to ${to.join(", ")} - Subject: ${subject}`;
  },
  {
    name: "send_email",
    description:
      "Send an email via email API. Requires properly formatted addresses.",
    schema: z.object({
      to: z.array(z.string()).describe("email addresses"),
      subject: z.string(),
      body: z.string(),
      cc: z.array(z.string()).optional().default([]),
    }),
  }
);

const getAvailableTimeSlots = tool(
  async ({ attendees, date, durationMinutes }) => {
    // Stub: In practice, this would query calendar APIs
    return ["09:00", "14:00", "16:00"];
  },
  {
    name: "get_available_time_slots",
    description:
      "Check calendar availability for given attendees on a specific date.",
    schema: z.object({
      attendees: z.array(z.string()),
      date: z.string().describe("ISO format: '2024-01-15'"),
      durationMinutes: z.number(),
    }),
  }
);

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

const llm = new ChatAnthropic({
  model: "gpt-5.4",
});

const calendarAgent = createAgent({
  model: llm,
  tools: [createCalendarEvent, getAvailableTimeSlots],
  systemPrompt: `
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.
  `.trim(),
});

const emailAgent = createAgent({
  model: llm,
  tools: [sendEmail],
  systemPrompt: `
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.
  `.trim(),
});

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

const scheduleEvent = tool(
  async ({ request }) => {
    const result = await calendarAgent.invoke({
      messages: [{ role: "user", content: request }],
    });
    const lastMessage = result.messages[result.messages.length - 1];
    return lastMessage.text;
  },
  {
    name: "schedule_event",
    description: `
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')
    `.trim(),
    schema: z.object({
      request: z.string().describe("Natural language scheduling request"),
    }),
  }
);

const manageEmail = tool(
  async ({ request }) => {
    const result = await emailAgent.invoke({
      messages: [{ role: "user", content: request }],
    });
    const lastMessage = result.messages[result.messages.length - 1];
    return lastMessage.text;
  },
  {
    name: "manage_email",
    description: `
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')
    `.trim(),
    schema: z.object({
      request: z.string().describe("Natural language email request"),
    }),
  }
);

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

const supervisorAgent = createAgent({
  model: llm,
  tools: [scheduleEvent, manageEmail],
  systemPrompt: `
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.
  `.trim(),
});

// ============================================================================
// Step 5: Use the supervisor
// ============================================================================

// Example: User request requiring both calendar and email coordination
const userRequest =
  "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.";

console.log("User Request:", userRequest);
console.log(`\n${"=".repeat(80)}\n`);

const stream = await supervisorAgent.stream({
  messages: [{ role: "user", content: userRequest }],
});

for await (const step of stream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

理解架构

Your system has three layers. The bottom layer contains rigid API tools that require exact formats. The middle layer contains sub-agents that accept natural language, translate it to structured API calls, and return natural language confirmations. The top layer contains the supervisor that routes to high-level capabilities and synthesizes results.

This separation of concerns provides several benefits: each layer has a focused responsibility, you can add new domains without affecting existing ones, and you can test and iterate on each layer independently.

6. 添加人机协作审查

It can be prudent to incorporate human-in-the-loop review of sensitive actions. LangChain includes built-in middleware to review tool calls, in this case the tools invoked by sub-agents.

Let’s add human-in-the-loop review to both sub-agents:

• We configure the create_calendar_event and send_email tools to interrupt, permitting all response types (approve, edit, reject)
• We add a checkpointer only to the top-level agent. This is required to pause and resume execution.

import { createAgent, humanInTheLoopMiddleware } from "langchain";
import { MemorySaver } from "@langchain/langgraph";

const calendarAgent = createAgent({
  model: llm,
  tools: [createCalendarEvent, getAvailableTimeSlots],
  systemPrompt: CALENDAR_AGENT_PROMPT,
  middleware: [ 
    humanInTheLoopMiddleware({
      interruptOn: { create_calendar_event: true },
      descriptionPrefix: "Calendar event pending approval",
    }),
  ],
});

const emailAgent = createAgent({
  model: llm,
  tools: [sendEmail],
  systemPrompt: EMAIL_AGENT_PROMPT,
  middleware: [ 
    humanInTheLoopMiddleware({
      interruptOn: { send_email: true },
      descriptionPrefix: "Outbound email pending approval",
    }),
  ],
});

const supervisorAgent = createAgent({
  model: llm,
  tools: [scheduleEvent, manageEmail],
  systemPrompt: SUPERVISOR_PROMPT,
  checkpointer: new MemorySaver(),
});

Let’s repeat the query. Note that we gather interrupt events into a list to access downstream:

const 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.";

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

const interrupts: any[] = [];
const stream = await supervisorAgent.stream(
  { messages: [{ role: "user", content: query }] },
  config
);

for await (const step of streamA) {
  for (const update of Object.values(step)) {
    for (const message of update.messages) {
      console.log(message.toFormattedString());
    }
    const interrupt = update.__interrupt__?.[0];
    interrupts.push(interrupt);
    console.log(`\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

This time we’ve interrupted execution. Let’s inspect the interrupt events:

for (const interrupt of interrupts) {
  for (const request of interrupt.value.actionRequests) {
    console.log(`INTERRUPTED: ${interrupt.id}`);
    console.log(`${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': ['[e-mail]'], '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]"}

We can specify decisions for each interrupt by referring to its ID using a Command. Refer to the human-in-the-loop guide for additional details. For demonstration purposes, here we will accept the calendar event, but edit the subject of the outbound email:

import { Command } from "@langchain/langgraph";

const resume: Record<string, any> = {};
for (const interrupt of interrupts) {
  const actionRequest = interrupt.value.actionRequests[0];
  if (actionRequest.name === "send_email") {
    // Edit email
    const editedAction = { ...actionRequest };
    editedAction.args.subject = "Mockups reminder";
    resume[interrupt.id] = {
      decisions: [{ type: "edit", editedAction }]
    };
  } else {
    resume[interrupt.id] = { decisions: [{ type: "approve" }] };
  }
}

const resumeStream = await supervisorAgent.stream(
  new Command({ resume }),
  config
);

for await (const step of resumeStream) {
  for (const update of Object.values(step)) {
    if (update && typeof update === "object" && "messages" in update) {
      for (const message of update.messages) {
        console.log(message.toFormattedString());
      }
    }
  }
}

================================= 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: [e-mail]
- 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!

The run proceeds with our input.

7. 进阶：控制信息流

By default, sub-agents receive only the request string from the supervisor. You might want to pass additional context, such as conversation history or user preferences.

向子智能体传递额外的对话上下文

import { getCurrentTaskInput } from "@langchain/langgraph";
import { type BuiltInState, HumanMessage } from "langchain";

const scheduleEvent = tool(
  async ({ request }, config) => {
    // Customize context received by sub-agent
    // Access full thread messages from the config
    const currentMessages = getCurrentTaskInput<BuiltInState>(config).messages;
    const originalUserMessage = currentMessages.find(HumanMessage.isInstance);
    const prompt = `
You are assisting with the following user inquiry:

${originalUserMessage?.content || "No context available"}

You are tasked with the following sub-request:

${request}
    `.trim();

    const result = await calendarAgent.invoke({
      messages: [{ role: "user", content: prompt }],
    });
    const lastMessage = result.messages[result.messages.length - 1];
    return lastMessage.text;
  },
  {
    name: "schedule_event",
    description: "Schedule calendar events using natural language.",
    schema: z.object({
      request: z.string().describe("Natural language scheduling request"),
    }),
  }
);

This allows sub-agents to see the full conversation context, which can be useful for resolving ambiguities like “schedule it for the same time tomorrow” (referencing a previous conversation).

You can see the full context received by the sub agent in the chat model call of the LangSmith trace.

控制监督者接收的内容

You can also customize what information flows back to the supervisor:

const scheduleEvent = tool(
  async ({ request }) => {
    const result = await calendarAgent.invoke({
      messages: [{ role: "user", content: request }]
    });

    const lastMessage = result.messages[result.messages.length - 1];

    // Option 1: Return just the confirmation message
    return lastMessage.text;

    // Option 2: Return structured data
    // return JSON.stringify({
    //   status: "success",
    //   event_id: "evt_123",
    //   summary: lastMessage.text
    // });
  },
  {
    name: "schedule_event",
    description: "Schedule calendar events using natural language.",
    schema: z.object({
      request: z.string().describe("Natural language scheduling request"),
    }),
  }
);

Important: Make sure sub-agent prompts emphasize that their final message should contain all relevant information. A common failure mode is sub-agents that perform tool calls but don’t include the results in their final response.

For a complete working example that demonstrates the full supervisor pattern with human-in-the-loop review and advanced information flow control, check out supervisor_complete.ts in the LangChain.js examples.

8. 关键要点

The supervisor pattern creates layers of abstraction where each layer has a clear responsibility. When designing a supervisor system, start with clear domain boundaries and give each sub-agent focused tools and prompts. Write clear tool descriptions for the supervisor, test each layer independently before integration, and control information flow based on your specific needs.

When to use the supervisor pattern

Use the supervisor pattern when you have multiple distinct domains (calendar, email, CRM, database), each domain has multiple tools or complex logic, you want centralized workflow control, and sub-agents don’t need to converse directly with users.

For simpler cases with just a few tools, use a single agent. When agents need to have conversations with users, use handoffs instead. For peer-to-peer collaboration between agents, consider other multi-agent patterns.

后续步骤

Learn about handoffs for agent-to-agent conversations, explore context engineering to fine-tune information flow, read the multi-agent overview to compare different patterns, and use LangSmith to debug and monitor your multi-agent system.

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