概述
主管模式是一种多智能体架构,其中中央主管智能体协调专门的工作者智能体。这种方法在任务需要不同类型专业知识时表现出色。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主管协调的专注专家。 在本教程中,您将构建一个个人助理系统,通过一个真实的工作流来展示这些优势。该系统将协调两个职责根本不同的专家:- 日历智能体:处理日程安排、可用性检查和事件管理。
- 电子邮件智能体:管理通信、起草消息和发送通知。
为什么使用主管?
多智能体架构允许您将工具分配给工作者,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电子邮件 API 的智能体:它必须从许多相似的工具中选择,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所帮助(部分原因是为了管理迭代改进)。概念
我们将涵盖以下概念:设置
安装
本教程需要langchain 包:
npm install langchain
LangSmith
设置 LangSmith 以检查智能体内部发生的情况。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:- OpenAI
- Anthropic
- Azure
- Google Gemini
- Bedrock Converse
👉 阅读 OpenAI 聊天模型集成文档
npm install @langchain/openai
import { initChatModel } from "langchain";
process.env.OPENAI_API_KEY = "your-api-key";
const model = await initChatModel("gpt-5.2");
👉 阅读 Anthropic 聊天模型集成文档
npm install @langchain/anthropic
import { initChatModel } from "langchain";
process.env.ANTHROPIC_API_KEY = "your-api-key";
const model = await initChatModel("claude-sonnet-4-6");
👉 阅读 Azure 聊天模型集成文档
npm install @langchain/azure
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-5.2");
👉 阅读 Google GenAI 聊天模型集成文档
npm install @langchain/google-genai
import { initChatModel } from "langchain";
process.env.GOOGLE_API_KEY = "your-api-key";
const model = await initChatModel("google-genai:gemini-2.5-flash-lite");
👉 阅读 AWS Bedrock 聊天模型集成文档
npm install @langchain/aws
import { initChatModel } from "langchain";
// 按照此处步骤配置您的凭据:
// https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
const model = await initChatModel("bedrock:gpt-5.2");
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,这些工具会调用实际的 API(Google Calendar、SendGrid 等)。在本教程中,您将使用存根来演示该模式。import { tool } from "langchain";
import { z } from "zod";
const createCalendarEvent = tool(
async ({ title, startTime, endTime, attendees, location }) => {
// 存根:在实践中,这将调用 Google Calendar API、Outlook API 等。
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 }) => {
// 存根:在实践中,这将调用 SendGrid、Gmail API 等。
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 }) => {
// 存根:在实践中,这将查询日历 API
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. 创建专门的子智能体
接下来,我们将创建处理每个领域的专门子智能体。创建日历智能体
日历智能体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。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,
});
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!
create_calendar_event,并返回自然语言确认。
创建电子邮件智能体
电子邮件智能体处理消息的撰写和发送。它专注于提取收件人信息、撰写合适的主题行和正文文本,以及管理电子邮件通信。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,
});
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: ['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!
send_email,并返回确认。每个子智能体都有狭窄的关注点,配备领域特定的工具和提示,使其能够在特定任务上表现出色。
3. 将子智能体包装为工具
现在将每个子智能体包装为一个工具,供主管调用。这是创建分层系统的关键架构步骤。主管将看到高级工具,如 “schedule_event”,而不是低级工具,如 “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"),
}),
}
);
4. 创建主管智能体
现在创建协调子智能体的主管。主管只看到高级工具,并在领域级别做出路由决策,而不是在单个 API 级别。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. 使用主管
现在使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!
schedule_event,日历智能体处理日期解析和事件创建。
要全面了解信息流,包括每个聊天模型调用的提示和响应,请查看上述运行的 LangSmith 追踪。
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!
schedule_event,然后为提醒调用 manage_email。每个子智能体完成其任务,主管将两个结果综合成一个连贯的响应。
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
以下是可运行脚本中的所有内容:Show 查看完整代码
Show 查看完整代码
/**
* 个人助理主管示例
*
* 此示例演示了多智能体系统的工具调用模式。
* 主管智能体协调专门的子智能体(日历和电子邮件),
* 这些子智能体被包装为工具。
*/
import { tool, createAgent } from "langchain";
import { ChatAnthropic } from "@langchain/anthropic";
import { z } from "zod";
// ============================================================================
// 步骤 1:定义低级 API 工具(存根)
// ============================================================================
const createCalendarEvent = tool(
async ({ title, startTime, endTime, attendees, location }) => {
// 存根:在实践中,这将调用 Google Calendar API、Outlook API 等。
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 }) => {
// 存根:在实践中,这将调用 SendGrid、Gmail API 等。
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 }) => {
// 存根:在实践中,这将查询日历 API
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(),
}),
}
);
// ============================================================================
// 步骤 2:创建专门的子智能体
// ============================================================================
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(),
});
// ============================================================================
// 步骤 3:将子智能体包装为供主管使用的工具
// ============================================================================
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"),
}),
}
);
// ============================================================================
// 步骤 4:创建主管智能体
// ============================================================================
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(),
});
// ============================================================================
// 步骤 5:使用主管
// ============================================================================
// 示例:需要日历和电子邮件协调的用户请求
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
您的系统有三层。底层包含需要精确格式的刚性 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智能体。顶层包含主管,它路由到高级功能并综合结果。 这种关注点分离提供了几个好处:每个层都有明确的职责,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个层。6. 添加人机回环审查
对于敏感操作,加入人机回环审查是谨慎的做法。LangChain 包含内置中间件来审查工具调用,在本例中是子智能体调用的工具。 让我们向两个子智能体添加人机回环审查:- 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) - 我们仅向顶级智能体添加一个检查点。这是暂停和恢复执行所必需的。
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(),
});
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
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': ['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]"}
Command 通过其 ID 为每个中断指定决策。有关更多详细信息,请参阅人机回环指南。出于演示目的,这里我们将接受日历事件,但编辑外发电子邮件的主题:
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") {
// 编辑电子邮件
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: 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!
7. 高级:控制信息流
默认情况下,子智能体仅接收来自主管的请求字符串。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) => {
// 自定义子智能体接收的上下文
// 从配置中访问完整的线程消息
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"),
}),
}
);
您可以在 LangSmith 追踪的聊天模型调用中看到子智能体接收的完整上下文。
控制主管接收的内容
您还可以自定义流回主管的信息:const scheduleEvent = tool(
async ({ request }) => {
const result = await calendarAgent.invoke({
messages: [{ role: "user", content: request }]
});
const lastMessage = result.messages[result.messages.length - 1];
// 选项 1:仅返回确认消息
return lastMessage.text;
// 选项 2:返回结构化数据
// 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"),
}),
}
);
有关演示完整主管模式(包括人机回环审查和高级信息流控制)的完整工作示例,请查看 LangChain.js 示例中的
supervisor_complete.ts。8. 关键要点
主管模式创建了抽象层,其中每层都有明确的职责。设计主管系统时,从明确的领域边界开始,并为每个子智能体提供专注的工具和提示。为主管编写清晰的工具描述,在集成前独立测试每一层,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逻辑,您希望集中控制工作流,并且子智能体不需要直接与用户对话时,使用主管模式。对于只有几个工具的简单情况,请使用单个智能体。当智能体需要与用户对话时,请使用交接。对于智能体之间的点对点协作,请考虑其他多智能体模式。
后续步骤
了解用于智能体间对话的交接,探索上下文工程以微调信息流,阅读多智能体概述以比较不同模式,并使用 LangSmith 调试和监控您的多智能体系统。通过 MCP 将这些文档连接到 Claude、VSCode 等,以获取实时答案。

