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多智能体

多智能体

单个智能体可能难以处理需要专攻多个领域或管理大量工具的情况。为了解决这个问题，您可以将智能体拆分为更小、独立的智能体，并将它们组合成一个多智能体系统。

在多智能体系统中，智能体需要相互通信。它们通过交接来实现——这是一种原语，描述将控制权移交给哪个智能体以及要发送给该智能体的有效负载。

两种最流行的多智能体架构是：

  • supervisor —— 各个智能体由中央监督智能体协调。监督者控制所有通信流和任务委派，根据当前上下文和任务要求决定调用哪个智能体。
  • swarm —— 智能体根据其专长在彼此之间动态交接控制权。系统记住哪个智能体最后处于活动状态，确保在后续交互时与该智能体恢复对话。

Supervisor

Supervisor

使用 langgraph-supervisor 库创建监督者多智能体系统：

npm install @langchain/langgraph-supervisor
import { ChatOpenAI } from "@langchain/openai";
// highlight-next-line
import { createSupervisor } from "@langchain/langgraph-supervisor";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const bookHotel = tool(
  async (input: { hotel_name: string }) => {
    return `Successfully booked a stay at ${input.hotel_name}.`;
  },
  {
    name: "book_hotel",
    description: "Book a hotel",
    schema: z.object({
      hotel_name: z.string().describe("The name of the hotel to book"),
    }),
  }
);

const bookFlight = tool(
  async (input: { from_airport: string; to_airport: string }) => {
    return `Successfully booked a flight from ${input.from_airport} to ${input.to_airport}.`;
  },
  {
    name: "book_flight",
    description: "Book a flight",
    schema: z.object({
      from_airport: z.string().describe("The departure airport code"),
      to_airport: z.string().describe("The arrival airport code"),
    }),
  }
);

const llm = new ChatOpenAI({ modelName: "gpt-4o" });

// 创建专业智能体
const flightAssistant = createReactAgent({
  llm,
  tools: [bookFlight],
  prompt: "You are a flight booking assistant",
  // highlight-next-line
  name: "flight_assistant",
});

const hotelAssistant = createReactAgent({
  llm,
  tools: [bookHotel],
  prompt: "You are a hotel booking assistant",
  // highlight-next-line
  name: "hotel_assistant",
});

// highlight-next-line
const supervisor = createSupervisor({
  agents: [flightAssistant, hotelAssistant],
  llm,
  prompt: "You manage a hotel booking assistant and a flight booking assistant. Assign work to them, one at a time.",
}).compile();

const stream = await supervisor.stream({
  messages: [{
    role: "user",
    content: "first book a flight from BOS to JFK and then book a stay at McKittrick Hotel"
  }]
});

for await (const chunk of stream) {
  console.log(chunk);
  console.log("\n");
}

Swarm

Swarm

使用 langgraph-swarm 库创建蜂群多智能体系统：

npm install @langchain/langgraph-swarm
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { ChatAnthropic } from "@langchain/anthropic";
// highlight-next-line
import { createSwarm, createHandoffTool } from "@langchain/langgraph-swarm";

const transferToHotelAssistant = createHandoffTool({
  agentName: "hotel_assistant",
  description: "Transfer user to the hotel-booking assistant.",
});

const transferToFlightAssistant = createHandoffTool({
  agentName: "flight_assistant",
  description: "Transfer user to the flight-booking assistant.",
});


const llm = new ChatAnthropic({ modelName: "claude-3-5-sonnet-latest" });

const flightAssistant = createReactAgent({
  llm,
  tools: [bookFlight, transferToHotelAssistant],
  prompt: "You are a flight booking assistant",
  name: "flight_assistant",
});

const hotelAssistant = createReactAgent({
  llm,
  tools: [bookHotel, transferToFlightAssistant],
  prompt: "You are a hotel booking assistant",
  name: "hotel_assistant",
});

// highlight-next-line
const swarm = createSwarm({
  agents: [flightAssistant, hotelAssistant],
  defaultActiveAgent: "flight_assistant",
}).compile();

const stream = await swarm.stream({
  messages: [{
    role: "user",
    content: "first book a flight from BOS to JFK and then book a stay at McKittrick Hotel"
  }]
});

for await (const chunk of stream) {
  console.log(chunk);
  console.log("\n");
}

Handoffs

多智能体交互中的常见模式是交接，其中一个智能体将控制权移交给另一个智能体。交接允许您指定：

  • 目标：要导航到的目标智能体
  • 有效负载：要传递给该智能体的信息

这被 langgraph-supervisor（监督者向各个智能体交接）和 langgraph-swarm（单个智能体可以向其他智能体交接）使用。

要使用 createReactAgent 实现交接，您需要：

  1. 创建一个可以将控制权转移到不同智能体的特殊工具

    const transferToBob = tool(
  async (_) => {
    return new Command({
      // 要前往的智能体（节点）名称
      // highlight-next-line
      goto: "bob",
      // 发送给智能体的数据
      // highlight-next-line
      update: { messages: ... },
      // 向 LangGraph 指示我们需要导航到
      // 父图中的智能体节点
      // highlight-next-line
      graph: Command.PARENT,
    });
  },
  {
    name: ...,
    schema: ...,
    description: ...
  }
);

  2. 创建具有交接工具访问权限的单个智能体：

    const flightAssistant = createReactAgent(
  ..., tools: [bookFlight, transferToHotelAssistant]
)
const hotelAssistant = createReactAgent(
  ..., tools=[bookHotel, transferToFlightAssistant]
)

  3. 定义一个包含单个智能体作为节点的父图：

    import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";

const multiAgentGraph = new StateGraph(MessagesAnnotation)
  .addNode("flight_assistant", flightAssistant)
  .addNode("hotel_assistant", hotelAssistant)
  ...

将这些组合在一起，以下是如何实现一个简单的多智能体系统，包含两个智能体——航班预订助手和酒店预订助手：

import { ChatAnthropic } from "@langchain/anthropic";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { StateGraph, MessagesAnnotation, Command, START, getCurrentTaskInput, END } from "@langchain/langgraph";
import { tool } from "@langchain/core/tools";
import { z } from "zod";
import { ToolMessage } from "@langchain/core/messages";

interface CreateHandoffToolParams {
  agentName: string;
  description?: string;
}

const createHandoffTool = ({
  agentName,
  description,
}: CreateHandoffToolParams) => {
  const toolName = `transfer_to_${agentName}`;
  const toolDescription = description || `Ask agent '${agentName}' for help`;

  const handoffTool = tool(
    async (_, config) => {
      const toolMessage = new ToolMessage({
        content: `Successfully transferred to ${agentName}`,
        name: toolName,
        tool_call_id: config.toolCall.id,
      });

      // 注入当前智能体状态
      const state =
        // highlight-next-line
        getCurrentTaskInput() as (typeof MessagesAnnotation)["State"];  // (1)!
      return new Command({  // (2)!
        // highlight-next-line
        goto: agentName,  // (3)!
        // highlight-next-line
        update: { messages: state.messages.concat(toolMessage) },  // (4)!
        // highlight-next-line
        graph: Command.PARENT,  // (5)!
      });
    },
    {
      name: toolName,
      schema: z.object({}),
      description: toolDescription,
    }
  );

  return handoffTool;
};

const bookHotel = tool(
  async (input: { hotel_name: string }) => {
    return `Successfully booked a stay at ${input.hotel_name}.`;
  },
  {
    name: "book_hotel",
    description: "Book a hotel",
    schema: z.object({
      hotel_name: z.string().describe("The name of the hotel to book"),
    }),
  }
);

const bookFlight = tool(
  async (input: { from_airport: string; to_airport: string }) => {
    return `Successfully booked a flight from ${input.from_airport} to ${input.to_airport}.`;
  },
  {
    name: "book_flight",
    description: "Book a flight",
    schema: z.object({
      from_airport: z.string().describe("The departure airport code"),
      to_airport: z.string().describe("The arrival airport code"),
    }),
  }
);

const transferToHotelAssistant = createHandoffTool({
  agentName: "hotel_assistant",
  description: "Transfer user to the hotel-booking assistant.",
});

const transferToFlightAssistant = createHandoffTool({
  agentName: "flight_assistant",
  description: "Transfer user to the flight-booking assistant.",
});

const llm = new ChatAnthropic({ modelName: "claude-3-5-sonnet-latest" });

const flightAssistant = createReactAgent({
  llm,
  // highlight-next-line
  tools: [bookFlight, transferToHotelAssistant],
  prompt: "You are a flight booking assistant",
  // highlight-next-line
  name: "flight_assistant",
});

const hotelAssistant = createReactAgent({
  llm,
  // highlight-next-line
  tools: [bookHotel, transferToFlightAssistant],
  prompt: "You are a hotel booking assistant",
  // highlight-next-line
  name: "hotel_assistant",
});

const multiAgentGraph = new StateGraph(MessagesAnnotation)
  .addNode("flight_assistant", flightAssistant, { ends: ["hotel_assistant", END] })
  .addNode("hotel_assistant", hotelAssistant, { ends: ["flight_assistant", END] })
  .addEdge(START, "flight_assistant")
  .compile();

const stream = await multiAgentGraph.stream({
  messages: [{
    role: "user",
    content: "book a flight from BOS to JFK and a stay at McKittrick Hotel"
  }]
});

for await (const chunk of stream) {
  console.log(chunk);
  console.log("\n");
}
  1. 访问智能体状态
  2. Command 原语允许将状态更新和节点转换指定为单个操作，使其可用于实现交接。
  3. 要交接到的智能体或节点名称。
  4. 获取智能体的消息并作为交接的一部分添加到父状态中。下一个智能体将看到父状态。
  5. 向 LangGraph 指示我们需要导航到父多智能体图中的智能体节点。

!!! 注意 此交接实现假设：

  • 每个智能体接收多智能体系统中的整体消息历史（跨所有智能体）作为其输入
  • 每个智能体将其内部消息历史输出到多智能体系统的整体消息历史

查看 LangGraph supervisor 和 swarm 文档以了解如何自定义交接。

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多智能体SupervisorSwarmHandoffs