快速开始

几分钟内构建你的第一个 Deep Agent

本教程将带你一步步创建一个具备任务规划、文件系统工具和子 Agent 能力的 Deep Agent。我们将构建一个能够执行研究并撰写报告的研究型 Agent。

提示

在使用 AI 编程助手？

安装 LangChain Docs MCP server，让你的 Agent 获取最新的 LangChain 文档和示例。
安装 LangChain Skills，提升 Agent 在 LangChain 生态任务中的表现。

前置条件

开始之前，请确保你已拥有某个模型供应商的 API Key（例如 Gemini、Anthropic、OpenAI）。

注意

Deep Agents 需要模型支持工具调用（tool calling）。关于如何配置模型，请参考自定义配置。

第一步：安装依赖

npmyarnpnpm

bash

npm install deepagents langchain @langchain/core @langchain/tavily

bash

yarn add deepagents langchain @langchain/core @langchain/tavily

bash

pnpm add deepagents langchain @langchain/core @langchain/tavily

注意

本教程使用 Tavily 作为示例搜索服务，你也可以替换为任何搜索 API（如 DuckDuckGo、SerpAPI、Brave Search）。

第二步：设置 API Key

bash

export GOOGLE_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

export OPENAI_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

export ANTHROPIC_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

export OPENROUTER_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

export FIREWORKS_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

export BASETEN_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

# Local: Ollama must be running on your machine
# Cloud: Set your Ollama API key for hosted inference
export OLLAMA_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

bash

# Set the API key for your provider
export <PROVIDER>_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-api-key"

Deep Agents 支持任意 LangChain 聊天模型，请根据你的供应商设置对应的 API Key。

第三步：创建搜索工具

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { z } from "zod";

const internetSearch = tool(
  async ({
    query,
    maxResults = 5,
    topic = "general",
    includeRawContent = false,
  }: {
    query: string;
    maxResults?: number;
    topic?: "general" | "news" | "finance";
    includeRawContent?: boolean;
  }) => {
    const tavilySearch = new TavilySearch({
      maxResults,
      tavilyApiKey: process.env.TAVILY_API_KEY,
      includeRawContent,
      topic,
    });
    return await tavilySearch._call({ query });
  },
  {
    name: "internet_search",
    description: "Run a web search",
    schema: z.object({
      query: z.string().describe("The search query"),
      maxResults: z
        .number()
        .optional()
        .default(5)
        .describe("Maximum number of results to return"),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general")
        .describe("Search topic category"),
      includeRawContent: z
        .boolean()
        .optional()
        .default(false)
        .describe("Whether to include raw content"),
    }),
  },
);

第四步：创建 Deep Agent

通过 model 参数传入 provider:model 格式的字符串，或传入一个已初始化的模型实例。所有支持的供应商请见supported models，经过测试的推荐模型请见suggested models。

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "google-genai:gemini-3.5-flash",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "openai:gpt-5.4",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "openrouter:anthropic/claude-sonnet-4-6",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

// System prompt to steer the agent to be an expert researcher
const researchInstructions = `You are an expert researcher. Your job is to conduct thorough research and then write a polished report.

You have access to an internet search tool as your primary means of gathering information.

## \`internet_search\`

Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
`;

const agent = createDeepAgent({
  model: "ollama:devstral-2",
  tools: [internetSearch],
  systemPrompt: researchInstructions,
});

第五步：运行 Agent

const result = await agent.invoke({
  messages: [{ role: "user", content: "What is langgraph?" }],
});

// Print the agent's response
console.log(result.messages[result.messages.length - 1].content);

提示

使用 LangSmith 追踪 Agent 的规划步骤、工具调用和子 Agent 委派过程。按照可观测性快速入门完成设置。

我们还建议你配置 LangSmith Engine，它会监控你的追踪记录、检测问题并提出修复建议。

运行原理是什么？

你的 Deep Agent 会自动完成以下步骤：

规划方案：使用内置的 write_todos 工具拆解研究任务。
执行研究：调用 internet_search 工具收集信息。
管理上下文：使用文件系统工具（write_file、read_file）卸载大量搜索结果。
派生子 Agent：根据需要将复杂子任务委派给专用子 Agent。
综合报告：将研究发现汇总为连贯的最终回复。

示例

更多 Agent 模式和应用，请参考 GitHub 上的 Examples。

流式传输

Deep Agents 基于 LangGraph 内置了流式传输能力，可以实时获取 Agent 执行的更新。这让你能够逐步观察输出，审查和调试 Agent 及子 Agent 的工作过程，包括工具调用、工具结果和 LLM 响应。

下一步

现在你已经构建了第一个 Deep Agent，可以继续探索：

自定义你的 Agent：了解自定义选项，包括自定义系统提示词、工具和子 Agent。
添加长期记忆：启用跨会话的持久记忆。
部署到生产环境：使用 Managed Deep Agents 在 LangSmith 中创建、运行和管理 Deep Agent。

本文基于 Deep Agents 官方文档翻译并二次创作。

快速开始

快速开始 ​

前置条件 ​

第一步：安装依赖 ​

第二步：设置 API Key ​

第三步：创建搜索工具 ​

第四步：创建 Deep Agent ​

第五步：运行 Agent ​

运行原理是什么？ ​

示例 ​

流式传输 ​

下一步 ​

快速开始

前置条件

第一步：安装依赖

第二步：设置 API Key

第三步：创建搜索工具

第四步：创建 Deep Agent

第五步：运行 Agent

运行原理是什么？

示例

流式传输

下一步