Quickstart

This guide walks you through creating your first deep agent with planning, file system tools, and subagent capabilities. You’ll build a research agent that can conduct research and write reports.

Using an AI coding assistant?

Install the LangChain Docs MCP server to give your agent access to up-to-date LangChain documentation and examples.
Install LangChain Skills to improve your agent’s performance on LangChain ecosystem tasks.

Prerequisites

Before you begin, make sure you have an API key from a model provider (e.g., Gemini, Anthropic, OpenAI).

Deep Agents require a model that supports tool calling. See customization for how to configure your model.

Step 1: Install dependencies

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

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

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

This guide uses Tavily as an example search provider, but you can substitute any search API (e.g., DuckDuckGo, SerpAPI, Brave Search).

Step 2: Set up your API keys

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

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

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

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

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

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

# 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"

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

Deep Agents work with any LangChain chat model. Set the API key for your provider.

Step 3: Create a search tool

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"),
    }),
  },
);

Step 4: Create a deep agent

Pass a model string in provider:model format, or an initialized model instance. See supported models for all providers and suggested models for tested recommendations.

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",
  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,
});

Step 5: Run the 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);

Trace your agent’s planning steps, tool calls, and subagent delegation with LangSmith. Follow the observability quickstart to get set up.

How does it work?

Your deep agent automatically:

Plans its approach using the built-in write_todos tool to break down the research task.
Conducts research by calling the internet_search tool to gather information.
Manages context by using file system tools (write_file, read_file) to offload large search results.
Spawns subagents as needed to delegate complex subtasks to specialized subagents.
Synthesizes a report to compile findings into a coherent response.

Examples

For agents, patterns, and applications you can build with Deep Agents, see Examples.

Streaming

Deep Agents have built-in streaming for real-time updates from agent execution using LangGraph. This allows you to observe output progressively and review and debug agent and subagent work, such as tool calls, tool results, and LLM responses.

Next steps

Now that you’ve built your first deep agent:

Customize your agent: Learn about customization options, including custom system prompts, tools, and subagents.
Add long-term memory: Enable persistent memory across conversations.
Deploy to production: Use Managed Deep Agents to create, run, and operate deep agents in LangSmith.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.

Get started

Deployment

Core capabilities

Frontend

Protocols

Code

Prerequisites

Step 1: Install dependencies

Step 2: Set up your API keys

Step 3: Create a search tool

Step 4: Create a deep agent

Step 5: Run the agent

How does it work?

Examples

Streaming

Next steps

​Prerequisites

​Step 1: Install dependencies

​Step 2: Set up your API keys

​Step 3: Create a search tool

​Step 4: Create a deep agent

​Step 5: Run the agent

​How does it work?

​Examples

​Streaming

​Next steps

Prerequisites

Step 1: Install dependencies

Step 2: Set up your API keys

Step 3: Create a search tool

Step 4: Create a deep agent

Step 5: Run the agent

How does it work?

Examples

Streaming

Next steps