Sandbox::https://docs.langchain.com/oss/javascript/deepagents/customization/

Build the harness around your goal. create_deep_agent gives you a production-ready foundation: connect it to your data, shape its behavior, and add the capabilities your use case needs. createDeepAgent ships with a pre-assembled harness: filesystem, summarization, subagents, and prompt caching by default. The parameters below let you define the agent’s persona, connect it to your data and tools, and extend the Deep Agents stack with additional middleware.

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "google:gemini-3.6-flash",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "openai:gpt-6.1-sol",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  systemPrompt: "You are a helpful assistant.",
  tools: [search, fetchUrl],
  memory: ["./AGENTS.md"],
  skills: ["./skills/"],
});

Parameter	What it does
`model`	Which model to use
`systemPrompt`	Custom instructions for the agent
`tools`	Domain tools the agent can call
`memory`	AGENTS.md files loaded at startup
`skills`	Skills directory for on-demand knowledge
`backend`	Filesystem backend (StateBackend by default)
`permissions`	Path-level access control for the filesystem
`subagents`	Custom subagents for delegated tasks
`middleware`	Extra middleware appended to the Deep Agents stack
`interruptOn`	Pause before tool calls for human approval
`responseFormat`	Structured output schema
`contextSchema`	Per-run runtime context schema (user IDs, API keys, feature flags)

For the full parameter list, see the createDeepAgent API reference. To compose a fully custom harness from scratch, see Configure the harness or follow the step-by-step Build a deep agent from scratch guide.

As you add tools, subagents, and backends, use LangSmith to trace how each piece behaves together. Follow the observability quickstart to get set up.We recommend you also set up LangSmith Engine, which monitors your traces, detects issues, and proposes fixes.

Model

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.

Use the provider:model format (for example openai:gpt-6.1-sol) to quickly switch between models.

👉 Read the OpenAI chat model integration docs

npm install @langchain/openai deepagents

pnpm install @langchain/openai deepagents

yarn add @langchain/openai deepagents

bun add @langchain/openai deepagents

import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({ model: "gpt-6.1-sol" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

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

const model = await initChatModel("gpt-6.1-sol");
const agent = createDeepAgent({
  model,
  temperature: 0,
});

import { ChatOpenAI } from "@langchain/openai";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: new ChatOpenAI({
    model: "gpt-6.1-sol",
    apiKey: "your-api-key",
    temperature: 0,
  }),
});

👉 Read the Anthropic chat model integration docs

npm install @langchain/anthropic deepagents

pnpm install @langchain/anthropic deepagents

yarn add @langchain/anthropic deepagents

bun add @langchain/anthropic deepagents

import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({ model: "anthropic:claude-sonnet-4-6" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

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

const model = await initChatModel("claude-sonnet-4-6");
const agent = createDeepAgent({
  model,
  temperature: 0,
});

import { ChatAnthropic } from "@langchain/anthropic";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: new ChatAnthropic({
    model: "claude-sonnet-4-6",
    apiKey: "your-api-key",
    temperature: 0,
  }),
});

👉 Read the Azure chat model integration docs

npm install @langchain/azure deepagents

pnpm install @langchain/azure deepagents

yarn add @langchain/azure deepagents

bun add @langchain/azure deepagents

import { createDeepAgent } from "deepagents";

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 agent = createDeepAgent({ model: "azure_openai:gpt-6.1-sol" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

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-6.1-sol");
const agent = createDeepAgent({
  model,
  temperature: 0,
});

import { AzureChatOpenAI } from "@langchain/openai";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: new AzureChatOpenAI({
    model: "gpt-6.1-sol",
    azureOpenAIApiKey: "your-api-key",
    azureOpenAIApiEndpoint: "your-endpoint",
    azureOpenAIApiVersion: "your-api-version",
    temperature: 0,
  }),
});

👉 Read the ChatGoogle chat model integration docs

npm install @langchain/google deepagents

pnpm install @langchain/google deepagents

yarn add @langchain/google deepagents

bun add @langchain/google deepagents

import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({ model: "google:gemini-3.1-pro-preview" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

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

const model = await initChatModel("google:gemini-3.1-pro-preview");
const agent = createDeepAgent({
  model,
  temperature: 0,
});

import { ChatGoogle } from "@langchain/google/node";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: new ChatGoogle({
    model: "gemini-3.1-pro-preview",
    apiKey: "your-api-key",
    temperature: 0,
  }),
});

👉 Read the AWS Bedrock chat model integration docs

npm install @langchain/aws deepagents

pnpm install @langchain/aws deepagents

yarn add @langchain/aws deepagents

bun add @langchain/aws deepagents

import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({ model: "bedrock:anthropic.claude-sonnet-4-6" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

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

const model = await initChatModel("bedrock:anthropic.claude-sonnet-4-6");
const agent = createDeepAgent({
  model,
  temperature: 0,
});

import { ChatBedrockConverse } from "@langchain/aws";
import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({
  model: new ChatBedrockConverse({
    model: "anthropic.claude-sonnet-4-6",
    region: "us-east-2",
    temperature: 0,
  }),
});

Pass any supported model string, or an initialized model instance:

import { initChatModel } from "langchain";
import { createDeepAgent } from "deepagents";

const model = await initChatModel("provider:model-name");
const agent = createDeepAgent({ model });

Chat models automatically retry transient API failures (with exponential backoff). For defaults, limits, and code samples for tuning max_retries / timeout live on the LangChain Models page.

Tools

In addition to built-in tools for file management and subagent spawning, you can provide custom tools:

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  tools: [internetSearch],
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  tools: [internetSearch],
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  tools: [internetSearch],
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  tools: [internetSearch],
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  tools: [internetSearch],
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

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

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  tools: [internetSearch],
});

MCP tools

Deep Agents fully support Model Context Protocol (MCP) tools. You can load tools from any MCP server—databases, APIs, file systems, and more—and pass them directly to create_deep_agent.

Install @langchain/mcp-adapters to connect to MCP servers:

npm install @langchain/mcp-adapters

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "google:gemini-3.6-flash",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "openai:gpt-6.1-sol",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "anthropic:claude-sonnet-5",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "openrouter:z-ai/glm-5.2",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "fireworks:accounts/fireworks/models/glm-5p2",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "baseten:zai-org/GLM-5.2",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

import { createDeepAgent } from "deepagents";

const { MultiServerMCPClient } = await import("@langchain/mcp-adapters");

const client = new MultiServerMCPClient({
    my_server: {
        transport: "http",
        url: "http://localhost:8000/mcp",
    },
});

const tools = await client.getTools();

const agent = await createDeepAgent({
    model: "ollama:north-mini-code-1.0",
    tools,
});

const result = await agent.invoke({
    messages: [{ role: "user", content: "Use the MCP server to help me." }],
});

For detailed configuration options including stdio servers, OAuth authentication, tool filtering, and stateful sessions, see the full MCP guide.

System prompt

Pass system_prompt= to give the agent your own instructions:

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  systemPrompt: researchInstructions,
});

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

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

import { createDeepAgent } from "deepagents";

const researchInstructions =
  `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  systemPrompt: researchInstructions,
});

Besides a string, the main agent also accepts a SystemMessage with structured content blocks; Deep Agents preserve those blocks (subagent dictionary specs remain strings).

Subagent prompts

Declarative subagents resolve profile overlays against their own model, then apply the resolved profile’s base_system_prompt / system_prompt_suffix to the subagent’s authored system_prompt. A profile that ships only a system_prompt_suffix (the common case for built-in Anthropic / OpenAI profiles) appends to the authored prompt. A profile that sets base_system_prompt replaces it outright.

General-purpose subagent prompt

The auto-added general-purpose subagent resolves its base prompt as general_purpose_subagent.system_prompt (if set) -> HarnessProfile.base_system_prompt (if set) -> SDK general-purpose default, with the profile suffix layered on top. When both override fields are set, the general-purpose-specific one wins so a caller tuning both fields never sees their GP override silently dropped:

from deepagents import (
    GeneralPurposeSubagentProfile,
    HarnessProfile,
    register_harness_profile,
)

register_harness_profile(
    "anthropic",
    HarnessProfile(
        base_system_prompt="You are ACME's support orchestrator.",  # main agent
        general_purpose_subagent=GeneralPurposeSubagentProfile(
            system_prompt="You are a research subagent. Cite sources.",  # GP subagent
        ),
        system_prompt_suffix="Always think step by step.",
    ),
)

Stack	Final system prompt
Main agent	`"You are ACME's support orchestrator." + SUFFIX`
GP subagent	`"You are a research subagent. Cite sources." + SUFFIX`

Middleware

Deep Agents support any middleware, including the built-in middleware listed below, prebuilt middleware from LangChain, provider-specific middleware, and custom middleware you write yourself. Pass middleware to the middleware argument of createDeepAgent. Each instance is merged into the Deep Agents stack by its .name. An instance whose name matches a built-in entry replaces that entry in place. Anything else is inserted after PatchToolCallsMiddleware.

Deep Agents stack

createDeepAgent builds middleware in a fixed order. The bare stack is what you get with only a model. The full stack is the complete assembly order, including slots that appear only when you pass optional arguments or when the resolved harness profile contributes them.

Bare stack

With only a model (no other optional arguments), the main agent typically includes:

FilesystemMiddleware
SubAgentMiddleware (because the general-purpose subagent is auto-added unless a harness profile disables it)
SummarizationMiddleware
PatchToolCallsMiddleware
Prompt caching middleware (added for supported providers; no-ops elsewhere)
Harness profile extras, if the resolved model profile defines them
modelRefusalMiddleware
Excluded-tool filtering, if the resolved model profile defines them
UnsupportedContentMiddleware

Full stack

From first to last:

FilesystemMiddleware: Handles file system operations such as reading, writing, and navigating directories. When you pass permissions, filesystem permissions enforcement is included here so it can evaluate every tool the agent might call.
SubAgentMiddleware: Only when at least one synchronous subagent is available. Spawns and coordinates subagents for delegating tasks. Included in the bare stack because the general-purpose subagent is auto-added by default; omit it by disabling that subagent and passing no synchronous subagents. See Running without subagents.
SummarizationMiddleware: Condenses message history to stay within context limits when conversations grow long (via createSummarizationMiddleware).
PatchToolCallsMiddleware: Repairs dangling tool calls in message history when a run resumes after an interruption or receives malformed tool-call arguments. Runs before Anthropic prompt caching and the tail stack below.
AsyncSubAgentMiddleware: Only when you configure async subagents.
Your middleware argument: Optional middleware you pass as the middleware argument is merged after Patch but before the rest of the stack. An instance whose .name matches a built-in entry replaces that entry in place instead of duplicating it. Anything else lands here.
Harness profile extras: Provider-specific middleware from the resolved model profile, if any.
SkillsMiddleware: Only when you pass skills, or pass a createSkillsMiddleware instance in middleware. Either way, it runs after your middleware and directly before prompt caching, so it sees the summarized conversation and the model that is actually called.
Prompt caching (AnthropicPromptCachingMiddleware and BedrockPromptCachingMiddleware): Added automatically for Anthropic models and Amazon Bedrock Converse models, respectively. Both run after Patch and after your middleware so the cached prefix matches what is actually sent to the model.
MemoryMiddleware: Only when you pass memory.
MemoryMiddleware is placed after profile extras and the prompt caching middleware so updates to injected memory are less likely to invalidate the cache prefix. The same ordering concern is called out in the createDeepAgent implementation comments.
HumanInTheLoopMiddleware: Only when you pass interruptOn. Pauses for human approval or input at configured tool calls.
modelRefusalMiddleware: Throws ModelRefusalError when the model refuses. It runs after your middleware, so a modelFallbackMiddleware you pass retries the request on the next model first. See Handle a refusal for ways to respond.
Excluded-tool filtering: When the harness profile lists excluded tools, middleware removes those tools from the agent.
UnsupportedContentMiddleware: Rewrites unsupported multimodal content in requests with a text placeholder.

Synchronous subagent stack

The built-in general-purpose subagent and each declarative synchronous SubAgent graph use a stack that createDeepAgent builds in code. It matches the main agent in broad shape (filesystem, summarization, Patch, profile extras, skills, Anthropic and Bedrock caching, optional permissions), except that there is no SubAgentMiddleware inside a subagent graph (only the parent agent exposes the task tool). When a declarative subagent sets interruptOn, that value is forwarded to createAgent for the subagent, which wires up human-in-the-loop handling for the configured tool calls.

Prebuilt middleware

LangChain exposes additional prebuilt middleware that let you add-on various features, such as retries, fallbacks, or PII detection. See Prebuilt middleware for more. The deepagents package also exposes createSummarizationMiddleware for the same workflow. For more detail, see Summarization.

Provider-specific middleware

For provider-specific middleware that is optimized for specific LLM providers, see Middleware integrations.

Custom middleware

You can provide additional middleware to extend functionality, add tools, or implement custom hooks:

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "google:gemini-3.6-flash",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "openai:gpt-6.1-sol",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  },
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`,
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});

Do not mutate attributes after initializationIf you need to track values across hook invocations (for example, counters or accumulated data), use graph state. Graph state is scoped to a thread by design, so updates are safe under concurrency.Do this:

const customMiddleware = createMiddleware({
  name: "CustomMiddleware",
  beforeAgent: async (state) => {
    return { x: (state.x ?? 0) + 1 }; // Update graph state instead
  },
});

Do not do this:

let x = 1;

const customMiddlewareBad = createMiddleware({
  name: "CustomMiddleware",
  beforeAgent: async () => {
    x += 1; // Mutation causes race conditions
  },
});

Mutation in place, such as modifying state.x in beforeAgent, mutating a shared variable in beforeAgent, or changing other shared values in hooks, can lead to subtle bugs and race conditions because many operations run concurrently (subagents, parallel tools, and parallel invocations on different threads).If you must use mutation in custom middleware, consider what happens when subagents, parallel tools, or concurrent agent invocations run at the same time.

Override a default middleware instance

Pass a middleware instance whose .name matches an entry in the Deep Agents stack, such as SummarizationMiddleware, to replace that built-in instance in place instead of appending a duplicate. Any middleware you pass whose .name does not match a built-in entry is not replaced, it lands after the last core middleware entry and before the profile, prompt-caching, and memory. See Full stack for the complete ordering.

import {
  createDeepAgent,
  createSummarizationMiddleware,
  StateBackend,
} from "deepagents";

const backend = new StateBackend();
const model = "openai:gpt-6.1-sol";

const customSummarization = createSummarizationMiddleware({
  model,
  backend,
  summaryPrompt: "Your custom summary prompt here.",
});

const agent = createDeepAgent({
  model,
  middleware: [customSummarization], // replaces the default SummarizationMiddleware
});

An override replaces the default middleware instance, it is not merged with it. That means your replacement must be fully configured with any settings it needs. This is especially important for FilesystemMiddleware: if you override it, you must pass the backend (and permissions, if applicable) directly to createFilesystemMiddleware, since it will not inherit the backend and permissions passed to createDeepAgent. To restrict the available filesystem tools, pass a tools allowlist to your custom FilesystemMiddleware instance; see Virtual filesystem access.

The general-purpose subagent, which Deep Agents adds automatically, inherits overrides for its default middleware from the main agent, without carrying over middleware that is specific to the main agent. Declarative subagents defined via subagents do not inherit the main agent’s middleware customization. Pass the override directly in that subagent’s own middleware field to apply it there; that field is matched against the synchronous subagent stack, the same way middleware is matched against the main agent’s.

Examples

Keep binary file content out of checkpointed history

Override FilesystemMiddleware with offloadBinaryContent: true to store binary read_file payloads and inline HumanMessage media on the backend under /blobs instead of in checkpointed message history. State keeps a small reference; model requests are rehydrated from a per-run cache or the backend. This keeps checkpoint size small when the agent reads many binary files across turns.

Binary content offloading requires deepagents>=1.14.2. Use a backend that stores blobs outside graph state, such as a sandbox or FilesystemBackend. Offloading has no effect when /blobs routes to StateBackend. Pass the same backend to createFilesystemMiddleware that you pass to createDeepAgent.

import {
  createDeepAgent,
  createFilesystemMiddleware,
  FilesystemBackend,
} from "deepagents";

const backend = new FilesystemBackend({
  rootDir: "/path/to/project",
  virtualMode: true,
});

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  backend,
  middleware: [
    createFilesystemMiddleware({ backend, offloadBinaryContent: true }),
  ],
});

For more information, see Offload binary content.

Interpreters

Use interpreters to add an eval tool that runs JavaScript in a scoped QuickJS runtime. Interpreters are useful when the agent needs to compose tools programmatically, batch work, handle errors in code, or transform structured data without a full shell environment.

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  middleware: [createCodeInterpreterMiddleware()],
});

import { createDeepAgent } from "deepagents";
import { createCodeInterpreterMiddleware } from "@langchain/quickjs";

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  middleware: [createCodeInterpreterMiddleware()],
});

For setup, programmatic tool calling, subagent orchestration, and limits, see Interpreters.

Subagents

To isolate detailed work and avoid context bloat, use subagents:

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "google:gemini-3.6-flash", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "openai:gpt-6.1-sol", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "anthropic:claude-sonnet-5", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "openrouter:z-ai/glm-5.2", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "fireworks:accounts/fireworks/models/glm-5p2", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "baseten:zai-org/GLM-5.2", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "ollama:north-mini-code-1.0", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  subagents,
});

For more information, see Subagents.

Backends

Tools for a deep agent can make use of virtual file systems to store, access, and edit files. By default, deep agents use a StateBackend. If you are using skills or memory, you must add the expected skill or memory files to the backend before creating the agent.

A thread-scoped filesystem backend stored in langgraph state.Files persist across turns within a thread (via your checkpointer) and are not shared across threads.

import { createDeepAgent, StateBackend } from "deepagents";

// By default we provide a StateBackend
const agent = createDeepAgent();

// Under the hood, it looks like
const agent2 = createDeepAgent({
  backend: new StateBackend(),
});

The local machine’s filesystem.

This backend grants agents direct filesystem read/write access. Use with caution and only in appropriate environments. For more information, see FilesystemBackend.

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

import { createDeepAgent, FilesystemBackend } from "deepagents";

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
});

Wrap FilesystemBackend in a CompositeBackend to prevent internal agent data (offloaded tool results, conversation history) from being written to disk alongside your project files. See the recommended pattern.

A filesystem with shell execution directly on the host. Provides filesystem tools plus the execute tool for running commands.

This backend grants agents direct filesystem read/write access and unrestricted shell execution on your host. Use with extreme caution and only in appropriate environments. For more information, see LocalShellBackend.

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend,
});

import { createDeepAgent, LocalShellBackend } from "deepagents";

const backend = new LocalShellBackend({ workingDirectory: "." });

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend,
});

A filesystem that provides long-term storage that is persisted across threads.

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore(); // Good for local dev; omit for LangSmith Agent Server

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

When deploying to LangSmith Agent Server, omit the store parameter. The platform automatically provisions a store for your agent.

The namespace parameter controls data isolation. For multi-user deployments, always set a namespace factory to isolate data per user or tenant.

Durable filesystem storage in a LangSmith Hub repo.For more details, see ContextHubBackend.

A flexible backend where you can specify different routes in the filesystem to point towards different backends.

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

import {
  createDeepAgent,
  CompositeBackend,
  StateBackend,
  StoreBackend,
} from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend: new CompositeBackend(new StateBackend(), {
    "/memories/": new StoreBackend({
      namespace: () => ["memories"],
    }),
  }),
  store,
});

For more information, see Backends.

Sandboxes

Sandboxes are specialized backends that run agent code in an isolated environment with their own filesystem and an execute tool for shell commands. Use a sandbox backend when you want your deep agent to write files, install dependencies, and run commands without changing anything on your local machine. You configure sandboxes by passing a sandbox backend to backend when creating your deep agent:

import { createDeepAgent, LangSmithSandbox } from "deepagents";
import { ChatAnthropic } from "@langchain/anthropic";
import { SandboxClient } from "langsmith/sandbox";

const client = new SandboxClient();
const lsSandbox = await client.createSandbox();

try {
  const agent = createDeepAgent({
    model: new ChatAnthropic({ model: "claude-opus-4-8" }),
    systemPrompt: "You are a coding assistant with sandbox access.",
    backend: new LangSmithSandbox({ sandbox: lsSandbox }),
  });

  const result = await agent.invoke({
    messages: [
      {
        role: "user",
        content: "Create a hello world Python script and run it",
      },
    ],
  });
} finally {
  await client.deleteSandbox(lsSandbox.name);
}

For more information, see Sandboxes.

Human-in-the-loop

Some tool operations may be sensitive and require human approval before execution. You can configure the approval for each tool:

import { tool } from "langchain";
import { createDeepAgent } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";
import { z } from "zod";

const removeFile = tool(
  async ({ path }: { path: string }) => {
    return `Deleted ${path}`;
  },
  {
    name: "remove_file",
    description: "Delete a file from the filesystem.",
    schema: z.object({
      path: z.string(),
    }),
  },
);

const fetchFile = tool(
  async ({ path }: { path: string }) => {
    return `Contents of ${path}`;
  },
  {
    name: "fetch_file",
    description: "Read a file from the filesystem.",
    schema: z.object({
      path: z.string(),
    }),
  },
);

const notifyEmail = tool(
  async ({
    to,
    subject,
    body,
  }: {
    to: string;
    subject: string;
    body: string;
  }) => {
    return `Sent email to ${to}`;
  },
  {
    name: "notify_email",
    description: "Send an email.",
    schema: z.object({
      to: z.string(),
      subject: z.string(),
      body: z.string(),
    }),
  },
);

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  tools: [removeFile, fetchFile, notifyEmail],
  interruptOn: {
    remove_file: true, // Default: approve, edit, reject, respond
    fetch_file: false, // No interrupts needed
    notify_email: { allowedDecisions: ["approve", "reject"] }, // No editing
  },
  checkpointer, // Required!
});

You can configure interrupt for agents and subagents on tool call as well as from within tool calls. For more information, see Human-in-the-loop.

Skills

You can use skills to provide your deep agent with new capabilities and expertise. While tools tend to cover lower level functionality like native file system actions, skills can contain detailed instructions on how to complete tasks, reference info, and other assets, such as templates. These files are only loaded by the agent when the agent has determined that the skill is useful for the current prompt. This progressive disclosure reduces the amount of tokens and context the agent has to consider upon startup. For example skills, see Deep Agents example skills. To add skills to your deep agent, pass them as an argument to create_deep_agent:

StateBackend
StoreBackend
FilesystemBackend

import { createDeepAgent, StateBackend, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();
const backend = new StateBackend();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content: content.split("\n"),
    created_at: now,
    modified_at: now,
  };
}

const skillsFiles: Record<string, FileData> = {};
const skillUrl =
  "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md";
const response = await fetch(skillUrl);
const skillContent = await response.text();

skillsFiles["/skills/langgraph-docs/SKILL.md"] = createFileData(skillContent);

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  backend,
  checkpointer, // Required !
  // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
  skills: ["/skills/"],
});

const config = { configurable: { thread_id: `thread-${Date.now()}` } };
const result = await agent.invoke(
  {
    messages: [{ role: "user", content: "what is langraph?" }],
    files: skillsFiles,
  },
  config,
);

View example trace

Open a public LangSmith run for this example.

import { createDeepAgent, StoreBackend } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();
const store = new InMemoryStore();
const backend = new StoreBackend({
  namespace: () => ["filesystem"],
  store,
});

const skillUrl =
  "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md";

const response = await fetch(skillUrl);
const skillContent = await response.text();

await backend.uploadFiles([
  ["/skills/langgraph-docs/SKILL.md", new TextEncoder().encode(skillContent)],
]);

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  backend,
  store,
  checkpointer,
  // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
  skills: ["/skills/"],
});

const config = {
  recursionLimit: 50,
  configurable: { thread_id: `thread-${Date.now()}` },
};
const result = await agent.invoke(
  { messages: [{ role: "user", content: "what is langraph?" }] },
  config,
);

View example trace

Open a public LangSmith run for this example.

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const skillUrl =
  "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md";
const response = await fetch(skillUrl);
const skillContent = await response.text();

let backend = new FilesystemBackend({
  rootDir: process.cwd(),
  virtualMode: true,
});
await backend.uploadFiles([
  ["/skills/langgraph-docs/SKILL.md", new TextEncoder().encode(skillContent)],
]);

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  backend,
  // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
  skills: ["/skills/"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required for filesystem operations!
});

const config = { configurable: { thread_id: `thread-${Date.now()}` } };
const result = await agent.invoke(
  { messages: [{ role: "user", content: "what is langraph?" }] },
  config,
);

View example trace

Open a public LangSmith run for this example.

Memory

Use AGENTS.md files to provide extra context to your deep agent.

To generate a repository wiki that coding agents discover through AGENTS.md, see OpenWiki.

You can pass one or more file paths to the memory parameter when creating your deep agent:

StateBackend
StoreBackend
Filesystem

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "google:gemini-3.6-flash",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "openai:gpt-6.1-sol",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, type FileData } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);
const checkpointer = new MemorySaver();

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const agent = await createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  memory: ["/AGENTS.md"],
  checkpointer: checkpointer,
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
    // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
    files: { "/AGENTS.md": createFileData(agentsMd) },
  },
  { configurable: { thread_id: "12345" } },
);

View example trace

Open a public LangSmith run for this example.

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
import { InMemoryStore, MemorySaver } from "@langchain/langgraph";

const AGENTS_MD_URL =
  "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

async function fetchText(url: string): Promise<string> {
  const res = await fetch(url);
  if (!res.ok) {
    throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
  }
  return await res.text();
}

const agentsMd = await fetchText(AGENTS_MD_URL);

function createFileData(content: string): FileData {
  const now = new Date().toISOString();
  return {
    content,
    mimeType: "text/plain",
    created_at: now,
    modified_at: now,
  };
}

const store = new InMemoryStore();
const fileData = createFileData(agentsMd);
await store.put(["filesystem"], "/AGENTS.md", fileData);

const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend: new StoreBackend({
    namespace: () => ["filesystem"],
  }),
  store: store,
  checkpointer: checkpointer,
  memory: ["/AGENTS.md"],
});

const result = await agent.invoke(
  {
    messages: [
      {
        role: "user",
        content: "Please tell me what's in your memory files.",
      },
    ],
  },
  { configurable: { thread_id: "12345" } },
);

View example trace

Open a public LangSmith run for this example.

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "google:gemini-3.6-flash",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "openai:gpt-6.1-sol",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = await createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
  memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
});

Structured output

Deep Agents support structured output. You can set a desired structured output schema by passing it as the responseFormat argument to the call to createDeepAgent(). Wrap the schema in a strategy such as toolStrategy() or providerStrategy() from langchain. When the model generates the structured data, it’s captured, validated, and returned in the ‘structuredResponse’ key of the agent’s state.

import { tool, toolStrategy } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
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),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const weatherReportSchema = z.object({
  location: z.string().describe("The location for this weather report"),
  temperature: z.number().describe("Current temperature in Celsius"),
  condition: z
    .string()
    .describe("Current weather condition (e.g., sunny, cloudy, rainy)"),
  humidity: z.number().describe("Humidity percentage"),
  windSpeed: z.number().describe("Wind speed in km/h"),
  forecast: z.string().describe("Brief forecast for the next 24 hours"),
});

const agent = await createDeepAgent({
  responseFormat: toolStrategy(weatherReportSchema),
  tools: [internetSearch],
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "What's the weather like in San Francisco?",
    },
  ],
});

console.log(result.structuredResponse);
// {
//   location: 'San Francisco, California',
//   temperature: 18.3,
//   condition: 'Sunny',
//   humidity: 48,
//   windSpeed: 7.6,
//   forecast: 'Clear skies with temperatures remaining mild. High of 18°C (64°F) during the day, dropping to around 11°C (52°F) at night.'
// }

View example trace

Open a public LangSmith run for this example.

For more information and examples, see response format.

Advanced

createDeepAgent pre-assembles a middleware stack on top of createAgent. To build a fully custom agent—choosing exactly which capabilities to include—see Configure the harness.

Connect these docs to your agent of choice via MCP for real-time answers.

Edit this page on GitHub or file an issue.

Get started

Deployment

Execution environment

Context management

Delegation

Steering

Middleware

Frontend

Protocols

Customize Deep Agents

Model

Tools

MCP tools

System prompt

Middleware

Deep Agents stack

Bare stack

Full stack

Synchronous subagent stack

Prebuilt middleware

Provider-specific middleware

Custom middleware

Override a default middleware instance

Examples

Interpreters

Subagents

Backends

Sandboxes

Human-in-the-loop

Skills

View example trace

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Memory

View example trace

View example trace

Structured output

View example trace

Advanced