Customize Deep Agents - Docs by LangChain

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 stack with additional middleware.

import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent({
  model: "anthropic:claude-sonnet-4-6",
  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 to add to the stack
`interruptOn`	Pause before tool calls for human approval
`responseFormat`	Structured output schema

For the full parameter list, see the createDeepAgent API reference. To compose a fully custom harness from scratch, see Configure the harness.

As you add tools, subagents, and backends, use LangSmith to trace how each piece behaves together. Follow the observability quickstart to get set up, and see Going to production for deployment on LangSmith.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-5.4) to quickly switch between models.

👉 Read the OpenAI chat model integration docs

npm install @langchain/openai deepagents

import { createDeepAgent } from "deepagents";

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

const agent = createDeepAgent({ model: "gpt-5.4" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

👉 Read the Anthropic chat model integration docs

npm install @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

👉 Read the Azure chat model integration docs

npm install @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-5.4" });
// this calls initChatModel for the specified model with default parameters
// to use specific model parameters, use initChatModel directly

👉 Read the Google GenAI chat model integration docs

npm install @langchain/google-genai deepagents

import { createDeepAgent } from "deepagents";

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

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

👉 Read the AWS Bedrock chat model integration docs

npm install @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

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 planning, 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-genai:gemini-3.5-flash",
  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 { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createDeepAgent } from "deepagents";

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-5.4",
    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

Deep Agents come with a built-in system prompt. A deep agent’s value comes from the orchestration layer the SDK provides on top of the model—planning, virtual-filesystem tools, and subagents—and the model needs to know those exist and when to reach for them. The built-in prompt teaches the agent how to use that scaffolding so you don’t have to re-derive it for every project; tweak it through a profile or your own system_prompt= rather than copying it verbatim. When middleware add special tools, like the filesystem tools, it appends them to the system prompt. Each deep agent should also include a custom system prompt specific to its specific use case:

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-genai:gemini-3.5-flash",
  systemPrompt: researchInstructions,
});

Prompt assembly

Deep Agents builds the system prompt from up to four named parts so that caller-supplied instructions, the SDK’s built-in agent guidance, and any model-specific profile overrides can coexist with predictable precedence. Without this layering, a profile suffix tuned for Claude (for example) could overwrite or be overwritten by your system_prompt= argument depending on call order; the named slots make the ordering explicit and stable. In practice, most callers only encounter two slots: USER (your system_prompt=) and BASE (the SDK default). Selecting a model with a built-in profile—Anthropic or OpenAI today—adds a SUFFIX. The full four-part assembly is mainly relevant when you author a custom HarnessProfile or debug why a profile’s text appears where it does. The four named parts (each may be absent):

Name	Source	Notes
`USER`	`system_prompt=` argument to `create_deep_agent`	`str` or `SystemMessage`; omitted when unset.
`BASE`	The SDK default (`BASE_AGENT_PROMPT`)	Always present unless replaced by a profile’s `CUSTOM`.
`CUSTOM`	`HarnessProfile.base_system_prompt`	Replaces `BASE` outright when a matching profile sets it.
`SUFFIX`	`HarnessProfile.system_prompt_suffix`	Appended last when a matching profile sets it.

The order is always USER -> (BASE or CUSTOM) -> SUFFIX, joined by blank lines (\n\n). Two invariants follow:

USER is always at the front. The caller’s text precedes any SDK or profile content, so persona/instructions take precedence regardless of which model is selected.
SUFFIX is always at the end. Profile suffixes sit closest to the conversation history, where model-tuning guidance lands most reliably.

Assembled shapes (✓ = field is set, - = field is unset):

`system_prompt=`	profile `base_system_prompt` (`CUSTOM`)	profile `system_prompt_suffix` (`SUFFIX`)	Final assembled system prompt
`None`	-	-	`BASE`
`None`	-	✓	`BASE` + `SUFFIX`
`None`	✓	-	`CUSTOM`
`None`	✓	✓	`CUSTOM` + `SUFFIX`
`str`	-	-	`USER` + `BASE`
`str`	-	✓	`USER` + `BASE` + `SUFFIX`
`str`	✓	-	`USER` + `CUSTOM`
`str`	✓	✓	`USER` + `CUSTOM` + `SUFFIX`

Worked example—built-in profiles (Anthropic, OpenAI) ship only a system_prompt_suffix, so a typical call lands in the str + - + ✓ row:

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    system_prompt="You are a customer-support agent for ACME Corp.",
)
# Final = USER + BASE + SUFFIX
#       = "You are a customer-support agent for ACME Corp."
#         + "\n\n"
#         + BASE_AGENT_PROMPT
#         + "\n\n"
#         + <Claude-specific guidance>

Passing a SystemMessage (rather than a string) triggers a different concatenation path: the right-hand assembly (BASE-or-CUSTOM plus any SUFFIX) is appended as an additional text content block onto the message’s existing content_blocks. The same logical ordering applies (caller blocks first), and any cache_control markers on the caller’s blocks are preserved—useful for placing explicit Anthropic prompt-cache breakpoints.

Subagent prompts

The prompt assembly overlay rules also apply to declarative subagents: each subagent re-runs profile resolution against its own model, then applies the resolved profile’s base_system_prompt / system_prompt_suffix to its authored system_prompt. The subagent’s system_prompt plays the BASE role; CUSTOM and SUFFIX come from the profile that matches the subagent’s model (which may differ from the main agent’s profile).

`spec["system_prompt"]`	profile `base_system_prompt` (`CUSTOM`)	profile `system_prompt_suffix` (`SUFFIX`)	Final subagent system prompt
authored	-	-	authored
authored	-	✓	authored + `SUFFIX`
authored	✓	-	`CUSTOM`
authored	✓	✓	`CUSTOM` + `SUFFIX`

There is no USER segment for subagents. The spec’s authored system_prompt is the closest analog and stays in the BASE slot. A profile that ships only a system_prompt_suffix (the common case for built-in Anthropic / OpenAI profiles) just appends to whatever the subagent author wrote. A profile that sets base_system_prompt will replace the authored prompt outright.

General-purpose subagent prompt

The auto-added general-purpose subagent follows the prompt assembly overlay rules with one extra layer: the GP base prompt is resolved as general_purpose_subagent.system_prompt (if set) -> HarnessProfile.base_system_prompt (if set) -> SDK general-purpose default. The profile suffix layers on top either way.The two override fields can both carry a base-prompt replacement, but they are not interchangeable. general_purpose_subagent.system_prompt is general-purpose-specific configuration; base_system_prompt is a global override that primarily targets the main agent. When both are set, the general-purpose-specific intent wins for the general-purpose subagent so a user tuning both fields never sees their GP override silently dropped:

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`

If general_purpose_subagent.system_prompt is unset, the GP subagent falls back to base_system_prompt (when set) and finally to the SDK general-purpose default.

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. By default, Deep Agents have access to the following middleware:

Default stack (main agent)

From first to last:

TodoListMiddleware: Tracks and manages todo lists for organizing agent tasks and work.
SkillsMiddleware: Only when you pass skills. Injected immediately after the todo middleware and before filesystem middleware so skill metadata is available before file tools run.
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: Spawns and coordinates subagents for delegating tasks to specialized agents.
SummarizationMiddleware: Condenses message history to stay within context limits when conversations grow long (via createSummarizationMiddleware).
PatchToolCallsMiddleware: Automatic message history fixes when tool calls are interrupted or cancelled before receiving results. 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 appended here (after Patch, before the tail stack).
Harness profile extras: Provider-specific middleware from the resolved model profile, if any. tools from the agent.
AnthropicPromptCachingMiddleware: Automatically added when you are using an Anthropic mode. Runs 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 Anthropic prompt caching so updates to injected memory are less likely to invalidate the Anthropic 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.
Excluded-tool filtering: When the harness profile lists excluded tools, middleware removes those ```

Default stack (synchronous subagents)

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 (todo list, filesystem, summarization, Patch, profile extras, Anthropic caching, optional permissions) but differs in two ways:

Skills run after PatchToolCallsMiddleware on these inner agents (on the main agent, skills run before filesystem middleware when skills is set).
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 Official integrations and Community 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-genai:gemini-3.5-flash",
  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.

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-genai:gemini-3.5-flash",
  middleware: [createCodeInterpreterMiddleware()],
});

For setup, programmatic tool calling, interpreter skills, 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-genai:gemini-3.5-flash", // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "google_genai:gemini-3.5-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_genai:gemini-3.5-flash",
  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_genai:gemini-3.5-flash",
  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 Deployment
const agent = createDeepAgent({
  model: "google_genai:gemini-3.5-flash",
  backend: new StoreBackend({
    namespace: (rt) => [rt.serverInfo.user.identity],
  }),
  store,
});

When deploying to LangSmith Deployment, 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_genai:gemini-3.5-flash",
  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 } from "deepagents";
import { ChatAnthropic } from "@langchain/anthropic";
import { DenoSandbox } from "@langchain/deno";

// Create and initialize the sandbox
const sandbox = await DenoSandbox.create({
  memoryMb: 1024,
  lifetime: "10m",
});

try {
  const agent = createDeepAgent({
    model: new ChatAnthropic({ model: "claude-opus-4-6" }),
    systemPrompt: "You are a JavaScript coding assistant with sandbox access.",
    backend: sandbox,
  });

  const result = await agent.invoke({
    messages: [
      {
        role: "user",
        content:
          "Create a simple HTTP server using Deno.serve and test it with curl",
      },
    ],
  });
} finally {
  await sandbox.close();
}

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_genai:gemini-3.5-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 or planning, 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 { createCodeInterpreterMiddleware } from "@langchain/quickjs";
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: "google-genai:gemini-3.1-pro-preview",
  backend,
  checkpointer, // Required !
  // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
  skills: ["/skills/"],
  middleware: [createCodeInterpreterMiddleware({ skillsBackend: backend })],
});

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

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

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

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

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();
const fileData = createFileData(skillContent);

await store.put(["filesystem"], "/skills/langgraph-docs/SKILL.md", fileData);

const agent = await createDeepAgent({
  model: "google-genai:gemini-3.1-pro-preview",
  backend,
  store,
  checkpointer,
  // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
  skills: ["/skills/"],
  middleware: [createCodeInterpreterMiddleware({ skillsBackend: backend })],
});

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

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

const checkpointer = new MemorySaver();
const backend = new FilesystemBackend({ rootDir: process.cwd() });

const agent = await createDeepAgent({
  model: "google-genai:gemini-3.1-pro-preview",
  backend,
  skills: ["./examples/skills/"],
  interruptOn: {
    read_file: true,
    write_file: true,
    delete_file: true,
  },
  checkpointer, // Required!
  middleware: [createCodeInterpreterMiddleware({ skillsBackend: backend })],
});

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

Memory

Use AGENTS.md files to provide extra context to your deep agent. 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-genai:gemini-3.5-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, 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-genai:gemini-3.5-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, 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-genai:gemini-3.5-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!
});

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(). When the model generates the structured data, it’s captured, validated, and returned in the ‘structuredResponse’ key of the agent’s state.

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 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: 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.'
// }

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 Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.

Documentation Index

​Model

​Tools

​MCP tools

​System prompt

​Prompt assembly

​Middleware

​Default stack (main agent)

​Default stack (synchronous subagents)

​Prebuilt middleware

​Provider-specific middleware

​Custom middleware

​Interpreters

​Subagents

​Backends

​Sandboxes

​Human-in-the-loop

​Skills

​Memory

​Structured output

​Advanced

Model

Tools

MCP tools

System prompt

Prompt assembly

Middleware

Default stack (main agent)

Default stack (synchronous subagents)

Prebuilt middleware

Provider-specific middleware

Custom middleware

Interpreters

Subagents

Backends

Sandboxes

Human-in-the-loop

Skills

Memory

Structured output

Advanced