> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Define a Managed Deep Agent

> Configure the model and core capabilities of a Managed Deep Agent.

The agent definition selects the model and core capabilities of a Managed Deep Agent.

<Note>
  Managed Deep Agents is in **public [beta](/langsmith/release-stages)** and available on [LangSmith Cloud](/langsmith/cloud) in the US region only.
</Note>

## Project structure

The agent entry lives at the project root:

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
my-agent/
  agent.py
```

Export the agent definition as a named `agent`.

## Define an agent

Use `define_deep_agent`:

<CodeGroup>
  ```python OpenAI theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="openai:gpt-5.5",
  )
  ```

  ```python Anthropic theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="anthropic:claude-sonnet-4-6",
  )
  ```

  ```python Google Gemini theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="google_genai:gemini-3.6-flash",
  )
  ```
</CodeGroup>

| Parameter                                | What it does                                                          |
| ---------------------------------------- | --------------------------------------------------------------------- |
| [`name=`](#name)                         | Sets the agent and default deployment name                            |
| [`model=`](#model)                       | Selects the chat model                                                |
| [`tools=`](#tools)                       | Adds tools the agent can call                                         |
| [`middleware=`](#middleware)             | Adds behavior around model calls, tool calls, and the agent lifecycle |
| [`subagents=`](#subagents)               | Defines specialized agents for delegated tasks                        |
| [`permissions=`](#permissions)           | Controls path-level access for filesystem tools                       |
| [`interrupt_on=`](#human-in-the-loop)    | Pauses before selected tool calls for human approval                  |
| [`response_format=`](#structured-output) | Defines a structured output schema                                    |

## Name

`name` is required. Pass a static string that starts with a letter and contains only letters, numbers, underscores, or hyphens, such as `"research-assistant"`.

MDA uses the name as the LangGraph assistant ID and the default LangSmith deployment name. You can override the deployment name with `mda deploy --name` without changing the agent definition.

## Model

Set `model` to the chat model the agent uses. The simplest option is a `provider:model` string. Add the provider's API key to `.env` so the model works locally and in the deployment.

<CodeGroup>
  ```python OpenAI theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="openai:gpt-5.5",
  )
  ```

  ```python Anthropic theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="anthropic:claude-sonnet-4-6",
  )
  ```

  ```python Google Gemini theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from managed_deepagents import define_deep_agent

  agent = define_deep_agent(
      name="research-assistant",
      model="google_genai:gemini-3.6-flash",
  )
  ```
</CodeGroup>

Pass a LangChain chat model instance instead when you need to configure model parameters in code. For model options and supported providers, see [Models](/oss/python/deepagents/models).

### Using LangSmith Gateway

You can use [LangSmith Gateway](langsmith/llm-gateway) to control rate limits, fallbacks, and more.

In order to use, you should:

* Use the ChatOpenAI model directly
* Set a base url of `https://gateway.smith.langchain.com/v1`
* Set an environment variable of `LANGSMITH_GATEWAY_API_KEY` to be your LangSmith API key.

This should look like (illustrative):

```py theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import os

from managed_deepagents import define_deep_agent
from langchain_openai import ChatOpenAI

api_key = os.environ.get(
    "LANGSMITH_GATEWAY_API_KEY",
    "missing-langsmith-gateway-api-key",
)
base_url = "https://gateway.smith.langchain.com/v1"

agent = define_deep_agent(
    name="my-agent",
    model=ChatOpenAI(
        model="moonshotai/Kimi-K3",
        api_key=api_key,
        base_url=base_url,
    ),
)
```

<Note>
  The model slug should be `provider/model-name` when using Gateway. When NOT using Gateway, it is normally `provider:model-name`
</Note>

In order to scaffold your project to use Gateway from the start, you can pass a `--gateway` flag when initializing your agent:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
mda init my-agent --gateway
```

## Tools

Pass tools in the `tools` list to let the agent call application logic or external services.

Define tools in local modules, import them into the agent entry, and add them to the definition. See [Custom tools](/langsmith/python/managed-deep-agents-tools).

## Middleware

Pass middleware in the `middleware` list to add behavior around model calls, tool calls, and the agent lifecycle. Middleware runs in list order.

See [Custom middleware](/langsmith/python/managed-deep-agents-middleware).

## Subagents

Pass subagent definitions in `subagents` when the agent should delegate specialized or context-heavy work. Each subagent can have its own prompt, model, and tools. See [Subagents](/oss/python/deepagents/subagents).

## Permissions

Pass filesystem permission rules in `permissions` to control which paths the agent's built-in filesystem tools can read or write. See [Permissions](/oss/python/deepagents/permissions).

## Human-in-the-loop

Set `interrupt_on` to pause before selected tool calls.

Use this for actions that require a person to approve, edit, or reject the call before it runs. See [Human-in-the-loop](/langsmith/python/managed-deep-agents-tools#human-in-the-loop).

## Structured output

Set `response_format` when the agent must return data that matches a schema instead of an unconstrained text response.

See [Structured output](/oss/python/langchain/structured-output).

Configure the system prompt, skills, memory, sandbox, identity, channels, and schedules through their project files rather than the agent definition. See [Project structure](/langsmith/python/managed-deep-agents-project-structure).

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/langsmith/managed-deep-agents-agent-definition.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>