> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-ramonn-1789138246-3410f5f.mintlify.site/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>

The agent entry lives at the project root:

```text theme={null}
my-agent/
  agent.ts
```

You can also use `agent.tsx`.

For the full project layout, see [Project structure](/langsmith/javascript/managed-deep-agents-project-structure).

To define the agent, use `defineDeepAgent`:

<CodeGroup>
  ```ts OpenAI theme={null}
  import { defineDeepAgent } from "managed-deepagents";

  export const agent = defineDeepAgent({
    name: "research-assistant",
    model: "openai:gpt-5.5",
  });
  ```

  ```ts Anthropic theme={null}
  import { defineDeepAgent } from "managed-deepagents";

  export const agent = defineDeepAgent({
    name: "research-assistant",
    model: "anthropic:claude-sonnet-4-6",
  });
  ```

  ```ts Google Gemini theme={null}
  import { defineDeepAgent } from "managed-deepagents";

  export const agent = defineDeepAgent({
    name: "research-assistant",
    model: "google-genai:gemini-3.6-flash",
  });
  ```
</CodeGroup>

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

## Parameters

| Parameter        | What it does                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name`           | Required. Set the agent and default deployment name. Pass a static string that starts with a letter and contains only letters, numbers, underscores, or hyphens, such as `"research-assistant"`.<br /><br /> Managed Deep Agents 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 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.<br /><br /> 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/javascript/deepagents/models).<br /><br /> To connect to LLM Gateway, see [Use LLM Gateway](#use-llm-gateway).           |
| `tools`          | Add tools the agent can call. Pass tools in the `tools` array so the agent can call application logic or external services.<br /><br /> Define tools in local modules, import them into the agent entry, and add them to the definition. See [Custom tools](/langsmith/javascript/managed-deep-agents-tools). To add tools from remote MCP servers without importing them into the agent entry, use [MCP connectors](/langsmith/javascript/managed-deep-agents-mcp-connectors). |
| `middleware`     | Add behavior around model calls, tool calls, and the agent lifecycle. Pass middleware in the `middleware` array. Middleware runs in array order. See [Custom middleware](/langsmith/javascript/managed-deep-agents-middleware).                                                                                                                                                                                                                                                 |
| `subagents`      | Define specialized agents for delegated tasks. Pass subagent definitions when the agent should delegate specialized or context-heavy work. Each subagent can have its own prompt, model, and tools. See [Subagents](/oss/javascript/deepagents/subagents).                                                                                                                                                                                                                      |
| `permissions`    | Control path-level access for filesystem tools. Pass filesystem permission rules to control which paths the agent's built-in filesystem tools can read or write. See [Permissions](/oss/javascript/deepagents/permissions).                                                                                                                                                                                                                                                     |
| `interruptOn`    | Pause before selected tool calls for human approval. Set `interruptOn` to pause before selected tool calls, so a person can approve, edit, or reject the call before it runs. See [Human-in-the-loop](/langsmith/javascript/managed-deep-agents-tools#human-in-the-loop).                                                                                                                                                                                                       |
| `responseFormat` | Set when the agent must return data that matches a schema instead of an unconstrained text response. See [Structured output](/oss/javascript/langchain/structured-output).                                                                                                                                                                                                                                                                                                      |

## Use LLM Gateway

You can use [LLM Gateway](/langsmith/llm-gateway) to apply rate limits, fallbacks, and other policies to model calls.

Prefix the gateway model ID with `langsmith:`:

```ts theme={null}
import { defineDeepAgent } from "managed-deepagents";

export const agent = defineDeepAgent({
  name: "my-agent",
  model: "langsmith:moonshotai/kimi-k3",
});
```

<Note>
  Gateway model IDs use a slash between provider and model (`langsmith:provider/model-name`). Model strings that call a provider directly use a colon (`provider:model-name`).
</Note>

The gateway routes each request by model ID. `moonshotai/kimi-k3` is a LangChain-hosted model, so it requires no provider secret and draws on [Gateway Credits](/langsmith/llm-gateway-credits). A model ID that starts with a provider your workspace has configured, such as `anthropic/claude-opus-5`, uses that [provider secret](/langsmith/llm-gateway-admin-setup#1-add-provider-secrets) and bills to your own provider account.

For more information, see [LLM Gateway](/langsmith/llm-gateway).

To scaffold a project that uses Gateway from the start, pass `--gateway` when initializing:

```bash theme={null}
mda init my-agent --gateway
```

***

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