> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-remove-1782434199-4494406.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Managed Deep Agents quickstart

> Create and deploy your first Managed Deep Agent with the CLI.

LangChain hosts [Managed Deep Agents](/langsmith/managed-deep-agents-overview), so you can deploy a Deep Agent without setting up infrastructure. This quickstart shows how to scaffold a local project, edit the agent instructions, and deploy it with the CLI.

For the full deploy workflow and all backend options, see [Deploy an agent](/langsmith/managed-deep-agents-deploy).

<Note>
  Managed Deep Agents is in **private beta**, available on [LangSmith Cloud](/langsmith/cloud) in the US region only. [Join the waitlist](https://www.langchain.com/langsmith-managed-deep-agents-waitlist) to request access.
</Note>

## Prerequisites

Before you start, make sure you have:

* Managed Deep Agents [private beta access](https://www.langchain.com/langsmith-managed-deep-agents-waitlist).
* A [LangSmith API key](/langsmith/create-account-api-key) for a workspace with private beta access.
* The `deepagents-cli>=0.2.2` package.

## Create and deploy an agent

<Steps>
  <Step title="Install the CLI" id="install-the-cli">
    Install `deepagents-cli` with `uv` (preferred) or `pip`:

    <CodeGroup>
      ```bash uv theme={null}
      uv tool install "deepagents-cli>=0.2.2"
      ```

      ```bash pip theme={null}
      pip install -U "deepagents-cli>=0.2.2"
      ```
    </CodeGroup>

    To upgrade an existing CLI installation, run `uv tool upgrade deepagents-cli` (or `pip install -U "deepagents-cli"` if you installed with pip).
  </Step>

  <Step title="Set your API key">
    Set a LangSmith API key for a workspace with private beta access:

    ```bash theme={null}
    export LANGSMITH_API_KEY="<LANGSMITH_API_KEY>"
    ```

    <Note>
      If a request returns 401 or 403, confirm your API key belongs to a workspace with private beta access.
    </Note>
  </Step>

  <Step title="Create a project">
    Create a Managed Deep Agents project:

    ```bash theme={null}
    deepagents init research-assistant
    cd research-assistant
    ```

    The CLI creates:

    | File or directory       | Purpose                                                         |
    | ----------------------- | --------------------------------------------------------------- |
    | `agent.json`            | Agent metadata, model, backend, and optional target `agent_id`. |
    | `AGENTS.md`             | Main agent instructions.                                        |
    | `tools.json`            | Empty MCP tool configuration.                                   |
    | `skills/example-skill/` | Example skill you can edit or remove.                           |
    | `subagents/researcher/` | Example subagent you can edit or remove.                        |
    | `.gitignore`            | Excludes local environment files.                               |
  </Step>

  <Step title="Edit the instructions">
    Open `AGENTS.md` and replace the generated instructions with the behavior you want:

    ```md AGENTS.md theme={null}
    # Research assistant

    You are a careful research assistant. Search for sources, keep notes, and return
    concise answers with citations.
    ```

    The generated project uses the `state` backend so the agent can deploy without sandbox-specific configuration. Switch to the `sandbox` backend when the agent needs a [LangSmith sandbox](/langsmith/sandboxes) for code execution, filesystem work, or long-running tasks. For options, see [Choose a backend](/langsmith/managed-deep-agents-deploy#choose-a-backend).

    If the agent calls MCP tools, [connect tools](/langsmith/managed-deep-agents-mcp) before deploying the agent.
  </Step>

  <Step title="Deploy the agent">
    Deploy the local project:

    ```bash theme={null}
    deepagents deploy
    ```

    On success, the CLI prints the agent name, ID, short revision, agent URL, and a post-deploy MCP health check:

    ```text theme={null}
    Deployed: research-assistant
      agent_id: e2de7a35-9dda-462b-b982-9e57051993bc
      revision: 13ac11f1
      https://smith.langchain.com/o/-/agents/e2de7a35-9dda-462b-b982-9e57051993bc
      health:   {'agent_id': '...', 'mcp_check': {'ok': True, 'servers': []}, ...}
    ```

    Open the printed agent URL in LangSmith to inspect the deployed agent, managed files, revisions, traces, and runtime behavior.
  </Step>
</Steps>

## Next steps

<CardGroup cols={2}>
  <Card title="Connect tools" icon="plug" href="/langsmith/managed-deep-agents-mcp">
    Register MCP servers and reference their tools from `tools.json`.
  </Card>

  <Card title="Deploy an agent" icon="upload" href="/langsmith/managed-deep-agents-deploy">
    Review the complete CLI deploy workflow and project file layout.
  </Card>

  <Card title="CLI reference" icon="terminal" href="/langsmith/managed-deep-agents-cli">
    Review all deploy commands, flags, and validation rules.
  </Card>

  <Card title="LangSmith sandboxes" icon="terminal-2" href="/langsmith/sandboxes">
    Learn how sandboxes provide isolated execution environments.
  </Card>
</CardGroup>

***

<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-quickstart.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>