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

# Installation

> Set up mcp-agent, the CLI, and optional provider extras.

## Prerequisites

<CardGroup cols={2}>
  <Card title="Python 3.10+" icon="python">
    mcp-agent targets Python 3.10 or newer.
  </Card>

  <Card title="Node.js (optional)" icon="node-js">
    Some MCP servers (e.g. filesystem) are distributed via npx and require Node.js.
  </Card>
</CardGroup>

## Run the CLI with `uvx`

You can run `mcp-agent` commands without a global install by using [`uvx`](https://docs.astral.sh/uv/reference/cli/#uvx):

```bash theme={null}
uvx mcp-agent --version
```

The first run downloads the package into uv's cache; subsequent runs are instant. Whenever you see a command such as `uvx mcp-agent deploy …`, you can paste it directly into your terminal. If you prefer a persistent install, `pip install mcp-agent` still works—but `uvx` keeps things simple.

## Add mcp-agent to your project

Inside your project directory, initialize a new project and add mcp-agent as a dependency:

```bash theme={null}
uv init
uv add mcp-agent
```

Or with pip:

```bash theme={null}
pip install mcp-agent
```

`uv add` writes to `pyproject.toml` and `uv.lock`, which the project templates (`uvx mcp-agent init …`) expect.

### Provider extras

mcp-agent includes optional extras for different LLM providers. Install the ones you need:

<AccordionGroup>
  <Accordion title="OpenAI">
    ```bash theme={null}
    uv add "mcp-agent[openai]"
    pip install "mcp-agent[openai]"
    ```
  </Accordion>

  <Accordion title="Anthropic">
    ```bash theme={null}
    uv add "mcp-agent[anthropic]"
    uv add "mcp-agent[anthropic_bedrock]"
    uv add "mcp-agent[anthropic_vertex]"
    pip install "mcp-agent[anthropic]"
    ```
  </Accordion>

  <Accordion title="Azure OpenAI">
    ```bash theme={null}
    uv add "mcp-agent[azure]"
    pip install "mcp-agent[azure]"
    ```
  </Accordion>

  <Accordion title="AWS Bedrock">
    ```bash theme={null}
    uv add "mcp-agent[bedrock]"
    pip install "mcp-agent[bedrock]"
    ```
  </Accordion>

  <Accordion title="Google Gemini">
    ```bash theme={null}
    uv add "mcp-agent[google]"
    pip install "mcp-agent[google]"
    ```
  </Accordion>

  <Accordion title="All Providers">
    ```bash theme={null}
    uv add "mcp-agent[openai,anthropic,azure,bedrock,google]"
    pip install "mcp-agent[openai,anthropic,azure,bedrock,google]"
    ```
  </Accordion>
</AccordionGroup>

## Verify your setup

<Steps>
  <Step title="Create a project folder">
    ```bash theme={null}
    mkdir mcp-agent-playground
    cd mcp-agent-playground
    ```
  </Step>

  <Step title="Bootstrap your project">
    ```bash theme={null}
    uvx mcp-agent init
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv init
    uv add mcp-agent
    ```
  </Step>
</Steps>

<Tip>
  Refer to the [CLI reference](/reference/cli) for the complete command list, then head to the [Quickstart](/get-started/quickstart) to scaffold a project with `uvx mcp-agent init`.
</Tip>

## Next steps

* [Quickstart](/get-started/quickstart) – create a basic agent and run it locally.
* [Deploy to Cloud](/get-started/cloud) – deploy your agent with `uvx mcp-agent deploy` as a remote MCP server.
* [MCP Servers](/mcp-agent-sdk/mcp/overview) – learn how `mcp-agent` supports various MCP capabilities natively.