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

# Installation

> Install Mirage for Python with mirage-ai from PyPI or uv, then add extras for S3, Redis, FUSE, Google Workspace, and other resources.

## Quick Install

Install the base package into your project:

```bash theme={null}
uv add mirage-ai
```

This is enough to start with local workflows such as the RAM resource and to understand the Mirage execution model.

If you are not using uv:

```bash theme={null}
pip install mirage-ai
```

## Use Mirage as a CLI

If you only want the `mirage` CLI (not the library), there are two convenient paths.

**One-shot run with `uvx`**, no install, just runs:

```bash theme={null}
uvx mirage-ai --help
uvx mirage-ai workspace create workspace.yaml --id demo
```

**Persistent install with `uv tool install`**, drops `mirage` on your PATH globally:

```bash theme={null}
uv tool install mirage-ai
mirage --help
```

To upgrade later:

```bash theme={null}
uv tool install --upgrade mirage-ai
```

## Resource Extras

Install extras when a resource needs optional dependencies:

```bash theme={null}
uv add "mirage-ai[s3]"
uv add "mirage-ai[r2]"
uv add "mirage-ai[redis]"
uv add "mirage-ai[fuse]"
```

Use the resource docs in the Python section when you are ready to connect real systems.

## Install From Source

If you are contributing to Mirage or want the full local development setup:

```bash theme={null}
git clone https://github.com/strukto-ai/mirage.git
cd mirage/python
uv sync --all-extras --no-extra camel
```

The `camel` extra conflicts with the `openai` stack, so excluding it keeps everything else installable in one shot.

## Recommended Path

1. Install the base package.
2. Follow the [Python Quickstart](/python/quickstart) with the RAM resource.
3. Add the resource extras and follow the [FUSE setup](/python/setup/fuse) if you need to expose mounts as a real filesystem.