> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tai42.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation

> Install the tai runtime and its packages.

Install the runtime from source. Nothing is published to PyPI yet, so you clone
the three packages side by side and let `uv` resolve them from the sibling
checkouts.

## Requirements

* **Python 3.13 or newer.**
* **[`uv`](https://docs.astral.sh/uv/)** to resolve and sync dependencies.
* **Redis 7.0+ and Postgres** only for the features that need them — access
  control, connectors, and interactions. Local tools and mounted MCP servers
  need no services at all.

## Install from source

`tai42-skeleton`'s `[tool.uv.sources]` points at `../tai-contract` and
`../tai-kit`, so the three repositories must sit next to each other. Clone them,
then sync the skeleton:

```bash theme={null}
git clone https://github.com/tai42ai/tai-contract
git clone https://github.com/tai42ai/tai-kit
git clone https://github.com/tai42ai/tai-skeleton
cd tai-skeleton
uv sync
```

<Note>
  Once the packages are published, `pip install tai42-skeleton` replaces the
  from-source flow. Until then, install from the side-by-side checkouts.
</Note>

## Add the standard toolbox

The `toolbox` extra pulls in the `tai42-toolbox` contrib package — its generic
tools and its composition extensions (`chain`, `batch`) load straight from a
manifest:

```bash theme={null}
uv sync --extra toolbox
```

Once the packages are published, `pip install tai42-skeleton[toolbox]` works too.
See the [standard toolbox guide](/guides/standard-toolbox) for what ships in it.

## The `tai` command

Installing the package puts the entrypoint on your path under two names, `tai`
and `tai42`. Both run the same command tree — `tai` is the documented one, and
`tai42` is an alias for environments where an unrelated `tai` already sits on
your PATH. Three subcommands run the long-lived processes:

* `tai serve` — run the MCP server.
* `tai backend` — run the agent / worker backend process.
* `tai metrics` — serve the Prometheus metrics endpoint.

Run `tai --help` for the full command tree, or browse the
[CLI reference](/reference/cli/index).

Next, boot a server and call a tool in the [quickstart](/getting-started/quickstart).