> ## Documentation Index
> Fetch the complete documentation index at: https://hud-f5fd7c15-lukass-phys-experimental.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Install the HUD SDK, write your first task, run it against a model, and read the reward returned by the grader - the full quickstart in five minutes.

**Fastest path - hand the docs to your coding agent first.** The HUD docs skill scaffolds correct v6 environments and flags weak task designs as you build:

```bash theme={"dark"}
npx skills add https://docs.hud.ai
```

The rest of this page walks the setup path by hand.

## 1. Install

<CodeGroup>
  ```bash uv theme={"dark"}
  uv tool install hud --python 3.12
  ```

  ```bash pip theme={"dark"}
  pip install hud
  ```
</CodeGroup>

## 2. Set your API key

Get a key from [hud.ai/project/api-keys](https://hud.ai/project/api-keys) - one key both routes models through the HUD gateway and traces every rollout.

```bash theme={"dark"}
hud set HUD_API_KEY=your-key-here
```

## 3. Create a Test Environment

Scaffold a complete, runnable example to start from:

```bash theme={"dark"}
hud init my-env
cd my-env
```

This includes a ready-to-run task in `tasks.py`:

```python tasks.py theme={"dark"}
from hud import Environment

env = Environment(name="letter-count")

@env.template()
async def count_letter(word: str = "strawberry", letter: str = "r"):
    answer = yield f"How many '{letter}'s are in '{word}'? Reply with just the number."
    yield 1.0 if answer and str(word.count(letter)) in answer else 0.0

tasks = [count_letter(word=w) for w in ("strawberry", "raspberry", "blueberry")]
```

## 4. Run it

```bash theme={"dark"}
hud eval tasks.py claude
```

`hud eval` spawns the environment locally, runs the `claude` agent, and grades it. Every rollout generates a replayable trace on [hud.ai](https://hud.ai).