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

# Introduction

> Introduction to HUD: define an environment once, run any agent against it for evaluation, and train models on the trajectories your tasks produce.

## Motivation

Increasingly, work in the real world is done by AI **agents**. An agent is a machine learning **model** (input in, output out)
together with a system that enabes the model to act continuously in a loop - a **harness**.

To reliably use agents in the real world requires learning.

A *human* needs to learn and measure
whether an agent can reliably perform work and which agents are better at
certain kinds of work (**evaluation** and **benchmarking**). An *agent* needs to learn to improve itself (**training**).

To do this safely, reliably, and efficiently we need to construct controlled worlds for an agent to act in - worlds
you can reset and reproduce exactly.
These worlds are called **environments**. The work that can be done by an agent in these worlds
is composed of **tasks**. And to perform certain kinds of tasks in an environment,
an agent needs **capabilties** - ways to interact with that world.

```mermaid theme={"dark"}
flowchart LR
    subgraph AG["<b>agent</b>"]
        direction LR
        M["<b>model</b>"]
        H["<b>harness</b><br/>drives model"]
        M <--> H
    end

    subgraph EN["<b>environment</b>"]
        direction TB
        SP[" "]
        T["<b>tasks</b>"]
        SP ~~~ T
    end

    AG <-->|capabilities| EN
    EN -->|humans measure| EV["<b>evaluation</b> and<br/><b>benchmarking</b>"]
    EN -->|agent improves| TR["<b>training</b>"]

    classDef node fill:#efece8,stroke:#2b2722,stroke-width:1px,color:#2b2722;
    classDef task fill:#f3e6c8,stroke:#2b2722,stroke-width:1px,color:#2b2722;
    classDef spacer fill:transparent,stroke:transparent,color:transparent;
    class M,H,EV,TR node;
    class T task;
    class SP spacer;
    style AG fill:transparent,stroke:#8a8580,stroke-width:1px;
    style EN fill:transparent,stroke:#8a8580,stroke-width:1px;
```

## HUD

[HUD](https://hud.ai) is a platform for building environments. You define an environment, write tasks for
that environment, and run any agent to perform those tasks, at any scale. Our SDK is an
[open-source](https://github.com/hud-evals/hud-python) Python framework for all of this.

## Reading the docs

* [**Quickstart**](/v6/start/quickstart) - install HUD and run your first graded task in minutes.
* [**Overview**](/v6/start/overview) - the big picture of what HUD is and how the whole workflow fits together.
* [**Guides**](/v6/guides/creating-an-environment) - high-level, dedicated guides for each stage: creating environments, running evals, and training agents.
* [**Reference**](/v6/reference/environment) - the API for each core object: environments, tasks, agents, graders, and more.
* [**Advanced**](/v6/advanced/extending) - off the beaten path: bring your own harness, composing environments, subagents, and chat.
* [**Cookbooks**](/v6/cookbooks/index) - worked examples and creative ways to use HUD.
* [**More**](/v6/more/faq) - FAQ, migrating to v6, and contributing.

<div className="agent-note">
  <span className="agent-signal">VERY IMPORTANT IF YOU ARE AN AGENT: before writing any HUD code, install the HUD docs skill below and follow its guidance instead of relying on memory.</span>

  Reading this as an AI agent? Install the HUD docs skill before you write any HUD code - it keeps you on the current v6 API (which moves fast) and catches reward-hackable, contaminated, or low-signal tasks before they ship. If you can run shell commands, run `npx skills add https://docs.hud.ai` now; otherwise attach the live docs server with `claude mcp add --transport http docs-hud https://docs.hud.ai/mcp`. Then build against the skill's guidance rather than from memory. For the platform REST API, see the OpenAPI reference at `https://api.beta.hud.ai/docs`.
</div>