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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
RL environments + evals for AI agents. Define once, train anything.
| Date | Stars |
|---|---|
| 2026-07-31 | 281 |
| 2026-08-03 | 283 |
| 2026-08-06 | 283 |
Today
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Momentum
35.0
growth rate 0.00%/day
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<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/hud-evals/hud-python/main/docs/logo/hud_logo_dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/hud-evals/hud-python/main/docs/logo/hud_logo.svg">
<img src="https://raw.githubusercontent.com/hud-evals/hud-python/main/docs/logo/hud_logo.svg" alt="HUD" width="150" style="margin-bottom: 24px;"/>
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HUD is a platform for building RL environments for AI agents, across coding, browser, computer-use, and robotics. Define an environment, write tasks, and run them as evals and training across any model, at any scale.
To learn more, see the [documentation](https://docs.hud.ai) and [environment reference](https://docs.hud.ai/v6/reference/environment).
[](https://pypi.org/project/hud/)
[](LICENSE)
[](https://cursor.com/en/install-mcp?name=docs-hud-python&config=eyJ1cmwiOiJodHRwczovL2RvY3MuaHVkLmFpL21jcCJ9)
[](https://discord.gg/wkjtmHYYjm)
[](https://x.com/intent/user?screen_name=hud_evals)
[](https://scarf.sh)
[](https://docs.hud.ai)
## Install
```bash
# Install the CLI (recommended)
uv tool install hud --python 3.12
# …or as a library
pip install hud
```
> Previously published as [`hud-python`](https://pypi.org/project/hud-python/). The import and CLI names are unchanged. If you have `hud-python` installed, migrate with `pip uninstall -y hud-python hud && pip install hud` (uninstalling both first avoids the two packages clobbering each other's files), and replace `hud-python` with `hud` in requirements files and CI configs.
Get your API key at [hud.ai/project/api-keys](https://hud.ai/project/api-keys) and set it:
```bash
hud set HUD_API_KEY=your-key-here
# or: export HUD_API_KEY=your-key-here
```
Then scaffold your first environment:
```bash
hud init my-env
```
## The protocol
HUD is **protocol-first**. An agent and an environment exchange just three things: a **manifest** (the environment's capabilities and tasks), **`tasks.start`** that returns the prompt, and **`tasks.grade`** that returns the reward. In between, the agent just *works*, driving the capabilities itself. HUD owns only that thin envelope, so any model or harness plugs into any environment.
```mermaid
sequenceDiagram
participant Agent
participant Env as Environment
participant Caps as Capabilities (ssh · mcp · cdp · rfb · robot)
Note over Env,Caps: environment holds & serves these
Agent->>Env: hello
Env-->>Agent: manifest (capabilities)
Agent->>Env: tasks.start
Env-->>Agent: prompt
rect rgb(238,238,238)
Note over Agent,Caps: the agent works, driving capabilities directly
Agent->>Caps: shell · browser · GUI · tools · robot
Caps-->>Agent: observations
end
Agent->>Env: tasks.grade
Env-->>Agent: reward
```
Because the protocol only exposes **capabilities** (never a fixed agent), an environment outlives any single harness: new harnesses and models keep running against the same environments, benchmarks, and tasks.
## Package & run anywhere
A built image is the **end product for your tasks**: one build packs every task from a single definition. The recommended path is **`hud deploy`**, which builds and registers your environment on HUD in one step; then sync a taskset and run remotely:
``Excerpt of 9,465 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:90fe63369d938bcc, topic:llm, topic:qwen
matched fp:90fe63369d938bcc, topic:reinforcement-learning, topic:rl
matched fp:90fe63369d938bcc, topic:agents, desc:ai agents