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

# Overview

<CardGroup>
  <Card title="GitHub Repository" icon="github" href="https://github.com/PrimeIntellect-ai/verifiers">
    Verifiers Repo
  </Card>
</CardGroup>

Verifiers is our library for creating environments to train and evaluate LLMs.

Environments contain everything required to run and evaluate a model on a particular task:

* A *dataset* of task inputs
* A *harness* for the model (tools, sandboxes, context management, etc.)
* A reward function or *rubric* to score the model's performance

Environments can be used for training models with reinforcement learning (RL), evaluating capabilities, generating synthetic data, experimenting with agent harnesses, and more.

Verifiers is tightly integrated with the [Environments Hub](https://app.primeintellect.ai/dashboard/environments?ex_sort=most_stars), as well as our training framework [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) and our [Hosted Training](https://app.primeintellect.ai/dashboard/training) platform.

## Getting Started

Ensure you have `uv` installed, as well as the `prime` [CLI](https://docs.primeintellect.ai/cli-reference/introduction) tool:

```bash theme={null}
# install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# install the prime CLI
uv tool install prime
# log in to the Prime Intellect platform
prime login
```

To set up a new workspace for developing environments, do:

```bash theme={null}
# ~/dev/my-lab
prime lab setup 
```

This sets up a Python project if needed (with `uv init`), installs `verifiers` (with `uv add verifiers`), creates the recommended workspace structure, and downloads useful starter files:

```
configs/
├── endpoints.toml      # OpenAI-compatible API endpoint configuration
├── rl/                 # Example configs for Hosted Training
├── eval/               # Example multi-environment eval configs
└── gepa/               # Example configs for prompt optimization
.prime/
└── skills/             # Bundled workflow skills for create/browse/review/eval/GEPA/train/brainstorm
environments/
└── AGENTS.md           # Documentation for AI coding agents
AGENTS.md               # Top-level documentation for AI coding agents
CLAUDE.md               # Claude-specific pointer to AGENTS.md
```

Alternatively, add `verifiers` to an existing project:

```bash theme={null}
uv add verifiers && prime lab setup --skip-install
```

Optional features are installed with extras:

| Extra      | Install                        | Enables                                                                   |
| ---------- | ------------------------------ | ------------------------------------------------------------------------- |
| `modal`    | `uv add "verifiers[modal]"`    | The v1 Modal sandbox runtime                                              |
| `notebook` | `uv add "verifiers[notebook]"` | `Environment.generate_sync()` inside Jupyter or another active event loop |
| `quest`    | `uv add "verifiers[quest]"`    | QUEST PDF parsing and evaluation                                          |

Environments built with Verifiers are self-contained Python modules. To initialize a fresh environment template, do:

```bash theme={null}
prime env init my-env # creates a v0 stub in ./environments/my_env
```

This will create a new module called `my_env` with a runnable environment
template.

```
environments/my_env/
├── my_env.py           # Main implementation
├── pyproject.toml      # Dependencies and metadata
└── README.md           # Documentation
```

Environment modules should expose a `load_environment` function which returns an
environment object. For simple legacy environments, this can still be a direct
constructor:

```python theme={null}
# my_env.py
import verifiers as vf

def load_environment(dataset_name: str = 'gsm8k') -> vf.Environment:
    dataset = vf.load_example_dataset(dataset_name) # 'question'
    async def correct_answer(completion, answer) -> float:
        completion_ans = completion[-1]['content']
        return 1.0 if completion_ans == answer else 0.0
    rubric = vf.Rubric(funcs=[correct_answer])
    env = vf.SingleTurnEnv(dataset=dataset, rubric=rubric)
    return env
```

To run a local evaluation with any OpenAI-compatible model, do:

```bash theme={null}
prime eval run my-env -m openai/gpt-5-nano # run and save eval results locally
```

Evaluations use [Prime Inference](https://docs.primeintellect.ai/inference/overview) by default; configure your own API endpoints in `./configs/endpoints.toml`.

View local evaluation results in the terminal UI:

```bash theme={null}
prime eval view
```

The TUI opens a single run browser (`environment -> model -> run`). Press `Enter` on a run to open rollout details, `b` to go back, `tab` to cycle panes, `e` and `x` to expand or collapse history, `pageup` and `pagedown` to scroll history, and `c` for Copy Mode.

To publish the environment to the [Environments Hub](https://app.primeintellect.ai/dashboard/environments?ex_sort=most_stars), do:

```bash theme={null}
prime env push my-env # equivalent to --path ./environments/my_env
```

To run an evaluation directly from the Environments Hub, do:

```bash theme={null}
prime eval run primeintellect/math-python
```

## Documentation

**[Environments](/verifiers/environments)** — Create datasets, rubrics, and custom multi-turn interaction protocols.

**[Evaluation](/verifiers/evaluation)** - Evaluate models using your environments.

**[Training](/verifiers/training)** — Train models in your environments with reinforcement learning.

**[Development](/verifiers/development)** — Contributing to verifiers

**[API Reference](/verifiers/reference)** — Understanding the API and data structures

**[FAQs](/verifiers/faqs)** - Other frequently asked questions.