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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, as well as our training framework prime-rl and our Hosted Training platform.

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Getting Started

Ensure you have uv installed, as well as the prime CLI: 
# install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# install the prime CLI
uv tool install prime
To set up a new workspace for developing environments, do: 
# ~/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.py        # OpenAI-compatible API endpoint configuration
└── lab/                # Example configs for Hosted Training
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: 
uv add verifiers && prime lab setup --skip-install
Environments built with Verifiers are self-contained Python modules. To initialize a fresh environment template, do: 
prime env init my-env # creates a new template in ./environments/my_env
This will create a new module called my_env with a basic 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 instance of the Environment object, and which can accept custom arguments. For example: 
# 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 = Rubric(funcs=[correct_answer])
    env = vf.SingleTurnEnv(dataset=dataset, rubric=rubric)
    return env
To install the environment module into your project, do: 
prime env install my-env # installs from ./environments/my_env
To install an environment from the Environments Hub into your project, do: 
prime env install primeintellect/math-python
To run a local evaluation with any OpenAI-compatible model, do: 
prime eval run my-env -m gpt-5-nano # run and save eval results locally
Evaluations use Prime Inference by default; configure your own API endpoints in ./configs/endpoints.py. View local evaluation results in the terminal UI: 
prime eval tui
To publish the environment to the Environments Hub, do: 
prime env push --path ./environments/my_env
To run an evaluation directly from the Environments Hub, do: 
prime eval run primeintellect/math-python

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Documentation

Environments — Create datasets, rubrics, and custom multi-turn interaction protocols. Evaluation - Evaluate models using your environments. Training — Train models in your environments with reinforcement learning. Development — Contributing to verifiers API Reference — Understanding the API and data structures FAQs - Other frequently asked questions.