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

# prime lab setup

> Initialize a development workspace for building RL environments

## Overview

The `prime lab setup` command initializes a new workspace for developing environments with Verifiers. It creates the recommended directory structure, downloads starter configurations, and optionally installs prime-rl for local training.

## Usage

```bash theme={null}
prime lab setup [OPTIONS]
```

## What It Does

1. **Initializes a Python project** (if not already present) using `uv init`
2. **Installs verifiers** via `uv add verifiers`
3. **Creates directory structure**:
   ```
   configs/
   ├── endpoints.toml      # API endpoint configuration
   ├── rl/                 # Training configs for Hosted Training
   ├── eval/               # Multi-environment eval configs
   └── gepa/               # Prompt optimization configs
   .prime/
   └── skills/             # Bundled workflow skills
   environments/
   └── AGENTS.md           # Documentation for AI coding agents
   AGENTS.md               # Top-level agent documentation
   CLAUDE.md               # Claude-specific pointer
   ```
4. **Downloads starter files** from the verifiers repository
5. **Optionally installs prime-rl** for local training

## Options

<ParamField path="--prime-rl" type="flag">
  Install prime-rl and download prime-rl training configurations. Use this if you want to train models locally.
</ParamField>

<ParamField path="--skip-install" type="flag">
  Skip `uv init` and `uv add verifiers` steps. Use when adding to an existing project.
</ParamField>

<ParamField path="--skip-agents-md" type="flag">
  Skip downloading AGENTS.md, CLAUDE.md, and environments/AGENTS.md files.
</ParamField>

<ParamField path="--agents" type="string">
  Comma-separated list of coding agents to scaffold (codex, claude, cursor, opencode, amp).

  Example: `--agents codex,claude`
</ParamField>

<ParamField path="--no-interactive" type="flag">
  Disable interactive prompts for coding agent selection.
</ParamField>

## Examples

### Basic Setup

Initialize a new workspace in an empty directory:

```bash theme={null}
prime lab setup
```

**Output:**

```
No pyproject.toml found, initializing uv project...
Running: uv init
Running: uv add verifiers
Downloaded configs/endpoints.toml from https://github.com/primeintellect-ai/verifiers
Downloaded AGENTS.md from https://github.com/primeintellect-ai/verifiers
```

### Add to Existing Project

Add verifiers to an existing Python project:

```bash theme={null}
# First add verifiers to your project
uv add verifiers

# Then run setup without the install step
prime lab setup --skip-install
```

### Setup with Local Training

Install prime-rl for training models locally:

```bash theme={null}
prime lab setup --prime-rl
```

This will:

* Clone and install prime-rl
* Download prime-rl training configurations
* Install all environments from `environments/` into the prime-rl workspace

### Specify Coding Agents

Configure skill directories for specific coding agents:

```bash theme={null}
prime lab setup --agents codex,opencode
```

## Next Steps

After running `prime lab setup`, you can:

1. **Create an environment:**
   ```bash theme={null}
   prime env init my-env
   ```

2. **Run an evaluation:**
   ```bash theme={null}
   prime eval run my-env -m gpt-4.1-mini -n 5
   ```

3. **View results:**
   ```bash theme={null}
   prime eval tui
   ```

4. **Train a model** (if you used `--prime-rl`):
   ```bash theme={null}
   uv run prime-rl configs/prime-rl/wiki-search.toml
   ```

## Configuration Files

The setup command downloads several starter configuration files:

### Endpoints Configuration

`configs/endpoints.toml` defines model API endpoints:

```toml theme={null}
[[endpoint]]
endpoint_id = "gpt-4.1-mini"
model = "gpt-4.1-mini"
url = "https://api.openai.com/v1"
key = "OPENAI_API_KEY"

[[endpoint]]
endpoint_id = "claude-sonnet"
model = "claude-sonnet-4-5-20250929"
url = "https://api.anthropic.com"
key = "ANTHROPIC_API_KEY"
api_client_type = "anthropic_messages"
```

### Evaluation Configs

`configs/eval/` contains multi-environment evaluation configurations:

```toml theme={null}
# configs/eval/multi-env.toml
model = "openai/gpt-4.1-mini"
num_examples = 50

[[eval]]
env_id = "gsm8k"
num_examples = 100

[[eval]]
env_id = "math-python"
```

### Training Configs

`configs/rl/` contains training configurations for use with Hosted Training or prime-rl.

## Troubleshooting

### uv Not Found

If `uv` is not installed:

```bash theme={null}
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### Permission Errors

Ensure you have write permissions in the target directory.

### Git Conflicts

If you're in a git repository, the setup may create files that conflict with existing files. Review changes carefully before committing.