> ## 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

> Create, manage and share environments for reinforcement learning and evaluation

## About

Environments Hub is a community-powered platform for aggregating and showcasing environments, both for RL training and downstream evaluation.
You can view all available environments on the [Environments Hub](https://app.primeintellect.ai/dashboard/environments).<br />

### Motivation

There are a few inter-related issues we see with the current ecosystem for both evals and RL environments which we're aiming to address with the Environments Hub:

* Despite the rapidly growing interest in training LLMs with RL, there is currently **no established community platform** for exploring and sharing train-ready environments.
* Environment implementations are often tied to a specific training RL stack and can be difficult to adapt to a new trainer.
* Popular evaluation suites (lm\_eval, lighteval, openbench, simple-evals, HELM) offer convenient entrypoints into many single-turn Q\&A evals, but these suites generally **lack support for tasks which are agentic in nature** or require complex infrastructure setups (TAU-bench, TerminalBench, SWE-bench), resulting in a proliferation of independent eval repos without shared entrypoints or specs.
* RL environments and agent evals are **basically the same thing** (dataset + harness + scoring rules), but current open-source efforts generally treat them as fundamentally separate.
* Realistic agent environments can be complex pieces of software requiring **dependencies and versioning**, and are ill-served by monorepo structures for environment collections which can quickly become unmaintainable.

With the Environments Hub, we’ve built a community platform that doubles as a proper Python package registry. Environments are modules which declare dependencies in a `pyproject.toml` and are distributed as wheels. By adopting the [`verifiers`](https://github.com/PrimeIntellect-ai/verifiers) spec, development efforts can focus on task-specific components (datasets, tools or harnesses, reward functions) and automatically leverage existing infrastructure for running evaluations or [training models with RL](https://github.com/primeintellect-ai/prime-rl).

### Resources

* **Prime CLI**: Command-line tool to install, upload and manage environments
  * Github: [https://github.com/PrimeIntellect-ai/prime-cli](https://github.com/PrimeIntellect-ai/prime-cli)
  * Documentation: [CLI Overview](/cli-reference/introduction)
* **Verifiers**: A library of modular components for creating RL environments and training LLM agents.
  * Github: [https://github.com/PrimeIntellect-ai/verifiers](https://github.com/PrimeIntellect-ai/verifiers)
  * Documentation: [Verifiers Docs](/verifiers/overview)
* **Prime RL**: A library for large-scale RL training with FSDP.
  * Github: [https://github.com/PrimeIntellect-ai/prime-rl](https://github.com/PrimeIntellect-ai/prime-rl)

### Support

Join the [Prime Intellect discord](https://discord.gg/primeintellect) to discuss, share feedback, and ask any questions.

<CardGroup cols={2}>
  <Card title="Create Environment" icon="upload" href="/tutorials-environments/create">
    Build and upload custom environments to the registry
  </Card>

  <Card title="Install Environment" icon="download" href="/tutorials-environments/install">
    Install and use environments in your projects
  </Card>
</CardGroup>