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🤗 HuggingEnvs: Open RL Environments

HuggingEnvs is a home for end-to-end RL environment recipes, built to make it easier to explore, reproduce, train, and evaluate agent systems.

Explore complete and reproducible environment projects from us and the community, including:

🌍 Open RL environments
🧩 End-to-end environment recipes
💻 Complete implementations
📦 Models, datasets, and artifacts
🧪 Training and evaluation setups
🚀 Demos and Spaces
📚 Tutorials and guides

All the reproducible code — environments, rollouts, training configs, notebooks, article and slide sources — lives in one repo: github.com/adithya-s-k/HuggingEnvs. The artifacts those produce live here on the Hub.

HuggingEnvs Projects

A growing collection of open projects, environments, resources, and artifacts.

Project	What it is	Explore
HuggingEnvs Academy	Articles, guides, tutorials, slides, and hands-on resources for learning how to build RL environments and agent systems.	Explore →
Data Agent	Training SLMs for data science with multi-harness RL environments.	Explore →

Articles & Talks

	What it covers	Read / Watch
📖 The Ultimate Guide to RL Environments	Building and scaling RL environments in the LLM era — how frameworks are built, how rewards are wired, how they scale to thousands of concurrent sessions.	Read →
🎞️ RL Environments 101	From "what is an env?" to training your own: RL fundamentals → environment anatomy → OpenEnv → training with TRL.	Watch →
📈 Scaling RL for LLMs	RL environments and RL training — what an environment is, how reward hacking happens, how to train against your own. AMD AI Dev Day.	Watch →
🔀 Multi-Harness Training	OpenEnv × Harbor — why an environment's failure model decides whether it can be trained against.	Watch →

Environments

Three reference environments, each implemented across six frameworks — openenv, ors, nemo_gym, verifiers, skyrl_gym, gem. Same logic, six dialects. Source →

Environment	Tools	OpenEnv	ORS	NeMo Gym
Jupyter agent — real code execution in an E2B sandbox	4	Space	Space	Space
Wordle — multi-turn, pure Python, no backend	1	Space	Space	Space
Desktop — computer-use, vision-driven Linux desktop	19	Space	Space	—

Build your own

Five agent skills turn a plain-English description into a runnable RL environment across four frameworks — works with Claude Code, Cursor, Codex, OpenCode, Gemini CLI and others.