OpenEnv: Agentic Execution Environments

An e2e framework for creating, deploying and using isolated execution environments for agentic RL training, built using Gymnasium style simple APIs.

Quick Start

Install the OpenEnv package:

pip install openenv

Install an environment client (e.g., Echo):

pip install git+https://huggingface.co/spaces/openenv/echo_env

Then use the environment:

import asyncio
from echo_env import CallToolAction, EchoEnv

async def main():
    # Connect to a running Space (async context manager)
    async with EchoEnv(base_url="https://openenv-echo-env.hf.space") as client:
        # Reset the environment
        result = await client.reset()
        print(result.observation.echoed_message)  # "Echo environment ready!"

        # Send messages
        result = await client.step(
            CallToolAction(
                tool_name="echo_message",
                arguments={"message": "Hello, World!"},
            )
        )
        print(result.observation.result)  # "Hello, World!"
        print(result.reward)

asyncio.run(main())

Synchronous usage is also supported via the .sync() wrapper:

from echo_env import CallToolAction, EchoEnv

# Use .sync() for synchronous context manager
with EchoEnv(base_url="https://openenv-echo-env.hf.space").sync() as client:
    result = client.reset()
    result = client.step(
        CallToolAction(
            tool_name="echo_message",
            arguments={"message": "Hello, World!"},
        )
    )
    print(result.observation.result)

For a detailed quick start, check out the docs page.

Overview

OpenEnv provides a standard for interacting with agentic execution environments via simple Gymnasium style APIs - step(), reset(), state(). Users of agentic execution environments can interact with the environment during RL training loops using these simple APIs.

In addition to making it easier for researchers and RL framework writers, we also provide tools for environment creators making it easier for them to create richer environments and make them available over familiar protocols like HTTP and packaged using canonical technologies like docker. Environment creators can use the OpenEnv framework to create environments that are isolated, secure, and easy to deploy and use.

The OpenEnv CLI (openenv) provides commands to initialize new environments and deploy them to Hugging Face Spaces.

⚠️ Early Development Warning OpenEnv is currently in an experimental stage. You should expect bugs, incomplete features, and APIs that may change in future versions. The project welcomes bugfixes, but significant changes should be discussed before implementation so the technical committee and community can coordinate scope, compatibility, and release timing. It's recommended that you signal your intention to contribute in the issue tracker, either by filing a new issue or by claiming an existing one.

RFCs

Below is a list of active and historical RFCs for OpenEnv. RFCs are proposals for major changes or features. Please review and contribute!

• RFC 001: Baseline API and Interface Specifications
• RFC 002: Discoverability of environment tools by agents
• RFC 003: Add MCP (Model Context Protocol) support
• RFC 004: Add delayed rewards support for trajectory-based scoring
• RFC 005: Agentic Harness Integration

CLI Commands

The OpenEnv CLI provides commands to manage environments:

• openenv init <env_name> - Initialize a new environment from template
• openenv push [--repo-id <repo>] [--private] - Deploy environment to Hugging Face Spaces
• openenv serve - Serve an environment locally with optional auto-reload
• openenv build - Build the Docker image for an environment
• openenv fork <space-id> - Fork a Space from HF Hub to your account
• openenv validate - Validate an environment configuration

Quick Start

# Create a new environment
openenv init my_game_env

# Deploy to Hugging Face (will prompt for login if needed)
cd my_game_env
openenv push

For detailed options run any command with --help.

Integrations

OpenEnv works with a growing ecosystem of RL frameworks and platforms. If your project supports OpenEnv, open a PR to add it here.

TRL

See the TRL example on how to integrate OpenEnv environments with GRPO training.

torchforge

See GRPO BlackJack training example: examples/grpo_blackjack/

Unsloth

See the 2048 game example based on gpt-oss: Colab notebook

SkyRL

See the SkyRL example on how to train on OpenEnv environments with SkyRL.

ART

See the ART example on how OpenEnv environments can be used to train models with ART.

Oumi

See the Oumi example on how OpenEnv environments can be used to train models with Oumi.

Lightning AI

Lightning AI templates

Browse the full catalog of community environments at huggingface.co/docs/openenv/environments.

Community Support & Acknowledgments

OpenEnv is governed by a technical committee that coordinates project direction, major technical decisions, RFCs, and release planning through the public issue tracker, pull requests, and RFC process. Current committee members: Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face.

The project is also supported by a broader community of organizations. If you would like to add your project or organization here, please open a pull request for maintainer review.

Supporters include: Meta-PyTorch, Hugging Face, Scaler AI Labs, Patronus AI, Surge AI, LastMile AI, Unsloth, Reflection, vLLM, SkyRL (UC-Berkeley), Lightning AI, Axolotl AI, Stanford Scaling Intelligence Lab, Mithril, OpenMined, Fleet AI, Halluminate, Turing, Scale AI, Scorecard

And we'd also like to acknowledge the team at Farama Foundation as the OpenEnv API was heavily inspired by the work you all have done on Gymnasium. Cheers!