OpenEnv: Agentic Execution Environments

An end-to-end framework for creating, deploying and using isolated execution environments for agentic RL, with a simple Gymnasium-style API.

What you get

• One API for every environment. reset(), step() and state(), sync or async, over a WebSocket.
• Isolated, deployable environments. Each environment is a Docker image that runs locally, on a cloud sandbox, or as a Hugging Face Space.
• 40+ ready-to-use environments, from games and coding sandboxes to browsers, finance and simulators, in the environment catalog.
• Train with your framework. TRL, Unsloth, SkyRL, ART, Oumi, torchforge, Miles and more (see Integrations).
• Train real coding agents. The Harbor integration runs Claude Code, Codex, OpenCode, mini-swe-agent and other harnesses on Harbor tasks, and captures the exact tokens for RL.
• Serve tools to agents. MCP environments expose their tools to agents over /mcp in production mode, while training keeps using step() and rewards.
• Rewards and evals built in. Compose rewards with rubrics and evaluate with Inspect AI.

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.metadata["message"])  # "Echo environment ready!"

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

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

Train an agent

Any training framework that can call an environment can train on it. Training with OpenEnv maps the ways to train and the frameworks that support each one.

• Environments as tools (white-box). The trainer runs the multi-turn tool loop and the environment supplies the tools and the reward. Each framework under Integrations has its own example. With TRL, GRPOTrainer takes an environment_factory: start with the Wordle GRPO tutorial or TRL's OpenEnv guide.
• Real agent harnesses (loop-owning). The agent runs its own loop, and OpenEnv's Harbor integration captures every model call as a framework-neutral TrainingTrace. TRL's AsyncGRPOTrainer trains on those captures today: see examples/async_grpo_harbor and The ultimate guide to multi-harness RL.

Build your own environment

openenv init my_env       # scaffold an environment
openenv validate my_env --level static --skip-build   # quick check against the OpenEnv contract
openenv push my_env       # deploy it to Hugging Face Spaces

See Your First Environment and Deploying an Environment. openenv import wraps an existing environment from ORS/OpenReward or Verifiers.

Environments

A few to start with:

Browse all of them in the environment catalog, or on the OpenEnv Hub organization.

Integrations

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

Learn more

• Documentation: concepts, guides and tutorials
• Core Concepts and the CLI reference
• Tutorials, and the Zero to Hero tutorial from our GPU Mode lecture
• FineEnvs: end-to-end projects that build, deploy and train against OpenEnv environments
• RFCs, the proposals behind major changes
• Contributing: development setup, tests and the PR process

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, Microsoft, Hugging Face, RadixArk, and Nebius.

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, Snorkel AI, SGLang, Miles, Nebius

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!

License

BSD 3-Clause License (see LICENSE file)