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.

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 familar 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.

⚠️ 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 to make sure things are well coordinated you should discuss any significant change before starting the work. 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

Architecture

Component Overview

┌─────────────────────────────────────────────────────────┐
│                    Client Application                   │
│  ┌────────────────┐              ┌──────────────────┐   │
│  │  EchoEnv       │              │  CodingEnv       │   │
│  │ (HTTPEnvClient)│              │  (HTTPEnvClient) │   │
│  └────────┬───────┘              └────────┬─────────┘   │
└───────────┼───────────────────────────────┼─────────────┘
            │ HTTP                          │ HTTP
            │ (reset, step, state)          │
┌───────────▼───────────────────────────────▼─────────────┐
│              Docker Containers (Isolated)               │
│  ┌──────────────────────┐    ┌──────────────────────┐   │
│  │ FastAPI Server       │    │ FastAPI Server       │   │
│  │   EchoEnvironment    │    │ PythonCodeActEnv     │   │
│  │ (Environment base)   │    │ (Environment base)   │   │
│  └──────────────────────┘    └──────────────────────┘   │
└─────────────────────────────────────────────────────────┘

Core Components

1. Environment (Server-Side)

Base class for implementing environment logic:

• reset(): Initialize a new episode, returns initial Observation
• step(action): Execute an Action, returns resulting Observation
• state(): Access episode metadata (State with episode_id, step_count, etc.)

2. HTTPEnvClient (Client-Side)

Base class for HTTP communication:

• Handles HTTP requests to environment server
• Contains a utility to spin up a docker container locally for the corresponding environment
• Type-safe action/observation parsing

3. Container Providers

Manage container deployment:

• LocalDockerProvider: Run containers on local Docker daemon
• KubernetesProvider: Deploy to K8s clusters (future)

4. Models

Type-safe data structures:

• Action: Base class for environment actions
• Observation: Base class for environment observations
• State: Episode state tracking
• StepResult: Combines observation, reward, done flag

Project Structure

For Environment Creators

When building a new environment, create the following structure:

src/envs/your_env/
├── __init__.py           # Export YourAction, YourObservation, YourEnv
├── models.py             # Define Action, Observation, State dataclasses
├── client.py             # Implement YourEnv(HTTPEnvClient)
├── README.md             # Document your environment
└── server/
    ├── your_environment.py  # Implement YourEnvironment(Environment)
    ├── app.py               # Create FastAPI app
    └── Dockerfile           # Define container image

See src/envs/README.md for a complete guide on building environments.

For Environment Users

To use an environment:

1. Import from envs.your_env: from envs.echo_env import EchoAction, EchoEnv
2. Create client: client = EchoEnv.from_docker_image("echo-env:latest")
3. Interact: client.reset(), client.step(action), client.state()
4. Cleanup: client.close()

See example scripts in examples/ directory.

Design Principles

1. Separation of Concerns: Clear client-server boundaries
2. Type Safety: Strongly-typed actions, observations, and state
3. Container Isolation: Each environment runs in its own container
4. Simple APIs: Minimal, intuitive interfaces

Quick Start

Using the Echo Environment(Example)

from envs.echo_env import EchoAction, EchoEnv

# Automatically start container and connect
client = EchoEnv.from_docker_image("echo-env:latest")

# Reset the environment
result = client.reset()
print(result.observation.echoed_message)  # "Echo environment ready!"

# Send messages
result = client.step(EchoAction(message="Hello, World!"))
print(result.observation.echoed_message)  # "Hello, World!"
print(result.reward)  # 1.3 (based on message length)

# Cleanup
client.close()  # Stops and removes container

Requirements

• Python 3.11+
• Docker Desktop or Docker Engine
• FastAPI >= 0.104.0
• Uvicorn >= 0.24.0
• Requests >= 2.25.0
• smolagents (for coding environment)

Supported RL Tools

The goal of this project is to support a broad set of open and closed tools to help standardize the agentic RL community. If you have a project that supports OpenEnv environments, please put up a PR to add your tool name along with a link to your documentation.

torchforge

(coming soon)

TRL

(coming soon}

Unsloth

(coming soon)

SkyRL

(coming soon)

Example Environments

Echo Environment

A simple environment that echoes back messages with metadata. Perfect for:

• Testing the HTTP server infrastructure
• Learning the framework basics
• Verifying container deployment

See: src/envs/echo_env/README.md

Coding Environment

Executes arbitrary Python code in a sandboxed environment. Features:

• Safe code execution using smolagents
• Capture stdout, stderr, and exit codes
• Persistent execution context within episodes
• Error handling with detailed messages

See: src/envs/coding_env/README.md

Community support & Acknowledgments

This is an open and community centric project. If you would like to add your name here, please put up a pull request and tag @jspisak for review. Ty!!

Supporters include: Meta-PyTorch, Hugging Face, Unsloth AI, Reflection AI, vLLM, SkyRL (UC-Berkeley), LightningAI, ..

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)