Getting Started

Install OpenEnv, load an environment, and run your first step.

Install OpenEnv

pip install openenv

This installs the full OpenEnv runtime: the environment server, the client, the openenv CLI, the web interface, and MCP support. Environments depend on openenv directly.

Optional dependencies

A few integrations ship as optional extras. Install them with pip install openenv[<extra>]:

Extra	Pulls in
`inspect`	The Inspect AI evaluation harness
`harbor`	The Harbor integration and its sandbox backends (Python 3.12+)
`daytona`, `aca`, `modal`, `novita`	Cloud sandbox providers (see Runtime Providers)

Try an Environment

Use AutoEnv to load the Echo client from its Hugging Face Space. It asks for confirmation before installing the environment package, then connects to the running Space. Echo exposes its actions as MCP tools.

from openenv import AutoEnv

env = AutoEnv.from_env(
    "openenv/echo_env",
    base_url="https://openenv-echo-env.hf.space",
)

with env.sync() as client:
    result = client.reset()
    print(result.observation.metadata["message"])  # "Echo environment ready!"

    message = client.call_tool("echo_message", message="Hello, OpenEnv!")
    print(message)  # "Hello, OpenEnv!"

For installed environment packages, AutoEnv.from_env() also accepts the common name forms below. Without a base_url, these start a local Docker container and require Docker and the environment image:

from openenv import AutoEnv

AutoEnv.from_env("echo")
AutoEnv.from_env("echo-env")
AutoEnv.from_env("echo_env")

Connect to a Running Environment

OpenEnv clients are async by default. Use the async client for production code, parallel environment runs, and integrations with async frameworks.

import asyncio

from echo_env import CallToolAction, EchoEnv


async def main():
    async with EchoEnv(base_url="https://openenv-echo-env.hf.space") as client:
        await client.reset()

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


asyncio.run(main())

For scripts and notebooks, use .sync():

from echo_env import CallToolAction, EchoEnv

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

Use Containers or Local Servers

You can run an environment from a Docker image:

import asyncio

from echo_env import EchoEnv


async def main():
    client = await EchoEnv.from_docker_image(
        "registry.hf.space/openenv-echo-env:latest"
    )
    async with client:
        result = await client.reset()
        print(result.observation)


asyncio.run(main())

If you run the Hugging Face Space image yourself with docker run, expose port 7860 and connect to that port:

docker run -it -p 7860:7860 --platform=linux/amd64 \
    registry.hf.space/openenv-echo-env:latest

from echo_env import EchoEnv

with EchoEnv(base_url="http://localhost:7860").sync() as client:
    result = client.reset()

Or connect to a local server:

cd path/to/echo-env
uv venv
source .venv/bin/activate
uv pip install -e .
uv run server --host 0.0.0.0 --port 8000

from echo_env import EchoEnv

with EchoEnv(base_url="http://localhost:8000").sync() as client:
    result = client.reset()

The same client also runs environments on cloud sandboxes (Daytona, Modal, Novita, Azure Container Apps, Hugging Face) through runtime providers. HFSandboxProvider ships with core OpenEnv and needs a Hugging Face token for an account that can run Jobs (the sandboxes are billed as Jobs): it runs an environment’s Space image in a Hugging Face sandbox (example). See the Runtime Providers guide to pick one.

Next Steps

Train an agent with your training framework, or train a coding agent through Harbor
Explore environments
Build your first environment
Concepts

Update on GitHub