{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"OpenEnv provides a Gymnasium-style framework for creating and deploying isolated execution environments that agents can interact with via standard `reset()`, `step()`, and `state()` APIs over HTTP and WebSocket.","skillfed_tags":["reinforcement-learning","agent-training","environment-framework"],"use_cases":["Train agents to play games or solve tasks by wrapping task logic in an OpenEnv environment and connecting agents via the client API.","Deploy custom agent execution sandboxes as isolated Docker containers accessible over HTTP, with automatic tool discovery and action routing.","Build interactive web-based debugging interfaces for environment behavior using the built-in web UI and real-time WebSocket updates.","Integrate RL training loops with fastapi and uvicorn by using the async client to step environments and collect rewards during training.","Share reproducible environments across teams or platforms by packaging them as Docker images and hosting on remote services."],"what_it_does":"OpenEnv is a framework for building and deploying isolated execution environments that agents can train against using reinforcement learning. It standardizes the agent-environment interaction using Gymnasium-style APIs (`reset`, `step`, `state`) and handles the networking layer via WebSocket and HTTP, allowing agents to interact with environments running in Docker containers or on remote services.\n\nThe package provides both server-side components (base classes for implementing environments, FastAPI integration, web UI) and client-side components (async/sync wrappers for connecting to environments, type-safe action/observation handling). It is built on top of fastapi, pydantic, uvicorn, and other production frameworks, and integrates with huggingface_hub and openai. The framework is currently experimental and undergoing active development.","worth_installing":"Yes, if you are actively building or training agents against custom environments and can tolerate experimental APIs. The framework is actively maintained and has low install friction, but it is explicitly in early development with expected breaking changes. Verify the license before use. Not recommended for production systems requiring API stability."},"id":"openenv-core","links":{"html":"https://skillfed.io/packages/openenv-core","md":"https://skillfed.io/packages/openenv-core.md","pypi":"https://pypi.org/project/openenv-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-11","license_spdx":null,"license_treatment":"unclear","name":"openenv-core","python_support":"supports_current","summary":"A unified framework for reinforcement learning environments"},"popularity":{"monthly_downloads":452791,"position":6580,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}