OpenSandbox is a versatile, open-source sandbox platform designed to facilitate the development and deployment of AI applications. Developed by Alibaba, it provides a secure and isolated environment for executing code, running agents, and conducting evaluations, particularly in the context of AI-driven tasks. The platform's core purpose is to offer a robust and flexible infrastructure for AI developers, enabling them to experiment, test, and deploy their applications with confidence.

The platform's key features center around its multi-language SDKs, unified sandbox APIs, and support for Docker and Kubernetes runtimes. The multi-language SDKs are a significant advantage, providing developers with the flexibility to work in their preferred programming languages, including Python, Java/Kotlin, JavaScript/TypeScript, C#/.NET, and, with a planned Go SDK. These SDKs simplify the interaction with the sandbox environment, allowing developers to easily create, manage, and interact with sandboxed processes.

The unified sandbox APIs are another crucial aspect of OpenSandbox. They define a standardized way to manage the lifecycle of sandboxes and execute commands within them. This standardization allows for the extension of custom sandbox runtimes, providing developers with the ability to tailor the environment to their specific needs. The platform's built-in support for Docker and Kubernetes runtimes further enhances its versatility. Docker enables local execution and testing, while Kubernetes facilitates large-scale distributed scheduling, making OpenSandbox suitable for a wide range of deployment scenarios.

OpenSandbox offers a variety of built-in sandbox environments, including Command, Filesystem, and Code Interpreter implementations. These environments provide essential functionalities for interacting with the sandbox, such as executing shell commands, managing files, and interpreting code. The platform also includes examples covering various use cases, such as Coding Agents (e.g., Claude Code), browser automation (Chrome, Playwright), and desktop environments (VNC, VS Code). The network policy feature provides a unified Ingress Gateway with multiple routing strategies and per-sandbox egress controls, enhancing security and control over network traffic.

The repository provides comprehensive examples demonstrating how to use OpenSandbox in different scenarios. These examples cover basic sandbox operations, such as creating and managing sandboxes, executing commands, and interacting with files. More advanced examples showcase integrations with coding agents like Claude Code, Gemini CLI, and OpenAI Codex CLI, as well as browser and desktop environments. The inclusion of examples for ML and training, specifically RL training, highlights the platform's suitability for AI research and development.

The project's structure is well-defined, with separate directories for SDKs, specifications, the server, Kubernetes deployment, execution daemons, ingress components, egress controls, runtime implementations, examples, documentation, tests, and scripts. This organization promotes modularity and maintainability. The documentation, including architecture and design documents, SDK guides, and API specifications, provides developers with the necessary information to understand and utilize the platform effectively. The project is licensed under the Apache 2.0 License, promoting open-source collaboration and usage. The roadmap outlines future development plans, including the Go SDK, persistent storage, and Kubernetes Helm charts, indicating the project's ongoing evolution and commitment to providing a comprehensive sandbox solution for AI applications.