This project provides a secure, containerized environment for executing arbitrary code, primarily targeting AI applications that require isolated compute. It simplifies the process of setting up and managing Docker containers for code execution, offering features like file transfer, command execution, and real-time logging.

How It Works

The system leverages Docker to create isolated execution environments. Users can initialize a sandbox with a specified Docker image (defaulting to python:3.12-slim-bookworm), transfer files into and out of the container, execute shell commands, and stream logs. This approach ensures that code runs in a predictable and secure manner, isolated from the host system and other processes.

Quick Start & Requirements

• Installation:
  • Linux/macOS: curl -fsSL https://raw.githubusercontent.com/Automata-Labs-team/code-sandbox-mcp/main/install.sh | bash
  • Windows: irm https://raw.githubusercontent.com/Automata-Labs-team/code-sandbox-mcp/main/install.ps1 | iex
• Prerequisites: Docker installed and running.
• Setup Time: Minimal, dependent on Docker installation and binary download speed.
• Documentation: DEVELOPMENT.md (for local builds/contributions).

Highlighted Details

• Supports custom Docker images for flexible environment setup.
• Enables real-time streaming of container logs and command output.
• Offers auto-update functionality for the binary.
• Cross-platform compatibility (Linux, macOS, Windows).

Maintenance & Community

No specific community channels (Discord/Slack) or notable contributors are mentioned in the README. Development details are referenced in a DEVELOPMENT.md file.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

The README does not detail specific resource limitations that can be imposed on containers, nor does it mention any known bugs or unsupported features. The project appears to be focused on providing the core functionality for code sandboxing.