Cube Sandbox is a hardware-isolated sandbox service designed to execute untrusted LLM-generated code safely. It boots instances in under 60ms with less than 5MB of memory overhead and supports high-density deployments.
Project overview
The project addresses shared-kernel escapes by providing dedicated Guest OS kernels for untrusted code, targeting high-density AI agent workloads.
Project type
AI Agent · Infrastructure
Use cases
Coding & Development · Automation
Deployment
Refer to project documentation
License
License pending
Best for
Operations teams and AI engineers managing high-density deployments of AI-generated code requiring fast boot times and low memory overhead.
Key capabilities
Provides each sandbox with its own Guest OS kernel to prevent Docker shared-kernel escapes, allowing safe execution of untrusted LLM-generated code.
Visual management console accessible in the browser to manage sandboxes, templates, nodes, and version matrix.
Automatically suspends idle sandboxes and wakes them on the next request for cost optimization.
Limitations and risks
Requires an x86_64 Linux environment with KVM support, with poor performance in non-KVM dev environments.
Data boundary is external optional. Telemetry practices are currently not documented. Coding requirements are optional and no paid external services are required for operation.
Getting started
Provision a server via bare metal, cloud VM, or Terraform. Install Cube Sandbox, create a sandbox template, and access the web console at port 12088 to run code.
Alternatives and comparisons
An open-source super agent harness that orchestrates sub-agents, memory, and sandboxes for long-horizon tasks.
Provides a unified multi-provider LLM API, an agent runtime with tool calling, and an interactive coding agent CLI.
Orchestrates swarms of CLI-based coding agents in parallel across isolated git worktrees.