What CubeSandbox Does
CubeSandbox provides ephemeral, sandboxed compute environments designed specifically for AI agents that need to execute code. Think of it as a secure, browser-accessible virtual machine that spins up in seconds and tears down after use — perfect for AI coding assistants, data analysis agents, and automated testing pipelines.
- Instant environments — Spin up a fully configured development environment in under 2 seconds
- Language support — Python, Node.js, Go, Rust, Java, and C++ with pre-installed packages
- Network isolation — Each sandbox runs in an isolated container with controlled network access
- File system persistence — Files persist within a session but are destroyed when the sandbox terminates
- API-first design — REST API for programmatic sandbox creation, code execution, and output retrieval
The Security Model
Running arbitrary AI-generated code is inherently risky. CubeSandbox addresses this with a multi-layered security approach:
- Container isolation — Each sandbox runs in a separate container with restricted capabilities
- Resource limits — CPU, memory, and execution time caps prevent runaway processes
- Network policies — Outbound network access is restricted by default, with opt-in for specific domains
- No host access — Sandboxes cannot access the host filesystem, other containers, or internal services
- Automatic teardown — Sessions expire after a configurable timeout (default: 10 minutes)
Real-World Use Cases
We tested CubeSandbox across several practical scenarios:
- AI coding agents — An LLM writes and executes Python code to analyze a dataset, produces charts, returns results. Works reliably.
- Automated testing — Run student code submissions against test suites in isolated environments. Clean and fast.
- Data analysis pipeline — Upload CSV, execute Pandas transformations, download results. The API makes this trivial.
- Interactive tutorials — Embed live code execution in documentation. Readers can run examples without local setup.
Performance and Limits
In our testing, performance was solid for the intended use cases:
- Sandbox startup — 1.2-2.1 seconds for Python, 1.5-2.5 seconds for Node.js
- Code execution — No observable overhead vs local execution for CPU-bound tasks
- Concurrent sandboxes — Free tier supports 5 concurrent sessions; Pro tier supports 50
- Memory limit — 512MB per sandbox (free), 4GB (Pro). Sufficient for most AI agent tasks, tight for ML training.
The most interesting thing about CubeSandbox isn't the sandbox itself — it's the API design for AI agents. Traditional sandboxes (JSFiddle, Replit) are designed for humans clicking buttons. CubeSandbox is designed for LLMs making API calls. That distinction matters: when an AI agent needs to execute code, verify output, and iterate, the API-first approach with automatic teardown is exactly right. This is infrastructure built for the agentic era.