Run AI Agents in Secure Sandboxes on Your Own Infrastructure
OpenSandbox gives AI agents isolated environments to execute code, run commands, manage files, and operate browsers or desktops on your own infrastructure.
OpenSandbox is a secure, fast sandbox runtime that lets AI agents execute code, run commands, manage files, and operate browsers in isolated environments on your own infrastructure. Small businesses can automate tasks like data reconciliation without exposing credentials or risking data leaks.
What OpenSandbox Does
OpenSandbox is a secure, fast, and extensible sandbox runtime for AI agents. It gives AI applications isolated environments to execute code, run commands, manage files, and operate browsers or desktops. You start locally with Docker and deploy on Kubernetes through a unified sandbox API.
The project is hosted on GitHub under the opensandbox-group organization and has received over 16,700 stars and 1,600 forks. It is licensed under Apache-2.0 and is part of the Agentic AI Foundation (AAIF) project.
Key Features for Small Businesses
OpenSandbox offers several features that matter to small-business owners. The fast sandbox runtime provides high-density sandboxes on Kubernetes, with Firecracker creation times of 64 ms P50 (serial) and 125.4 ms P99 (10 concurrent). Firecracker sandboxes support pause and resume with memory and disk state preserved.
Agent working environments allow you to execute commands, manage files, and run code with built-in APIs. Integration examples show how to run coding agents, browsers, and desktops inside sandboxes.
Network access control routes inbound traffic through a unified ingress gateway and controls outbound access with per-sandbox egress policies. The credential vault lets agents call external services without exposing real credentials to sandbox workloads.
You can start with Docker and deploy on Kubernetes through a unified lifecycle API. Resource pools and batch creation support agent evaluation and reinforcement learning training workloads.
Small-Business Use Case
Consider a local accounting firm that wants to automate client data reconciliation. Instead of manually running scripts on sensitive spreadsheets, they deploy an AI agent inside an OpenSandbox container. The agent runs Python scripts, reads and writes files, and calls external APIs — all without exposing credentials or risking data leaks. The sandbox destroys itself after the job.
This replaces manually running scripts on a local machine, sharing credentials with third-party tools, or trusting sensitive data to cloud-only AI platforms. OpenSandbox gives you control over where your data lives and how it is processed.
Getting Started
To get started, you need Docker (required for local execution) and Python 3.10 or later (required for examples and local runtime). The sandbox server can be installed and configured using uvx. The sandbox SDK is available for Python via pip, and there are SDKs for Java/Kotlin, TypeScript/JavaScript, C#/.NET, and Go.
OpenSandbox also provides a terminal CLI called osb for common sandbox workflows: create sandboxes, run commands, move files, inspect diagnostics, and manage runtime egress policy. The MCP server exposes sandbox creation, command execution, and text file operations to MCP-capable clients such as Claude Code and Cursor.
Official container images are available for deployment.
Takeaways
- OpenSandbox lets you run AI agents in isolated environments on your own infrastructure, keeping sensitive data under your control.
- You can start with Docker locally and scale to Kubernetes, with fast sandbox creation times and support for pause/resume.
- The credential vault and network access control features protect your credentials and data from exposure.
Where would this work in your business?
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