OpenSandbox: The Complete Guide to the AI Sandbox Platform
Running AI-generated code in production is inherently risky. Coding agents, GUI agents, RL training — all need isolated, secure environments to execute untrusted code. OpenSandbox is Alibaba's answer: a general-purpose, open-source sandbox platform for AI applications with 6,600+ GitHub stars, multi-language SDKs, Docker/Kubernetes runtimes, and enterprise-grade security (gVisor, Kata Containers, Firecracker).
What Is OpenSandbox?
OpenSandbox is a general-purpose sandbox platform for AI applications. It provides unified sandbox APIs and multi-language SDKs so you can create isolated environments for coding agents, GUI agents, agent evaluation, AI code execution, and RL training.
- Language: Python
- License: Apache 2.0
- Stars: 6,600+ ⭐
- Forks: 479
- Releases: 50
- Contributors: 31
- Team: Alibaba
Key Features
Multi-Language SDKs
Build sandbox integrations in your language of choice:
- Python — Primary SDK
- Java/Kotlin — JVM support
- JavaScript/TypeScript — Node.js
- C#/.NET — .NET ecosystem
- Go — On roadmap
Sandbox Protocol
A formal specification for sandbox lifecycle management and execution APIs. Extend with custom sandbox runtimes — not locked into a single runtime.
Docker & Kubernetes Runtimes
- Docker — Local development and single-machine workloads
- Kubernetes — High-performance distributed scheduling for production scale
- Horizontal scaling with Kubernetes-native controllers
Sandbox Environments
Built-in implementations for common AI agent scenarios:
- Command execution — Run shell commands
- Filesystem — Read/write files
- Code Interpreter — Execute Python, JavaScript, and more
- Browser automation — Chrome, Playwright
- Desktop environments — VNC, VS Code Server
Network Policy
- Ingress Gateway — Multiple routing strategies for accessing sandbox services
- Egress Controls — Per-sandbox outbound network policies
Strong Isolation
Enterprise-grade security with multiple container runtime options:
- gVisor — User-space kernel, syscall interception
- Kata Containers — VM-level isolation with container compatibility
- Firecracker microVM — Lightweight VMs for maximum isolation
Use Cases
Coding Agents
Run AI coding agents in isolated sandboxes with built-in examples for:
- Claude Code — Run Claude Code inside OpenSandbox
- Gemini CLI — Google's coding agent
- Codex CLI — OpenAI's coding agent
- Kimi CLI — Moonshot AI's coding agent
- LangGraph — State-machine workflows with sandbox jobs
- Google ADK — Google Agent Development Kit integration
GUI Agents
- Chrome sandbox — Chromium with VNC and DevTools
- Playwright — Headless browser automation
- Desktop — Full desktop environment via VNC
- VS Code — code-server for remote development
Agent Evaluation
Run evaluation benchmarks in isolated, reproducible environments with consistent container images.
RL Training
Reinforcement learning environments running in sandboxes for safe exploration.
Quick Start
1. Install and Start the Server
uv pip install opensandbox-server
opensandbox-server init-config ~/.sandbox.toml --example docker
opensandbox-server
2. Install the Code Interpreter SDK
uv pip install opensandbox-code-interpreter
3. Create a Sandbox and Execute Code
import asyncio
from datetime import timedelta
from code_interpreter import CodeInterpreter, SupportedLanguage
from opensandbox import Sandbox
from opensandbox.models import WriteEntry
async def main():
# Create a sandbox
sandbox = await Sandbox.create(
"opensandbox/code-interpreter:v1.0.1",
entrypoint=["/opt/opensandbox/code-interpreter.sh"],
env={"PYTHON_VERSION": "3.11"},
timeout=timedelta(minutes=10),
)
async with sandbox:
# Execute a shell command
execution = await sandbox.commands.run("echo 'Hello OpenSandbox!'")
print(execution.logs.stdout[0].text)
# Write and read a file
await sandbox.files.write_files([
WriteEntry(path="/tmp/hello.txt", data="Hello World", mode=644)
])
content = await sandbox.files.read_file("/tmp/hello.txt")
print(f"Content: {content}")
# Execute Python code
interpreter = await CodeInterpreter.create(sandbox)
result = await interpreter.codes.run(
"result = 2 + 2\nresult",
language=SupportedLanguage.PYTHON,
)
print(result.result[0].text) # 4
await sandbox.kill()
asyncio.run(main())
Architecture
sdks/ → Multi-language client SDKs
specs/ → Sandbox Protocol specification
server/ → Sandbox server (lifecycle management)
kubernetes/ → Kubernetes runtime controller
components/
execd/ → In-sandbox execution daemon
ingress/ → Ingress gateway
egress/ → Egress network controls
sandboxes/ → Sandbox image definitions
examples/ → Usage examples
Roadmap (March 2026)
- SDK: Connection pool management, pre-provisioned sandboxes (ms-level startup), Go SDK
- Runtime: Persistent volumes, lightweight local sandbox for PCs, secure container improvements
- Deployment: Self-hosted Kubernetes cluster guide
OpenSandbox vs Alternatives
Category: This tool is an open-source sandbox platform for AI agent code execution.
| Feature | OpenSandbox | Daytona | E2B |
|---|---|---|---|
| Focus | General-purpose AI sandbox platform | Secure elastic infrastructure for AI code | Cloud sandbox for AI agents |
| Stars | 6.6K ⭐ | 62.9K ⭐ | 11.2K ⭐ |
| License | Apache 2.0 | AGPL-3.0 | Apache 2.0 |
| Language | Python | TypeScript | MDX/TypeScript |
| Multi-Language SDKs | ✅ Python, Java, JS, C#, Go (roadmap) | Python, TypeScript | Python, JS/TS |
| Docker Runtime | ✅ | ✅ | ❌ (Firecracker only) |
| Kubernetes Runtime | ✅ Native K8s controller | ❌ | ❌ |
| Isolation Options | ✅ gVisor + Kata + Firecracker | Docker, optional Kata | Firecracker microVM |
| Code Interpreter | ✅ Built-in SDK | ✅ | ✅ |
| Network Policy | ✅ Ingress + Egress controls | ❌ | ❌ |
| Browser/Desktop | ✅ Chrome, Playwright, VNC, VS Code | ❌ | ❌ |
| Coding Agent Examples | ✅ Claude Code, Gemini CLI, Codex CLI, Kimi CLI, LangGraph, ADK | ❌ | ✅ |
| Self-Hosted | ✅ Docker + K8s | ✅ | ✅ (self-hosted or cloud) |
| Cold Start | Standard | ✅ Sub-90ms | Fast (~150ms) |
| Team | Alibaba | Daytona.io | e2b-dev |
When to choose OpenSandbox: You need a general-purpose sandbox platform with Kubernetes-native distributed scheduling, multiple isolation options (gVisor, Kata, Firecracker), multi-language SDKs (Python, Java, JS, C#), and built-in browser/desktop environments. Best for enterprise teams needing production-scale sandbox infrastructure with fine-grained network policies.
When to choose Daytona: You want the fastest cold start (sub-90ms) and largest community (62.9K stars). Elastic infrastructure with simple SDK. Best for teams needing ultra-fast sandbox provisioning.
When to choose E2B: You want Firecracker microVM isolation out of the box with a simple, cloud-first SDK. Best for startups and teams wanting a managed sandbox service with self-hosted option.
Conclusion
OpenSandbox stands out as the most comprehensive, general-purpose sandbox platform for AI applications. With multi-language SDKs, a formal Sandbox Protocol, Docker and Kubernetes runtimes, three isolation options (gVisor, Kata, Firecracker), built-in browser/desktop environments, and network policy controls, it addresses the full spectrum of AI agent sandbox needs — from local development to production-scale Kubernetes deployments. Built by Alibaba with Apache 2.0 licensing.