OpenSandbox

Documentation | 中文文档

OpenSandbox is a general-purpose sandbox platform for AI applications, offering multi-language SDKs, unified sandbox APIs, and Docker/Kubernetes runtimes for scenarios like Coding Agents, GUI Agents, Agent Evaluation, AI Code Execution, and RL Training.

OpenSandbox is now listed in the CNCF Landscape.

Features

• Multi-language SDKs: Provides sandbox SDKs in Python, Java/Kotlin, JavaScript/TypeScript, C#/.NET, Go.
• Sandbox Protocol: Defines sandbox lifecycle management APIs and sandbox execution APIs so you can extend custom sandbox runtimes.
• Sandbox Runtime: Built-in lifecycle management supporting Docker and high-performance Kubernetes runtime, enabling both local runs and large-scale distributed scheduling.
• Sandbox Environments: Built-in Command, Filesystem, and Code Interpreter implementations. Examples cover Coding Agents (e.g., Claude Code), browser automation (Chrome, Playwright), and desktop environments (VNC, VS Code).
• Network Policy: Unified Ingress Gateway with multiple routing strategies plus per-sandbox egress controls.
• Strong Isolation: Supports secure container runtimes like gVisor, Kata Containers, and Firecracker microVM for enhanced isolation between sandbox workloads and the host. See Secure Container Runtime Guide for details.

SDKs

Python:

pip install opensandbox

Java/Kotlin (Gradle Kotlin DSL):

dependencies {
    implementation("com.alibaba.opensandbox:sandbox:{latest_version}")
}

Java/Kotlin (Maven):

<dependency>
    <groupId>com.alibaba.opensandbox</groupId>
    <artifactId>sandbox</artifactId>
    <version>{latest_version}</version>
</dependency>

JavaScript/TypeScript:

npm install @alibaba-group/opensandbox

C#/.NET:

dotnet add package Alibaba.OpenSandbox

Go:

go get github.com/alibaba/OpenSandbox/sdks/sandbox/go

CLI

OpenSandbox also provides osb, a terminal CLI for the common sandbox workflow: create sandboxes, run commands, move files, inspect diagnostics, and manage runtime egress policy.

Install:

pip install opensandbox-cli
# or
uv tool install opensandbox-cli

Quick start:

osb config init
osb config set connection.domain localhost:8080
osb config set connection.protocol http
osb config set connection.api_key <your-api-key>
osb sandbox create --image python:3.12 --timeout 30m -o json
osb command run <sandbox-id> -o raw -- python -c "print(1 + 1)"

See the CLI README for the full command reference.

MCP

The OpenSandbox MCP server exposes sandbox creation, command execution, and text file operations to MCP-capable clients such as Claude Code and Cursor.

Install and run:

pip install opensandbox-mcp
opensandbox-mcp --domain localhost:8080 --protocol http

Minimal stdio config:

{
  "mcpServers": {
    "opensandbox": {
      "command": "opensandbox-mcp",
      "args": ["--domain", "localhost:8080", "--protocol", "http"]
    }
  }
}

See the MCP README for client-specific setup.

Getting Started

Requirements:

• Docker (required for local execution)
• Python 3.10+ (required for examples and local runtime)

Install and Configure the Sandbox Server

uvx opensandbox-server init-config ~/.sandbox.toml --example docker

uvx opensandbox-server

# Show help
# uvx opensandbox-server -h

Create a Code Interpreter and Execute Commands/Codes

Install the Code Interpreter SDK

uv pip install opensandbox-code-interpreter

Create a sandbox and execute commands and codes.

import asyncio
from datetime import timedelta

from code_interpreter import CodeInterpreter, SupportedLanguage
from opensandbox import Sandbox
from opensandbox.models import WriteEntry

async def main() -> None:
    # 1. Create a sandbox
    sandbox = await Sandbox.create(
        "opensandbox/code-interpreter:v1.0.2",
        entrypoint=["/opt/opensandbox/code-interpreter.sh"],
        env={"PYTHON_VERSION": "3.11"},
        timeout=timedelta(minutes=10),
    )

    async with sandbox:

        # 2. Execute a shell command
        execution = await sandbox.commands.run("echo 'Hello OpenSandbox!'")
        print(execution.logs.stdout[0].text)

        # 3. Write a file
        await sandbox.files.write_files([
            WriteEntry(path="/tmp/hello.txt", data="Hello World", mode=644)
        ])

        # 4. Read a file
        content = await sandbox.files.read_file("/tmp/hello.txt")
        print(f"Content: {content}") # Content: Hello World

        # 5. Create a code interpreter
        interpreter = await CodeInterpreter.create(sandbox)

        # 6. Execute Python code (single-run, pass language directly)
        result = await interpreter.codes.run(
              """
                  import sys
                  print(sys.version)
                  result = 2 + 2
                  result
              """,
              language=SupportedLanguage.PYTHON,
        )

        print(result.result[0].text) # 4
        print(result.logs.stdout[0].text) # 3.11.14

    # 7. Cleanup the sandbox
    await sandbox.kill()

if __name__ == "__main__":
    asyncio.run(main())

More Examples

OpenSandbox provides examples covering SDK usage, agent integrations, browser automation, and training workloads. All example code is located in the examples/ directory.

🎯 Basic Examples

• code-interpreter - End-to-end Code Interpreter SDK workflow in a sandbox.
• aio-sandbox - All-in-One sandbox setup using the OpenSandbox SDK.
• agent-sandbox - Example integration for running OpenSandbox workloads on Kubernetes with kubernetes-sigs/agent-sandbox.
• Volumes — Docker PVC / named volumes, Docker OSSFS, Kubernetes PVC: persistent and shared storage patterns.

🤖 Coding Agent Integrations

• Coding CLIs — Claude Code, Gemini CLI, OpenAI Codex CLI, Qwen Code, Kimi CLI: run each vendor CLI inside OpenSandbox.
• langgraph - LangGraph state-machine workflow that creates/runs a sandbox job with fallback retry.
• google-adk - Google ADK agent using OpenSandbox tools to write/read files and run commands.
• openclaw - Launch an OpenClaw Gateway inside a sandbox.

🌐 Browser and Desktop Environments

• chrome - Chromium sandbox with VNC and DevTools access for automation and debugging.
• playwright - Playwright + Chromium headless scraping and testing example.
• desktop - Full desktop environment in a sandbox with VNC access.
• vscode - code-server (VS Code Web) running inside a sandbox for remote dev.

🧠 ML and Training

• rl-training - DQN CartPole training in a sandbox with checkpoints and summary output.

For more details, please ref