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A universal sandbox platform for AI application scenarios, providing multi-language SDKs, unified sandbox protocols, and sandbox runtimes for LLM-related capabilities.

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OpenSandbox logo

OpenSandbox

GitHub stars Ask DeepWiki license PyPI version npm version E2E Status


English | δΈ­ζ–‡

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.

Features

  • Multi-language SDKs: Provides sandbox SDKs in Python, Java/Kotlin, JavaScript/TypeScript, Go (Roadmap), and more.
  • Sandbox Protocol: Defines sandbox lifecycle management API and sandbox execution API. You can extend your own sandbox runtime through these sandbox protocols.
  • Sandbox Runtime: Implements sandbox lifecycle management by default, supports Docker and Kubernetes runtimes, enabling large-scale distributed sandbox scheduling.
  • Sandbox Environments: Built-in implementations for Command, Filesystem, Code Interpreter. And provides examples for Coding Agents (Claude Code, etc.), Browser automation (Chrome, Playwright), and Desktop environments (VNC, VS Code).

Examples

Basic Sandbox Operations

Requirements:

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

1. Clone the Repository

git clone https://github.com/alibaba/OpenSandbox.git
cd OpenSandbox

2. Start the Sandbox Server

cd server
uv sync
cp example.config.toml ~/.sandbox.toml # Copy configuration file
uv run python -m src.main # Start the service

3. Create a Code Interpreter and Execute Commands

Install the Code Interpreter SDK

uv pip install opensandbox-code-interpreter

Create a sandbox and execute commands

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:v1.0.1",
        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 rich examples demonstrating sandbox usage in different scenarios. All example code is located in the examples/ directory.

🎯 Basic Examples

πŸ€– Coding Agent Integrations

  • claude-code - Run Claude Code inside OpenSandbox.
  • gemini-cli - Run Google Gemini CLI inside OpenSandbox.
  • codex-cli - Run OpenAI Codex CLI inside OpenSandbox.
  • iflow-cli - Run iFLow 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.

🌐 Browser and Desktop Environments

  • chrome - Headless Chromium with VNC and DevTools access for automation/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 refer to examples and the README files in each example directory.

Project Structure

Directory Description
sdks/ Multi-language SDKs (Python, Java/Kotlin, TypeScript/JavaScript)
specs/ OpenAPI specs and lifecycle specifications
server/ Python FastAPI sandbox lifecycle server
kubernetes/ Kubernetes deployment and examples
components/execd/ Sandbox execution daemon (commands and file operations)
components/ingress/ Sandbox traffic ingress proxy
components/egress/ Sandbox network egress control
sandboxes/ Runtime sandbox implementations
examples/ Integration examples and use cases
oseps/ OpenSandbox Enhancement Proposals
docs/ Architecture and design documentation
tests/ Cross-component E2E tests
scripts/ Development and maintenance scripts

For detailed architecture, see docs/architecture.md.

Documentation

License

This project is open source under the Apache 2.0 License.

Roadmap

SDK

  • Go SDK - Go client SDK for sandbox lifecycle management and command execution, file operations.

Server Runtime

  • OpenSandbox Kubernetes Runtime - High-performance sandbox scheduling implementation (see kubernetes/)
  • kubernetes-sigs/agent-sandbox Support - Integration with kubernetes-sigs/agent-sandbox (see OSEP-0002 and Example)
  • Declarative Network Isolation - Network egress control with allow/deny rules for specific domains (see OSEP-0001)
    • DNS-based egress control (Layer 1)
    • Network-based egress control (Layer 2)

Contact and Discussion

  • Issues: Submit bugs, feature requests, or design discussions through GitHub Issues

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A universal sandbox platform for AI application scenarios, providing multi-language SDKs, unified sandbox protocols, and sandbox runtimes for LLM-related capabilities.

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  • Python 46.3%
  • Go 30.0%
  • TypeScript 8.6%
  • Kotlin 8.5%
  • Java 3.3%
  • Shell 1.6%
  • Other 1.7%