Enhanced Sandbox SDK

Python sandbox execution environment with comprehensive MCP server support, featuring enhanced artifact management, interactive REPL, and Manim animation capabilities.

🎬 Demo: Manim Animation in Action

See the Sandbox MCP server creating beautiful mathematical animations with Manim:

Alternative formats: MP4 Video | GIF Animation

Example: 3D mathematical animation generated automatically by the sandbox

🚀 Quick Start

# Clone the repository
git clone https://github.com/scooter-lacroix/sandbox-mcp.git
cd sandbox-mcp

# Install with uv (recommended)
uv venv && uv pip install -e .

# Run the MCP server
uv run sandbox-server-stdio

✨ Features

🔧 Enhanced Python Execution

• Code Validation: Automatic input validation and formatting
• Virtual Environment: Auto-detects and activates .venv
• Persistent Context: Variables persist across executions
• Enhanced Error Handling: Detailed diagnostics with colored output
• Interactive REPL: Real-time Python shell with tab completion

🎨 Intelligent Artifact Management

• Automatic Capture: Matplotlib plots and PIL images
• Categorization: Smart file type detection and organization
• Multiple Formats: JSON, CSV, and structured output
• Recursive Scanning: Deep directory traversal
• Smart Cleanup: Configurable cleanup by type or age

🎬 Manim Animation Support

• Pre-compiled Examples: One-click animation execution
• Quality Control: Multiple rendering presets
• Video Generation: Auto-saves MP4 animations
• Example Library: Built-in templates and tutorials
• Environment Verification: Automatic dependency checking

🌐 Web Application Hosting

• Flask & Streamlit: Launch web apps with auto port detection
• Process Management: Track and manage running servers
• URL Generation: Returns accessible endpoints

🔒 Security & Safety

• Command Filtering: Blocks dangerous operations
• Sandboxed Execution: Isolated environment
• Timeout Control: Configurable execution limits
• Resource Monitoring: Memory and CPU usage tracking

🔌 MCP Integration

• Dual Transport: HTTP and stdio support
• LM Studio Ready: Drop-in AI model integration
• FastMCP Powered: Modern MCP implementation
• Comprehensive Tools: 12+ available MCP tools

📦 Installation

Prerequisites

• Python 3.9+
• uv (recommended) or pip

Method 1: Direct Git Installation (Recommended)

For immediate use with AI applications like LM Studio, Claude Desktop, or VS Code:

uvx git+https://github.com/scooter-lacroix/sandbox-mcp.git

This automatically installs and runs the MCP server without manual setup.

Method 2: Local Development Installation

For development, customization, or contributing:

Using uv (Recommended)

git clone https://github.com/scooter-lacroix/sandbox-mcp.git
cd sandbox-mcp
uv venv
uv pip install -e .

Using pip

git clone https://github.com/scooter-lacroix/sandbox-mcp.git
cd sandbox-mcp
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate  # Windows
pip install -e .

Method 3: Package Installation

Install from package manager (when available):

# Using uv
uvx sandbox-mcp

# Using pip
pip install sandbox-mcp

🖥️ Usage

Command Line Interface

# Start HTTP server (web integration)
sandbox-server

# Start stdio server (LM Studio integration)
sandbox-server-stdio

MCP Integration

The Sandbox MCP server supports multiple integration methods:

Method 1: Direct Git Integration (Recommended)

For LM Studio, Claude Desktop, VS Code, and other MCP-compatible applications:

{
  "mcpServers": {
    "sandbox": {
      "command": "uvx",
      "args": ["git+https://github.com/scooter-lacroix/sandbox-mcp.git"],
      "env": {},
      "start_on_launch": true
    }
  }
}

Method 2: Local Installation

For locally installed versions:

{
  "mcpServers": {
    "sandbox": {
      "command": "sandbox-server-stdio",
      "args": [],
      "env": {},
      "start_on_launch": true
    }
  }
}

Method 3: HTTP Server Mode

For web-based integrations:

# Start HTTP server
python -m sandbox.mcp_sandbox_server --port 8765

Then configure your application:

{
  "mcpServers": {
    "sandbox": {
      "transport": "http",
      "url": "http://localhost:8765/mcp",
      "headers": {
        "Authorization": "Bearer your-token-here"
      }
    }
  }
}

Application-Specific Configurations

VS Code/Cursor/Windsurf (using MCP extension):

{
  "mcp.servers": {
    "sandbox": {
      "command": "sandbox-server-stdio",
      "args": [],
      "env": {},
      "transport": "stdio"
    }
  }
}

Jan AI:

{
  "mcp_servers": {
    "sandbox": {
      "command": "sandbox-server-stdio",
      "args": [],
      "env": {}
    }
  }
}

OpenHands:

{
  "mcp": {
    "servers": {
      "sandbox": {
        "command": "sandbox-server-stdio",
        "args": [],
        "env": {}
      }
    }
  }
}

Available MCP Tools

💡 Examples

Enhanced SDK Usage

Local Python Execution

import asyncio
from sandbox import PythonSandbox

async def local_example():
    async with PythonSandbox.create_local(name="my-sandbox") as sandbox:
        # Execute Python code
        result = await sandbox.run("print('Hello from local sandbox!')")
        print(await result.output())
        
        # Execute code with artifacts
        plot_code = """
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(8, 6))
plt.plot(x, y)
plt.title('Sine Wave')
plt.show()  # Automatically captured as artifact
"""
        result = await sandbox.run(plot_code)
        print(f"Artifacts created: {result.artifacts}")
        
        # Execute shell commands
        cmd_result = await sandbox.command.run("ls", ["-la"])
        print(await cmd_result.output())

asyncio.run(local_example())

Remote Python Execution (with microsandbox)

import asyncio
from sandbox import PythonSandbox

async def remote_example():
    async with PythonSandbox.create_remote(
        server_url="http://127.0.0.1:5555",
        api_key="your-api-key",
        name="remote-sandbox"
    ) as sandbox:
        # Execute Python code in secure microVM
        result = await sandbox.run("print('Hello from microVM!')")
        print(await result.output())
        
        # Get sandbox metrics
        metrics = await sandbox.metrics.all()
        print(f"CPU usage: {metrics.get('cpu_usage', 0)}%")
        print(f"Memory usage: {metrics.get('memory_usage', 0)} MB")

asyncio.run(remote_example())

Node.js Execution

import asyncio
from sandbox import NodeSandbox

async def node_example():
    async with NodeSandbox.create(
        server_url="http://127.0.0.1:5555",
        api_key="your-api-key",
        name="node-sandbox"
    ) as sandbox:
        # Execute JavaScript code
        js_code = """
console.log('Hello from Node.js!');
const sum = [1, 2, 3, 4, 5].reduce((a, b) => a + b, 0);
console.log(`Sum: ${sum}`);
"""
        result = await sandbox.run(js_code)
        print(await result.output())

asyncio.run(node_example())

Builder Pattern Configuration

import asyncio
from sandbox import LocalSandbox, SandboxOptions

async def builder_example():
    config = (SandboxOptions.builder()
              .name("configured-sandbox")
              .memory(1024)
              .cpus(2.0)
              .timeout(300.0)
              .env("DEBUG", "true")
              .build())
    
    async with LocalSandbox.create(**config.__dict__) as sandbox:
        result = await sandbox.run("import os; print(os.environ.get('DEBUG'))")
        print(await result.output())  # Should print: true

asyncio.run(builder_example())

MCP Server Examples

Basic Python Execution

# Execute simple code
result = execute(code="print('Hello, World!')")

Matplotlib Artifact Generation

code = """
import matplotlib.pyplot as plt
import numpy as np

# Generate plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(8, 6))
plt.plot(x, y, 'b-', linewidth=2)
plt.title('Sine Wave')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.show()  # Automatically captured as artifact
"""

result = execute(code)
# Returns JSON with base64-encoded PNG

Flask Web Application

flask_code = """
from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/')
def home():
    return '<h1>Sandbox Flask App</h1>'

@app.route('/api/status')
def status():
    return jsonify({"status": "running", "server": "sandbox"})
"""

result = start_web_app(flask_code, "flask")
# Returns URL where app is accessible

Shell Command Execution

# Install packages via shell
result = shell_execute("uv pip install matplotlib")

# Check environment
result = shell_execute("which python")

# List directory contents
result = shell_execute("ls -la")

# Custom working directory and timeout
result = shell_execute(
    "find . -name '*.py' | head -10", 
    working_directory="/path/to/search",
    timeout=60
)

Manim Animation Creation

# Simple circle animation
manim_code = """
from manim import *

class SimpleCircle(Scene):
    def construct(self):
        circle = Circle()
        circle.set_fill(PINK, opacity=0.5)
        self.play(Create(circle))
        self.wait(1)
"""

result = create_manim_animation(manim_code, quality="medium_quality")
# Returns JSON with video path and metadata

# Mathematical graph visualization
math_animation = """
from manim import *

class GraphPlot(Scene):
    def construct(self):
        axes = Axes(
            x_range=[-3, 3, 1],
            y_range=[-3, 3, 1],
            x_length=6,
            y_length=6
        )
        axes.add_coordinates()
        
        graph = axes.plot(lambda x: x**2, color=BLUE)
        graph_label = axes.get_graph_label(graph, label="f(x) = x^2")
        
        self.play(Create(axes))
        self.play(Create(graph))
        self.play(Write(graph_label))
        self.wait(1)
"""

result = create_manim_animation(math_animation, quality="high_quality")

# List all animations
animations = list_manim_animations()

# Get example code snippets
examples = get_manim_examples()

Error Handling

# Import error with detailed diagnostics
result = execute(code="import nonexistent_module")
# Returns structured error with sys.path info

# Security-blocked shell command
result = shell_execute("rm -rf /")
# Returns security error with blocked pattern info

🏗️ Architecture

Project Structure

sandbox-mcp/
├── src/
│   └── sandbox/                   # Main package
│       ├── __init__.py           # Package initialization
│       ├── mcp_sandbox_server.py # HTTP MCP server
│       ├── mcp_sandbox_server_stdio.py # stdio MCP server
│       ├── server/               # Server modules
│       │   ├── __init__.py
│       │   └── main.py
│       └── utils/                # Utility modules
│           ├── __init__.py
│           └── helpers.py
├── tests/
│   ├── test_integration.py       # Main test suite
│   └── test_simple_integration.py
├── pyproject.toml                # Package configuration
├── README.md                     # This file
├── .gitignore
└── uv.lock                       # Dependency lock file

Core Components

ExecutionContext

Manages the execution environment:

• Project Root Detection: Dynamic path resolution
• Virtual Environment: Auto-detection and activation
• sys.path Management: Intelligent path handling
• Artifact Management: Temporary directory lifecycle
• Global State: Persistent execution context

Monkey Patching System

Non-intrusive artifact capture:

• matplotlib.pyplot.show(): Intercepts and saves plots
• PIL.Image.show(): Captures image displays
• Conditional Patching: Only applies if libraries available
• Original Functionality: Preserved through wrapper functions

MCP Integration

FastMCP-powered server with:

• Dual Transport: HTTP and stdio protocols
• Tool Registry: 7 available MCP tools
• Streaming Support: Ready for real-time interaction
• Error Handling: Structured error responses

📚 Documentation

For comprehensive usage information, troubleshooting guides, and advanced features:

• FAQ and Limitations - Common issues and sandbox restrictions
• Enhanced Features Guide - Advanced capabilities and examples
• API Reference - Complete API documentation

🧪 Testing

Run the test suite to verify installation:

uv run pytest tests/ -v

Test categories include:

• Package import and sys.path tests
• Error handling and ImportError reporting
• Artifact capture (matplotlib/PIL)
• Web application launching
• Virtual environment detection

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Run tests: uv run pytest
4. Submit a pull request

For development setup:

uv venv && uv pip install -e ".[dev]"

License

Apache License

Attribution

This project includes minor inspiration from:

• Microsandbox - Referenced for secure microVM isolation concepts

The majority of the functionality in this project is original implementation focused on MCP server integration and enhanced Python execution environments.

Changelog

v0.3.0 (Enhanced SDK Release)

• 🚀 Enhanced SDK: Complete integration with microsandbox functionality
• 🔄 Unified API: Single interface for both local and remote execution
• 🛡️ MicroVM Support: Secure remote execution via microsandbox server
• 🌐 Multi-Language: Python and Node.js execution environments
• 🏗️ Builder Pattern: Fluent configuration API with SandboxOptions
• 📊 Metrics & Monitoring: Real-time resource usage tracking
• ⚡ Async/Await: Modern Python async support throughout
• 🔒 Enhanced Security: Improved command filtering and validation
• 📦 Artifact Management: Comprehensive file artifact handling
• 🎯 Command Execution: Safe shell command execution with timeouts
• 🔧 Configuration: Flexible sandbox configuration options
• 📝 Documentation: Comprehensive examples and usage guides

v0.2.0

• Manim Integration: Complete mathematical animation support
• 4 New MCP Tools: create_manim_animation, list_manim_animations, cleanup_manim_animation, get_manim_examples
• Quality Control: Multiple animation quality presets
• Video Artifacts: Auto-saves MP4 animations to artifacts directory
• Example Library: Built-in Manim code examples
• Virtual Environment Manim: Uses venv-installed Manim executable

v0.1.0

• Initial enhanced package structure
• Dynamic project root detection
• Robust virtual environment integration
• Enhanced error handling with detailed tracebacks
• Artifact management with matplotlib/PIL support
• Web application launching (Flask/Streamlit)
• Comprehensive test suite
• MCP server integration (HTTP and stdio)
• CLI entry points
• LM Studio compatibility