LLM Sandbox

Securely Execute LLM-Generated Code with Ease

LLM Sandbox is a lightweight and portable sandbox environment designed to run Large Language Model (LLM) generated code in a safe and isolated mode. It provides a secure execution environment for AI-generated code while offering flexibility in container backends and comprehensive language support, simplifying the process of running code generated by LLMs.

Documentation: https://vndee.github.io/llm-sandbox/

🚀 Key Features

🛡️ Security First

• Isolated Execution: Code runs in isolated containers with no access to host system
• Security Policies: Define custom security policies to control code execution
• Resource Limits: Set CPU, memory, and execution time limits
• Network Isolation: Control network access for sandboxed code

🏗️ Flexible Container Backends

• Docker: Most popular and widely supported option
• Kubernetes: Enterprise-grade orchestration for scalable deployments
• Podman: Rootless containers for enhanced security

🌐 Multi-Language Support

Execute code in multiple programming languages with automatic dependency management:

• Python - Full ecosystem support with pip packages
• JavaScript/Node.js - npm package installation
• Java - Maven and Gradle dependency management
• C++ - Compilation and execution
• Go - Module support and compilation

🔌 LLM Framework Integration

Seamlessly integrate with popular LLM frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI, and more.

📊 Advanced Features

• Artifact Extraction: Automatically capture plots and visualizations
• Library Management: Install dependencies on-the-fly
• File Operations: Copy files to/from sandbox environments
• Custom Images: Use your own container images

📦 Installation

Basic Installation

pip install llm-sandbox

With Specific Backend Support

# For Docker support (most common)
pip install 'llm-sandbox[docker]'

# For Kubernetes support
pip install 'llm-sandbox[k8s]'

# For Podman support
pip install 'llm-sandbox[podman]'

# All backends
pip install 'llm-sandbox[docker,k8s,podman]'

Development Installation

git clone https://github.com/vndee/llm-sandbox.git
cd llm-sandbox
pip install -e '.[dev]'

🏃‍♂️ Quick Start

Basic Usage

from llm_sandbox import SandboxSession

# Create and use a sandbox session
with SandboxSession(lang="python") as session:
    result = session.run("""
print("Hello from LLM Sandbox!")
print("I'm running in a secure container.")
    """)
    print(result.stdout)

Installing Libraries

from llm_sandbox import SandboxSession

with SandboxSession(lang="python") as session:
    result = session.run("""
import numpy as np

# Create an array
arr = np.array([1, 2, 3, 4, 5])
print(f"Array: {arr}")
print(f"Mean: {np.mean(arr)}")
    """, libraries=["numpy"])

    print(result.stdout)

Multi-Language Support

JavaScript

with SandboxSession(lang="javascript") as session:
    result = session.run("""
const greeting = "Hello from Node.js!";
console.log(greeting);

const axios = require('axios');
console.log("Axios loaded successfully!");
    """, libraries=["axios"])

Java

with SandboxSession(lang="java") as session:
    result = session.run("""
public class HelloWorld {
    public static void main(String[] args) {
        System.out.println("Hello from Java!");
    }
}
    """)

C++

with SandboxSession(lang="cpp") as session:
    result = session.run("""
#include <iostream>

int main() {
    std::cout << "Hello from C++!" << std::endl;
    return 0;
}
    """)

Go

with SandboxSession(lang="go") as session:
    result = session.run("""
package main
import "fmt"

func main() {
    fmt.Println("Hello from Go!")
}
    """)

Capturing Plots and Visualizations

with SandboxSession(lang="python") as session:
    result = session.run("""
import matplotlib.pyplot as plt
import numpy as np

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

plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.title("Sine Wave")
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.grid(True)
plt.savefig("sine_wave.png", dpi=150, bbox_inches="tight")
plt.show()
    """, libraries=["matplotlib", "numpy"])

    # Extract the generated plot
    artifacts = session.get_artifacts()
    print(f"Generated {len(artifacts)} artifacts")

🔧 Configuration

Basic Configuration

from llm_sandbox import SandboxSession

# Create a new sandbox session
with SandboxSession(image="python:3.9.19-bullseye", keep_template=True, lang="python") as session:
    result = session.run("print('Hello, World!')")
    print(result)

# With custom Dockerfile
with SandboxSession(dockerfile="Dockerfile", keep_template=True, lang="python") as session:
    result = session.run("print('Hello, World!')")
    print(result)

# Or default image
with SandboxSession(lang="python", keep_template=True) as session:
    result = session.run("print('Hello, World!')")
    print(result)

LLM Sandbox also supports copying files between the host and the sandbox:

from llm_sandbox import SandboxSession

with SandboxSession(lang="python", keep_template=True) as session:
    # Copy a file from the host to the sandbox
    session.copy_to_runtime("test.py", "/sandbox/test.py")

    # Run the copied Python code in the sandbox
    result = session.execute_command("python /sandbox/test.py")
    print(result)

    # Copy a file from the sandbox to the host
    session.copy_from_runtime("/sandbox/output.txt", "output.txt")

Custom runtime configs

from llm_sandbox import SandboxSession

pod_manifest = {
    "apiVersion": "v1",
    "kind": "Pod",
    "metadata": {
        "name": "test",
        "namespace": "test",
        "labels": {"app": "sandbox"},
    },
    "spec": {
        "containers": [
            {
                "name": "sandbox-container",
                "image": "test",
                "tty": True,
                "volumeMounts": {
                    "name": "tmp",
                    "mountPath": "/tmp",
                },
            }
        ],
        "volumes": [{"name": "tmp", "emptyDir": {"sizeLimit": "5Gi"}}],
    },
}
with SandboxSession(
    backend="kubernetes",
    image="python:3.9.19-bullseye",
    dockerfile=None,
    lang="python",
    keep_template=False,
    verbose=False,
    pod_manifest=pod_manifest,
) as session:
    result = session.run("print('Hello, World!')")
    print(result)

Remote Docker Host

import docker
from llm_sandbox import SandboxSession

tls_config = docker.tls.TLSConfig(
    client_cert=("path/to/cert.pem", "path/to/key.pem"),
    ca_cert="path/to/ca.pem",
    verify=True
)
docker_client = docker.DockerClient(base_url="tcp://<your_host>:<port>", tls=tls_config)

with SandboxSession(
    client=docker_client,
    image="python:3.9.19-bullseye",
    keep_template=True,
    lang="python",
) as session:
    result = session.run("print('Hello, World!')")
    print(result)

Kubernetes Support

from kubernetes import client, config
from llm_sandbox import SandboxSession

# Use local kubeconfig
config.load_kube_config()
k8s_client = client.CoreV1Api()

with SandboxSession(
    client=k8s_client,
    backend="kubernetes",
    image="python:3.9.19-bullseye",
    lang="python",
    pod_manifest=pod_manifest, # None by default
) as session:
    result = session.run("print('Hello from Kubernetes!')")
    print(result)

Podman Support

from llm_sandbox import SandboxSession

with SandboxSession(
    backend="podman",
    lang="python",
    image="python:3.9.19-bullseye"
) as session:
    result = session.run("print('Hello from Podman!')")
    print(result)

🤖 LLM Framework Integration

LangChain Tool

from langchain.tools import BaseTool
from llm_sandbox import SandboxSession

class PythonSandboxTool(BaseTool):
    name = "python_sandbox"
    description = "Execute Python code in a secure sandbox"

    def _run(self, code: str) -> str:
        with SandboxSession(lang="python") as session:
            result = session.run(code)
            return result.stdout if result.exit_code == 0 else result.stderr

Use with OpenAI Functions

import openai
from llm_sandbox import SandboxSession

def execute_code(code: str, language: str = "python") -> str:
    """Execute code in a secure sandbox environment."""
    with SandboxSession(lang=language) as session:
        result = session.run(code)
        return result.stdout if result.exit_code == 0 else result.stderr

# Register as OpenAI function
functions = [
    {
        "name": "execute_code",
        "description": "Execute code in a secure sandbox",
        "parameters": {
            "type": "object",
            "properties": {
                "code": {"type": "string", "description": "Code to execute"},
                "language": {"type": "string", "enum": ["python", "javascript", "java", "cpp", "go"]}
            },
            "required": ["code"]
        }
    }
]

🏗️ Architecture

graph LR
    A[LLM Client] --> B[LLM Sandbox]
    B --> C[Container Backend]

    A1[OpenAI] --> A
    A2[Anthropic] --> A
    A3[Local LLMs] --> A
    A4[LangChain] --> A
    A5[LangGraph] --> A
    A6[LlamaIndex] --> A

    C --> C1[Docker]
    C --> C2[Kubernetes]
    C --> C3[Podman]

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style A1 fill:#fff3e0
    style A2 fill:#fff3e0
    style A3 fill:#fff3e0
    style A4 fill:#fff3e0
    style A5 fill:#fff3e0
    style A6 fill:#fff3e0
    style C1 fill:#e0f2f1
    style C2 fill:#e0f2f1
    style C3 fill:#e0f2f1

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📚 Documentation

• Full Documentation - Complete documentation
• Getting Started - Installation and basic usage
• Configuration - Detailed configuration options
• Security - Security policies and best practices
• Backends - Container backend details
• Languages - Supported programming languages
• Integrations - LLM framework integrations
• API Reference - Complete API documentation
• Examples - Real-world usage examples

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/vndee/llm-sandbox.git
cd llm-sandbox

# Install in development mode
make install

# Run pre-commit hooks
uv run pre-commit run -a

# Run tests
make test

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Star History

If you find LLM Sandbox useful, please consider giving it a star on GitHub!

📞 Support & Community

• GitHub Issues: Report bugs or request features
• GitHub Discussions: Join the community
• PyPI: pypi.org/project/llm-sandbox
• Documentation: vndee.github.io/llm-sandbox

Contributors