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 manner using Docker containers. This project aims to provide an easy-to-use interface for setting up, managing, and executing code in a controlled Docker environment, simplifying the process of running code generated by LLMs.

Features

• Easy Setup: Quickly create sandbox environments with minimal configuration.
• Isolation: Run your code in isolated Docker containers to prevent interference with your host system.
• Flexibility: Support for multiple programming languages.
• Portability: Use predefined Docker images or custom Dockerfiles.
• Scalability: Support Kubernetes and remote Docker host.

Installation

Using Poetry

1. Ensure you have Poetry installed.
2. Add the package to your project:

poetry add llm-sandbox # or poetry add llm-sandbox[kubernetes] or poetry add llm-sandbox[podman] or poetry add llm-sandbox[docker]

Using pip

1. Ensure you have pip installed.
2. Install the package:

pip install llm-sandbox # or pip install llm-sandbox[kubernetes] or pip install llm-sandbox[podman] or pip install llm-sandbox[docker]

See CHANGELOG.md for more details.

Usage

Session Lifecycle

The SandboxSession class manages the lifecycle of the sandbox environment, including the creation and destruction of Docker containers. Here's a typical lifecycle:

1. Initialization: Create a SandboxSession object with the desired configuration.
2. Open Session: Call the open() method to build/pull the Docker image and start the Docker container.
3. Run Code: Use the run() method to execute code inside the sandbox. Currently, it supports Python, Java, JavaScript, C++, Go, and Ruby. See examples for more details.
4. Close Session: Call the close() method to stop and remove the Docker container. If the keep_template flag is set to True, the Docker image will not be removed, and the last container state will be committed to the image.

Basic Example

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)

Integration with AI Frameworks

Langchain Integration

from typing import Optional, List
from llm_sandbox import SandboxSession
from langchain import hub
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import AgentExecutor, create_tool_calling_agent


@tool
def run_code(lang: str, code: str, libraries: Optional[List] = None) -> str:
    """
    Run code in a sandboxed environment.
    :param lang: The language of the code.
    :param code: The code to run.
    :param libraries: The libraries to use, it is optional.
    :return: The output of the code.
    """
    with SandboxSession(lang=lang, verbose=False) as session:  # type: ignore[attr-defined]
        return session.run(code, libraries).text


if __name__ == "__main__":
    llm = ChatOpenAI(model="gpt-4o", temperature=0)
    prompt = hub.pull("hwchase17/openai-functions-agent")
    tools = [run_code]

    agent = create_tool_calling_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
    output = agent_executor.invoke(
        {
            "input": "Write python code to calculate Pi number by Monte Carlo method then run it."
        }
    )
    print(output)

    output = agent_executor.invoke(
        {
            "input": "Write python code to calculate the factorial of a number then run it."
        }
    )
    print(output)

    output = agent_executor.invoke(
        {"input": "Write python code to calculate the Fibonacci sequence then run it."}
    )
    print(output)

LlamaIndex Integration

from typing import Optional, List
from llm_sandbox import SandboxSession

from llama_index.llms.openai import OpenAI
from llama_index.core.tools import FunctionTool
from llama_index.core.agent import FunctionCallingAgentWorker


import nest_asyncio

nest_asyncio.apply()


def run_code(lang: str, code: str, libraries: Optional[List] = None) -> str:
    """
    Run code in a sandboxed environment.
    :param lang: The language of the code, must be one of ['python', 'java', 'javascript', 'cpp', 'go', 'ruby'].
    :param code: The code to run.
    :param libraries: The libraries to use, it is optional.
    :return: The output of the code.
    """
    with SandboxSession(lang=lang, verbose=False) as session:  # type: ignore[attr-defined]
        return session.run(code, libraries).text


if __name__ == "__main__":
    llm = OpenAI(model="gpt-4o", temperature=0)
    code_execution_tool = FunctionTool.from_defaults(fn=run_code)

    agent_worker = FunctionCallingAgentWorker.from_tools(
        [code_execution_tool],
        llm=llm,
        verbose=True,
        allow_parallel_tool_calls=False,
    )
    agent = agent_worker.as_agent()

    response = agent.chat(
        "Write python code to calculate Pi number by Monte Carlo method then run it."
    )
    print(response)

    response = agent.chat(
        "Write python code to calculate the factorial of a number then run it."
    )
    print(response)

    response = agent.chat(
        "Write python code to calculate the Fibonacci sequence then run it."
    )
    print(response)

    response = agent.chat("Calculate the sum of the first 10000 numbers.")
    print(response)

Contributing

We welcome contributions! Here are some areas we're looking to improve:

• Add support for more programming languages
• Enhance security scanning patterns
• Improve resource monitoring accuracy
• Add more AI framework integrations
• Implement container pooling for better performance
• Add support for distributed execution
• Enhance logging and monitoring features

License

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

Changelog

See CHANGELOG.md for a list of changes and improvements in each version.