---
id: llm-sandbox
version: "0.3.44"
license: unclear
license_treatment: unclear
maintenance: active
---

# llm-sandbox — LLM Sandbox is a lightweight and portable sandbox environment designed to run large language model (LLM) generated code in a safe and isolated mode.

License: unclear · Maintenance: active · Downloads: 661.6K/mo

## What it is and what it does

LLM Sandbox is a lightweight execution environment that runs code generated by large language models inside isolated containers, protecting the host system from untrusted or malicious code. It abstracts away container orchestration details and provides a unified Python API for spawning sandboxed sessions across multiple backends (Docker, Kubernetes, Podman) and programming languages (Python, JavaScript, Java, C++, Go, R).

The package is designed to integrate with LLM agent frameworks and Claude Desktop via the Model Context Protocol. It handles dependency installation, resource limits (CPU, memory, execution time), network isolation, and artifact extraction (plots, visualizations). Interactive sessions maintain state across multiple code runs, simulating notebook-style workflows. The single runtime dependency is pydantic, and it supports Python 3.10 through 3.13.

Use it for:

- Run code snippets generated by Claude, ChatGPT, or other LLMs without exposing your system to untrusted execution.
- Execute multi-language code (Python, JavaScript, Java, C++, Go, R) in a unified interface for polyglot AI agent workflows.
- Extract and save plots and visualizations generated by sandboxed Python or R code automatically.
- Deploy scalable code execution for AI agent frameworks (LangChain, CrewAI, Pydantic AI, etc.) with resource limits and security policies.
- Maintain state across multiple code executions in interactive Python sessions without manual serialization.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Executes code generated by large language models in isolated container environments (Docker, Kubernetes, or Podman) with configurable security policies, resource limits, and support for Python, JavaScript, Java, C++, Go, and R.

Yes, with conditions. Install if you need to safely execute LLM-generated code and have a container backend (Docker, Kubernetes, or Podman) available. The package is actively maintained, has low install friction, and integrates well with popular LLM frameworks. However, verify the unclear license terms before use in proprietary contexts, and ensure your deployment environment supports containerization.

## Install

pip install llm-sandbox
uv add llm-sandbox
poetry add llm-sandbox

## Installing llm-sandbox

Before you install: Low friction installation with a single runtime dependency (pydantic). Active maintenance with recent releases; last commit 2026-08-14 and 1104 repository stars indicate ongoing development.

License in practice: License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license terms in the repository before adopting in proprietary or restricted-use contexts.

Quickstart:

pip install llm-sandbox

from llm_sandbox import SandboxSession

with SandboxSession(lang="python") as session:
    result = session.run('print("Hello from sandbox")')
    print(result.stdout)

Requires a container backend (Docker, Kubernetes, or Podman) installed and running on the host system; Python 3.10 or later.

Verify before relying:

- Whether the unclear license permits commercial or proprietary use without restrictions.
- Performance overhead and startup time for container initialization in production workflows.
- Security audit status and third-party validation of the isolation guarantees claimed.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 661.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm code execution sandbox, secure container code runner, ai-generated code isolation, multi-language code sandbox, docker-based code execution, llm-integration, code-execution, containerization

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