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Code execution · MCP server

LLM Sandbox: MCP server, setup and security

LLM Sandbox is an open-source MCP server published on GitHub that lets an AI agent securely execute LLM-generated code in isolated containers across seven languages. Its repository has 1,137 stars and its last commit is from October 24, 2026 and it is licensed under MIT.

Score
76/100
Adoption (stars) 31/40
Maintenance 30/30
Framework (licence, registry, official) 15/30
Actively maintainedSystem accessTop 10% by stars

What LLM Sandbox does

According to its repository: “Securely run LLM-generated code in isolated containers across 7 languages and 3 container backends.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. LLM Sandbox is listed under Code execution, a category of 32 servers in the directory, where it ranks #3 by stars.

Topics declared by the repository: code-generation, code-interpreter, large-language-models, llm-sandbox.

Installing and connecting LLM Sandbox

Distribution modes declared in the official MCP registry:

  • PyPI package (Python, started with uvx or pip)

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: Python.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

Points found for LLM Sandbox:

  • System access: the Code execution category gives the agent control over files, a terminal, code or a browser. A booby-trapped instruction in content the agent reads (prompt injection) can then turn into an action.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on June 27, 2024, the vndee/llm-sandbox repository has 1,137 stars and 107 forks, with 19 open issues. It has more stars than 92% of the servers in the directory. It received changes this month. Version published in the registry: 0.3.43 (August 3, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

Container useContainer use : 4.1K4.1KMATLAB MCP ServerMATLAB MCP Server : 1.7K1.7KLLM SandboxLLM Sandbox : 1.1K1.1KTidewave PhoenixTidewave Phoenix : 858858Tidewave RailsTidewave Rails : 476476SuperserveSuperserve : 466466IPhone UseIPhone Use : 345345OrcaReplayOrcaReplay : 281281
GitHub stars of the most followed servers in the category (log scale); LLM Sandbox in orange. Checked October 25, 2026.

Verdict

LLM Sandbox is one of the most solid servers in its category: followed, maintained and well framed.

Strengths

  • Active project: recent commit
  • Widely followed: 1.1K stars
  • Listed in the official MCP registry
  • Permissive licence (MIT)

Avoid if…

  • You cannot isolate the agent (sandbox, container)

Frequently asked questions

How do I install LLM Sandbox?

LLM Sandbox is distributed as PyPI package (Python, started with uvx or pip). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of vndee/llm-sandbox.

Is LLM Sandbox free?

The code is published under the MIT licence and can be used free of charge.

Is LLM Sandbox safe?

Points to watch: system access. Give it minimum rights and approve sensitive actions.

Is it an official server?

Nothing says so: it is a project published by vndee on GitHub. Check who maintains it before giving it access.

What are the alternatives to LLM Sandbox?

In the same category: Container use, Tidewave Phoenix, Superserve, and OrcaReplay.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score