Monty: A Secure Python Interpreter for AI, Built in Rust

If you're working with AI, you've probably felt the tension between needing the flexibility of Python and wanting the speed and safety of a lower-level language. Running untrusted Python code—like user-submitted models, plugins, or data processing scripts—in a production AI system is a classic recipe for anxiety. What if you could sandbox that code in something fast and secure, without sacrificing the Python experience?

Enter Monty. It’s a new project from Pydantic that describes itself as "a secure Python interpreter for AI written in minimalist Rust." In short, it’s a fresh take on embedding Python, built with modern needs in mind.

What It Does

Monty is a Python interpreter built as a library in Rust. It’s not a full CPython replacement, but rather a secure, constrained environment designed specifically for executing Python code in contexts where safety and performance are critical—think AI agents, plugin systems, or sandboxed data transformations. It leverages Rust's memory safety and the ruffle crate for parsing, creating a minimalist but powerful execution engine.

Why It's Cool

The cool factor here is in the specific choices the Pydantic team made. First, it’s written in Rust. This gives you the inherent security and performance benefits right out of the gate. The interpreter runs in a tightly controlled environment, making it a great fit for scenarios where you can't trust the code being executed.

Second, it’s built by Pydantic, a team known for their pragmatic and developer-friendly tools in the Python ecosystem. This isn't a theoretical research project; it's built to solve real problems that arise when building AI applications. The focus on a "minimalist" implementation suggests a design that avoids bloat and stays focused on the core task of secure execution.

Finally, it’s purpose-built for AI. As AI applications increasingly involve chains of execution, tool calling, and dynamically generated code, having a dedicated, safe space to run that logic is becoming essential. Monty looks like a direct answer to that emerging need.

How to Try It

The project is on GitHub, and because it's a Rust library, you can try it out by adding it to your Cargo.toml. The repository includes examples to get you started.

Head over to the GitHub repository to clone it and explore the examples:

https://github.com/pydantic/monty

Check the README for the latest setup instructions. Since it's early days, the API might evolve, but the examples should give you a clear idea of how to embed Monty in your Rust application to start running Python snippets safely.

Final Thoughts

Monty feels like a tool that's arriving right on time. As we push more AI systems into production, the ability to safely execute dynamic code isn't just a nice-to-have—it's a requirement for many advanced features. Using Rust as the foundation is a smart move that addresses both security and performance concerns in one go.

If you're building AI agents, a platform that allows user code, or any system where "eval" is a tempting but terrifying idea, Monty is definitely worth a look. It represents a practical step towards more robust and secure AI applications.

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