Minimal, Fast, and Secure: Integrating the Monty Python Interpreter


Minimal, Fast, and Secure: Integrating the Monty Python Interpreter

pydantic/monty

2026-02-11

That’s where Monty comes in. Developed by the team at Pydantic, it’s a specialized Python interpreter designed to be a "sandbox" for AI-generated code.

In the traditional workflow, if you want an AI to solve a math problem or analyze data, it writes Python code, and you run it in a standard environment. The risks? Infinite loops, high memory usage, or unauthorized file access.

Monty solves this by being

Written in Rust
This provides memory safety and high performance at the core level.

Highly Constrained
It doesn't allow the AI to reach into your host machine's files or network unless you explicitly allow it.

Wasm-Ready
It’s designed to run in WebAssembly, making it perfect for browser-based AI tools.

Safety First
You can execute untrusted, AI-generated snippets without worrying about a rm -rf / command ruining your day.

Determinism
You get consistent results regardless of the underlying OS.

Low Overhead
Because it’s a "minimal" interpreter, it starts up instantly—perfect for serverless functions or agentic workflows.

Since Monty is still in its early, experimental stages, you'll want to ensure you have the Rust toolchain installed.

You can add it to your project via pip (if using the Python bindings)

pip install monty-python

Here is a simple example of how you might use Monty to safely evaluate code that an AI has written.

from monty import MontyInterpreter

# Imagine this string was generated by an LLM
ai_generated_code = """
def calculate_growth(initial, rate, years):
    return initial * (1 + rate) ** years

result = calculate_growth(1000, 0.05, 10)
"""

# Initialize the minimal interpreter
interpreter = MontyInterpreter()

# Execute the code within the sandbox
state = interpreter.run(ai_generated_code)

# Access the variables from the sandbox environment
final_value = state.get("result")
print(f"The AI calculated: {final_value}")

When you use Python's built-in exec(), the code has access to your entire environment. With Monty, the code lives in its own little bubble. If the AI tries to import os to look at your environment variables, Monty simply won't let it unless you've configured that module.

It's important to note that Monty is experimental. It doesn't support the entire Python standard library (yet!), so it's best used for

Mathematical logic.

Data transformation.

Simple algorithmic tasks.


pydantic/monty