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Pydantic's Monty: Rust-Based Sandboxed Python in WebAssembly

WebAssembly Rust Python Sandboxing Pydantic AI LLMs
February 06, 2026
Source: Simon Willison
Viqus Verdict Logo Viqus Verdict Logo 8
Iteration Amplified
Media Hype 6/10
Real Impact 8/10

Article Summary

Simon Willison has created 'Monty', a sandboxed Python environment built using Rust and WebAssembly, designed to execute LLM-generated code with exceptional speed and security. Monty leverages Rust’s safety features while offering a subset of Python, suitable for coding agents needing to evaluate and refine their responses. The key benefit is startup times measured in microseconds, drastically reducing latency compared to container-based sandboxes. Monty offers strict limits on memory, CPU time, and network access, significantly enhancing security. Crucially, it’s designed for iteration—a coding agent can receive an error message and retry with a modified approach. The project is offered as both a standalone WASM module and a Pyodide-compatible wheel file, providing flexibility across web browsers and Pyodide environments. Willison demonstrates how easily it's possible to compile Rust or C code into WebAssembly and run it in a browser or Pyodide, creating a rapidly evolving toolset for AI-assisted programming. The project's simplicity and accessibility are central to its appeal, furthering the exploration of sandboxed AI environments.

Key Points

  • Monty is a Rust-based Python subset in WebAssembly designed for fast, sandboxed LLM execution.
  • It achieves sub-microsecond startup times, drastically reducing latency for coding agents.
  • Monty offers strict limits on memory, CPU, and network access, bolstering security and enabling iterative development.

Why It Matters

This news is significant for the broader AI research and development community. The rapid prototyping of LLM-based coding agents requires efficient and secure execution environments. Monty’s performance and flexibility directly address this need, demonstrating a viable path forward for building robust and responsive AI assistants. The success of this project highlights the growing importance of WebAssembly and Rust in the development of AI tools, and the potential for iterative refinement through sandboxed execution. This advances the possibilities for AI-assisted programming, especially for tasks requiring fast feedback loops and safe experimentation.

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