Pydantic Built a 175-Point Python Interpreter That Ditches Classes for Speed
Pydantic just dropped a Python interpreter that throws out half the language on purpose. And the AI community is absolutely here for it.
Monty isn't your typical Python interpreter. Built in Rust by the team behind Pydantic (you know, the data validation library with 4k GitHub stars), it's designed specifically for AI agents that need to execute untrusted code fast and safely. No classes. No bloated standard library. No "general faff" as they put it.
<> "LLMs can adapt by rewriting code upon errors" - HN user discussing Monty's missing class support/>
This is brilliant pragmatism. Why give an AI agent access to Python's entire kitchen sink when it just needs to crunch numbers and manipulate data? Monty gives LLMs exactly what they need: loops, functions, basic data types, and nothing else.
The Real Story: From Validation to Virtualization
Here's what caught everyone off guard - Pydantic wasn't supposed to build interpreters. They're the validation company. The type hints people. Simon Willison himself expressed surprise at the project's origin, noting he was "surprised by Pydantic origin."
But dig deeper and it makes perfect sense. Pydantic has been pivoting hard into AI tooling:
- pydantic-ai: GenAI Agent Framework
- logfire: observability platform for LLMs
- pydantic-core: already built in Rust
Monty is the natural evolution. They already had the Rust expertise from pydantic-core. They already understood the pain points of running AI-generated code in production. Why not build the perfect sandbox?
Speed Demons and Security Freaks
The technical implications are chef's kiss perfect for 2024's AI landscape. Rust's memory safety gives you a smaller attack surface than CPython. WASM compatibility means you can run this thing in browsers - Simon Willison already documented a WASM build in February.
But here's the kicker: LLMs don't actually need Python's flexibility. As HN users preciousoo and kodablah argued, AIs benefit more from simple specs than full language wiggle room. When your "developer" is a large language model, you want constraints, not infinite possibilities.
Missing classes? Not a problem. LLMs just rewrite their code when they hit errors. It's like having a developer who never gets frustrated at compiler messages.
The Hacker News Verdict: 175 Points Don't Lie
The community response has been overwhelmingly positive. 175 points and 77 comments within hours of dmpetrov posting it to Hacker News. That's not just interest - that's validation of a real pain point.
Developers are already comparing it to alternatives like mental32/monty and eryx-org/eryx, but Monty has something those don't: the backing of a company that actually ships production AI tools.
My Hot Take: This Changes Agent Architecture
Monty isn't just another Python interpreter. It's a signal that the industry is maturing beyond "throw everything at the wall" approaches to AI safety.
Instead of sandboxing full Python environments (expensive, complex, slow), we're moving toward purpose-built execution environments. Monty proves you can give AI agents real computational power without the security nightmares.
The best part? It's open source. Pydantic could have kept this internal for their premium tools, but they're betting on ecosystem growth over proprietary moats.
Smart money says we'll see Monty integration across Pydantic's AI stack within months. LogFire observability + PydanticAI agents + Monty execution = a compelling end-to-end platform for production AI systems.
Sometimes the best innovation comes from throwing away everything you don't need.
