pydantic / monty

Monty 
 A secure Python sandbox, written in Rust, for code written by AI. 
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Monty avoids the latency, complexity and cost of a container based sandbox for running LLM generated code.
It comes in two forms: **OSS Monty**, the MIT licensed Python 3.14 sandbox you install as a package, and
**Full Monty**, the commercial server that runs the same sandbox behind a WebSocket as a service.
A new OSS Monty sandbox takes under **1ms** from a running pool (Full Monty: about **2ms**)
compared to around **1500ms** for a sandbox service.
Filesystem, environment variables and network do not exist inside the sandbox: it reaches the host only through the
functions and mounts you pass in.
**Documentation: pydantic.dev/docs/monty**
## Install
 [code] 
The commercial Full Monty runs the same workers as a container
image.
## Example
The `code` string is what a model writes when asked how long a bar of chocolate could power a lightbulb:
 [code] 
`nutrition` ran on the host and the sandbox saw only its return value.
## Documentation
- Introduction with the latency measurements
- Comparison to alternatives: Docker, Pyodide, WASI,
 sandboxing services
- Getting started with Python,
 JavaScript or
 Rust
- Security model, resource
 limits,
 snapshots, the Python
 subset
- `docs/`: the source of the documentation site
Monty runs Code Mode in Pydantic
AI.
Community bindings: gomonty (Go) and
dart_monty (Dart / Flutter).
## Part of the Pydantic Stack
The Pydantic Stack is everything you need to ship production-grade AI agents:
- Pydantic AI - Type-safe
 agent framework
- Pydantic Logfire - AI-first,
 full-stack observability
- Logfire AI Gateway - Unified
 LLM proxy