一个用 Rust 编写的极简安全的 Python 解释器,供 AI 使用
AI 翻译
A minimal, secure Python interpreter written in Rust for use by AI
开发工具Rust · PythonGitHub @pydantic自行安装运营者未确认
试用过了吗? 点一下就好,无需登录。
正在加载完整 README。 以下为已保存的摘录。
A sandboxed Python interpreter, written in Rust, for code written by AI.
Hack Monty Round 3 is live - the last round before Monty V1. See pydantic.dev/monty for details.查看完整 README ↗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
uv add pydantic-monty # Python
npm install @pydantic/monty # JavaScript / TypeScript
cargo add monty # Rust
The commercial Full Monty runs the same workers as a container image.
The code string is what a model writes when asked how long a bar of chocolate could power a lightbulb:
from pydantic_monty import Monty
code = """
kcal = nutrition('chocolate bar')['kcal']
hours = kcal * 4184 / (bulb_watts * 3600)
print(f'a chocolate bar powers a {bulb_watts} W bulb for {hours:.1f} hours')
"""
with Monty() as pool:
with pool.checkout() as session:
session.feed_run(
code,
inputs={'bulb_watts': 10},
external_lookup={'nutrition': lambda food: {'kcal': 230}},
)
#> a chocolate bar powers a 10 W bulb for 26.7 hours
nutrition ran on the host and the sandbox saw only its return value.
docs/: the source of the documentation siteMonty runs Code Mode in Pydantic AI. Community bindings: gomonty (Go) and dart_monty (Dart / Flutter).
The Pydantic Stack is everything you need to ship production-grade AI agents: