Monty: A minimal, secure Python interpreter written in Rust for use by AI
github.com82 points by dmpetrov 4 hours ago
82 points by dmpetrov 4 hours ago
I got a WebAssembly build of this working and fired up a web playground for trying it out: https://simonw.github.io/research/monty-wasm-pyodide/demo.ht...
It doesn't have class support yet!
But it doesn't matter, because LLMs that try to use a class will get an error message and rewrite their code to not use classes instead.
Notes on how I got the WASM build working here: https://simonwillison.net/2026/Feb/6/pydantic-monty/
This is a really interesting take on the sandboxing problem. This reminds me of an experiment I worked on a while back (https://github.com/imfing/jsrun), which embedded V8 into Python to allow running JavaScript with tightly controlled access to the host environment. Similar in goal to run untrusted code in Python.
I’m especially curious about where the Pydantic team wants to take Monty. The minimal-interpreter approach feels like a good starting point for AI workloads, but the long tail of Python semantics is brutal. There is a trade-off between keeping the surface area small (for security and predictability) and providing sufficient language capabilities to handle non-trivial snippets that LLMs generate to do complex tasks
Can't be sure where this might end, but the primary goal is to enable codemode/programmatic tool calling, using the external function call mechanism for anything more complicated.
I think in the near term we'll add support for classes, dataclasses, datetime, json. I think that should be enough for many use cases.
there’s no way around VMs for secure, untrusted workloads. everything else, like Monty has too many tradeoffs that makes it non-viable for any real workloads
disclaimer: i work at E2B, opinions my own
As discussed on twitter, v8 shows that's not true.
But to be clear, we're not even targeting the same "computer use" use case I think e2b, daytona, cloudflare, modal, fly.io, deno, google, aws are going after - we're aiming to support programmatic tool calling with minimal latency and complexity - it's a fundamentally different offering.
Chill, e2b has its use case, at least for now.
This feels like the time I was a Mercurial user before I moved to Git.
Everyone was using git for reasons to me that seemed bandwagon-y, when Mercurial just had such a better UX and mental model to me.
Now, everyone is writing agent `exec`s in Python, when I think TypeScript/JS is far better suited for the job (it was always fast + secure, not to mention more reliable and information dense b/c of typing).
But I think I'm gonna lose this one too.
A big benefit of letting agents run code is they can process data without bloating their context.
LLMs are really good at writing python for data processing. I would suspect its due to Python having a really good ecosystem around this niche
And the type safety/security issues can hopefully be mitigated by ty and pyodide (already used by cf’s python workers)
(Pydantic AI lead here) That’s exactly what we built this for: we’re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 which will use Monty by default, with abstractions to use other runtimes / sandboxes.
Monty’s overhead is so low that, assuming we get the security / restrictions right (Samuel can comment on this more), you could always have it enabled on your agents with basically no downsides, which can’t be said for many other code execution sandboxes which are often over-kill for the code mode use case anyway.
For those not familiar with the concept, the idea is that in “traditional” LLM tool calling, the entire (MCP) tool result is sent back to the LLM, even if it just needs a few fields, or is going to pass the return value into another tool without needing to see the intermediate value. Every step that depends on results from an earlier step also requires a new LLM turn, limiting parallelism and adding a lot of overhead.
With code mode, the LLM can chain tool calls, pull out specific fields, and run entire algorithms using tools with only the necessary parts of the result (or errors) going back to the LLM.
These posts by Cloudflare: https://blog.cloudflare.com/code-mode/ and Anthropic: https://platform.claude.com/docs/en/agents-and-tools/tool-us... explain the concept and its advantages in more detail.
Tangentially i wonder if the recent changes in the GIL will percolate to mercurial as any improvements.
Yep still using good old hg for personal repos - interop for outside project defaults to git since almost all the hg host withered.
Can we please make as little js as possible?
Why would one drag this god forsaken abomination on server-side is beyond me.
Even effing C# nowdays can be run in script-like manner from a single file.
—
Even the latest Codex UI app is Electron. The one that is supposed to write itself with AI wonders but couldn’t manage native swiftui, winui, and qt or whatever is on linux this days.
My favourite languages are F# and OCaml, and from my perspective, TypeScript is a far better language than C#.
Typescript’s types are far more adaptable and malleable, even with the latest C# 15 which is belatedly adding Sum Types. If I set TypeScript to its most strict settings, I can even make it mimic a poor man’s Haskell and write existential types or monoids.
And JS/TS have by far the best libraries and utilities for JSON and xml parsing and string manipulation this side of Perl (the difference being that the TypeScript version is actually readable), and maybe Nushell but I’ve never used Nushell in production.
Recently I wrote a Linux CLI tool for managing podman/quadlett containers and I wrote it in TypeScript and it was a joy to use. The Effect library gave me proper Error types and immutable data types and the Bun Shell makes writing shell commands in TS nearly as easy as Bash. And I got it to compile a single self contained binary which I can run on any server and has lower memory footprint and faster startup time than any equivalent .NET code I’ve ever written.
And yes had I written it in rust it would have been faster and probably even safer but for a quick a dirty tool, development speed matters and I can tell you that I really appreciated not having to think about ownership and fighting the borrow checker the whole time.
TypeScript might not be perfect, but it is a surprisingly good language for many domains and is still undervalued IMO given what it provides.
I would say the same about Python, a language that has clearly got far too big for its boots.
Maybe a dumb question, but couldn't you use seccomp to limit/deny the amount of syscalls the Python interpreter has access to? For example, if you don't want it messing with your host filesystem, you could just deny it from using any filesystem related system calls? What is the benefit of using a completely separate interpreter?
Yours is a valid approach. But you always gotta wonder if there’s some way around it. Starting with runtime that has ways of accessing every aspect of your system - there are a lot of ways an attacker might try to defeat the blocks you put in place. The point of starting with something super minimal is that the attack surface is tiny. Really hard to see how anything could break out.
agree. you still need a secure boundary like VM to isolate the tenants in case the model breaks out of the sandbox.
everything that you don’t want your agent to access should live outside of the sandbox.
Monty is the missing link that's made me ship my rust-based RLM implementation - and I'm certain it'll come in handy in plenty of other contexts.
Just beware of panics!
Well I love the name, so definitely trying this out later, but first...
And now for something, completely different.
I'm enjoying watching the battle for where to draw the sandbox boundaries (and I don't have any answers, either!)
best answer is probably to have a layered approach - use this to limit what the generated code can do, wrap it in a secure VM to prevent leaking out to other tenants.
> Instead, it let's you run safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.
Perhaps if the interpreter is in turn embedded in the executable and runs in-process, but even a do-nothing `uv` invocation takes ~10ms on my system.
I like the idea of a minimal implementation like this, though. I hadn't even considered it from an AI sandboxing perspective; I just liked the idea of a stdlib-less alternative upon which better-thought-out "core" libraries could be stacked, with less disk footprint.
Have to say I didn't expect it to come out of Pydantic.
Pydantic + FastAPI are my two favorite python shops right now, they’re always dropping fun new projcts
I don't quite understand the purpose. Yes, it's clearly stated, but, what do you mean "a reasonable subset of Python code" while "cannot use the standard library"? 99.9% of Python I write for anything ever uses standard library and then some (requests?). What do you expect your LLM-agent to write without that? A pseudo-code sorting algorithm sketch? Why would you even want to run that?
They plan to use to for "Code Mode" which mean the LLM will use this to run Python code that it writes to run tools instead of having to load the tools up front into the LLM context window.
(Pydantic AI lead here) We’re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 with support for Monty and abstractions to use other runtimes / sandboxes.
The idea is that in “traditional” LLM tool calling, the entire (MCP) tool result is sent back to the LLM, even if it just needs a few fields, or is going to pass the return value into another tool without needing to see the intermediate value. Every step that depends on results from an earlier step also requires a new LLM turn, limiting parallelism and adding a lot of overhead.
With code mode, the LLM can chain tool calls, pull out specific fields, and run entire algorithms using tools with only the necessary parts of the result (or errors) going back to the LLM.
These posts by Cloudflare: https://blog.cloudflare.com/code-mode/ and Anthropic: https://platform.claude.com/docs/en/agents-and-tools/tool-us... explain the concept and its advantages in more detail.
It's pydantic, they're verifying types and syntax, those don't require the stdlib. Type hints, syntax checks, likely logical issues,etc.. static type checking is good with that, but LLMs can take to the next level where they analyze the intended data flow and find logical bugs, or good syntax and typing but not the intended syntax.
For example, incorrect levels of indentation. Let me use dots instead of space because of HN formatting:
for key,val in mydict.items():
..if key == "operation":
....logging.info("Executing operation %s",val)
..if val == "drop_table":
....self.drop_table()
This uses good syntax, and I the logging part is not in the stdlib, so I assume it would ignore it or replace it with dummy code? That shouldn't prevent it from analyzing that loop and determining that the second if-block was intended to be under the first, and the way it is written now, the key check isn't done.
In other words, if you don't want to do validate proper stdlib/module usage, but proper __Python__ usage, this makes sense. Although I'm speculating on exactly what they're trying to do.
EDIT: I think I my speculation was wrong, it looks like they might have developed this to write code for pydantic-ai: https://github.com/pydantic/pydantic-ai , i'll leave the comment above as-is though, since I think it would still be cool to have that capability in pydantic.
If we’re going to have LLMs write the code, why not something more performant? Like pages and pages of Java maybe?
this is pretty performant for short scripts if you measure time "from code to rust" which can be as low as 1us.
Of course it's slow for complex numerical calculations, but that's the primary usecase.
I think the consensus is that LLMs are very good at writing python and ts/js, generally not quite as good at writing other languages, at least in one shot. So there's an advantage to using python/js/ts.
I'm of the mind that it will be better to construct more strict/structured languages for AI use than to reuse existing ones.
My reasoning is 1) AIs can comprehend specs easily, especially if simple, 2) it is only valuable to "meet developers where they are" if really needing the developers' history/experience which I'd argue LLMs don't need as much (or only need because lang is so flexible/loose), and 3) human languages were developed to provide extreme human subjectivity which is way too much wiggle-room/flexibility (and is why people have to keep writing projects like these to reduce it).
We should be writing languages that are super-strict by default (e.g. down to the literal ordering/alphabetizing of constructs, exact spacing expectations) and only having opt-in loose modes for humans and tooling to format. I admit I am toying w/ such a lang myself, but in general we can ask more of AI code generations than we can of ourselves.
I think the hard part about that is you first have to train the model on a BUTT TON of that new language, because that's the only way they "learn" anything. They already know a lot of Python, so telling them to write restricted and sandboxed Python ("you can only call _these_ functions") is a lot easier.
But I'd be interested to see what you come up with.
I like the idea a lot but it's still unclear from the docs what the hard security boundary is once you start calling LLMs - can it avoid "breaking out" into the host env in practice?
without a VM boundary this is potentially dangerous as LLMs could figure out ways of escaping the lightweight sandbox.
Wow, a start latency of 0.06ms
It is absurd for any user to use a half baked Python interpreter, also one that will always majorly lag behind CPython in its support. I advise sandboxing CPython instead using OS features.
How do I sandbox CPython using OS features?
(Genuine question, I've been trying to find reliable, well documented, robust patterns for doing this for years! I need it across macOS and Linux and ideally Windows too. Preferably without having to run anything as root.)
Python already has a lot of half-baked (all the way up to nearly-fully-baked) interpreters, what's one more?
https://en.wikipedia.org/wiki/List_of_Python_software#Python...
The repo does make a case for this, namely speed, which does make sense.
True, but while CPython does have a reputation for slow startup, completely re-implementing isn't the only way to work around it - e.g. with eryx [1] I've managed to pre-initialize and snapshots the Wasm and pre-compile it, to get real CPython starting in ~15ms, without compromising on language features. It's doable!