steelix
steelix transpiles Python into Rust code that calls libpython through PyO3.
It is meant to help move Python projects to Rust. You transpile the project once and get a Rust program that behaves like the original. Semantics and error messages stay the same, the standard library and third party packages are still imported from Python, and tracebacks still point at the Python source. You can deploy that program right away, then replace its functions with ordinary Rust one at a time, keeping it working after every step.
It is a bit like Nuitka, but the output is Rust instead of C, and the goal is readability rather than speed. Nuitka's C is meant for a compiler; steelix's Rust is meant to be read and edited by people, and becomes the starting point of the port. It is plain PyO3 plus a small runtime crate.
Example
import json
class Item:
def __init__(self, name, price, qty=1):
self.name = name
self.price = price
self.qty = qty
def total(self):
return self.price * self.qty
def load(text):
try:
rows = json.loads(text)
except ValueError as e:
print(f"bad input: {e}")
return []
return [Item(**row) for row in rows if row["qty"] > 0]
items = load('[{"name": "pen", "price": 2, "qty": 3}]')
print(sum(item.total() for item in items))
steelix transpile inventory.py -o out/ writes a Cargo crate. An excerpt of out/src/inventory.rs:
// inventory.py:14
pub fn load<'py>(m: &rt::Module<'py>, text: &Bound<'py, PyAny>) -> PyResult<Bound<'py, PyAny>> {
let py = m.py();
rt::frame!(m, "load", 14, |frame| {
let rows = match (|| m.global("json")?.method1("loads", (text,)))() {
Ok(value) => value,
Err(err) if err.is_instance_of::<PyValueError>(py) => {
let e = frame.catch(err);
let _handling = rt::Handling::new(&e);
rt::builtins::print(
py,
(rt::fstring(py, ("bad input: ", rt::format(&e, "")?))?,),
)?;
return Ok(PyList::empty(py).into_any());
}
Err(err) => return Err(err),
};
let list = PyList::empty(py);
for row in rows.try_iter()? {
let row = row?;
if row.get_item("qty")?.gt(0)? {
list.append(rt::call!(m.global("Item")?, **row)?)?;
}
py.check_signals()?;
}
Ok(list.into_any())
})
}
pub mod item {
use super::*;
// inventory.py:5
pub fn __init__<'py>(
m: &rt::Module<'py>,
slf: &Bound<'py, PyAny>,
name: &Bound<'py, PyAny>,
price: &Bound<'py, PyAny>,
qty: &Bound<'py, PyAny>,
) -> PyResult<Bound<'py, PyAny>> {
rt::frame!(m, "Item.__init__", 5, |_| {
slf.set_attr("name", name)?;
slf.set_attr("price", price)?;
slf.set_attr("qty", qty)?;
Ok(rt::none(m.py()))
})
}
// inventory.py:10
pub fn total<'py>(m: &rt::Module<'py>, slf: &Bound<'py, PyAny>) -> PyResult<Bound<'py, PyAny>> {
rt::frame!(m, "Item.total", 10, |_| {
slf.attr("price")?.mul(slf.attr("qty")?)
})
}
}
cargo build in out/ produces an executable that embeds libpython and prints 6, like python inventory.py. The class is a real Python class, load is a real Python function (inspect.signature, pickling and monkeypatching still work), and the generator expression is evaluated lazily as in CPython.
Installation
steelix needs Python 3.14 and a Rust toolchain.
pip install steelix
# or
uv tool install steelix
The generated crates depend on the steelix-runtime crate from crates.io.
Usage
steelix transpile app.py -o out/ # a crate for app.py and the project modules it imports
steelix transpile -m pkg --root src/ -o out/ # the same for `python -m pkg`
steelix build app.py # transpile, build and assemble a relocatable dist/app/
steelix test out/ -- tests/ # run the project's pytest suite against the native modules
steelix coverage app.py # how much of the code becomes Rust
steelix attrs app.py --class Point # who reads and writes the attributes of a class
When building a generated crate yourself, point PyO3 at the interpreter whose packages the program uses: PYO3_PYTHON=/path/to/venv/bin/python cargo build.
steelix build bundles the Python runtime and the site-packages of the build environment next to the executable, so the result runs on a machine without Python (Linux, glibc at least as new as the build machine's).
Rewriting
Each generated function keeps its Python calling convention, so it can be rewritten on its own: keep the signature as an adapter, move the logic into plain Rust, convert the arguments at the boundary. The rest of the program, including Python code that calls the function, does not notice. examples/logsum shows a small CLI before and after rewriting some of its functions.
Python containers can stay as they are at first (a PyDict is fine in Rust code), so the first step only changes the logic, not the data model.
The project's own tests check each step. steelix test out/ -- tests/ builds the crate and runs python -m pytest tests/ with the generated executable acting as the Python interpreter: every import of a transpiled module gets the native code, wherever the project directory is, and the run fails if a transpiled module was imported from its Python source instead. pytest comes from the environment of the interpreter the crate is built with (--python, default $PYO3_PYTHON).
Classes are rewritten the same way, method by method, with the instance state left in __dict__. Turning a class into a #[pyclass] struct has to wait until every function that reads or writes its attributes is rewritten, and in real projects those are often spread over several modules. steelix attrs app.py lists, for every class, each attribute and the functions that read, write or delete it (file and line), the accesses whose receiver's class it can not tell but whose name matches, and dynamic access such as setattr with a computed name. steelix attrs app.py --pyclass pkg.mod.Point prints a draft #[pyclass] for one class, with fields, method stubs and a header listing what complicates the change (subclasses, metaclasses, decorators, class body logic, dynamic access, reassigned class attributes). The draft is advice, never part of the build.
--mode ext builds a PyO3 extension module instead of an executable, for publishing a transpiled library. Install a package as pkg/__init__.so so that python -m pkg works.
What is missing
- Async generators (
async defwithyield), async comprehensions and a few rare uses ofawaitandyield(in a lambda, in anexceptclause,types.coroutine) are kept as Python. Coroutines and generator functions are native: they becomeasync fns whose.awaitpoints are the Pythonawaitandyieldexpressions, driven by a small runtime object that behaves like CPython's coroutine and generator. - OR patterns with captures in
matchstatements (case [x] | {"x": x}:) and a few rarer constructs (custom metaclasses,exec/evalwithout a namespace, ...) are kept as Python. Othermatchstatements are native: eachcasebecomes anif let .. && ..chain over small runtime checks that behave like CPython's pattern matching, down to the order oflen(),__eq__and attribute lookups. - Executables for Windows and macOS are not supported yet, only Linux.
- Free-threaded Python is not supported.
"Kept as Python" means the statement or function stays Python source inside the generated Rust and runs in the same module globals, so any program CPython runs can be transpiled. steelix transpile and steelix coverage report what was kept and why.
Status
Early, but tested against CPython: every test case, six command line tools of the standard library, the standard library json package and a small Scheme interpreter run with the same output and the same tracebacks as under CPython, and 154 standard library modules transpile into crates that compile without warnings.
License
BSD 3-Clause, see LICENSE.
Metadata
Release files for steelix 0.2.0
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