MCP Connector

Transpile Python to Safe, Idiomatic Rust

A Python-to-Rust transpiler with semantic verification and memory safety analysis - compiles typed Python to a native binary via a single CLI command.


91
Spark score
out of 100
Updated 3 months ago
Version 4.1.0

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Why it matters

Automate the translation of Python code to Rust, ensuring semantic equivalence and memory safety. This asset enables the creation of performant, secure native binaries from existing Python projects.

Outcomes

What it gets done

01

Convert Python code to Rust with type annotations.

02

Perform semantic verification to ensure code equivalence.

03

Analyze migration complexity from Python to Rust.

04

Conduct code quality analysis on transpiled Rust code.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-depyler | bash

Capabilities

Tools your agent gets

transpile_python

Convert Python code to Rust with type-driven transpilation using annotations

analyze_migration_complexity

Analyze the complexity of migrating Python code to Rust

verify_transpilation

Verify semantic equivalence between original Python and transpiled Rust code

pmat_quality_check

Perform code quality analysis on transpiled Rust code

Overview

Depyler MCP Server

A type-directed Python-to-Rust transpiler with memory safety analysis and property-based semantic verification, compiling Python straight to a native binary or to inspectable Rust source. Use when Python code with type annotations needs converting to Rust, or compiling Python directly to a native binary is the goal.

What it does

Translates annotated Python code into idiomatic Rust, preserving program semantics while adding compile-time safety guarantees, part of the PAIML Stack. Type-directed transpilation uses the source Python's own type annotations to generate the matching Rust types, memory safety analysis infers ownership and borrowing patterns automatically rather than requiring manual annotation, and semantic verification uses property-based testing to confirm the transpiled Rust actually behaves the same as the original Python. The pipeline itself runs Python AST through a high-level intermediate representation, type inference, a Rust AST, and finally code generation via syn/quote. The simplest path compiles Python straight to a native binary with depyler compile script.py; a lower-level path transpiles to a .rs file instead, optionally with --verify to run the semantic-equivalence checks, --trace to show the transpilation trace, or --explain to see the reasoning behind specific transformation decisions. A recursive Fibonacci function is a representative example - a typed Python function transpiles directly into:

fn fibonacci(n: i32) -> i32 {
    if n <= 1 {
        return n;
    }
    fibonacci(n - 1) + fibonacci(n - 2)
}

keeping the same recursive structure and type signature.

When to use - and when NOT to

Use it when Python code with type annotations needs converting to Rust for performance or memory-safety reasons, or when compiling Python directly to a native binary is the goal rather than working with the generated Rust source. 27 standard library modules are validated with 151 passing tests at 100% coverage, spanning serialization (json, struct, base64, csv), date/time, cryptography (hashlib, secrets), text processing, math, filesystem, data structures, functional tools, random, and sys. Supported Python language features cover typed functions, basic types, collections, control flow including match statements, comprehensions, generator expressions, exception handling mapped to Rust's Result<T, E>, classes and methods, async/await, and context managers. Explicitly not supported: dynamic features like eval and exec, runtime reflection, multiple inheritance, and monkey patching, since none of these have a sound Rust equivalent.

Capabilities

Single-command Python-to-binary compilation, standalone Python-to-Rust transpilation with optional semantic verification, 27 validated stdlib module mappings, a library API for programmatic transpilation with TranspileOptions, and a migration-complexity analysis command.

How to install

Install with cargo install depyler, requiring Rust 1.83.0 or later and Python 3.8+ for test validation; the current release reports a 92.7% compile rate on its stdlib corpus, 62.5% on a typed-CLI corpus, and 47.5% on an algorithms corpus, with an 80%+ single-shot compile rate across its internal example suite of 320 files.

Who it's for

Developers migrating performance- or safety-critical Python code to Rust who want a type-directed, semantically-verified transpiler rather than a manual rewrite, and who can work within its currently supported feature set. The project is MIT-licensed.

Source README
depyler

depyler

A Python-to-Rust transpiler with semantic verification and memory safety analysis.

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Depyler translates annotated Python code into idiomatic Rust, preserving program semantics while providing compile-time safety guarantees. Part of the PAIML Stack.

Table of Contents

What's New in v4.1.1

  • str(dict.get(key, default)) fix: No longer generates spurious .unwrap() calls
  • 25+ Python module mappings: tempfile, datetime, os, shutil, glob, and more
  • E0433 import resolution: Improved handling of unresolved imports
  • [profile.test] opt-level=1: Faster test execution

Multi-Corpus Convergence (v3.25.0+)

All three external corpus targets met:

Corpus Compile Rate Target
Tier 1 (stdlib) 92.7% (38/41) 80%
Tier 2 (typed-cli) 62.5% (10/16) 60%
Tier 5 (algorithms) 47.5% (48/101) 40%
Internal examples 80% (256/320) 80%

Features

  • Type-Directed Transpilation - Uses Python type annotations to generate appropriate Rust types
  • Memory Safety Analysis - Infers ownership and borrowing patterns automatically
  • Semantic Verification - Property-based testing to verify behavioral equivalence
  • Single-Command Compilation - Compile Python to native binaries with depyler compile
  • 27 Stdlib Modules - Production-ready support for common Python standard library modules
  • 80%+ Single-Shot Compile Rate - Most Python files compile on first transpilation attempt

Installation

cargo install depyler

Requirements

  • Rust 1.83.0 or later
  • Python 3.8+ (for test validation)

Quick Start

Compile to Binary

The fastest way to use Depyler:

# Compile Python to a standalone binary
depyler compile script.py

# Run the compiled binary
./script

Transpile to Rust

# Transpile a Python file to Rust
depyler transpile example.py

# Transpile with semantic verification
depyler transpile example.py --verify

Example

Input (fibonacci.py):

def fibonacci(n: int) -> int:
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

Output (fibonacci.rs):

fn fibonacci(n: i32) -> i32 {
    if n <= 1 {
        return n;
    }
    fibonacci(n - 1) + fibonacci(n - 2)
}

Usage

Compilation Options

# Compile with custom output name
depyler compile script.py -o my_app

# Debug build (faster compilation)
depyler compile script.py --profile debug

# Release build (optimized, default)
depyler compile script.py --profile release

Transpilation Options

# Show transpilation trace
depyler transpile example.py --trace

# Explain transformation decisions
depyler transpile example.py --explain

# Analyze migration complexity
depyler analyze example.py

Library Usage

use depyler::{transpile_file, TranspileOptions};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let options = TranspileOptions::default()
        .with_verification(true);

    let rust_code = transpile_file("example.py", options)?;
    println!("{}", rust_code);

    Ok(())
}

Supported Python Features

Feature Status
Functions with type annotations Supported
Basic types (int, float, str, bool) Supported
Collections (List, Dict, Tuple, Set) Supported
Control flow (if, while, for, match) Supported
Comprehensions (list, dict, set) Supported
Generator expressions Supported
Exception handling (→ Result<T, E>) Supported
Classes and methods Supported
Async/await Supported
Context managers Supported

Not Supported: Dynamic features (eval, exec), runtime reflection, multiple inheritance, monkey patching.

Stdlib Module Support

27 modules validated with 151 tests passing (100% coverage).

Category Modules
Serialization json, struct, base64, csv
Date/Time datetime, calendar, time
Cryptography hashlib, secrets
Text textwrap, re, string
Math math, decimal, fractions, statistics
File System os, pathlib, io
Data Structures collections, copy, memoryview, array
Functional itertools, functools
Random random
System sys

See validation report for details.

Architecture

Python AST → HIR → Type Inference → Rust AST → Code Generation
Component Description
Parser RustPython AST parser
HIR High-level intermediate representation
Type System Conservative type inference with annotation support
Verification Property-based testing for semantic equivalence
Codegen Rust code generation via syn/quote

Documentation

Quality Metrics

Metric Value
Single-Shot Compile Rate 80% (256/320 examples)
Line Coverage 87.85%
Function Coverage 92.85%
Total Tests 23,335+
Mutation Kill Rate 75%+

Run coverage locally:

cargo llvm-cov nextest --workspace --lib --summary-only

MSRV

Minimum Supported Rust Version: 1.83

Cookbook

See depyler-cookbook for examples and recipes.

FAQ

Common questions

Discussion

Questions & comments · 0

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