Depyler

Depyler is a Python‑to‑Rust transpiler that goes beyond simple syntax conversion. It reads annotated Python code, constructs a type‑directed intermediate representation, and emits idiomatic Rust while preserving the original program’s semantics. By performing semantic verification through property‑based testing and a rigorous memory safety analysis, Depyler guarantees that the generated Rust code upholds ownership, borrowing, and lifetime rules at compile time. This makes it an invaluable tool for developers who need to port performance‑critical or safety‑sensitive Python modules into a systems language without sacrificing correctness.

The server exposes an MCP (Model Context Protocol) interface, allowing AI assistants such as Claude to invoke Depyler’s capabilities directly. From an assistant’s perspective, the MCP server presents a set of high‑level tools—, , , and . These tools enable a conversational workflow where the assistant can request a Rust translation of a code snippet, receive an analysis of how many lines or functions need manual adjustment, and confirm that the output behaves identically to the source. This tight integration turns a typically manual migration process into an interactive, AI‑guided experience.

Key features include:

• Type‑directed transpilation that leverages Python type annotations to map to precise Rust types, reducing boilerplate and improving safety.
• Ownership inference that automatically translates Python’s dynamic memory model into Rust’s ownership and borrowing system, including conversion of exception handling to patterns.
• Semantic verification that runs property‑based tests against the original Python implementation to ensure behavioral equivalence.
• Multi‑backend support allowing output in Rust or Ruchy script, giving developers flexibility in target ecosystems.
• Comprehensive coverage of Python constructs such as generators, async/await, context managers, and comprehensions, while clearly documenting unsupported dynamic features.

Real‑world scenarios where Depyler shines include:

• Performance optimization: Migrating computational kernels from Python to Rust for speed‑critical applications in data science or machine learning pipelines.
• Safety hardening: Replacing unsafe Python code (e.g., raw pointer manipulations via C extensions) with Rust’s guarantees to eliminate memory errors.
• Cross‑platform deployment: Generating native binaries from existing Python libraries to embed in embedded systems or mobile apps.
• Legacy code modernization: Gradually refactoring a large Python codebase into Rust while maintaining functional parity, guided by the migration complexity analysis tool.

Integrating Depyler with AI workflows is straightforward: an assistant can parse user intent, extract the relevant Python snippet, invoke the tool through MCP, and present the Rust output along with a confidence score from semantic verification. The assistant can then ask follow‑up questions, suggest refactoring hints, or schedule a code review. This synergy reduces manual effort, speeds up migration cycles, and ensures that the transition to Rust is both safe and verifiable.