Legacy C code doesn't translate cleanly to Python. The idioms are different, the patterns are different, and naive line-by-line translation produces code that's technically correct but unmaintainable. I built a RAG-powered migration platform that understands code semantics and produces Python that actually looks like Python was written, not C wearing a Python costume.
The Challenge
The codebase was substantial—thousands of lines of C that needed to become Python. Manual translation was theoretically possible but would take months. Worse, human translators tend to preserve C patterns even when Python has better approaches.
The Real Problem
Simple translation tools exist, but they produce terrible output. They'll turn a C for-loop into a Python for-loop, missing that Python's list comprehension would be cleaner. They'll preserve pointer arithmetic patterns that make no sense in a garbage-collected language. The output "works" but nobody wants to maintain it.
What I needed was translation that understood intent, not just syntax. A system that could recognize what the C code was trying to accomplish and produce the idiomatic Python equivalent.
My Approach
The insight was that code translation is fundamentally a context problem. To translate a function well, you need to understand what it does, how it's used, and what patterns make sense in the target language. RAG (Retrieval-Augmented Generation) is perfect for this—it lets the model access relevant context when making translation decisions.
My system architecture:
- AST parsing: Parse C code into abstract syntax trees to understand structure
- Dependency analysis: Build a graph of how functions and types relate
- Context retrieval: When translating a function, retrieve related code for context
- Local LLM translation: Use LM Studio for the actual translation, informed by retrieved context
- Validation: Syntax checking and basic semantic verification of output
The Solution
The platform processes C files through a multi-stage pipeline that preserves semantics while producing idiomatic Python.
AST-Based Understanding
Before any translation happens, I parse the C code into an AST. This gives me a structured understanding of what's in the code—functions, types, control flow, data dependencies. I can answer questions like "what does this function depend on?" and "where is this type used?" programmatically.
Dependency-Aware Context
When translating a function, context matters. If a function calls helper functions, I retrieve their signatures. If it uses custom types, I retrieve their definitions. This context gets included in the prompt, so the model understands the broader picture.
The RAG Advantage
Without RAG, the model would translate each function in isolation. With RAG, it understands context—the types being used, the calling conventions, the patterns established elsewhere in the codebase. This context is the difference between technically correct output and actually usable output.
Chunking Strategy
You can't feed an entire codebase to an LLM at once. I developed a chunking strategy that breaks code into translatable units while preserving enough context for accurate translation. Functions are natural boundaries, but sometimes you need to group related functions together.
Local LLM Execution
All translation runs through LM Studio locally. At scale—thousands of functions—cloud API costs would have been prohibitive. Local inference makes the project economically viable and eliminates concerns about proprietary code leaving the network.
Results & Impact
Idiomatic Output
Python that looks like Python, using appropriate idioms and patterns.
Semantic Accuracy
RAG context enables translations that preserve intent, not just syntax.
Zero Translation Costs
Local LLM inference eliminates per-file API charges at scale.
Scalable Processing
Process entire codebases systematically with consistent quality.
Lessons Learned
- Context is everything. The difference between good and bad translation is almost entirely about context. RAG makes context tractable at scale.
- AST parsing pays dividends. Structured code understanding enables intelligent chunking, dependency analysis, and validation. It's worth the upfront investment.
- Human review is still essential. The system produces good first drafts, but human eyes catch subtle semantic issues. The goal is acceleration, not full automation.
- Test the output. When possible, run the translated code. Passing tests is the ultimate validation of semantic accuracy.
Have Legacy Code to Modernize?
If you're sitting on a legacy codebase that needs modernization, let's discuss how RAG-powered translation could accelerate your migration.
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