Just sharing my experience: I had a pretty rough time with technical debt in a FastAPI project last year. Honestly, relying on GitHub Copilot led to some *seriously* unsafe habits because I just blindly accepted its code.

1. Claude 3.5 Sonnet vs OpenAI GPT-4o: Claude feels way more 'Pythonic' to me. It actually catches weird safety bugs and suggests clean type hints, whereas GPT-4o sometimes gives me convoluted logic that's hard to maintain.
2. Manual linting: Ngl, no AI replaces a solid config. I started using strict rules which helped clean up the mess those tools left behind... but yeah, technical debt is a real mood. good luck!

Respectfully, I'd consider another option before sinking money into a monthly sub like Cursor. Honestly, I've had a different experience where expensive AI tools just added more bloat because I wasn't fixing my workflow first. If you're worried about technical debt in a FastAPI backend, throwing a paid AI at it isn't always the fix... unfortunately.

I mean, Claude 3.5 Sonnet is basically the gold standard for refactoring right now and it's free to try. But for a really clean, PEP 8 codebase without the high cost, I'd actually suggest using PyCharm Community Edition paired with Ruff Linter.

Claude 3.5 Sonnet vs PyCharm Community Edition:
- Claude is better at logic but can hallucinate weird library versions if you aren't careful.
- PyCharm + Ruff is boring, but it FORCES you to write clean code via real-time linting and suggests those type hints you're after for free.

Maybe try a free LLM first and see if that cleans up the spaghetti before paying? What's your current linting setup looking like tho?

Tbh, I think the biggest mistake people make is expecting the tool to just 'know' what clean looks like without a strict frame of reference. I've been doing this for years and honestly, a DIY approach to your custom instructions is way more effective than switching apps every week. You basically need to build a 'Clean Code' persona that explicitly prioritizes SOLID principles and PEP 8 in your system prompts. A few warnings from my own workflow: - **Context Contamination:** If you paste too much of your old messy code, the AI actually starts to mimic your bad habits. It sees the spaghetti and thinks thats the style you want. Its better to use a 'clean slate' prompt for every new module.
- **Over-Abstraction:** AI loves to create 'AbstractBaseClass' hell. Itll try to be too clever with inheritance when a simple flat function would be way more Pythonic and easier to maintain.
- **Dependency Bloat:** Watch out for it suggesting new 3rd party libraries for things that the Python standard library already does perfectly well.
- **Type Hint Overkill:** It might suggest complex Nested Generics that are technically 'correct' but make the code a total nightmare for humans to read. Basically, dont let the AI drive the architecture. You define the structure, and let it fill in the logic one small piece at a time.

Saving this whole thread. So much good info here you guys are awesome.

Sooo, I went through this last year. I’m still kinda new to Python, but I totally get the frustration because my first FastAPI project turned into such a mess... like, actual spaghetti. I started using Claude for refactoring, but I gotta WARN you about a few things first:

1. Honestly, don't trust it to know your whole folder structure. It'll suggest moving logic into files that don't even exist.
2. Watch out for it hallucinating Pydantic v1 syntax when you're probably using v2. It’s super annoying to fix.
3. It loves to over-complicate things with abstract base classes when a simple function would work.

But yeah, I tried doing the same thing with the tool mentioned in the first reply, and while it was okay, I had issues with it making weird assumptions about my database migrations. Basically, it’s not as good as expected if you don't double-check every line. Still learning though! gl!

The thread has already touched on the heavy hitters like Claude and Cursor for handling messy FastAPI code. In my experience over the years, the biggest headache isnt the AIs logic—its the compatibility mess. You ask for a clean refactor and the AI gives you code that only works on a newer Python version than what youre running, or it mixes up Pydantic V1 and V2 syntax. ^ This. Also, if youre looking for something cost-effective that keeps your workflow in the IDE, try the Continue.dev VS Code Extension paired with the DeepSeek Coder V2 API. It gives you that specialized coding power without the $20 monthly hit. My main tip: always define your environment in your system prompt—it keeps the code clean and actually runnable.

For your situation, I've found that switching to Cursor AI Code Editor is honestly a total game-changer for FastAPI projects. I used to rely on standard ChatGPT, but Cursor is basically a fork of VS Code that actually understands your entire codebase, not just the file you're looking at. It's SO good at refactoring because you can highlight a messy block and just tell it to "make this more Pythonic and PEP 8 compliant" and it actually gets it right since it sees your existing patterns.

I mean, I've been there with the spaghetti code mess, and the Claude 3.5 Sonnet model inside Cursor is way better at modern Python than GPT-4o imo. It handles f-strings and complex list comprehensions without overdoing it. Plus, it's like $20/month which feels worth it for the time you save cleaning up technical debt. It's definitely the most "clean architecture" friendly tool I've used lately. Good luck tho!

Just catching up on this thread and honestly, I have this exact same problem. Ive been dealing with it for like three months now with my own hobby projects and still cant find a workflow that actually feels safe. Im so paranoid about breaking my logic that I usually just end up staring at the screen because I dont trust the AI to not turn my work into a complete mess... it is so frustrating. I was really hoping for a clear winner here but everything still feels like a huge gamble tbh.

Late to the party but i've spent way too much time obsessing over this lately. If you want to avoid the technical debt trap, you gotta look at how the AI sees your whole project, not just one file. I've been bouncing between two main setups for my own projects:

• Sourcegraph Cody is my go-to for daily stuff. It's a solid alternative to the more expensive editors that plugs right into VS Code. The best part is the context fetching; it actually looks at your FastAPI schemas when you're writing your routes so the type hints stay consistent across the app.
• Aider AI pair programmer is what i use for the heavy lifting. It's a terminal tool that's kinda scary at first, but it's incredible for refactoring. If you run it with Anthropic Claude 3.5 Sonnet, you can literally tell it to refactor a specific module to use dependency injection and it handles the multi-file changes way better than a standard chat window. Honestly, Cody is better for daily flow while Aider is king for cleaning up the spaghetti mess after a long session. Neither is perfect but they're way more surgical than just copy-pasting stuff into a web browser and hoping for the best.