Regarding what #1 said about "Based on my testing, Anthropic Claude 3.5 Sonnet...", im actually really satisfied with Aider AI Coding Assistant combined with the OpenAI GPT-4o API. While Claude is strong, Aiders repo mapping is what really saves me on complex Django async issues.

• It tracks dependencies across your whole project better than chat.
• No complaints about context loss since it uses a local repo map. Works well for me, good luck with the deadline!

Agree with #2, context is everything. Im super satisfied with Sourcegraph Cody AI Pro lately tho.

• reliable repo indexing
• doesnt hallucinate async logic

Based on my testing, Anthropic Claude 3.5 Sonnet 200k Context is currently the most effective for debugging async Python. The reasoning benchmarks for logic are higher than OpenAI GPT-4o Omnimodel, and it shows in complex middleware stacks. GPT-4o frequently truncates code blocks in long conversations, which is a major bottleneck when you need the full logic flow. Quick tip: provide the specific Django version and your full middleware list from settings.py. Async errors in Django often stem from mid-stack blocking calls that arent obvious from a single file. For the best context handling, I recommend Cursor AI Code Editor Pro using the Claude 3.5 API. It builds a local RAG index of your project which helps the model understand cross-file dependencies better than a browser copy-paste. In my experience, Microsoft GitHub Copilot Individual struggles with the specific nuances of async_to_sync wrappers. Last week I had a deadlocked middleware that Claude identified as a thread-safety issue in under a minute by analyzing the stack trace. It is more reliable for strict logic tasks than the current OpenAI models. If you have a deadline on Friday, the reduced hallucination rate on Claude is gonna save you a lot of time.

A day late to this thread but I was stuck in a similar spot last month with a legacy project. Spent almost $50 in API credits in one afternoon just trying to get the model to understand how my signals were interacting with the middleware chain. It kept suggesting the same fix over and over because I hadnt properly trimmed the file headers. You gotta be careful with those long context windows because they get expensive fast if you keep dumping the whole repo in there. I eventually found that feeding it specific stack traces and just the relevant parts of my settings.py worked way better than some full context approach. Here are a few things I learned the hard way:

• Watch your token count per request or your monthly bill will spike before you even fix the bug.
• Always double check the imports it suggests for async libraries since it tends to mix up sync_to_async wrappers.
• Strip out the docstrings if you are tight on context space to save money. Honestly, the biggest headache for me was the model confusing my local dev environment with older Python versions. Are you running this on Python 3.11 or are you still on an older 3.8 stack? Also, is this happening only under heavy load or can you reproduce it with a single local test case? Identifying that might save you a lot of wasted tokens.

Can vouch for this