Shadow-debugger – Background LLM error diagnosis for Python and FastAPI (github.com)

The newly announced Shadow-Debugger tool provides an innovative solution for diagnosing errors in Python applications and FastAPI web APIs. When an exception occurs, such as an HTTP 500 error, Shadow-Debugger captures the relevant stack frame and sends it to a Large Language Model (LLM) for analysis while allowing the application to continue functioning normally. This results in a detailed Markdown error report generated asynchronously, helping developers understand the root cause and suggesting fixes without disrupting the user experience. This tool is significant for the AI/ML community as it streamlines the debugging process by integrating AI capabilities directly into software development workflows. It leverages various LLMs, including Ollama, Gemini, and OpenAI models, and ensures that sensitive information is protected through automatic redaction of secrets. Developers can use it across several frameworks, such as Flask and Django, with options to customize settings to fit different deployment needs. By reducing time spent on error diagnosis while maintaining application integrity, Shadow-Debugger represents a meaningful advancement in leveraging AI for practical software development challenges.