AI API Failure Debugger

An AI-powered developer tool that analyzes API failures, detects probable root causes, calculates confidence scores, and generates intelligent debugging insights using FastAPI, React, PostgreSQL, and OpenAI.

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Live Demo

Frontend

https://ai-api-debugger-nu.vercel.app/

Backend API Docs

https://ai-api-debugger-jrmm.onrender.com/docs

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Features

• AI-powered API error analysis
• Intelligent error pattern detection
• Confidence score calculation
• Root cause identification
• Suggested debugging solutions
• OpenAI-powered debugging explanations
• PostgreSQL database integration
• Modern dashboard UI
• FastAPI backend architecture
• Cloud deployment with Render, Neon, and Vercel

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Dashboard Preview

Add screenshots here

Main Dashboard

assets/dashboard.png

Analysis Result

assets/result.png

Swagger API Documentation

assets/swagger.png

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Problem Statement

Developers often spend significant time debugging API failures such as authentication errors, rate limiting issues, timeout problems, and server-side failures.

Traditional debugging requires manually analyzing logs, documentation, and error messages.

This project provides an AI-powered debugging assistant that automatically analyzes API failures, identifies probable causes, calculates confidence scores, and suggests troubleshooting strategies.

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Solution

The system combines:

• Rule-based error classification
• Pattern matching engine
• Confidence scoring system
• AI-enhanced debugging explanations
• Persistent PostgreSQL storage

to create a developer-focused debugging platform.

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Tech Stack

Frontend

• React
• Vite
• Tailwind CSS
• Axios

Backend

• FastAPI
• Python
• SQLAlchemy
• PostgreSQL

AI Integration

• OpenAI API

Deployment

• Vercel
• Render
• Neon PostgreSQL

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System Architecture

React Frontend
       ↓
FastAPI Backend
       ↓
Validation Layer
       ↓
Pattern Detection Engine
       ↓
Confidence Scoring System
       ↓
OpenAI API
       ↓
PostgreSQL Database

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Project Structure

ai-api-debugger/
│
├── backend/
│   ├── models/
│   ├── routes/
│   ├── services/
│   ├── utils/
│   ├── database.py
│   ├── main.py
│   └── requirements.txt
│
├── frontend/
│   ├── src/
│   ├── public/
│   └── package.json
│
└── README.md

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API Endpoint

Analyze Error

POST

/analyze

Request

{
  "api_name": "OpenAI",
  "error_message": "429 Too Many Requests"
}

Response

{
  "category": "Rate Limiting",
  "cause": "Too many requests in short time",
  "solutions": [
    "Use retry logic",
    "Reduce request frequency",
    "Add exponential backoff"
  ],
  "confidence_score": "75%",
  "ai_explanation": "Detailed AI-generated debugging explanation..."
}

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Key Engineering Concepts Implemented

• REST API Development
• Backend Architecture Design
• Layered Service Structure
• Rule-Based Classification Engine
• AI Integration Workflows
• PostgreSQL Database Management
• ORM with SQLAlchemy
• Cloud Deployment
• Environment Variable Management
• CORS Handling
• Production Debugging

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Challenges Solved

• API error classification
• OpenAI integration
• Database persistence
• Cross-origin request handling (CORS)
• Cloud deployment configuration
• Environment variable management
• Production debugging

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Future Improvements

• Authentication System
• User Accounts
• Error History Dashboard
• Log File Upload Support
• Docker Deployment
• Advanced AI Reasoning
• Analytics Dashboard
• Multi-Model AI Support

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Learning Outcomes

This project helped in understanding:

• Full Stack Development
• Backend Engineering
• FastAPI Development
• React Frontend Architecture
• PostgreSQL Integration
• AI-Powered Applications
• Cloud Deployment
• System Design Fundamentals
• Production-Level Debugging

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Author

Anuman Shailesh Modi

Artificial Intelligence & Data Science Student

GitHub: https://github.com/Anuman18

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