DebugPilot AI — AI-Powered Debugging Workflow

[Yashyr190]

DebugPilot AI

DebugPilot AI is an AI-powered debugging workflow designed to help developers triage runtime errors, deployment failures, API issues, and stack traces more efficiently.

Instead of generic conversational responses, the system focuses on structured engineering workflows including:

• severity classification
• subsystem detection
• root cause analysis
• debugging checklists
• prevention guidance
• confidence scoring

🚀 Live Demo

https://debugpilot-ai.vercel.app/

📂 GitHub Repository

https://github.com/Yashyr190/debugpilot-ai

🎥 Demo Video

https://drive.google.com/file/d/1B6vfclYXRypmdNDDFDD16DtDiTtuYRL2/view?usp=sharing

🛠️ Tech Stack

• Next.js 14
• TypeScript
• Tailwind CSS

✨ Features

• AI-powered incident triage
• Structured debugging workflows
• Severity detection
• Likely subsystem identification
• Immediate next-step guidance
• Confidence scoring
• Modern developer-focused UI

🌱 Future Improvements

• GitHub repository-aware debugging
• CI/CD log ingestion
• AI-generated PR fixes
• Multi-agent debugging workflows

Files Added

• submissions/debugpilot-ai (301 lines): Comprehensive project README documenting DebugPilot AI, an AI-powered debugging workflow solution for incident triage and root cause analysis.

Workflow Overview

The submission does not include a flow.json configuration file. Instead, the workflow is described within the README as a linear debugging pipeline with the following stages:

1. Input Error/Logs - Accepts stack traces, CI/CD failures, runtime exceptions, API errors, and infrastructure logs
2. Issue Classification - Categorizes the type of error/issue
3. Severity Detection - Classifies into Low, Medium, High, or Critical
4. Subsystem Identification - Identifies affected engineering layers (Frontend API Layer, React Rendering Layer, Deployment Build Pipeline, Backend Validation Layer, Database Connection Layer)
5. Root Cause Analysis - Provides actionable debugging explanations
6. Fix Suggestions - Generates structured fix recommendations
7. Debugging Checklist - Produces operational debugging steps
8. Prevention Guidance - Offers engineering practices to prevent recurrence

Project Summary

DebugPilot AI is a lightweight Next.js 14 debugging assistant that transforms unstructured technical failures into practical engineering reports. The application features:

• AI-Powered Incident Triage - Converts raw logs and stack traces into structured debugging reports
• Severity Scoring - Four-tier severity classification system
• Subsystem Detection - Identifies relevant engineering layers
• Confidence Scoring - Estimates reliability of generated insights
• Heuristic Fallback - Operates without external APIs using built-in local analysis mode
• Modern Developer UI - Dark glassmorphism-inspired interface with responsive design

Tech Stack: Next.js 14, TypeScript, Tailwind CSS, with optional OpenAI API integration

Deployment: Production-ready demo at https://debugpilot-ai.vercel.app/

Architecture: Lightweight Next.js API route with dual-mode analysis (LLM-based when API key provided, or local heuristic-based for demos and offline use)

[coderabbitai]

No actionable comments were generated in the recent review. 🎉

ℹ️ Recent review info

⚙️ Run configuration

Configuration used: Repository UI (base), Organization UI (inherited)

Review profile: ASSERTIVE

Plan: Pro

Run ID: d5cbff2e-f344-4055-be32-5a4312c99ee7

📥 Commits

Reviewing files that changed from the base of the PR and between 6e60d64 and a4325d1.

📒 Files selected for processing (1)

• submissions/debugpilot-ai

---

Walkthrough

This pull request adds a comprehensive README for the DebugPilot AI project. The documentation covers the problem statement, solution approach, core features, system architecture with LLM and fallback modes, tech stack, setup instructions, and future roadmap. No code changes are included.

Changes

DebugPilot AI Project Documentation

Layer / File(s)

Summary

Complete README with features, architecture, and setup guide submissions/debugpilot-ai

Documentation introduces DebugPilot AI as an AI-powered debugging assistant, explains the problem of unstructured incident workflows, and documents features including severity classification, subsystem identification, root cause analysis, checklists, and confidence scoring. Describes the Next.js architecture with optional LLM integration or local heuristic fallback, tech stack details, step-by-step setup instructions, optional OPENAI_API_KEY configuration, future improvements, project rationale, and MIT license.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description lacks required checklist items and file structure validation details specified in the template (config.json, flows structure, env files, etc.).	Complete the PR checklist template: confirm contribution type (Kit/Bundle/Template), verify folder naming convention, document file structure compliance (config.json, flows, etc.), and confirm local testing validation.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly describes the main change—a README addition for DebugPilot AI, an AI-powered debugging workflow project submission.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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[Yashyr190]

Hi maintainers

This PR was submitted for the agentkit-challenge.

I don’t appear to have permission to add labels from my side, so requesting the agentkit-challenge label to enable review automation.

Thank you!