CompletedPythonCLILLM+4 more
Airror
A CLI-based AI code debugger that analyzes errors directly from codebases and provides root cause, explanation, and fixes using a vectorless retrieval approach.
Timeline
2-3 Days
Role
AI/Backend Developer
Team
Solo
Status
CompletedTechnology Stack
Python
CLI
LLM
Vectorless Retrieval
AST Parsing
Code Analysis
NLP
Key Challenges
- Error Context Extraction from Codebase
- Accurate Root Cause Detection
- Vectorless Retrieval Design
- Handling Diverse Error Types
- CLI UX Design
Key Learnings
- Code Understanding using LLMs
- AST-based Parsing
- Prompt Engineering for Debugging
- Context Injection without Vector DB
- Developer Tooling Design
Overview
Airror is a CLI-based AI debugging tool that helps developers identify, understand, and fix errors directly from their codebase. Instead of relying on vector databases, it uses a vectorless retrieval approach to extract relevant context and provide precise debugging insights.
The tool analyzes code, detects the origin of errors, and returns structured outputs including the root cause, explanation, and suggested fixes — all within the terminal.
Key Features
Core Functionalities
- AI Code Debugging: Analyze errors and provide fixes in real-time
- Root Cause Detection: Identify where and why the error occurred
- Codebase Awareness: Understand project-level context, not just single files
- Vectorless Retrieval: Efficient context extraction without embeddings
- CLI Tooling: Lightweight and developer-friendly interface
- Detailed Explanations: Clear breakdown of issues and solutions
How It Works
- Error Input → Developer provides error/log or stack trace
- Code Parsing → Relevant files and context extracted (AST / pattern-based)
- Context Building → Important snippets selected without vector DB
- LLM Processing → Model analyzes error + context
- Output Generation → Returns cause, explanation, and fix
Example Workflow
$ debug-search "TypeError: undefined is not a function"