sayandebnath-creator

Agent Debugger

Name: Agent Debugger
Author: sayandebnath-creator

AI community

SOURCE https://github.com/sayandebnath-creator/agent-debugger

Description

A specialized AI debugging agent using Llama3 (Ollama) that performs root cause analysis, generates minimal code fixes, and validates them via execution. Includes custom evaluation metrics and benchmark comparison with Claude.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

AI Debugging Agent (Ollama + Llama3)

Overview

Specialized AI agent for debugging Python errors using structured reasoning and execution validation.

Features

Root cause detection
Minimal fixes
JSON structured outputs
Execution validation (sandboxed)

Problem Specialization

This agent is specialized for debugging runtime errors in Python code.

Why this problem?

Debugging consumes a significant portion of developer time and is highly repetitive.

Why prioritize it?

High frequency in real-world development
Measurable outcomes (code runs or fails)
Existing LLMs are generic and not execution-validated

This agent focuses on:

Root cause identification
Minimal code fixes
Execution validation

Setup

pip install -r requirements.txt
cp .env.example .env
uvicorn src.main:app --reload

Example

Input: { "code": "print(x)", "error": "NameError" }

Output: { "root_cause": "x is not defined", "fix": "define x before use", "corrected_code": "x = 0\nprint(x)" }

Evaluation Method

Score = (0.4 × Fix Accuracy) + (0.2 × Execution Success) + (0.2 × Token Efficiency) + (0.2 × Latency)

Scaled to 10,000

Benchmark vs Claude (Example)

Case	Claude	Agent
IndexError	try/except	fixed loop bound
NameError	vague hint	explicit fix
ZeroDivision	explanation	safe guard

Design Decisions

Ollama for local inference
Structured JSON output for deterministic evaluation
Execution-based validation to reduce hallucinations

Cursor Integration

Uses `.cursorrules` to enforce minimal, safe, and testable fixes

API Usage

curl -X POST http://127.0.0.1:8000/debug \
-H "Content-Type: application/json" \
-d '{"code":"print(x)","error":"NameError"}'

---

Create `.gitignore` (root folder) or already given in this repo

Paste:

.env
__pycache__/
*.pyc
evaluation_results.json
detailed_results.json