All Toolsโ€บSmart Debugger
๐Ÿ’ฌ LLM-Powered Code GenerationJune 2, 2026โœ… Tests passing

Smart Debugger

A Python library that analyzes Python traceback errors and provides suggestions to fix the issue using LLM-based reasoning. This tool is useful for developers who want smarter debugging capabilities directly in their development environment.

What It Does

  • Analyze Python traceback strings.
  • Get human-readable explanations and suggestions for fixing errors.

Installation

Install the required dependencies:

pip install openai pytest

Usage

Run the tool from the command line:

python smart_debugger.py <traceback_file>

Replace <traceback_file> with the path to a file containing the Python traceback.

Source Code

import openai
import traceback
import argparse

def debug_error(traceback_string: str) -> dict:
    """
    Analyzes a Python traceback string and provides suggestions to fix the issue.

    Args:
        traceback_string (str): The Python traceback string to analyze.

    Returns:
        dict: A dictionary containing human-readable explanations and suggestions.
    """
    if not traceback_string.strip():
        return {"error": "Empty traceback string provided."}

    try:
        # Call OpenAI API for analysis (mocked in tests)
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a helpful assistant that analyzes Python errors."},
                {"role": "user", "content": f"Analyze this Python traceback and suggest fixes:\n{traceback_string}"}
            ]
        )
        explanation = response['choices'][0]['message']['content']
        return {"traceback": traceback_string, "suggestions": explanation.strip()}

    except openai.error.OpenAIError as e:
        return {"error": f"Failed to analyze traceback: {str(e)}"}

    except Exception as e:
        return {"error": f"Unexpected error occurred: {str(e)}"}


def main():
    parser = argparse.ArgumentParser(description="Smart Debugger: Analyze Python tracebacks and get suggestions.")
    parser.add_argument("traceback_file", type=str, help="Path to a file containing the Python traceback.")
    args = parser.parse_args()

    try:
        with open(args.traceback_file, "r") as f:
            traceback_string = f.read()

        result = debug_error(traceback_string)
        if "error" in result:
            print(f"Error: {result['error']}")
        else:
            print("Traceback Analysis:")
            print(result["suggestions"])

    except FileNotFoundError:
        print("Error: The specified file was not found.")
    except Exception as e:
        print(f"Unexpected error: {str(e)}")

if __name__ == "__main__":
    main()

Community

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Details

Tool Name
smart_debugger
Category
LLM-Powered Code Generation
Generated
June 2, 2026
Tests
Passing โœ…
Fix Loops
3

Quick Install

Clone just this tool:

git clone --depth 1 --filter=blob:none --sparse \
  https://github.com/ptulin/autoaiforge.git
cd autoaiforge
git sparse-checkout set generated_tools/2026-06-02/smart_debugger
cd generated_tools/2026-06-02/smart_debugger
pip install -r requirements.txt 2>/dev/null || true
python smart_debugger.py
Smart Debugger โ€” AI Tools by AutoAIForge