๐ฌ LLM-Powered Code GenerationJune 2, 2026โ
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 pytestUsage
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 โ
- 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