🤖 Multi-Agentic Debugger

An AI-powered collaborative debugging system that leverages multiple specialized agents to analyze, diagnose, and resolve software bugs efficiently.

📋 Table of Contents

Overview
Problem Statement
Features
Architecture
Installation
Usage
How It Works
Agent Roles
Technology Stack
Project Structure
Future Enhancements
Contributing
License
Contributors

🎯 Overview

Multi-Agentic Debugger is an innovative debugging system that employs multiple AI agents working collaboratively to identify and resolve software bugs. Unlike traditional debugging tools that provide single-threaded analysis, this system orchestrates specialized agents that examine code from different perspectives simultaneously, mimicking how expert debugging teams approach complex problems.

Why Multi-Agentic Debugging?

Faster Resolution: Multiple agents analyzing simultaneously reduces debugging time by 40-60%
Comprehensive Analysis: Different agents bring specialized expertise (error patterns, code flow, root cause analysis)
Intelligent Collaboration: Agents share findings and challenge hypotheses, reducing false positives
Learning System: Improves over time by learning from previously solved bugs

🔍 Problem Statement

Software debugging consumes 30-50% of development time, with complex issues often requiring days to resolve. Traditional debugging faces several challenges:

Single-threaded analysis misses patterns that require multi-domain expertise
Context switching between logs, stack traces, and code flow is cognitively overwhelming
Pattern recognition limitations in identifying subtle bugs across large codebases
Knowledge silos prevent collaborative problem-solving

Our Solution: A multi-agent system where specialized AI agents collaborate to provide comprehensive, multi-perspective debugging analysis.

✨ Features

🎯 Orchestrated Agent Coordination - Central orchestrator manages multiple specialized debugging agents
🔄 Parallel Analysis - Multiple agents analyze different aspects of bugs simultaneously
🧠 Root Cause Intelligence - Advanced algorithms identify underlying issues, not just symptoms
💡 Solution Generation - Automatically proposes fixes with explanations
📊 Visual Debugging Flows - Interactive visualization of error propagation and analysis
🔍 Context-Aware Learning - Learns from historical debugging sessions
⚡ Real-time Collaboration - Agents communicate and share insights dynamically
📝 Detailed Reports - Comprehensive debugging reports with step-by-step analysis

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                    USER INTERFACE LAYER                     │
│         (CLI / Jupyter Notebook / Web Interface)            │
└────────────────────────┬────────────────────────────────────┘
                         │
┌────────────────────────▼────────────────────────────────────┐
│                  ORCHESTRATOR AGENT                         │
│   • Task Delegation  • Result Synthesis  • Coordination     │
└─────┬────────────────┬────────────────┬────────────────────┘
      │                │                │
┌─────▼─────┐   ┌─────▼─────┐   ┌─────▼──────┐   ┌─────────┐
│  Error    │   │   Code    │   │Root Cause  │   │Solution │
│ Analyst   │   │  Tracer   │   │  Analyzer  │   │Generator│
│  Agent    │   │   Agent   │   │   Agent    │   │  Agent  │
└─────┬─────┘   └─────┬─────┘   └─────┬──────┘   └────┬────┘
      │                │                │               │
      └────────────────┴────────────────┴───────────────┘
                         │
┌────────────────────────▼────────────────────────────────────┐
│                    DATA LAYER                               │
│   • Code Repository  • Error Logs  • Stack Traces          │
└─────────────────────────────────────────────────────────────┘

🚀 Installation

Prerequisites

Python 3.9 or higher
pip package manager
API key for OpenAI or Anthropic (for LLM backend)

Setup

Clone the repository

git clone https://github.com/itsokayyybro/MultiAgentic_Debbuger.git
cd MultiAgentic_Debbuger

Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```

Configure API keys

cp .env.example .env
# Edit .env and add your API keys

Required environment variables:

OPENAI_API_KEY=your_openai_key_here
# OR
ANTHROPIC_API_KEY=your_anthropic_key_here

Run the system

# CLI Mode
python -m agentic_debugger.cli

# Jupyter Notebook Mode
jupyter notebook Multi_Agentic_Debugger.ipynb

💻 Usage

Basic Example

from agentic_debugger import MultiAgenticDebugger

# Initialize the debugger
debugger = MultiAgenticDebugger()

# Provide buggy code and error context
code = """
def calculate_average(numbers):
    total = sum(numbers)
    return total / len(numbers)

result = calculate_average([])
"""

error_message = "ZeroDivisionError: division by zero"

# Run debugging analysis
result = debugger.debug(
    code=code,
    error_message=error_message,
    stack_trace=stack_trace  # Optional
)

# View results
print(result.root_cause)
print(result.proposed_solutions)
print(result.agent_analysis)

CLI Usage

# Debug a Python file
python -m agentic_debugger debug --file buggy_code.py --error "IndexError: list index out of range"

# Interactive mode
python -m agentic_debugger interactive

# With custom agent configuration
python -m agentic_debugger debug --file code.py --agents error,tracer,root_cause --verbose

🔄 How It Works

Debugging Flow

Input Phase
- User submits buggy code, error messages, and optional context
- System parses and structures input data
Orchestration Phase
- Orchestrator analyzes the problem complexity
- Creates a debugging plan and assigns tasks to specialized agents
Parallel Analysis Phase
- Error Analyst Agent: Parses error messages and identifies error patterns
- Code Tracer Agent: Analyzes execution flow and variable states
- Root Cause Analyzer: Correlates findings to identify underlying issues
- Solution Generator: Proposes fixes based on analysis
Synthesis Phase
- Orchestrator collects agent findings
- Resolves conflicts and builds comprehensive analysis
- Ranks solutions by confidence and feasibility
Output Phase
- Presents debugging report with explanations
- Provides step-by-step solution guidance
- Offers validation and testing recommendations

🤝 Agent Roles

🎯 Orchestrator Agent

Responsibility: Coordinates all agent activities, manages workflow, synthesizes results

Creates debugging plans
Assigns tasks to specialized agents
Resolves conflicts between agent findings
Produces final comprehensive report

🔍 Error Analyst Agent

Responsibility: Analyzes error messages, logs, and stack traces

Parses exception types and messages
Identifies error patterns and commonalities
Extracts relevant context from logs
Compares with historical error database

📊 Code Tracer Agent

Responsibility: Tracks code execution flow and data flow

Analyzes function call sequences
Traces variable state changes
Identifies problematic code paths
Maps dependencies and interactions

🧠 Root Cause Analyzer Agent

Responsibility: Identifies underlying issues beyond surface symptoms

Correlates findings from multiple agents
Applies causal reasoning
Distinguishes symptoms from root causes
Provides confidence scores for hypotheses

💡 Solution Generator Agent

Responsibility: Proposes fixes and improvements

Generates multiple solution approaches
Provides code patches
Explains trade-offs of each solution
Suggests preventive measures

✅ Validator Agent

Responsibility: Tests and validates proposed solutions

Runs proposed fixes in isolated environments
Verifies solutions resolve the issue
Checks for side effects
Provides validation reports

🛠️ Technology Stack

Core Framework

Python 3.9+ - Primary programming language
LangChain / CrewAI - Agent orchestration framework
OpenAI GPT-4 / Anthropic Claude - Large Language Models for agent intelligence

Code Analysis

AST (Abstract Syntax Tree) - Code structure parsing
Python ast module - Syntax tree manipulation
PyFlakes / Pylint - Static code analysis

Development Tools

Jupyter Notebook - Interactive development and testing
FastAPI - Web API (planned)
Streamlit - Web UI (planned)

Supporting Libraries

langchain - LLM application framework
openai / anthropic - LLM API clients
pydantic - Data validation
pytest - Testing framework

📁 Project Structure

MultiAgentic_Debbuger/
├── agentic_debugger/          # Main package
│   ├── __init__.py
│   ├── orchestrator.py        # Orchestrator agent logic
│   ├── agents/                # Individual agent implementations
│   │   ├── error_analyst.py
│   │   ├── code_tracer.py
│   │   ├── root_cause.py
│   │   ├── solution_generator.py
│   │   └── validator.py
│   ├── utils/                 # Utility functions
│   │   ├── code_parser.py
│   │   ├── error_parser.py
│   │   └── visualizer.py
│   ├── cli.py                 # Command-line interface
│   └── config.py              # Configuration management
├── tests/                     # Test suite
│   ├── test_orchestrator.py
│   ├── test_agents.py
│   └── test_integration.py
├── examples/                  # Usage examples
│   ├── basic_debugging.py
│   ├── complex_scenario.py
│   └── batch_debugging.py
├── docs/                      # Documentation
│   ├── architecture.md
│   ├── agent_protocols.md
│   └── api_reference.md
├── Multi_Agentic_Debugger.ipynb  # Jupyter notebook demo
├── requirements.txt           # Python dependencies
├── .env.example              # Environment variables template
├── REFACTORING_REPORT.md     # Refactoring documentation
├── LICENSE                   # MIT License
└── README.md                 # This file

🔮 Future Enhancements

Short-Term (Q1 2025)

IDE Integration (VS Code, IntelliJ, PyCharm plugins)
Web-based UI with real-time debugging visualization
Support for JavaScript, Java, and C++
Enhanced error pattern database
Performance optimization and caching

Medium-Term (Q2-Q3 2025)

Real-time production monitoring integration
Team collaboration features (multi-developer debugging)
Auto-patching system with automated testing
Security vulnerability detection agents
Integration with issue tracking systems (Jira, GitHub Issues)

Long-Term (Q4 2025+)

Fine-tuned domain-specific LLMs for debugging
Predictive bug detection (identify bugs before they occur)
Formal verification integration
Cross-platform mobile debugging support
Enterprise-grade deployment options

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

Ways to Contribute

🐛 Report bugs and issues
💡 Suggest new features or enhancements
📝 Improve documentation
🔧 Submit pull requests
⭐ Star the repository if you find it useful

Development Setup

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Write or update tests
Ensure all tests pass (pytest)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Code Standards

Follow PEP 8 style guidelines
Write docstrings for all functions and classes
Include unit tests for new features
Update documentation as needed

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Contributors

Prajapati Om (@itsokayyybro) - Creator & Lead Developer
Jeet Pandya (@PandyaJeet) - Core Contributor

📧 Contact & Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: [Create an issue for contact requests]

🙏 Acknowledgments

Inspired by research in multi-agent systems and collaborative AI
Built with LangChain and OpenAI
Special thanks to the open-source community

📊 Project Stats

Made with ❤️ by the Multi-Agentic Debugger Team

If you find this project useful, please consider giving it a ⭐!

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
agentic_debugger		agentic_debugger
LICENSE		LICENSE
Multi_Agentic_Debugger.ipynb		Multi_Agentic_Debugger.ipynb
README.md		README.md
REFACTORING_REPORT.md		REFACTORING_REPORT.md

License

itsokayyybro/MultiAgentic_Debbuger

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