SuperMon - SDLC Automation Platform

A comprehensive Software Development Life Cycle automation platform using MCP (Model Context Protocol) servers and agentic framework with Gemini 2.5 Pro integration.

🚀 Quick Start

Prerequisites

Python 3.11+ - Required for the modular script system
Conda (Miniconda or Anaconda) - Download here
Docker (optional) - For database containers
Git - Version control

Installation & Setup

# Clone the repository
git clone <repository-url>
cd SuperMon

# Make startup scripts executable
chmod +x start_all.sh supermon.py

# Setup environment and dependencies
./start_all.sh setup

# Start all services
./start_all.sh start

# Check status
./start_all.sh status

🛠️ Development Commands

# Run code quality checks
./start_all.sh quality

# Format code
./start_all.sh format

# Lint code
./start_all.sh lint

# Run tests
./start_all.sh test

# Stop all services
./start_all.sh stop

# Restart all services
./start_all.sh restart

📂 Project Structure

Modular Script System

The project now uses a modular Python-based script system for better maintainability:

supermon.py - Main Python script for managing all services
start_all.sh - Shell wrapper around the Python script
scripts/ - Directory containing modular components:
- utils.py - Utility functions
- environment.py - Environment and dependency management
- database.py - Database service management
- services.py - Backend, frontend, and MCP server management

This modular approach makes it easier to maintain and extend the platform.

🏗️ Architecture

Multi-Agent System

Requirements Agent: Analyzes conversations and extracts requirements
Planning Agent: Creates epics and user stories
Development Agent: Manages code and deployment
Testing Agent: Handles testing and quality assurance
Communication Agent: Manages stakeholder communication

MCP Server Integration

Slack: Real-time messaging and notifications
WhatsApp: Mobile communication
Webex: Video conferencing and meetings
Notion: Documentation and planning
GitHub: Repository and issue management
PostgreSQL: Database management
Redis: Caching and sessions
Docker: Containerization
Tl;dv: Meeting recording and transcription

Technology Stack

Backend: FastAPI + Python 3.11
Frontend: Next.js 14 + React 18
Database: PostgreSQL + Redis
AI: Gemini 2.5 Pro
Environment: Conda
Code Quality: Black, isort, ruff, mypy, pre-commit

🔧 Configuration

Environment Setup

The platform uses conda for environment management. The environment.yml file defines all dependencies:

name: supermon
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.11
  - pip
  - nodejs=18
  - postgresql=15
  - redis=7
  - pip:
    - fastapi==0.104.1
    - uvicorn==0.24.0
    # ... other dependencies

Required API Keys

Create a .env file with your API keys:

# AI Services
GEMINI_API_KEY=your_gemini_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

# Communication Services
SLACK_BOT_TOKEN=your_slack_bot_token_here
WHATSAPP_API_KEY=your_whatsapp_api_key_here
WEBEX_ACCESS_TOKEN=your_webex_token_here

# Project Management
NOTION_API_KEY=your_notion_api_key_here
GITHUB_TOKEN=your_github_token_here

# Database
DATABASE_URL=postgresql://supermon:supermon123@localhost/supermon
REDIS_URL=redis://localhost:6379

# Frontend
NEXT_PUBLIC_API_URL=http://localhost:8000

📊 Features

AI-Powered Requirements Analysis

Analyzes Slack/Webex conversations using Gemini 2.5 Pro
Extracts functional, non-functional, and technical requirements
Validates and prioritizes requirements automatically
Generates comprehensive requirements summaries

Automated Project Planning

Creates project epics from analyzed requirements
Generates user stories with acceptance criteria
Estimates story points and effort using AI
Maps dependencies automatically

Intelligent Meeting Management

Automated meeting scheduling with participant coordination
Integration with Tl;dv for recording and transcription
AI-powered action item extraction from meeting transcripts
Automatic follow-up on action items

Multi-Channel Communication

Slack: Team collaboration and notifications
WhatsApp: Stakeholder updates and mobile communication
Webex: Video conferencing and meeting management
Email: Formal communications and summaries

Development Automation

GitHub integration for repository management
Automatic issue creation and tracking
CI/CD pipeline integration
AI-assisted code review and quality checks

Documentation & Knowledge Management

Notion integration for automated documentation
AI-generated technical specifications
Automatic API documentation generation
Centralized knowledge base management

🎯 Workflow

Requirements Gathering: AI analyzes conversations from Slack/Webex
UI Enhancement: Additional requirements collected via web interface
Epic Creation: AI generates project epics and user stories
Meeting Scheduling: Automated meeting coordination
Development Tracking: Real-time progress monitoring
Documentation: Automated Notion documentation
Communication: Multi-channel stakeholder updates

🔍 Code Quality

The platform includes comprehensive code quality tools:

Python Code Quality

Black: Code formatting
isort: Import sorting
ruff: Fast Python linter
mypy: Static type checking
pytest: Testing framework
pre-commit: Automated quality checks

Frontend Code Quality

ESLint: JavaScript/TypeScript linting
Prettier: Code formatting
TypeScript: Static type checking

Quality Commands

# Run all quality checks
./start_all.sh quality

# Format code only
./start_all.sh format

# Lint code only
./start_all.sh lint

# Run tests only
./start_all.sh test

🚀 Deployment

Development Environment

# Start all services
./start_all.sh start

# Access points
# Frontend: http://localhost:3000
# API Docs: http://localhost:8000/docs
# Health: http://localhost:8000/health

Production Environment

# Build and deploy with Docker
docker-compose up -d

# Or deploy to cloud platforms
# - AWS ECS/Fargate
# - Google Cloud Run
# - Azure Container Instances
# - Heroku

📈 Monitoring & Observability

Health Checks

Application health endpoints
Database connection monitoring
MCP server status tracking
Agent performance metrics

Logging

Structured logging with JSON format
Log aggregation and analysis
Error tracking and alerting
Performance monitoring

Metrics

Project completion rates
Agent performance metrics
API response times
User engagement analytics

🔐 Security

Authentication & Authorization

JWT-based authentication
Role-based access control (RBAC)
API key management for external services
Secure token storage

Data Protection

Encrypted API communications
Secure database connections
Environment variable management
Audit logging

API Security

Rate limiting
Input validation
CORS configuration
Error handling without information leakage

🤝 Contributing

Development Setup

# Clone and setup
git clone <repository-url>
cd SuperMon

# Setup conda environment
conda env create -f environment.yml
conda activate supermon

# Install pre-commit hooks
pre-commit install

# Run quality checks
./start_all.sh quality

Code Standards

Follow PEP 8 for Python code
Use type hints throughout
Write comprehensive tests
Document all public APIs
Use conventional commits

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=app --cov-report=html

# Run specific test file
pytest tests/test_config.py

📚 Documentation

Setup Guide: docs/SETUP.md
Architecture: docs/ARCHITECTURE.md
API Documentation: http://localhost:8000/docs
Code Quality: See .pre-commit-config.yaml

🌐 Access Points

Once running, access the platform at:

Frontend Dashboard: http://localhost:3000
API Documentation: http://localhost:8000/docs
Health Check: http://localhost:8000/health
Admin Panel: http://localhost:3000/admin

🐛 Troubleshooting

Common Issues

Conda Environment Issues

# Recreate environment
conda env remove -n supermon
conda env create -f environment.yml

Database Connection Issues

# Check PostgreSQL
docker ps | grep postgres
docker logs supermon-postgres

# Check Redis
docker ps | grep redis
docker logs supermon-redis

Code Quality Issues

# Fix formatting
black .
isort .

# Fix linting issues
ruff check --fix .

# Check types
mypy app/

Logs Location

Backend logs: backend/logs/
Frontend logs: Browser console
MCP server logs: mcp_servers/*/logs/

📄 License

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

🆘 Support

Documentation: Check /docs directory
Issues: Create GitHub issues for bugs
Discussions: Use GitHub discussions for questions
Slack: Join our community Slack channel

SuperMon - Streamlining SDLC with AI-powered automation and multi-channel communication.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
backend/app		backend/app
docs		docs
frontend		frontend
mcp_servers		mcp_servers
scripts		scripts
tests		tests
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
environment.yml		environment.yml
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
start_all.sh		start_all.sh
supermon.py		supermon.py

amogh18t/SuperMon

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