🎯 SDLC Agent Workflow

AI-Powered Software Development Life Cycle Automation Platform

Transform your software development process with AI-powered automation. From meeting transcriptions to complete technical documentation, streamline your entire SDLC workflow.

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🚀 What is SDLC Agent Workflow?

The SDLC Agent Workflow is a production-ready AI platform that automates key aspects of software development, starting with audio transcription and document generation, with a comprehensive roadmap to become a complete SDLC automation solution.

🎯 Current Capabilities (Production Ready ✅)

• 🎤 Audio Transcription: High-quality transcription using OpenAI Whisper models
• 🤖 AI Meeting Analysis: Generate key meeting points and summaries with OpenAI GPT
• 📋 PRD Generation: Transform discussions into industry-standard Product Requirements Documents
• 🔧 Android TRD Generation: Convert PRDs into comprehensive Android Technical Requirements Documents
• 🎨 Figma MCP Integration: Model Context Protocol server for comprehensive Figma design data extraction
• 📱 Android MCP Integration: AI-powered Android device automation with LLM integration for intelligent mobile testing and interaction
• 📁 Multi-Format Support: MP3, WAV, M4A, FLAC, AAC, OGG, WMA, MP4, MOV, AVI
• ⚙️ Configurable Settings: Extensive customization through environment variables

🔮 Future Vision (2025-2026 Roadmap)

Complete SDLC automation platform covering:

• Requirements & Planning → Design & Architecture → Development Support → Testing & Quality → Deployment & Operations → Documentation & Knowledge

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⚡ Quick Start

Prerequisites

• Python 3.10 or higher
• OpenAI API key
• uv package manager (recommended) or pip

Installation

1. Clone the repository

   git clone git@github.com:tomdwipo/agent.git
   cd agent
   

2. Install dependencies

   # Using uv (recommended)
   uv sync
   
   # Or using pip
   pip install -r requirements.txt
   

3. Configure environment

   # Create .env file
   cp .env.example .env
   
   # Add your OpenAI API key
   echo "OPENAI_API_KEY=your_api_key_here" >> .env
   

4. Launch the application

   # Using uv
   uv run python transcribe_gradio.py
   
   # Or using python directly
   python transcribe_gradio.py
   

5. Access the interface Open your browser to http://localhost:7860

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🎯 Features Overview

✅ Production Features

Feature	Status	Description	Documentation
Audio Transcription	✅ Complete	OpenAI Whisper integration with multi-format support	API Docs
AI Meeting Analysis	✅ Complete	Key points extraction and meeting summaries	API Docs
PRD Generation v1.0	✅ Complete	8-section industry-standard Product Requirements Documents	Feature Docs
Android TRD Generation v1.0	✅ Complete	7-section Android Technical Requirements Documents	Feature Docs
Figma MCP Integration v1.0	✅ Complete	Model Context Protocol server for Figma design data extraction	Feature Docs
Android MCP Integration v1.0	✅ Complete	AI-powered Android device automation with LLM integration for intelligent mobile testing	Setup Guide

📋 Planned Features (2025-2026)

Phase	Timeline	Key Components	Expected Impact
Phase 1: Requirements & Planning	Q3 2025	Enhanced PRD + Project Planning Agent	50% planning time reduction
Phase 2: Design & Architecture	Q4 2025	System Design + UI/UX Design Agents	60% faster architecture documentation
Phase 3: Development Support	Q1 2026	Code Generation + Development Standards	70% boilerplate code reduction
Phase 4: Testing & Quality	Q2 2026	Test Planning + Quality Assurance Agents	80% test coverage automation
Phase 5: Deployment & Operations	Q3 2026	DevOps + Infrastructure Management	90% deployment automation
Phase 6: Documentation & Knowledge	Q4 2026	Documentation + Knowledge Management	75% documentation automation

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🏗️ Architecture

System Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   UI Layer      │    │  Service Layer  │    │ Configuration   │
│                 │    │                 │    │                 │
│ • Gradio UI     │◄──►│ • OpenAI Service│◄──►│ • Settings      │
│ • Components    │    │ • Whisper Service│   │ • Constants     │
│ • Interface     │    │ • File Service  │    │ • Environment   │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Technology Stack

• Backend: Python 3.10+, OpenAI API, Whisper
• Frontend: Gradio (Web UI)
• Package Management: uv with pyproject.toml
• Configuration: Environment variables with .env support
• Testing: Comprehensive test suite with pytest

Project Structure

agent/
├── main.py                 # Main application entry point
├── transcribe_gradio.py    # Gradio interface launcher
├── pyproject.toml         # Project configuration
├── requirements.txt       # Dependencies
├── config/               # Configuration management
│   ├── settings.py       # Application settings
│   ├── constants.py      # System constants
│   └── __init__.py
├── services/             # Core business logic
│   ├── openai_service.py # OpenAI API integration
│   ├── whisper_service.py# Audio transcription
│   ├── file_service.py   # File operations
│   └── __init__.py
├── ui/                   # User interface components
│   ├── gradio_interface.py# Main UI interface
│   ├── components.py     # UI components
│   └── __init__.py
├── tests/                # Test suite
├── demos/                # Demo applications
└── docs/                 # Comprehensive documentation

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📚 Documentation

🎯 For Users

• Quick Start Guide - Get up and running quickly
• Features Overview - Complete feature documentation
• User Manual - Comprehensive user guide

🛠️ For Developers

• Architecture Overview - Technical system design
• API Reference - Complete API documentation
• Contributing Guide - Development workflow
• Testing Guide - Testing procedures

📋 For Project Managers & Stakeholders

• Complete Project Proposal - Full business case and roadmap
• Architecture Evolution - Technical progress history
• Feature Status Tracking - Development progress

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🚀 Usage Examples

Basic Audio Transcription

from services.whisper_service import WhisperService

# Initialize service
whisper = WhisperService()

# Transcribe audio file
result = whisper.transcribe("meeting.mp3")
print(result["text"])

PRD Generation

from services.openai_service import OpenAIService

# Initialize service
openai_service = OpenAIService()

# Generate PRD from meeting transcript
prd = openai_service.generate_prd(transcript_text)
print(prd)

Complete Workflow

1. Upload Audio → Transcribe meeting recording
2. Generate Analysis → Extract key points and action items
3. Create PRD → Transform discussion into structured requirements
4. Generate TRD → Convert PRD into technical specifications
5. Download Documents → Export all generated documents

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🔧 Configuration

Environment Variables

# OpenAI Configuration
OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL=gpt-4
OPENAI_MAX_TOKENS=4000

# Whisper Configuration
WHISPER_MODEL=base
WHISPER_LANGUAGE=auto

# Application Settings
DEBUG=false
LOG_LEVEL=INFO

Advanced Configuration

See Configuration API Documentation for complete configuration options.

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🧪 Development

Setup Development Environment

# Clone repository
git clone git@github.com:tomdwipo/agent.git
cd agent

# Install development dependencies
uv sync --dev

# Run tests
uv run pytest

# Run with development settings
uv run python transcribe_gradio.py

Running Tests

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_prd_services.py

# Run with coverage
uv run pytest --cov=services

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Run the test suite (uv run pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

See Contributing Guidelines for detailed information.

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📈 Project Status & Roadmap

Current Status: Production Ready v1.0 ✅

• Core Foundation: Fully functional audio transcription and document generation
• Production Features: PRD and Android TRD generation complete
• Architecture: Modular, scalable design ready for expansion
• Documentation: Comprehensive documentation and testing

Success Metrics by Phase

• Phase 1: 50% planning time reduction
• Phase 2: 60% faster architecture documentation
• Phase 3: 70% boilerplate code reduction
• Phase 4: 80% test coverage automation
• Phase 5: 90% deployment automation
• Phase 6: 75% documentation automation

Complete Workflow Vision

Meeting/Discussion → Transcription → PRD → TRD → Architecture → Code → Tests → Deployment → Documentation

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🤝 Community & Support

Getting Help

• Documentation: Comprehensive guides in docs/
• Issues: Report bugs and request features via GitHub Issues
• Discussions: Join community discussions

Contributing

We welcome contributions! See our Contributing Guide for:

• Code contribution guidelines
• Development setup instructions
• Testing requirements
• Documentation standards

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📊 Metrics & Performance

Current Application Metrics

• Features Implemented: 5/5 core features (100%)
• Architecture Phases: 3/3 complete (Service Layer, Configuration, UI Components)
• Test Coverage: Comprehensive test suite
• Production Readiness: ✅ Ready for deployment

Performance Benchmarks

• Transcription Speed: Real-time processing for most audio formats
• PRD Generation: ~30 seconds for typical meeting transcript
• TRD Generation: ~45 seconds from PRD input
• Multi-format Support: 9 audio/video formats supported

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📄 License

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

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🎉 Acknowledgments

• OpenAI for Whisper and GPT API
• Gradio for the excellent web UI framework
• Python Community for the amazing ecosystem
• Contributors who help make this project better

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📞 Contact & Links

• Repository: github.com/tomdwipo/agent
• Documentation: Complete Documentation Hub
• Project Proposal: SDLC Agent Workflow Proposal
• Issues: GitHub Issues

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🚀 Ready to transform your SDLC workflow? Get started with the Quick Start guide above!