AI-Dev-Agent: Practical AI Development Toolkit

A pedagogical toolkit for building AI agent systems with practical software development techniques.

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What This Is

This is a foundational research project that teaches how to build AI agent systems using practical software development techniques. It's designed for learning and extracting production-ready components.

Not a production system - Use this to learn patterns and extract clean software artifacts.

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Key Features

🤖 Agent Framework

• LangChain/LangGraph-based agent coordination
• Specialized agent roles (architect, developer, tester)
• Context-aware rule loading system

🌈 Vibe Coding UI

• Streamlit interface for intuitive development
• Emotion-to-code translation for non-technical users
• Enhanced prompt generation for agent workflows

💾 Prompt Management System

• SQLite database for prompt storage and versioning
• A/B testing and optimization framework
• Template system with quality assessment

🔧 Development Tools

• Cross-platform git hooks and automation
• File organization enforcement
• Agile artifact management

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🚀 Quick Start

📖 Essential Reading: Complete Cursor Practical Guide

→ Complete Cursor Practical Guide 🎯

THE definitive guide for developers using our revolutionary AI development system:

• Full development workflows (coding, debugging, testing, deployment)
• Complete agile management with automation
• @keyword mastery for instant context switching
• Advanced features and troubleshooting

Prerequisites

• Python: Use Anaconda installation at C:\App\Anaconda\ (Windows)
• Free APIs: Google Gemini (no paid services required)
• Git: For version control

Installation

# Clone the repository
git clone https://github.com/your-username/ai-dev-agent.git
cd ai-dev-agent

# Create conda environment
conda env create -f environment.yml
conda activate ai-dev-agent

# Install dependencies
pip install -r requirements.txt

Try Vibe Coding

# Launch the UI
streamlit run apps/vibe_coding_ui.py

Run Agent System

# Start the main agent interface
python apps/main.py

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Project Structure

ai-dev-agent/
├── agents/           # Specialized AI agents
├── apps/            # User interfaces (Vibe Coding, etc.)
├── utils/           # Core utilities and frameworks
├── tests/           # Comprehensive test suite
├── docs/            # Documentation and guides
├── enforcement/     # Rules and principles
└── scripts/         # Automation and setup tools

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Core Components

Agent System

• Context-aware agents that adapt behavior based on development context
• Rule-based coordination using practical software patterns
• Wu Wei principles for natural, non-forcing system design

Vibe Coding Interface

• Intuitive development through emotion and metaphor
• Enhanced prompts that improve agent communication
• Working implementation - not just a concept

Prompt Engineering

• Database-driven prompt storage and optimization
• Scientific A/B testing for prompt effectiveness
• Template system for consistent, reusable patterns

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Learning Focus

This project teaches practical techniques for:

1. AI Agent Coordination - How to build multi-agent systems that work together
2. Context-Aware Systems - Loading different behaviors based on development context
3. Prompt Engineering - Scientific approach to optimizing LLM communication
4. Ethical AI Development - Building safeguards and value alignment
5. Clean Architecture - Organizing AI systems for maintainability

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Production Extraction

To generate clean, production-ready software from this research:

# Extract specific components
python scripts/extract.py --type agent-toolkit --output ./my-agents
python scripts/extract.py --type vibe-ui --output ./my-vibe-app
python scripts/extract.py --type prompt-manager --output ./my-prompts

See PROJECT_STRUCTURE_STRATEGY.md for details.

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Testing

# Run full test suite
pytest tests/ -v

# Run specific test categories
pytest tests/unit/ -v
pytest tests/integration/ -v

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Development Principles

• Test-driven development - Write tests first
• Clean code practices - Readable, maintainable implementations
• Evidence-based validation - No claims without proof
• Practical focus - Every concept must improve actual software
• No bullshit - Clear, honest documentation and implementation

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Contributing

1. Follow the development principles - practical, tested, clean code
2. Write comprehensive tests for all new functionality
3. Document clearly - focus on practical usage
4. No philosophical abstractions - keep it practical and teachable

See CONTRIBUTING.md for detailed guidelines.

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Technology Stack

• Python 3.8+ with Anaconda package management
• LangChain/LangGraph for agent orchestration
• Streamlit for user interfaces
• SQLite for data persistence
• Pytest for testing framework
• Google Gemini for LLM capabilities

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License

MIT License - see LICENSE for details.

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Acknowledgments

Built with:

• LangChain - Agent orchestration framework
• Streamlit - Beautiful Python web apps
• Google Gemini - LLM capabilities

Standing on the shoulders of software engineering giants:

• Donald Knuth, Robert C. Martin, Martin Fowler, Kent Beck
• Gang of Four, Steve McConnell, and the Python community

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Focus: Learn practical AI development techniques. Extract clean software. Build useful tools.

Mission: Teach how philosophy can improve software engineering when applied practically.