w3bdesign/ai-architect-planner

An AI-based architecht and planner for planning development of new projects

🏗️ AI Architect Planner

🤖 Your AI-powered companion for architecting enterprise-ready applications

✨ Features

🎯 Smart Architecture Planning

• Enterprise-grade application scaffolding
• Best practices implementation
• Technology stack optimization
• Project structure generation

🧠 AI-Powered Design

• Architecture pattern recommendations
• Technology stack suggestions
• Security considerations
• Scalability strategies

🎨 Interactive Experience

• User-friendly CLI interface
• Guided project setup
• Detailed documentation generation
• Clear project organization

🚀 Quick Start

Prerequisites

• Python 3.11 or higher
• Git
• PowerShell 7+

Installation

Option 1: Manual Setup

1️⃣ Clone the repository:

git clone https://github.com/w3bdesign/ai-architect-planner.git
cd ai-architect-planner

2️⃣ Create and activate virtual environment:

python -m venv venv

# Windows
venv\Scripts\activate

# Unix/MacOS
source venv/bin/activate

3️⃣ Install in development mode:

pip install -e ".[dev]"

Option 2: Automated Setup (Windows PowerShell)

After cloning the repository, you can use our automated setup script:

pwsh setup.ps1

This script will automatically:

• Verify Python 3.11+ is installed
• Create and activate a virtual environment
• Install the package in development mode

💻 Usage

Start the interactive planning session:

python -m ai_architect_planner

The tool will:

1. Guide you through project setup
2. Generate project structure
3. Create detailed architecture documentation
4. Provide technology recommendations

Generated Project Structure

For each project, the tool creates:

your-project/
├── src/           # Source code
├── docs/          # Documentation
│   └── ARCHITECT.md  # Architecture document
└── tests/         # Test files

🏛️ Project Structure

ai_architect_planner/
├── core/          # Core functionality
│   ├── interaction.py  # User interaction
│   └── project.py      # Project operations
├── services/      # Service modules
│   └── llm.py         # AI service (mock)
├── utils/         # Utilities
│   ├── config.py          # Configuration
│   ├── constants.py       # Constants
│   └── type_definitions.py # Type hints
└── cli/           # CLI interface
    └── cli.py          # Main CLI

Module Overview

Core

• interaction.py: User interaction handling
• project.py: Project structure operations

Services

• llm.py: AI service for architecture analysis (currently mocked)

Utils

• config.py: Project-wide configuration
• constants.py: Shared constants
• type_definitions.py: TypeScript-like type hints

CLI

• cli.py: Main CLI interface

🧪 Testing

The project uses pytest for testing and maintains code coverage through Codecov integration.

Running Tests Locally

# Run all tests with coverage
pytest --cov=ai_architect_planner --cov-report=term

# Run specific test file
pytest tests/cli/test_cli.py -v

Continuous Integration

Our GitHub Actions workflow automatically runs on push and pull requests to the main branch, performing:

1. Environment Setup

   • Python 3.11 setup
   • Installation of dependencies

2. Code Quality

   • Linting with Ruff
   • Format checking with Ruff (non-blocking)

3. Testing

   • Runs pytest with multiple coverage reports:
     • XML report for Codecov
     • HTML report for local viewing
     • Terminal output for immediate feedback
   • Generates JUnit XML test results

4. Artifacts

   • Uploads test results and coverage reports as artifacts
   • Pushes coverage data to Codecov for tracking

View the latest coverage reports on Codecov.

Test Structure

tests/
├── cli/          # CLI tests
├── core/         # Core module tests
├── services/     # Service tests
└── utils/        # Utility tests

📚 Generated Documentation

The tool generates a comprehensive ARCHITECT.md in your project containing:

• 📐 System Architecture
• 🔧 Technology Stack
• 🛠️ Implementation Guidelines
• 🔒 Security Considerations
• 📈 Scaling Strategies

🔄 Development Workflow

1. Create feature branch
2. Make changes
3. Run tests and linting:

   # Run tests with coverage
   pytest --cov=ai_architect_planner --cov-report=term
   
   # Run Ruff for linting and formatting
   ruff check
   ruff format

4. Submit pull request

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

---

Made with ❤️ by AI enthusiasts for AI developers