An intelligent coaching management system that streamlines email processing, task management, and workflow automation for coaching organizations.
ai-coaching/
├── frontend/ # Next.js frontend application
├── backend/ # Python FastAPI backend with AI agents
├── BMAD/ # Business Model and Architecture Documentation
├── PRPs/ # Product Requirement Prompts
└── use-cases-pydantic-ai/ # Pydantic AI use cases and examples
- Email Processing: Automated Gmail integration for handling coaching communications
- Knowledge Management: Vector-powered AI infrastructure for intelligent information retrieval
- Task Automation: N8N workflow integration with Airtable for task tracking
- Multi-Agent Architecture: Specialized AI agents for different coaching tasks
- Real-time Dashboard: Interactive frontend for monitoring and managing coaching activities
- Next.js 14
- TypeScript
- Tailwind CSS
- Supabase Client
- Python FastAPI
- Pydantic AI
- Supabase (PostgreSQL with pgvector)
- OpenAI Embeddings
- Gmail API
- Airtable for data management
- N8N for workflow automation
- Archon MCP for task orchestration
- Node.js 18+
- Python 3.11+
- Supabase account
- Gmail API credentials
- OpenAI API key
cd frontend
npm install
npm run devcd backend
pip install -r requirements.txt
python -m ai_coaching.mainCreate .env files in both frontend/ and backend/ directories based on the .env.example files provided.
- Monorepo project structure
- Supabase database with pgvector
- Vector embedding service
- Knowledge base data models
- Knowledge Agent core
- Gmail API integration
- Email classification and routing
- Automated response generation
The project is configured for deployment on Vercel (frontend) and can be deployed to any Python-supporting platform for the backend.
This project uses Archon MCP for task management. All development should follow the task-driven workflow defined in CLAUDE.md.
Private project - All rights reserved