Smart Personal Planner & Assistant

AI-powered personal planner with multi-agent orchestration, intelligent task prioritization, and natural language interface.

Features

• Multi-Agent Architecture: 4 specialized AI agents working together

  • Task Planner Agent: Prioritizes and schedules tasks
  • Suggestion Agent: Recommends what to do next
  • Notification Agent: Sends reminders and motivational messages
  • Analytics Agent: Tracks patterns and insights

• Multi-LLM Orchestration: Primary (GPT-4) with fallback (Gemini)

• Vector Database: Pinecone for context-aware suggestions

• Telegram Bot: Natural language task management

• REST API: Full CRUD for tasks and events

• React Dashboard: Visual calendar and task tracking

Architecture

Frontend (React)  ←→  FastAPI Backend  ←→  PostgreSQL
                           ↓
                  Multi-Agent System
                           ↓
                  Multi-LLM Layer (GPT-4/Gemini)
                           ↓
                  Vector DB (Pinecone)
                           ↓
                  Telegram Bot

Quick Start (Day 1)

Prerequisites

• Python 3.9+
• PostgreSQL 15+
• Node.js 16+
• Git

1. Clone Repository

git clone <your-repo-url>
cd smart-planner-ai

2. Backend Setup

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Setup PostgreSQL
psql postgres
# In psql:
CREATE DATABASE smart_planner_db;
CREATE USER planner_user WITH PASSWORD 'your_password';
GRANT ALL PRIVILEGES ON DATABASE smart_planner_db TO planner_user;
\q

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Initialize database
python scripts/setup_db.py

# Run server
uvicorn app.main:app --reload

Visit: http://localhost:8000/docs for API documentation

3. Get API Keys

Telegram Bot

1. Message @BotFather on Telegram
2. Send /newbot
3. Copy your bot token

OpenAI

• Visit: https://platform.openai.com/api-keys
• Create API key

Google Gemini

• Visit: https://makersuite.google.com/app/apikey
• Create API key

Pinecone

• Sign up: https://www.pinecone.io/
• Create index: smart-planner (dimension: 1536, metric: cosine)
• Copy API key

4. Test the Bot

1. Find your bot on Telegram
2. Send /start
3. Try commands:
   • /help
   • /addtask
   • /listtasks
   • /suggest

Project Structure

smart-planner-ai/
├── backend/
│   ├── app/
│   │   ├── models/          # Database models
│   │   ├── agents/          # AI agents
│   │   ├── llm/             # LLM providers
│   │   ├── vectordb/        # Pinecone integration
│   │   ├── telegram/        # Telegram bot
│   │   ├── api/             # REST endpoints
│   │   └── utils/           # Utilities
│   ├── scripts/             # Setup scripts
│   ├── tests/               # Tests
│   └── requirements.txt     # Dependencies
├── frontend/                # React app (Day 9-10)
└── docs/                    # Documentation

API Endpoints

Tasks

• POST /api/tasks/ - Create task
• GET /api/tasks/ - List tasks
• GET /api/tasks/{id} - Get task
• PUT /api/tasks/{id} - Update task
• DELETE /api/tasks/{id} - Delete task
• GET /api/tasks/suggestions/next - Get next task suggestion

Events

• POST /api/events/ - Create event
• GET /api/events/ - List events
• GET /api/events/{id} - Get event
• PUT /api/events/{id} - Update event
• DELETE /api/events/{id} - Delete event

Chat

• POST /api/chat/ - Natural language interface

Testing

# Run tests
pytest

# Run with coverage
pytest --cov=app tests/

Deployment

Backend (Render)

# Connect GitHub repo to Render
# Set environment variables in Render dashboard
# Deploy!

Frontend (Vercel)

cd frontend
npm run build
vercel deploy

Environment Variables

See .env.example for all required variables:

• Database credentials
• API keys (OpenAI, Gemini, Pinecone)
• Telegram bot token
• App settings

Contributing

This is a personal project for learning. Feel free to fork and experiment!

License

MIT

Acknowledgments

Built as a portfolio project to demonstrate:

• Multi-agent AI systems
• LLM orchestration with fallbacks
• Vector databases for RAG
• Production-ready FastAPI backend
• Modern React frontend