🏠 Life Admin Assistant

An AI-powered personal assistant that helps you manage life's administrative tasks — documents, subscriptions, and major life events with smart checklists.

✨ Features

📄 Document Tracking

• Track important documents with expiry dates (passport, license, insurance, etc.)
• Automatic reminders at 90, 30, and 7 days before expiry
• Organize by category and family member
• Visual urgency indicators (🔴 urgent, 🟠 warning, 🟡 upcoming)

💳 Subscription Management

• Track all your recurring subscriptions
• Monitor free trials to avoid surprise charges
• Calculate monthly and yearly spending summaries
• Spending breakdown by category

📋 Life Event Checklists

Pre-built checklists for major life events:

• 🏠 Moving - 16 tasks from notice to settling in
• 💼 New Job - Onboarding and first month tasks
• 🚗 Buying a Car - Research to registration
• 🏡 Buying a Home - Pre-approval to closing
• 💒 Getting Married - Planning to post-wedding tasks
• ✈️ Travel - Planning to departure checklist
• 🎯 Custom - Create your own checklists

🔔 Notifications

• Email reminders for expiring documents
• Free trial ending alerts
• Daily digest summaries

🚀 Quick Start

Prerequisites

• Python 3.10+
• GitHub Token for GitHub Models API

Installation

1. Clone the repository

   git clone https://github.com/yourusername/life-admin-assistant.git
   cd life-admin-assistant

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment Create a .env file in the project root:

   GITHUB_TOKEN=your_github_token_here
   MODEL_NAME=openai/gpt-4.1-mini
   DATABASE_PATH=data/life_admin.db
   
   # Optional: Email notifications
   SENDER_EMAIL=[email protected]
   SENDER_PASSWORD=your_app_password
   NOTIFICATION_EMAIL=[email protected]
   
   # Optional: Tracing
   TRACING_ENABLED=true
   OTLP_ENDPOINT=http://localhost:4317

Running the App

Web UI (Streamlit)

python run_web.py

Opens at http://localhost:8501

Command Line

python main.py

📁 Project Structure

life-admin-assistant/
├── main.py                 # CLI entry point
├── run_web.py              # Streamlit launcher
├── requirements.txt        # Python dependencies
├── .env                    # Environment variables (create this)
├── data/                   # SQLite database storage
├── evaluation/             # Evaluation framework
│   ├── evaluate_agent.py   # Evaluation runner
│   └── test_dataset.json   # Test cases
├── knowledge/              # Knowledge base files
└── src/
    ├── agent.py            # Main AI agent configuration
    ├── config.py           # Configuration management
    ├── cli.py              # Command-line interface
    ├── webapp.py           # Streamlit web UI
    ├── database/
    │   ├── models/         # Data models
    │   └── repository/     # Database operations
    ├── prompts/
    │   └── system_prompt.txt
    └── tools/              # Agent tools
        ├── documents.py    # Document tracking
        ├── subscriptions.py# Subscription management
        ├── checklists.py   # Life event checklists
        └── notifications.py# Email notifications

🛠️ Available Tools

Tool	Description
add_document	Track a new document with expiry date
list_documents	List all tracked documents
get_expiring_documents	Show documents expiring soon
add_subscription	Track a new subscription
get_spending_summary	Calculate subscription spending
get_trial_alerts	Check for ending free trials
start_life_event	Begin tracking a life event
get_checklist	View life event checklist
mark_task_complete	Mark a checklist task done
send_expiry_reminder	Send email notification
get_daily_digest	Get summary of items needing attention

🔍 Observability

The agent includes OpenTelemetry tracing for debugging:

1. Enable tracing in your .env:

   TRACING_ENABLED=true

2. View traces in VS Code:

   • Open Command Palette (Ctrl+Shift+P)
   • Run "AI Toolkit: Open Trace Viewer"

📊 Evaluation

Run evaluation to test agent quality:

python evaluation/evaluate_agent.py

This will:

1. Run test queries against the agent
2. Evaluate response quality and tool usage
3. Generate results in evaluation/results.json

☁️ Deployment (Streamlit Cloud)

1. Push code to GitHub
2. Connect repo to Streamlit Cloud
3. Add secrets in Streamlit dashboard:

   GITHUB_TOKEN = "your_token"
   MODEL_NAME = "openai/gpt-4.1-mini"
   DATABASE_PATH = "data/life_admin.db"

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Built with Microsoft Agent Framework
• Powered by GitHub Models
• UI built with Streamlit