Desktop AI Pet

A modern, transparent desktop pet application featuring an LLM-based agent with long-term memory, tool usage, and automated scheduling. Built with Python, PyQt6, and Asyncio.

Features

• Interactive Desktop Pet: A transparent, always-on-top window that acts as your AI companion.
• LLM-Powered Chat: Chat with your pet using OpenAI-compatible APIs. The agent maintains context of recent conversations.
• Long-Term Memory: Automatically generates and stores daily summaries of your interactions to maintain continuity over days.
• Tool Usage: The agent can perform actions like setting reminders for you.
• Scheduling:
  • Midnight Summary: Summarizes the day's events at 00:00.
  • Dynamic Reminders: The agent can schedule alerts based on your requests.
• Modern Tech Stack:
  • GUI: PyQt6 (with qasync for asyncio integration).
  • Database: Async SQLite (aiosqlite).
  • Scheduling: APScheduler.

Project Structure

desktop_aipet/
├── assets/          # Images and resources
├── config/          # Configuration files
│   └── config.json  # LLM and Pet settings
├── data/            # SQLite database storage
├── src/             # Source code
│   ├── agent_core.py       # LLM Agent logic and tools
│   ├── database.py         # Async DB handling
│   ├── main.py             # Entry point
│   ├── main_window.py      # GUI implementation
│   ├── memory_service.py   # Context and summary management
│   └── scheduler_service.py# Task scheduling
└── tests/           # Unit tests

Setup

1. Install Dependencies: Ensure you have Python 3.10+ installed.

   pip install -r requirements.txt

2. Configuration: Edit desktop_aipet/config/config.json to add your LLM API details:

   {
       "llm": {
           "api_type": "openai",
           "base_url": "https://api.openai.com/v1",
           "api_key": "YOUR_API_KEY_HERE",
           "model": "gpt-3.5-turbo"
       },
       "pet": {
           "name": "RoboPet"
       }
   }

Running the Application

To start the application, run the following command from the project root:

python -m desktop_aipet.src.main

Testing

Run the test suite to verify functionality:

python -m unittest discover desktop_aipet/tests