About KognitiveKompanion
KognitiveKompanion is a desktop AI interface for KDE and other desktop environments, aimed at users working with OpenAI, Ollama, or AMD Ryzen AI. It combines conversation management and visual or audio context features with multiple backend options.
Key features
- 01OpenAI, Ollama, and Ryzen AI backends
- 02Conversation history and export
- 03Screen capture for visual context
- 04Audio input support
- 05RAG toggle
- 06System tray and floating window
Use cases
- 01Chat through OpenAI or Ollama
- 02Use screen context in conversations
- 03Save and export chat history
MCP support
The project includes an MCP client component in its application structure, but the README does not describe its MCP server connections or management.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEKognitiveKompanion
A modern, versatile AI interface for KDE and other desktop environments, designed to provide a seamless interaction with various AI backends including OpenAI, Ollama, and AMD Ryzen AI hardware acceleration.
Features
- Multi-Backend Support:
- OpenAI API integration (GPT-4o, GPT-3.5-Turbo, etc.)
- Ollama backend for local models
- AMD Ryzen AI hardware acceleration
- Advanced UI:
- Collapsible sections for a clean interface
- Conversation sidebar for managing chat history
- Modern styling with KDE theming integration
- System tray icon and floating window option
- Context Features:
- Screen capture capability for visual context
- Audio input support
- RAG (Retrieval-Augmented Generation) toggle
- Conversation Management:
- Save and load conversations
- Export chat history
Requirements
- Python 3.8+ (Python 3.12 recommended)
- PyQt5
- KDE Plasma 5 Desktop (optional, works on other desktops too)
- One of the following backends:
- OpenAI API key
- Ollama running locally
- AMD Ryzen AI compatible hardware (optional)
Installation
- Clone the repository:
git clone https://github.com/MagicUnicornInc/KognitiveKompanion.git
cd KognitiveKompanion
- Install dependencies:
pip install -r requirements.txt
- Run the application with your preferred backend:
# OpenAI backend
./run_openai_gui.sh
# Ollama backend
./run_kde_ai_interface.py
# AMD Ryzen AI (if supported hardware is available)
./run_ryzen_ai_model.py
Configuration
See MULTI_BACKEND_GUIDE.md for detailed setup of different backends.
For AMD Ryzen AI specific setup, see README_RYZEN_AI_SETUP.md.
Project Structure
├── app_root/ # Core application code
│ ├── ui/ # UI components and widgets
│ ├── mcp/ # Model Control Protocol client
│ ├── config/ # Configuration management
│ ├── rag/ # Retrieval-Augmented Generation
│ ├── system/ # System integration and optimization
│ └── utils/ # Utility functions
├── amd-ryzen-ai/ # AMD Ryzen AI integration
├── quark-integration/ # Quantization toolkit integration
└── MCP-INTEGRATION-GUIDE.md # MCP server documentation
License
This project is licensed under the MIT License - see the LICENSE file for details.
Project Status
See PROJECT_STATUS.md for the current development status and roadmap.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Acknowledgments
- KDE Community
- OpenAI and Ollama projects
- AMD for Ryzen AI support
- PyQt/Qt developers
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.