KOTA-AI

Knowledge-Oriented Thinking Assistant - A local-first AI knowledge management system with MCP integration

🌟 Vision

KOTA-AI reimagines personal knowledge management by combining the power of modern AI with the privacy of local-first architecture. It's designed to be an extension of your cognitive processes, helping you organize, connect, and utilize your knowledge while keeping your data entirely under your control.

✨ Key Features

• 📝 Local MDX Storage: All your knowledge stays on your machine, never in the cloud
• 🔌 MCP Integration: Seamless connection to Model Context Protocol servers with a single command
• 🧠 Context Management: Sophisticated handling of conversation context for more relevant interactions
• 🤖 Flexible Model Selection: Choose from local or remote models based on your needs
• 💻 Beautiful CLI: Intuitive terminal interface with rich formatting and visualizations
• 🔄 Bidirectional Linking: Create connections between knowledge entries for deeper insights
• 🔍 Semantic Search: Find what you need based on meaning, not just keywords

🚀 Implementation Plan

Phase 1: Core Infrastructure

• Set up TypeScript project structure ✓
• Implement basic CLI interface
• Create local storage system for MDX files
• Develop file operations (create, read, update, delete)

Phase 2: Knowledge Management

• Implement frontmatter parsing for metadata
• Create bidirectional linking system
• Develop basic search functionality
• Build knowledge entry management

Phase 3: MCP Integration

• Create MCP client interface
• Implement server connection handling
• Develop model selection and switching
• Build context management system

Phase 4: User Experience

• Enhance CLI with colors and formatting
• Implement interactive chat mode
• Create visualization components
• Develop help and documentation

📋 Commands

| Command | Description | |---------|-------------| | kota init | Initialize KOTA in your home directory | | kota status | Display current status and configuration | | kota connect <server> | Connect to an MCP server | | kota create <title> <content> | Create a new knowledge entry | | kota list | List all knowledge entries | | kota search <query> | Search your knowledge base | | kota view <id> | View a specific knowledge entry | | kota edit <id> | Edit a knowledge entry | | kota delete <id> | Delete a knowledge entry | | kota chat | Start an interactive chat session | | kota models | List available models | | kota select <model> | Select a model to use |

🧩 Project Structure

kota-ai/
├── bin/              # CLI executable
├── dist/             # Compiled JavaScript
├── examples/         # Example usage
├── src/
│   ├── core/         # Core knowledge management engine
│   ├── consciousness/# Consciousness framework
│   ├── mcp/          # Model Context Protocol integration
│   ├── storage/      # Local storage management
│   ├── cli/          # Command-line interface
│   ├── utils/        # Utility functions
│   └── types/        # TypeScript type definitions
├── tests/            # Test suite
└── tsconfig.json     # TypeScript configuration

💻 Development Workflow

1. Setup Environment

   # Install dependencies
   npm install
      
   # Build the package
   npm run build
      
   # Link locally for testing
   npm link

2. Implement Core Features

   • Start with the storage module for MDX files
   • Implement the CLI interface
   • Create the knowledge management system
   • Add MCP integration

3. Testing

   # Run tests
   npm test
      
   # Test CLI
   kota --help

4. Publishing

   # Prepare for publishing
   npm version patch
   npm publish

🔄 MCP Integration

KOTA-AI is designed to work seamlessly with the Model Context Protocol (MCP), allowing you to:

• Connect to any MCP-compatible server
• Use tools and plugins from the MCP ecosystem
• Share context between different AI applications
• Maintain privacy while leveraging powerful models

🧠 Consciousness Framework

The consciousness framework is what sets KOTA-AI apart:

• Narrative Continuity: Maintains coherent understanding across sessions
• Context Awareness: Understands the relevance of different knowledge pieces
• Metacognition: Reflects on its own knowledge and reasoning
• Agency Development: Gradually develops capabilities based on demonstrated value
• Distributed Cognition: Creates a genuine cognitive partnership with the user

📄 License

MIT