AI Context Persistence System (ACPS)

A sophisticated system for managing AI assistant context, memories, and knowledge across conversations using GitHub repositories and Gists with MCP (Model Context Protocol) integration.

🎯 Purpose

This system creates a persistent "memory bank" for AI assistants that:

• Stores system prompts, rules, and behavioral instructions
• Maintains conversation memories and learned patterns
• Automatically fetches and caches external documentation
• Provides intelligent context loading for new conversations
• Syncs with companion Gists for sharing and personal use

🏗️ Architecture

Main Repository

The core system with full automation, complex workflows, and comprehensive memory management.

Companion Gists

• AI Context Templates (Public) - Templates for new users
• Personal AI Memory Bank (Private) - Personal preferences and memories
• Shareable Prompt Collection (Public) - Domain-specific expertise prompts
• Quick Context Snippets (Public) - Reusable context components

Quick Setup

1. Clone and Setup:

   git clone https://github.com/hypeitnow/ai-context-system.git
   cd ai-context-system

2. Environment Configuration:

   cp .env.template .env
   # Edit .env with your GitHub token and other settings

3. Initialize:

   python scripts/setup.py

4. Validate:

   python scripts/validate.py

📁 Repository Structure

ai-context-system/
├── src/                    # Core system code
├── prompts/               # System prompts and rules
├── memories/              # Conversation memories and preferences
├── external-sources/      # Cached external documentation
├── config/               # System configuration
├── scripts/              # Utility scripts
├── .github/              # GitHub workflows and templates
├── tests/                # Test suites
└── docs/                 # Documentation

🔧 Features

Context Management

• Intelligent Context Loading: Prioritizes relevant context based on conversation type
• Memory Persistence: Stores and retrieves conversation summaries and learnings
• Version Control: Tracks prompt evolution and performance
• External Knowledge: Automatically updates documentation from external sources

MCP Integration

• Fetch MCP: Retrieves and caches web content
• GitHub MCP: Seamless repository management
• Custom MCPs: Extensible for additional data sources

Automation

• Scheduled Updates: Daily external documentation refresh
• Memory Backup: Automatic conversation storage
• Context Validation: Ensures prompt integrity
• Performance Monitoring: Tracks system effectiveness

Gist Synchronization

• Template Sharing: Public templates for community use
• Personal Sync: Private gist for sensitive data
• Snippet Library: Reusable context components

📖 Documentation

• Installation Guide
• Usage Instructions
• API Reference
• Troubleshooting
• Contributing Guidelines

🔒 Security

• Sensitive data encrypted at rest
• Personal memories kept in private gists
• Configurable access controls
• Audit logging for all operations

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

MIT License - see LICENSE file for details.

🆘 Support

• Create an Issue for bugs or feature requests
• Check our Troubleshooting Guide
• Review existing Discussions