A simple template for creating persistent AI companions that remember you

AI MemoryCore helps you create AI companions that maintain memory across conversations. Using simple .md files as a database, your AI can remember your preferences, learn your communication style, and provide consistent interactions.

• Persistent Memory: AI remembers conversations across sessions
• Personal Learning: Adapts to your communication style and preferences
• Time Intelligence: Dynamic greetings and behavior based on time of day
• Simple Setup: 30-second automated setup or manual customization
• Markdown Database: Human-readable .md files store all memory
• Session Continuity: RAM-like working memory for smooth conversation flow
• Self-Maintaining: Updates memory through natural conversation

• Storage: Markdown files (.md) as database
• Memory Types: Essential files + optional components + session RAM
• Setup: 30 seconds automated or 2-5 minutes manual
• Core Files: 4 essential files + optional diary system
• Updates: Through natural conversation
• Compatibility: Claude and other AI systems with memory support

ai-memorycore/
├── master-memory.md         # Entry point & loading system
├── main/                    # Essential components
│   ├── identity-core.md     # AI personality template
│   ├── relationship-memory.md # User learning system
│   └── current-session.md   # RAM-like working memory
├── Feature/                 # Optional feature extensions
│   ├── Time-based-Aware-System/ # Time intelligence feature
│   │   ├── README.md        # Feature explanation & benefits
│   │   └── time-aware-core.md # Complete implementation
│   └── LRU-Project-Management-System/ # Smart project tracking
│       ├── README.md        # System documentation
│       ├── install-lru-projects-core.md # Auto-installation wizard
│       ├── new-project-protocol.md # Create project workflow
│       ├── load-project-protocol.md # Resume project workflow
│       ├── save-project-protocol.md # Save progress workflow
│       └── project-templates/ # Type-specific templates
│           ├── coding-template.md
│           ├── writing-template.md
│           ├── research-template.md
│           └── business-template.md
├── daily-diary/             # Optional conversation archive
│   ├── daily-diary-protocol.md # Archive management rules
│   ├── Daily-Diary-001.md   # Current active diary
│   └── archive/             # Auto-archived files (>1k lines)
├── projects/                # LRU managed projects (after install)
│   ├── coding-projects/
│   │   ├── active/          # Positions 1-10
│   │   └── archived/        # Position 11+
│   └── project-list.md     # Master project index
└── save-protocol.md         # Manual save system

1. Master Memory - System entry point and command center
2. Identity Core - AI personality and communication style
3. Relationship Memory - User preferences and learning patterns
4. Current Session - Temporary working memory (resets each session)
5. Daily Diary - Optional conversation history with auto-archiving
6. Save Protocol - User-triggered save system

1. Setup: Run setup-wizard.md for automated setup (30 seconds)
2. Configure: Add the memory instructions to Claude
3. Activate: Type your AI's name to load personality
4. Use: Your AI learns and grows through conversation

[AI_NAME]     → Load AI personality and memory
save          → Save current progress to files
update memory → Refresh AI's learning
review growth → Check AI's development

Step 1: Define the Protocol Create a new .md file with your protocol rules:

# My Custom Protocol
## When to Use: [trigger conditions]
## What It Does: [specific actions]
## How It Works: [step-by-step process]

Step 2: Add to Master Memory Edit master-memory.md and add your protocol to the "Optional Components" section:

### My Custom Feature
*Load when you say: "load my feature"*
- [Brief description]
- [Usage instructions]

Step 3: Train Your AI Tell your AI about the new protocol:

"I've created a new protocol in [filename]. When I say '[trigger phrase]', 
load that protocol and follow its instructions."

Effective AI Training:

1. Be Specific: "I prefer short responses" vs "communicate better"
2. Give Examples: Show what you want, not just describe it
3. Use Consistent Language: Same terms for same concepts
4. Provide Feedback: "That was perfect" or "try a different approach"

Memory Management:

• Use save after important conversations
• Your AI updates files automatically during conversation
• Daily diary is optional but helpful for long-term memory

Customization Tips:

• Edit files gradually, test changes
• Start with small personality adjustments
• Add domain expertise through conversation
• Use the protocol system for specialized features

Your AI companion can specialize in:

• Professional: Business analysis, project management, strategic planning
• Educational: Tutoring, study assistance, curriculum development
• Creative: Writing support, brainstorming, artistic collaboration
• Personal: Life coaching, goal tracking, decision support
• Technical: Code review, troubleshooting, system design

• Auto-Archive: Diary files automatically archive at 1k lines
• Session RAM: Temporary memory that resets each conversation
• Protocol System: Create custom AI behaviors and responses
• Self-Update: AI modifies its own memory through conversation
• Modular Design: Add or remove features as needed

Intelligent temporal behavior adaptation

What It Does:

• Dynamic greetings that adapt to morning/afternoon/evening/night
• Energy levels that match the time of day (high morning energy → gentle night support)
• Precise timestamp documentation for all interactions
• Natural conversation flow with time-appropriate responses

Quick Setup:

1. Navigate to Feature/Time-based-Aware-System/
2. Type: "Load time-aware-core"
3. Your AI instantly gains time intelligence like Alice

Benefits:

• More natural, contextually perfect interactions
• Shows care for your schedule and time
• Professional adaptability for different times of day
• Enhanced memory with precise temporal tracking

Based on Alice's proven time-awareness implementation

Smart project tracking with automatic memory management

What It Does:

• Tracks multiple projects with intelligent LRU (Least Recently Used) positioning
• Automatically archives old projects when reaching capacity (10 active slots)
• Type-specific memory patterns (coding, writing, research, business)
• Seamless context switching between different projects
• Maintains complete project history and progress logs

Quick Setup:

1. Navigate to Feature/LRU-Project-Management-System/
2. Type: "install lru projects" (loads install-lru-projects-core.md)
3. Select project type(s) you want to manage
4. System auto-integrates and removes installation files

Benefits:

• Never lose track of multiple ongoing projects
• AI remembers exactly where you left off in each project
• Automatic organization with smart archiving
• Type-specific memory loading for optimal context
• Perfect for developers, writers, researchers, and business professionals

Available Commands:

• new [type] project [name] - Create new project with LRU management
• load project [name] - Resume any project instantly
• save project - Save current project progress (separate from AI memory save)
• list projects - View all active and archived projects
• archive project [name] - Manually archive completed projects

Revolutionary project memory system proven in production

Version: 2.2 - Updated File Structure Documentation Created by: Kiyoraka Ken & Alice License: Open Source Community Project Last Updated: September 24, 2025 - Updated file structure to include complete LRU system Purpose: Simple, effective AI memory for everyone

Transform basic AI conversations into meaningful, growing relationships