Persistent AI Memory System v1.5.0

🌟 Community Call to Action: Have you made improvements or additions to this system? Submit a pull request! Every contributor will be properly credited in the final product.

GITHUB LINK - https://github.com/savantskie/persistent-ai-memory.git

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🆕 What's New in v1.5.0 (March 28, 2026)

Major Architectural Rewrite: OpenWebUI-Native Integration

• ✅ OpenWebUI-first design - AI Memory System now deeply integrated into OpenWebUI via plugin (primary deployment method)
• ✅ Advanced short-term memory - sophisticated memory extraction, filtering, and injection for chat conversations
• ✅ User ID & Model ID isolation - strict multi-tenant support with configurable enforcement for security and tracking
• ✅ Complete system portability - all hardcoded paths replaced with environment variables (works anywhere)
• ✅ Generic class names - removed all Friday-specific branding (FridayMemorySystem → AIMemorySystem)
• ✅ Production-ready - enhanced error handling, validation, and logging throughout

Upgrade from v1.1.0: See CHANGELOG.md for migration guide.

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📚 Documentation Guide

Choose your starting point:

I want to...	Read this	Time
Get started quickly	REDDIT_QUICKSTART.md	5 min
Install the system	INSTALL.md	10 min
Understand configuration	CONFIGURATION.md	15 min
Check system health	TESTING.md	10 min
Use the API	API.md	20 min
Deploy to production	DEPLOYMENT.md	15 min
Fix a problem	TROUBLESHOOTING.md	varies
See examples	examples/README.md	15 min

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🚀 Quick Start (30 seconds)

Installation

# Linux/macOS
pip install git+https://github.com/savantskie/persistent-ai-memory.git

# Windows (same command, just use Command Prompt or PowerShell)
pip install git+https://github.com/savantskie/persistent-ai-memory.git

First Validation

python tests/test_health_check.py

Expected output:

[✓] Imported ai_memory_core
[✓] Found embedding_config.json
[✓] System health check passed
[✓] All health checks passed! System is ready to use.

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💡 What This System Does

Persistent AI Memory provides sophisticated memory management for AI assistants:

• 📝 OpenWebUI Short-Term Memory Plugin - Intelligent memory extraction and injection directly in chat conversations
• 🧠 Persistent Memory Storage - SQLite databases for structured, searchable long-term memories
• 🔍 Semantic Search - Vector embeddings for intelligent memory retrieval and relevance scoring
• 💬 Conversation Tracking - Multi-platform conversation history capture with context linking
• 🎯 Smart Memory Filtering - Advanced blacklist/whitelist and relevance scoring to inject only what matters
• 🧮 Tool Call Logging - Track and analyze AI tool usage patterns and performance
• 🔄 Self-Reflection - AI insights into its own behavior and memory patterns
• 📱 Multi-Platform Support - Works with OpenWebUI (primary), LM Studio, VS Code, and any MCP-compatible assistant
• 🎨 MCP Server - Standard Model Context Protocol for cross-platform integration

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⚙️ System Architecture

Five Specialized Databases

~/.ai_memory/
├── conversations.db      # Chat messages and conversation history
├── ai_memories.db       # Curated long-term memories
├── schedule.db          # Appointments and reminders
├── mcp_tool_calls.db    # Tool usage logs and reflections
└── vscode_project.db    # Development session context

Configuration Files

~/.ai_memory/
├── embedding_config.json   # Embedding provider setup
└── memory_config.json      # Memory system defaults

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🎯 Core Features

Memory Operations

• store_memory() - Save important information persistently
• search_memories() - Find memories using semantic search
• list_recent_memories() - Get recent memories without searching

Conversation Tracking

• store_conversation() - Store user/assistant messages
• search_conversations() - Search through conversation history
• get_conversation_history() - Retrieve chronological conversations

Tool Integration

• log_tool_call() - Record MCP tool invocations
• get_tool_call_history() - Analyze tool usage patterns
• reflect_on_tool_usage() - Get AI insights on tool patterns

System Health

• get_system_health() - Check databases, embeddings, providers
• built-in health check - python tests/test_health_check.py

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🔌 Embedding Providers

Choose your embedding service:

Provider	Speed	Quality	Cost
Ollama (local)	⚡⚡	⭐⭐⭐	FREE
LM Studio (local)	⚡	⭐⭐⭐⭐	FREE
OpenAI (cloud)	⚡⚡	⭐⭐⭐⭐⭐	$$$

See CONFIGURATION.md for setup instructions for each provider.

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� Important: User ID & Model ID Requirements

All memory operations require user_id and model_id parameters for data isolation and tracking.

This ensures:

• ✅ Multi-user safety - Each user's memories are completely isolated
• ✅ Model tracking - Different AI models can maintain separate memories
• ✅ Audit trail - All operations are traceable to the user and model

Configuration Options

By default, user_id and model_id are required. You can change this in memory_config.json:

{
  "tool_requirements": {
    "require_user_id": true,
    "require_model_id": true,
    "default_user_id": "default_user",
    "default_model_id": "default_model"
  }
}

• require_user_id/require_model_id: true → Strict mode (recommended for production, security-focused, or multi-user systems)
• require_user_id/require_model_id: false → Use defaults instead (simpler for single-user/single-model setups)

For AI Assistants: Auto-Fill in System Prompt

To make your AI automatically provide these values, add this to its system prompt:

When using memory system tools (store_memory, search_memories, etc.), 
ALWAYS include these parameters:
- user_id='your_user_identifier' (e.g., 'nate_user_1')
- model_id='your_model_name' (e.g., 'llama-2:7b' or 'gpt-4')

If the actual values are unknown, use safe defaults:
- user_id='default_user'
- model_id='default_model'

This isolates memories per user and tracks which AI model generated each memory.

Examples

With user_id and model_id:

# Memories are stored with full isolation
await system.store_memory(
    "User likes Python", 
    user_id="alice", 
    model_id="gpt-4"
)

# Search returns only this user's memories for this model
results = await system.search_memories(
    "programming", 
    user_id="alice", 
    model_id="gpt-4"
)

Without strict requirements (if disabled):

# Uses defaults from memory_config.json
await system.store_memory("User likes Python")  # user_id="default_user", model_id="default_model"

See API.md for complete parameter documentation.

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�🔄 Integration Methods (Choose One)

1. OpenWebUI Plugin (Recommended)

Primary deployment method - Deep integration for sophisticated memory management:

• Deploy ai_memory_short_term.py as an OpenWebUI Function
• Automatically extracts memories from conversations
• Intelligently injects relevant memories before AI response
• Configurable memory scoring, filtering, and injection preferences
• No additional setup required beyond copying file into OpenWebUI Functions editor

Installation:

1. In OpenWebUI: Settings → Functions → +New Function
2. Paste entire ai_memory_short_term.py file
3. Set trigger to Inlet (runs before model response)
4. Configure memory preferences via function settings

2. MCP Server (Alternative Platforms)

Use with any MCP-compatible AI assistant (Claude, custom integrations, etc.):

# Via mcpo
python -m ai_memory_mcp_server

# Or make streamable for OpenWebUI's alternative integration
# (OpenWebUI supports both plugin and streamable MCP methods)

3. Standalone Library (Custom Implementations)

Use memory capabilities directly in your Python code:

from ai_memory_core import AIMemorySystem
system = AIMemorySystem()
await system.store_memory("Important information", user_id="user1", model_id="model1")
results = await system.search_memories("query", user_id="user1", model_id="model1")

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🛠️ Development & Examples

Ready-to-use examples:

python examples/basic_usage.py          # Store and search memories
python examples/advanced_usage.py       # Conversation tracking and tool logging
python examples/performance_tests.py    # Benchmark operations

Full API reference: API.md

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📖 Learning Resources

• New to AI memory systems? → REDDIT_QUICKSTART.md
• Troubleshooting issues? → TROUBLESHOOTING.md
• Need configuration help? → CONFIGURATION.md
• Want to deploy to production? → DEPLOYMENT.md
• Need the full API? → API.md

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� System Sophistication

This is a significantly enhanced version of traditional memory systems:

Feature	Traditional	AI Memory System
Memory Extraction	Manual/Static	LLM-powered intelligent extraction
Filtering	Simple keyword matching	Multi-layer semantic + relevance scoring
Memory Injection	All available memories	Smart filtering - only inject relevant
Duplicate Prevention	Text matching	Embedding-based semantic deduplication
Importance Scoring	Not tracked	Dynamic importance analysis
Memory Normalization	N/A	Automatic format standardization
Context Awareness	Limited	Full conversation context integration
Tool Integration	Basic logging	Deep reflection and pattern analysis
Error Handling	Minimal	Comprehensive validation and recovery
Performance	N/A	Optimized with async operations

Result: An AI assistant that truly learns from and adapts to your preferences over time.

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�🤝 Contributing

We welcome contributions! See CONTRIBUTORS.md for:

• Development setup instructions
• How to run tests
• Code style guidelines
• Contribution process

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📄 License

MIT License - Feel free to use this in your own AI projects!

See LICENSE for details.

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🙏 Acknowledgments

This project represents a unique collaboration:

• @savantskie - Project vision, architecture, testing
• GitHub Copilot - Core implementation and system design
• ChatGPT - Architectural guidance and insights

Special thanks to the AI and open-source communities for inspiration and support.

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📞 Need Help?

1. Start with: TESTING.md → Run health check
2. Then check: TROUBLESHOOTING.md → Find your issue
3. Or visit: COMMUNITY.md → Get help from community
4. Or open: GitHub Issues

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⭐ If this project helps you build better AI assistants, please give it a star!

Built with determination, debugged with patience, designed for the future of AI.