🌟 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

Path Independence & Configuration System Update

• ✅ Complete path independence - works on any system in any directory
• ✅ Configuration system - memory_config.json and embedding_config.json for flexible deployments
• ✅ Tag management - automatic tag extraction and normalization
• ✅ Improved health checks - better diagnostics with helpful error messages
• ✅ Docker enhancements - full container support with synced registries
• ✅ Documentation - 5 comprehensive guides (config, testing, API, deployment, troubleshooting)

Choose your starting point:

# 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

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.

Persistent AI Memory provides:

• 🧠 Persistent Memory Storage - SQLite databases for structured, searchable storage
• 🔍 Semantic Search - Vector embeddings for intelligent memory retrieval
• 💬 Conversation Tracking - Multi-platform conversation history capture
• 🧮 Tool Call Logging - Track and analyze AI tool usage patterns
• 🔄 Self-Reflection - AI insights into its own behavior and performance
• 📱 Multi-Platform - Works with LM Studio, VS Code, OpenWebUI, custom applications
• 🎯 Easy Integration - MCP server for any AI assistant compatible with Model Context Protocol

~/.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

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

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

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

• 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

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

Choose your embedding service:

See CONFIGURATION.md for setup instructions for each provider.

Use memory capabilities directly in your Python code:

from ai_memory_core import AIMemorySystem
system = await AIMemorySystem.create()
await system.store_memory("Important information")
results = await system.search_memories("query")

Deploy as an OpenWebUI filter to auto-inject memories into chat:

• Automatically extracts memories from conversations
• Injects relevant memories before responding
• Configurable via OpenWebUI filter settings

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

python -m ai_memory_mcp_server

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

• 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

We welcome contributions! See CONTRIBUTORS.md for:

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

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

See LICENSE for details.

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.

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

⭐ 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.