Memory-PlusRepository30/100 via “semantic-memory-recording-with-vector-embedding”
** a lightweight, local RAG memory store to record, retrieve, update, delete, and visualize persistent "memories" across sessions—perfect for developers working with multiple AI coders (like Windsurf, Cursor, or Copilot) or anyone who wants their AI to actually remember them.
Unique: Integrates Google Gemini embeddings with Qdrant vector database through a dedicated MemoryProtocol class that handles text chunking, versioning, and category-based filtering — enabling semantic search with full memory history tracking rather than simple key-value storage
vs others: Lighter and more focused than full RAG frameworks (LlamaIndex, LangChain) by specializing in agent memory persistence with built-in MCP protocol support, avoiding framework overhead while maintaining semantic search capabilities