Vektor addresses the critical problem of 'goldfish memory' in AI agents, where conventional RAG systems fall short by treating memory as disconnected facts. It introduces a revolutionary cognitive memory operating system that replaces flat vector storage with a structured, multi-layered architecture. By synthesizing research into a local-first SQLite implementation, Vektor builds a persistent, intelligent history for AI agents, enabling them to understand temporal sequences, causality, and entity relationships, thus fostering long-term reasoning and persona development.
Key Features
01Local-first SQLite implementation for data persistence
02Multi-layered associative graph memory (MAGMA)
03Zero API cost embeddings with local transformers
041 GitHub stars
05Autonomous REM cycle for memory management
06Model-agnostic LLM compatibility
