Phantom – A memory system for local LLMs that enriches itself while you sleep (github.com)

Phantom has introduced an innovative memory system designed for local large language models (LLMs) that operates seamlessly and enriches itself while idle. This system, called "MemoryDaemon," enhances LLM capabilities by creating a continuous enrichment loop. It not only stores facts but also analyzes and organizes them, classifying data, recognizing relationships, flagging outdated information, and consolidating profiles — all without impacting the performance of the LLM during operation. As a result, users can expect a smarter knowledge vault that grows more insightful over time. The significance of Phantom lies in its unique approach to memory management for LLMs, allowing for real-time memory processing using a tri-processor architecture. This setup includes a GPU for the LLM's reasoning, a CPU for memory extraction and storage, and an optional Apple Neural Engine (ANE) to further enrich data with minimal power consumption. The integration of this advanced memory framework promises to reduce cognitive load on users, offering efficient semantic recall and insights that enhance decision-making and task management. With easy access to a visualized knowledge graph and detailed stats, Phantom not only improves the functionality of LLMs but also sets a new standard for memory systems in AI applications.