Long-Term Memory for AI Study Assistants: The Complete Guide

AI assistants can lose continuity when context windows fill or sessions end, since processing larger token histories becomes increasingly costly and older material may be truncated or summarized. Long-term memory systems aim to preserve relevant information outside the active context window by combining episodic memory for interaction history, semantic memory for domain knowledge, and procedural memory for user preferences and workflows. The passage argues that memory graphs improve on simple vector retrieval by explicitly representing conceptual, temporal, and contradictory relationships, while structured forgetting through decay, compression, and triage helps prevent stale information and excessive token use. It presents Supermemory as a five-layer API platform with connectors, content extractors, retrieval-augmented generation, a memory graph, and user profiles, claiming sub-300 millisecond retrieval and benchmark advantages for single- and multi-session recall.

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