The article explains how AMS’s long‑term memory transforms discarded conversation data into reusable knowledge assets by defining various memory strategies, managing ownership, lifecycle, retrieval, and providing practical configuration and best‑practice guidance for AI assistants.
VitaBench 2.0, an open‑source benchmark from Meituan LongCat, evaluates large language models on long‑term, dynamic user interactions using 56 realistic users, 819 tasks, over 2 000 evolving preferences across up to 1 580 days, and reveals that even top models struggle with memory, personalization and proactive behavior.
The article examines eight common AI memory techniques—from simple full‑history retention to sophisticated vector‑store and knowledge‑graph approaches—detailing their principles, Python‑style implementations, advantages, drawbacks, and ideal application scenarios for large‑language‑model agents in production environments.
