This briefing document synthesizes information from several recent academic papers and a commercial announcement, highlighting cutting-edge developments in enhancing Large Language Models (LLMs) with robust memory and retrieval capabilities. Key themes include the use of hierarchical memory systems inspired by operating systems (MemGPT), the integration of temporal knowledge graphs for improved factual accuracy and reasoning (Zep, TempAgent), and the application of reinforcement learning for efficient memory management in multi-objective tasks (MEM1). The integration of FalkorDB as a backend for Graphiti by Zep underscores the growing industry recognition of graph databases for scalable, real-time agent memory, particularly in multi-tenant environments.