Recent advancements in Large Language Models (LLMs) emphasize the importance of memory for maintaining context in extended dialogues. Two notable architectures, HEMA and Mnemosyne, have emerged: HEMA enhances dialogue memory through dual systems inspired by human cognition, significantly improving recall and coherence without retraining; Mnemosyne is designed for low-resource environments, enabling sustained interactions. Key challenges include managing context window limits, ensuring security, and developing scalable solutions. As research progresses, effective memory systems could transform LLM capabilities.