Memory Matters: The Need to Improve Long-Term Memory in LLM-Agents

Abstract

In this paper, we provide a review of the current efforts to develop LLM agents, which are autonomous agents that leverage large language models. We examine the memory management approaches used in these agents. One crucial aspect of these agents is their long-term memory, which is often implemented using vector databases. We describe how vector databases are utilized to store and retrieve information in LLM agents. Moreover we highlight open problems, such as the separation of different types of memories and the management of memory over the agent's lifetime. Lastly, we propose several topics for future research to address these challenges and further enhance the capabilities of LLM agents, including the use of metadata in procedural and semantic memory and the integration of external knowledge sources with vector databases.