In a workshop focused on enhancing memory for AI agents, the discussion centers around using graph-based memory with tools like Graphiti and FalkorDB to prevent common issues such as hallucinations and outdated context in AI recommendations. By implementing knowledge graphs that incorporate temporal aspects, these tools aim to provide AI with better contextual understanding, which is crucial for generating accurate recommendations. QueryWeaver, a specific application discussed, maps database schemas into graphs to retain relationships and improve memory retention for up to a month, while also ensuring data privacy by not storing personal data. Plans for HIPAA compliance and the ability to handle complex database schemas are highlighted, along with strategies to include user feedback to refine AI responses. The workshop also addresses technical aspects of constructing memory graphs and managing access control, underscoring the balance between fixed and flexible ontologies to optimize memory capabilities.