There's a reason why knowledge graphs are so GOATED

Most people start with a flat list first bc it feels simpler to just store everything and they let similarity search sort it out at query time but that's fine if you're just messing around with it. It starts losing its shyte when multiple facts about the same thing come under different names and you get separate, disconnected entries for each one because nothing links them to that one reference so your memory breaks into small fragments and you don't notice until search starts giving incomplete or just weird contradictory answers. and you need this the moment your data has any references that get called more than one way, that's basically what most agent memory looks like and it's rarely just random unconnected facts. A graph that's just used to connect the dots fixes this without slowing every single query and you just keep the memory typed. facts and preferences get sorted by type when you search and the graph underneath is just there to figure out who or what something refers to before it gets stored. Just imho a flat list on its own isn't actually as simple as it feels at first, it just pushes the problem back until the same thing appears under two different names.