Atomic memories vs context dumps: how memory granularity affects recall quality

You just had a productive session with your AI agent. It learned your deploy workflow, your naming conventions, and that you hate tabs. Time to store all of that. Do you dump the whole session summary in one big memory, or break it into individual facts? This isn't a style question. It changes how well your agent remembers things later. What happens when you store a context dump Say your agent stores this after a session: memoclaw store "Session with Ana on March 6. Discussed the deploy pipeline, prefers 2-space indentation, wants PR descriptions to include testing section, moving API from Railway to Fly.io next month, likes English for work but Portuguese for casual chat." \ --importance 0.7 --namespace personal That's 5 distinct facts crammed into one memory. MemoClaw generates a single embedding vector for the whole block. That vector represents the average meaning of everything in there. Two weeks later, the agent needs to recall indentation preferences: memoclaw recall "code forma