Agent memory gets good not by writing more application code, but by tuning configuration. A practical guide from Weaviate. Title: Agent Memory with Engram: A Practical Guide URL: It walks through moving from "just add and search conversation data" to actually optimizing both memory quality and token cost. Three things stand out. 📝 Write topic descriptions as exclusions The description you write for each memory category acts directly as the extraction prompt. Rather than enumerating examples of what to keep, a single exclusion rule like "do not record events, incidents, or passing conditions" generalized better to cases nobody anticipated. The description also decides the shape of the output: atomic facts versus flowing prose. 🔒 Pin singular facts with bounded Marking a topic bounded caps it at one memory per scope, so facts get updated instead of accumulating. Across five fresh users, a bounded UserProfile held at exactly one memory on all five runs, while the unbounded UserKnowledge fluctuated between two and four each time. 💰 Where you put memories decides your bill Search results change every turn, so pasting them into the system prompt breaks the cache and bills the whole prompt again. Instead, fetch always-on memories once at session start and place them right after the system prompt, then append search results after the user message. Over 25-turn sessions, the final request was about 3,500 tokens with only roughly 100 of them not served from cache. Worth reading as a decision aid if you're weighing building a memory layer yourself versus outsourcing it. #AIAgents# #AgentMemory#