Agent memory placement that keeps prompt caching alive: a practical Engram guide
dl_weekly · x · 2026-09-30
A Weaviate developer advocate shares a practical guide on placing agent memories (via the Engram memory service) into prompts so KV caching survives — in a 3,500-token final request, only 100 tokens missed the cache.
The post argues that getting Engram running is easy, but four decisions remain yours:
- What gets remembered — topic descriptions drive memory extraction
- How many, whose — bounded topics and scope control memory volume and ownership
- What comes back — retrieval mode shapes returned content
- Where it goes — placement within the prompt, the key to cache survival
Engram processes raw conversation data via an async pipeline (extract → transform against stored memories → commit); memories.add returns a run rather than immediately searchable memories, and the guide advises against blocking on run completion.
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