Five-layer agent memory plus Jev to cut decision cost
A 13-page guide covers working, episodic, semantic, procedural and forgetting layers, cutting a query from 26k to 1,800 tokens and a Claude bill from $765 to $3 with Jev.
agent memory, in 13 pages the fix that took one setup from 26,000 tokens per query down to 1,800 your agent reads the same 12 files every morning, reaches the same conclusions and bills you again for both the 5 layers that stop it: > working memory - what it sees right now, gone once the window fills > episodic memory - a timestamped log, so the deploy that died at 3am gets explained once > semantic memory - facts in a knowledge graph, like "this user prefers TypeScript" > procedural memory - three approaches tried, the one that worked saved as a skill > forgetting - what to delete before contradictions pile up the numbers: > Mem0 - 1,800 tokens per query instead of 26,000, with 91% lower latency > Snowflake - one ontology layer, 20% more accuracy, 39% fewer tool calls the forgetting engine is the layer almost nobody builds, and it decides whether the other four stay worth trusting memory cuts what your agent reads. Jev cuts what it pays to decide, and on the decision side that took one Claude bill from $765 to $3 a month 👇
This post quotes another post: x.com/mr_buzzoni/status/2103689373774483815