replaced my RAG pipeline with a memory layer and my agent actually got smarter over time
been building an agent that runs autonomously (openclaw loop, every 30 min). classic setup — vector db, chunk + embed documents, retrieve top-k on every query.
problem was my agent kept re-learning the same stuff. it would extract that "user prefers dark mode" from a conversation, embed it, and then next session extract it again from a different conversation. after 2 weeks my vector db had like 40 near-duplicate chunks about dark mode preferences.
i also noticed something weird — my agent was great at recalling facts but terrible at recalling how it did things. like if it successfully debugged a deployment issue through 5 steps, that workflow was gone next session. RAG only gave back fragments, not the full sequence.
ended up ripping out the whole chunking pipeline and replacing it with something that separates memory into types — facts (user likes X), events (meeting happened on tuesday), and procedures (here's how I fixed the deploy). the procedures part is what surprised me most. the agent now reuses its own workflows and they actually improve over time as it encounters variations.
i know this isn't traditional RAG but figured this sub would appreciate the comparison since i came from a pure RAG setup. anyone else experimenting with structured memory vs pure vector retrieval?
