Part 1 of Retrieval-Augmented Self-Recall — the research track behind Claude Code, Beyond the Prompt. All code is open source: RE-call.
Almost every RAG tutorial you've read solves the same problem: you have a pile of documents, a user asks a question, and you retrieve the chunks that answer it. Rank the right passage to the top, stuff it in the prompt, done.
There's a second kind of RAG that behaves completely differently, and almost nobody writes about it: an agent retrieving from its own memory.
I ran into it building the operational brain behind an automated trading system. That agent accumulates memory as it works — over 700 typed markdown memos, about 5 MB, re-indexed daily. Decisions, dead ends, calibration notes, "we tried this and it failed" postmortems. When the agent starts a task, it queries that memory: have we tested this before? what did we decide about X? is this still true?
The moment your knowledge base is the agent's own growing memory, the RAG problem inverts — and standard retrieval quietly does the wrong thing.
