At hundreds of notes you blow a lot of tokens just to _find one thing_.
replyIndexing your corpus as you go makes retrieval a lot faster, and then the agent can dig into the specific file if it needs something more.
The memories are stored as OKF (Open Knowledge Format), which is markdown + frontmatter (+ constraints/schema imposed thereon).
replyHaving an inverted index (as with FTS5) is useful in that, for a basic single-term lookup, you reduce a sequential scan, O(N), down to O(log N). For small N, the performance difference might not be meaningful. Performance gap widens with more sophisticated queries (boolean operators, ranking, etc).
[dead]
replyim asking the same thing myself for personal projects seems markdown files is best.
replyi can see for public facing deployments agent memory like this could result in faster roundtrips.
for one, the mcp-server architecture makes it usable from claude.ai and other surfaces where you have mcp but no filesystem. there are claude-specific workarounds (workspaces) but you lose portability across systems.
replyHmm, ok. I guess I rarely use the web interface and everything that I have agents record as "memory" in markdown is always accessible locally. If I'm accessing something remotely, I use the ChatGPT app with remote which connects directly to the host computer.
reply[dead]
reply