Most "AI memory" tools ship zero benchmarks. I come at it from the other side: I wrote a paper on training-free multi-hop retrieval (at ItalySoft) https://zenodo.org/records/20668567, and WikiMoth is that engine packaged small.
artificial
Most "AI memory" tools ship zero benchmarks. I come at it from the other side: I wrote a paper on training-free multi-hop retrieval (at ItalySoft) https://zenodo.org/records/20668567, and WikiMoth is that engine packaged small.
on a real 356-note vault: - ~5k tokens to answer a question vs ~482k to paste the whole vault. -99%! - recall@8 = 1.00 on simple lookups: easy. - multi-hop (answer 2-3 links deep): keyword and vector score 0%, link-walking gets 100%! - same query...
artificial

Most "AI memory" tools ship zero benchmarks. I come at it from the other side: I wrote a paper on training-free multi-hop retrieval (at ItalySoft) https://zenodo.org/records/20668567, and WikiMoth is that engine packaged small.

Most "AI memory" tools ship zero benchmarks. I come at it from the other side: I wrote a paper on training-free multi-hop retrieval (at ItalySoft) https://zenodo.org/records/20668567, and WikiMoth is that engine packaged small.

on a real 356-note vault:
- ~5k tokens to answer a question vs ~482k to paste the whole vault. -99%!
- recall@8 = 1.00 on simple lookups: easy.
- multi-hop (answer 2-3 links deep): keyword and vector score 0%, link-walking gets 100%!
- same query, 5 runs, 1 result. Deterministic. it means that is code not a LLM!

`wikimoth install` wires it into Claude Code, and from then on it's hands-off:

each session you finish gets saved as one linked markdown note, and your recent notes load back into context at the start of the next session. Claude boots with your memory automatically, no manual step.

submitted by /u/ObjectiveEntrance740
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