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Recall returns memories.res["hits"] becomes res["memories"], and
each entry is one whole memory with its content rather than a scattering
of its sections. matched names the arms that found it, sections the
headings that matched, via what pulled in a neighbour.
Scores mean something.score is calibrated in [0, 1] and comparable
across queries, replacing a fused rank that only ordered one response. scores["coverage"] says how much of the query a memory accounted for.
recall takes filters — tiers, paths, tags, tags_all, since, until, min_score, context, detail, include_expired — applied
inside every retrieval arm server-side rather than after the fact.
answer(query) returns one text answer: a fast model over a retrieval,
or with agentic=True a stronger model that searches the memory itself with
tools and returns its trace. Raises GitloomError("no_answer") rather
than returning an empty string. Both meter as chats.
memory.vocab — learn, list, lookup, forget. A learned alias
makes a query for any surface form find memories written with another;
matched definitions come back as defined.
memory.skills — store and find, stored as memories under the skills/ tier.
candidates, filtered_out and timings report what retrieval did.