Local-first institutional memory technical report
How Froggit Uses DeepLake and HiveMind
A technical breakdown of the local vector and memory sidecars that support Froggit retrieval while Neo4j remains the source of truth.
2,207
Active capsules
100%
DeepLake coverage
100%
HiveMind coverage
Graph authority
Local recall
Governed results
Executive summary
Froggit uses DeepLake as a local vector sidecar for capsule embeddings and HiveMind as a local memory sidecar for capsule lifecycle memory.
Neo4j remains the authority. DeepLake proposes similar vectors, HiveMind mirrors local memory, and Froggit applies graph, trust, archive, and filtering logic before results are surfaced.
DeepLake in Froggit
DeepLake stores capsule embeddings in dimension-specific datasets and participates in semantic retrieval by returning candidate capsule IDs.
Those candidate IDs are rehydrated through Neo4j so normal authorization, archive, trust, type, tag, and domain filters still apply.
HiveMind in Froggit
HiveMind mirrors capsule lifecycle memory so local recall remains available around active knowledge. It complements semantic search with memory continuity.
Because both sidecars can be rebuilt from Neo4j, Froggit keeps the primary database clear and avoids treating an accelerator as the source of truth.
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