How Forge Uses DeepLake and HiveMind

A technical breakdown of the local vector and memory sidecars that support Forge 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

Forge 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 Forge applies graph, trust, archive, and filtering logic before results are surfaced.

DeepLake in Forge

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 Forge

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, Forge keeps the primary database clear and avoids treating an accelerator as the source of truth.