Every agent memory system picks one retrieval strategy and optimizes for one benchmark. But in production, the same agent needs to recall differently for every query. The right answer is to stop deciding for the agent and let it tune retrieval itself.
The complete technical walkthrough. Two-phase ingest pipeline, RocksDB storage schema with eight column families, four recall strategies with documented complexity, and how an AI agent uses HEBBS end-to-end in a real conversation loop.
HEBBS used to ship two binaries that stored memories in two separate places. They could not see each other. v0.3.0 fixes that: one binary, one RocksDB, one recall that searches everything.