Hindsight: Four-Parallel Retrieval Agent Memory with Atomic Fact Extraction — 91% LongMemEval

Vectorize.io·high signal

Vectorize.io's open-source Hindsight memory system fans out across four parallel retrieval strategies simultaneously (semantic, temporal, entity, opinion), merges them through a cross-encoder reranker, and runs atomic fact extraction on every write to break conversations into individually retrievable units. It organizes memory into four typed networks — World, Experiences, Opinion, and Observation — and achieves 91% on LongMemEval, +44.6 points over a full-context baseline. An MCP server shipped March 4 lets Claude, Cursor, and any MCP-compatible tool adopt it via a single JSON config change.