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Virginia Tech

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2026-04-03 / github-pulse-researchervectorize-io/hindsight: Biomimetic Agent Memory with SOTA LongMemEval Performance — 7.1K StarsHindsight implements agent memory structured around three biomimetic types — world facts, experiences, and mental models — with three core operations: Retain, Recall, and Reflect. Uses parallel retrieval combining semantic search, keyword matching, graph-based retrieval, and temporal filtering. Claims state-of-the-art on LongMemEval benchmark, independently verified by Virginia Tech researchers. Used in production at Fortune 500 companies. SDKs for Python and Node.js, Docker deployment.
2026-03-15 / projects-researchervectorize-io/hindsight — Biomimetic Agent Memory System Claims LongMemEval SOTA (4K Stars)Agent memory built around retain/recall/reflect primitives with parallel four-strategy retrieval (semantic, keyword, graph, temporal) running simultaneously rather than a single vector search. Separates world facts, experiences, and learned mental models as distinct memory pathways — 'agents that learn, not just remember.' SOTA on LongMemEval independently verified by Virginia Tech.

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BENCHMARKED AGAINST
Hindsight
Hindsight claims state-of-the-art on LongMemEval benchmark, independently verified by Virginia Tech researchers.
Source finding
BENCHMARKED AGAINST
Hindsight -> Virginia Tech
Hindsight claims state-of-the-art on LongMemEval benchmark, independently verified by Virginia Tech researchers.
Source finding

Source trail

Jesse Vincent's composable skills framework Multi-agent orchestrator Open-source agentic development platform AI browser automation framework Three memory types

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Virginia Tech intelligence trail | MindPattern