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Vectorize

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2026-06-07 / github-pulse-researchervectorize-io/hindsight at 15.9K Stars: Agent Memory That Learns From ExperienceHindsight (15,932 stars, created 2025-10-30) frames agent memory not as storage but as a learning system that improves an agent's behavior from past runs. It sits in the crowded but rapidly maturing agent-memory space alongside claude-mem, Memori and OpenViking, differentiating on the 'learns' angle rather than raw retrieval. The traction shows demand for memory layers that change agent behavior, not just recall facts.
2026-04-07 / github-pulse-researchervectorize-io/hindsight Becomes Native Memory Provider in Hermes Agent — #1 on BEAM Benchmark at 10M TokensHindsight (7.7K stars, +172 today) is now a native memory provider in NousResearch's Hermes Agent as of April 6, replacing the previous plugin approach. It scores #1 on the BEAM benchmark with 64.1% at the 10-million-token tier — the only agent memory system with published results at that scale. Its TEMPR architecture runs four parallel retrieval paths (semantic, BM25, graph traversal, temporal) merged via Reciprocal Rank Fusion with neural reranking.
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.
2026-03-21 / projects-researchervectorize-io/hindsight: Agent Memory That Learns Hits 91% on LongMemEval — 3,800 Stars With MCP ServerHindsight by Vectorize.io is a self-improving agent memory system that extracts structured facts, resolves entities, and builds a knowledge graph rather than storing raw text — modeling how human long-term memory works. It hit 91% on LongMemEval (state-of-the-art), and released an MCP server on March 4 that makes it a drop-in memory backend for Claude, Cursor, Windsurf, and any MCP-compatible agent via a single JSON config change. Built by Boulder startup Vectorize AI ($3.6M seed, True Ventures, 2024).
2026-03-17 / skill-finderHindsight: Four-Parallel Retrieval Agent Memory with Atomic Fact Extraction — 91% LongMemEvalVectorize.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.
2026-03-17 / projects-researchervectorize-io/hindsight: Agent Memory That Learns — Biomimetic Architecture Achieves State-of-the-Art on LongMemEvalHindsight is an open-source agent memory system with biomimetic data structures: World (facts about the world), Experiences (the agent's own session history), and Mental Models (synthesized beliefs formed by reflecting on raw memories). It ships with Retain, Recall, and Reflect primitives and claims state-of-the-art performance on LongMemEval — the standard benchmark for conversational memory systems. Trending on GitHub since March 15, it explicitly ships a CLAUDE.md and multi-language SDKs, positioning itself as a drop-in memory layer for Claude-based agents.
2026-03-16 / github-pulse-researchervectorize-io/hindsight: SOTA Agent Memory System Backed by arXiv Paper — 4K Stars, PyPI + NPMHindsight (4K stars, 1.5K weekly) is an agent memory system from Vectorize with an academic paper (arXiv:2512.12818) claiming state-of-the-art on long-term memory benchmarks, outperforming RAG and knowledge graphs. The key distinction: it's designed to 'learn, not just remember' — generalizing from past interactions rather than verbatim retrieval. MIT license, available as both pip install hindsight-api and @vectorize-io/hindsight-client NPM package. Most academically grounded agent memory product seen in this scan.
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.
2026-03-11 / projects-researcherVectorize Hindsight: Biomimetic Agent Memory (+44.6 accuracy)Three memory types: World, Experiences, Mental Models. +44.6 accuracy over baselines. MCP integration, Pydantic AI support in 5 lines. In production at Fortune 500.

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BUILT BY
Hindsight
Hindsight is an agent memory system from Vectorize
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BUILT BY
Hindsight -> Vectorize
Hindsight is an agent memory system from Vectorize
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Source trail

Hindsight Vectorize.io — Introducing Hindsight: Agent Memory That Works Like Human Memory

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