CITATION.cff

# Live citation metadata for the AGPL-3.0 engine.
cff-version: 1.2.0
message: "If you use hypermnesic in your research or work, please cite it as below."
title: hypermnesic
abstract: >-
  A git-native memory layer for AI agents: a markdown vault as the single source of
  truth with a disposable, rebuildable hybrid retrieval index (SQLite FTS5 + sqlite-vec
  KNN fused with reciprocal rank fusion), read-time convergence, a gated git-first write
  path, and one OAuth-secured MCP endpoint every client shares. On LongMemEval V1 (the
  500-question _s set), end-to-end QA accuracy is 88.6% overall / 89.7% task-averaged
  with a GPT-4.1 reader (83.2 / 86.6 with a GPT-4o reader), both graded by the canonical
  gpt-4o-2024-08-06 judge; session-level retrieval recall@10 is 0.949.
type: software
authors:
  - family-names: Sellem
    given-names: Leonard
    email: leonard@sellem.me
license: AGPL-3.0-only
version: 0.1.0
date-released: 2026-06-14
repository-code: "https://github.com/leonardsellem/hypermnesic"
url: "https://github.com/leonardsellem/hypermnesic"
keywords:
  - agent memory
  - retrieval-augmented generation
  - model context protocol
  - markdown
  - git
  - LongMemEval
references:
  - type: article
    title: "LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory"
    authors:
      - name: "Wu, Di and colleagues"
    year: 2024
    notes: "arXiv:2410.10813 — the benchmark hypermnesic is evaluated on (V1, _s)."
    url: "https://arxiv.org/abs/2410.10813"
  - type: article
    title: "Zep: A Temporal Knowledge Graph Architecture for Agent Memory"
    authors:
      - name: "Rasmussen, Preston and colleagues"
    year: 2025
    notes: "arXiv:2501.13956 — source of the GPT-4o-judge comparability anchors (Zep 71.2, full-context 60.2)."
    url: "https://arxiv.org/abs/2501.13956"