fluctlightdb-paper

FluctlightDB: A Memory Model of Data for AI Agents

Preprint · June 2026 · DOI: 10.5281/zenodo.20949890 · arXiv pending

Author: Ganesh S · ORCID 0009-0006-7758-4114 · [email protected]

One-line claim

Long-term agent memory is a third data model — not SQL rows, not vector ANN alone. FluctlightDB is an embedded engine with native experience() / activate() semantics.

Headline results

Frozen metrics: fluctlightdb-benchmarks

Abstract

For fifty years, data systems answered two questions: which records match a predicate (relational), and which vectors lie nearest a query (vector). Autonomous agents ask a third: what have I learned, and what of it can I trust?

We present FluctlightDB, an embedded brain-native database with write path experience() and read path activate(). On full LoCoMo it recalls 98.1% of gold evidence (warm and cold-start identical). On BEIR SciFact it matches a tuned Chroma baseline; on FAMB it scores 97–98% macro.

Install

pip install "fluctlightdb[native]"

from fluctlightdb import connect

brain = connect("/tmp/agent-brain")
brain.experience("User prefers dark mode", context="settings", salience=0.8)
print(brain.activate("theme preference"))
brain.checkpoint()

Links

Citation

@article{s2026fluctlightdb,
  title={FluctlightDB: A Memory Model of Data for AI Agents},
  author={S, Ganesh},
  year={2026},
  doi={10.5281/zenodo.20949890},
  url={https://doi.org/10.5281/zenodo.20949890},
  note={Preprint. Software: https://github.com/voxmastery/FluctlightDB}
}

See also CITATION.cff on GitHub.

Metric note

LoCoMo evidence recall = fraction of gold dialogue evidence in retrieved context (official RAG metric). Mem0/Zep often report LLM-as-judge end-to-end QA — a different, harder number. Do not compare 98.1% recall to ~92% QA without naming the metric.

License

MIT — engine, harnesses, and this paper draft.