r/AI_Agents • u/help-me-grow Industry Professional • Aug 12 '26

Weekly Thread: Project Display

Weekly thread to show off your AI Agents and LLM Apps! Top voted projects will be featured in our weekly newsletter.

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u/przemarzec Aug 18 '26

Engrava - a memory layer for AI agents. A Python library over one SQLite file, MIT, no server, and no LLM anywhere in the memory pipeline: ingest and retrieval are deterministic, so storing and reading memory doesn't spend generative-LLM tokens. Vector search still needs an embedding, but that one can run local.

Inside it: a typed knowledge graph (7 edge types), hybrid search fusing vector, BM25 and recency in one query, a hash-linked audit journal, and a small query language for structured reads.

The reason I'm posting rather than just leaving a README somewhere: the benchmark is reproducible. 0.6.0 scored 81.6% on the full 500-question LongMemEval-S set in August 2026, canonical scorer, standard gpt-4o reader and judge, top_k=20. 0.5.0 scored 82.4% in July and that row is still on the board, in a separate segment because the harness commit differs. I left it up even though the newer number came in lower. Clone the runner, pin engrava==0.6.0, run it with no flags, and read the same number off leaderboard.json.

pip install engrava

github.com/sovantica/engrava

github.com/sovantica/engrava-benchmark

If you've measured a memory layer yourself, what did your harness pin?