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Lemmalog

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2026-08-29 / projects-researcherLemmalog turns agent memory into a Datalog engine and beats full-transcript prompting on 38x less contextJordyZomer/lemmalog was created 2026-08-27, is 76 stars and MIT-licensed in Rust, and the author's write-up hit HN at 173 points on 2026-08-28. It stores LLM observations as provenance-tracked facts with stratified rules and incremental derivation, exposed to a harness through an MCP server, so the model does language extraction while the database keeps logical consistency. Reported results are 0.463 ±0.010 F1 on LongMemEval with 38x smaller context than full-transcript prompting and 0.533 ±0.001 F1 on LoCoMo at 6x smaller, strongest on knowledge updates and adversarial questions.

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BENCHMARKED AGAINST
LoCoMo
Lemmalog achieved 0.533 F1 on the LoCoMo benchmark.
Source finding
BENCHMARKED AGAINST
LongMemEval
Lemmalog achieved 0.463 F1 on the LongMemEval benchmark.
Source finding
USES
Datalog
Lemmalog uses a Datalog engine for agent memory.
Source finding
BUILT BY
JordyZomer
JordyZomer built Lemmalog as an agent memory system.
Source finding

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