QuarqLabs says its open-source agent scored 98.2% on LongMemEval-S

Quarq Agent uses local FAISS storage, layered memory and three separate LLM roles to tackle long-context recall.

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Why it matters

Long-term memory is becoming a practical battleground for agents. QuarqLabs' claim is notable because it emphasizes local storage and retrieval architecture, not just bigger models or longer context windows.

Illustration of Quarq Agent's layered memory architecture and recall process, showing stacked memory layers, FAISS storage, and connections among three LLM roles.

QuarqLabs says Quarq Agent, its open-source memory-first AI agent, scored 98.2% on LongMemEval-S, a long-memory benchmark built around 500 questions, about 57 million tokens of conversation data and roughly 50 sessions per question.…

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