Memori Labs Outperforms Every Other Memory System with 81.95% Accuracy at 4.98% the Cost of Full Context
Summary
Memori Labs released a benchmark paper showing its structured memory layer achieved 81.95% accuracy on the LoCoMo long-conversation memory benchmark, outperforming Zep, LangMem, and Mem0. The system used an average of 1,294 tokens per query — just 4.98% of the cost of full conversation context — delivering more than 20x lower context cost than full-context prompting and 67% fewer tokens than Zep. Memori converts raw conversational history into compact, retrieval-friendly memory assets (semantic triples and session summaries) via its Advanced Augmentation pipeline to address context rot. The company positions the platform as available via Memori Cloud and enterprise deployment options, promising lower inference costs, better cross-session continuity, and stronger recall for AI builders.
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