[2603.19935] Memori: A Persistent Memory Layer for Efficient, Context-Aware LLM Agents

Computer Science > Machine Learning arXiv:2603.19935 (cs) [Submitted on 20 Mar 2026] Title:Memori: A Persistent Memory Layer for Efficient, Context-Aware LLM Agents Authors:Luiz C. Borro, Luiz A. B. Macarini, Gordon Tindall, Michael Montero, Adam B. Struck View a PDF of the paper titled Memori: A Persistent Memory Layer for Efficient, Context-Aware LLM Agents, by Luiz C. Borro and 4 other authors View PDF HTML (experimental) Abstract:As large language models (LLMs) evolve into autonomous agents, persistent memory at the API layer is essential for enabling context-aware behavior across LLMs and multi-session interactions. Existing approaches force vendor lock-in and rely on injecting large volumes of raw conversation into prompts, leading to high token costs and degraded performance. We introduce Memori, an LLM-agnostic persistent memory layer that treats memory as a data structuring problem. Its Advanced Augmentation pipeline converts unstructured dialogue into compact semantic triples and conversation summaries, enabling precise retrieval and coherent reasoning. Evaluated on the LoCoMo benchmark, Memori achieves 81.95% accuracy, outperforming existing memory systems while using only 1,294 tokens per query (~5% of full context). This results in substantial cost reductions, including 67% fewer tokens than competing approaches and over 20x savings compared to full-context methods. These results show that effective memory in LLM agents depends on structured representations in...