MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents

• URL: http://arxiv.org/abs/2605.01386v1
• Date: Sat, 02 May 2026 11:20:34 GMT
• Title: MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents
• Abstract summary: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations.<n>Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context.<n>We introduce MemORAI, a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic weighted PageRank.
• Score: 14.16693343382686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.

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