Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
This work addresses the need for persistent and interpretable memory in conversational AI systems, particularly for industry applications requiring adaptive user experiences, though it appears incremental as it builds on existing concepts of agentic memory.
The paper tackles the problem of enabling large language models to maintain continuity and personalization in extended conversations by introducing Memoria, a scalable agentic memory framework that integrates dynamic summarization and a knowledge graph-based user modeling engine, resulting in improved short-term dialogue coherence and long-term personalization within token constraints.
Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical capabilities for deploying LLMs as truly interactive and adaptive agents. Agentic memory refers to the memory that provides an LLM with agent-like persistence: the ability to retain and act upon information across conversations, similar to how a human would. We present Memoria, a modular memory framework that augments LLM-based conversational systems with persistent, interpretable, and context-rich memory. Memoria integrates two complementary components: dynamic session-level summarization and a weighted knowledge graph (KG)-based user modelling engine that incrementally captures user traits, preferences, and behavioral patterns as structured entities and relationships. This hybrid architecture enables both short-term dialogue coherence and long-term personalization while operating within the token constraints of modern LLMs. We demonstrate how Memoria enables scalable, personalized conversational artificial intelligence (AI) by bridging the gap between stateless LLM interfaces and agentic memory systems, offering a practical solution for industry applications requiring adaptive and evolving user experiences.