Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users

Abstract

Conversational AI is now the most widely adopted platform for interfacing with LLMs. Alongside LLMs these AI systems rely on contexts derived from past conversations and preferences to provide accurate and the most relevant responses to users. The knowledge base and past experiences contribute to long-term memory, while processing ongoing conversations generates short-term memory. Both long-term and short-term memories together provide a comprehensive and coherent context to the user. While most architectures focus on a single user context, there is an emerging need in conversational AI to provide a system to generate context from multiple individuals and/or agents. Building on this foundation, we introduce memory fabric, a framework that allows conversational AI to leverage context drawn from multiple users to generate coherent responses in a multiuser setting. This review is a synthesis of foundational memory architectures, from early models such as Neural Turing Machines and Key-Value Memory Networks to recent systems like AutoGen, SRMT, AgentVerse, and SAMEP evaluated through the lens of memory fabric. It also addresses work on dialogue and agent memory, multi-agent shared-memory designs, privacy and provenance strategies, benchmarking standards, and system-level considerations. A systematic review of English articles using IEEE Xplore, ACM, arXiv, SpringerLink, and ACL anthology databases was performed. Only full-text studies reported between 2014 and 2025 were included for analysis. Following PRISMA guidelines, a total of 662 studies were identified, of which 27 studies met the inclusion criteria. Overall, this review presents a detailed analysis on leveraging contexts from multiple users in a shared environment in foundational memory architectures, offering an in-depth analysis of their strengths and shortcomings, along with recommendations and future research directions.

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