Conversational Agents: From RAG to LTM

Abstract

Large Language Models (LLMs) have significantly advanced Artificial Intelligence (AI), demonstrating impressive capabilities in language understanding, reasoning, and generation. However, their fixed context windows fundamentally limit their utility in sustained, complex human-computer interactions, leading to issues such as forgetting previous turns, lacking consistent personas, and an inability to perform long-horizon reasoning. While Retrieval-Augmented Generation (RAG) offers a promising solution by externalizing knowledge and providing LLMs with relevant information from external corpora, its traditional static retrieve-then-generate pipeline often struggles with dynamic knowledge integration, introduces noise, and overlooks structural relationships. This tutorial introduces the evolution from traditional RAG to advanced Long-Term Memory (LTM) mechanisms that equip LLM-based conversational agents with human-like memory capabilities. We will explore various LTM architectures, including textual, graph-based, and parametric memory, detailing their forms, operations (such as dynamic indexing, retrieval, updating, and consolidation), and multimodal integration strategies. The tutorial will cover cutting-edge systems (like Mem0), illustrating how they enable agents to maintain coherent conversations, personalize interactions, and perform complex reasoning over extended periods. We will also delve into evaluation benchmarks (e.g., LoCoMo and ZH-4O) as well as metrics that compr…