Memory in the Age of AI Agents

2025-12-15

AI Summary

Current AI agent memory research is highly fragmented: conceptual definitions are ambiguous, taxonomic criteria inconsistent, and evaluation frameworks absent; the traditional dichotomy of long-term versus short-term memory fails to capture its heterogeneity. This paper addresses these gaps via a systematic literature review and conceptual modeling, proposing the first three-dimensional analytical framework for agent memory—unified along dimensions of *form* (token-level, parametric, implicit), *function* (factual, experiential, working), and *dynamics* (formation, evolution, retrieval)—while rigorously distinguishing agent memory from LLM internal memory, RAG, and context engineering. Contributions include: (1) a consensus-driven terminology system and taxonomy; (2) the first evaluation paradigm covering all three dimensions; (3) formal recognition of memory as a core agent capability; and (4) identification of key research directions—memory automation, multimodal coordination, multi-agent memory sharing, and trustworthiness—providing foundational support for architectural design and empirical investigation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.

Problem

• Surveying fragmented research on AI agent memory systems
• Clarifying memory scope and distinguishing from related concepts
• Providing taxonomy and benchmarks for future agent development

Innovation

• Proposes token-level, parametric, latent memory forms
• Introduces factual, experiential, working memory taxonomy
• Analyzes memory formation, evolution, retrieval dynamics