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.

翻译：记忆已成为并仍将是基于基础模型的智能体的核心能力。随着智能体记忆研究的快速扩展并吸引前所未有的关注，该领域也日益呈现碎片化趋势。现有关于智能体记忆的研究在动机、实现方式和评估协议上往往存在显著差异，而定义松散的记忆术语的激增进一步模糊了概念的清晰度。传统的分类法（如长/短期记忆）已不足以涵盖当代智能体记忆系统的多样性。本文旨在提供当前智能体记忆研究的最新全景图。我们首先清晰界定智能体记忆的范畴，并将其与相关概念（如大语言模型记忆、检索增强生成（RAG）和上下文工程）进行区分。接着，我们通过形式、功能和动态三个统一视角审视智能体记忆。从形式视角，我们识别出三种主流的记忆实现方式：令牌级记忆、参数化记忆和潜在记忆。从功能视角，我们提出更细粒度的分类法，区分事实记忆、经验记忆和工作记忆。从动态视角，我们分析记忆如何随时间形成、演化和检索。为支持实际开发，我们汇编了记忆基准测试和开源框架的全面总结。在整合现有成果的基础上，我们阐述了关于新兴研究前沿的前瞻性视角，包括记忆自动化、强化学习集成、多模态记忆、多智能体记忆以及可信度问题。我们希望本综述不仅能作为现有工作的参考，更能为重新思考记忆作为未来智能体化智能设计中的一等原语提供概念基础。