From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

2026-05-06

AI Summary

Current research on memory mechanisms in large language model (LLM) agents is fragmented across operating systems engineering and cognitive science, lacking a unified evolutionary perspective. This work proposes a three-stage memory evolution framework—storage, reflection, and experience—that systematically integrates recent advances in the field and formally defines the core drivers and key capabilities of each stage, such as active exploration and cross-trajectory abstraction. By synthesizing theoretical insights from cognitive science and systems engineering through comprehensive review and framework-based modeling, this study establishes a unified evolutionary theory of memory for LLM agents. The resulting framework offers clear design principles and a developmental roadmap for next-generation agents, advancing memory systems from passive recording toward active experience generation.

Abstract

Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: Storage (trajectory preservation), Reflection (trajectory refinement), and Experience (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.

Problem

• LLM agent
• memory mechanisms
• theoretical fragmentation
• evolutionary framework
• continual learning

Innovation

• LLM agent memory
• evolutionary framework
• experience abstraction
• proactive exploration
• continual learning