Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects

作者：Yixin Liu, Guibin Zhang, Kun Wang, Shiyuan Li, Shirui Pan, Bo An · 发表于：IEEE Intelligent Systems · 年份：2026 · DOI：10.1109/mis.2025.3642667 · 被引用次数：2 · 研究领域：Advanced Graph Neural Networks、Topic Modeling、Multimodal Machine Learning Applications

Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in numerous real-world applications. While most LLMs are limited in several key agentic procedures, graphs can serve as a powerful auxiliary structure to enhance structure, continuity, and coordination in complex agent workflows. Given the rapid growth and fragmentation of research on Graph-augmented LLM Agents (GLA), this article offers a timely and comprehensive overview of recent advances and highlights key directions for future work. Specifically, we categorize existing GLA methods by their primary functions in LLM agent systems, including planning, memory, and tool usage, and then analyze how graphs and graph learning algorithms contribute to each. For multiagent systems, we further discuss how GLA solutions facilitate the orchestration, efficiency optimization, and trustworthiness of MAS. Finally, we highlight key future directions to advance this field, from improving structural adaptability to enabling unified, scalable, and multimodal GLA systems.