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

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.

Authors

• Yixin Liu Griffith University, Queensland, Australia
• Guibin Zhang Tongji University
• Kun Wang PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, China
• Shiyuan Li Griffith University
• Shirui Pan Griffith University, Queensland, Australia
• Bo An Nanyang Technological University

Keywords

• Autonomous agents
• Large language models
• Artificial intelligence
• Agentic AI
• Multi-agent systems
• Optimization models
• Graphical models
• Learning systems
• Algorithm design and theory
• Trusted computing
• Multisensory integration
• Scalability
• Market research
• Memory management
• Language Model
• Modularity
• Agentic
• Graph Structure
• Graph Neural Networks
• Task Planning
• Dynamic Graph
• External Tools
• Graph Learning
• Broad Range Of Fields
• Graphical Representation
• Semantic Similarity
• Application Programming Interface
• Memory System
• Semantic Memory
• Agent Interactions
• User Requests
• Efficient Adaptation
• Planning Module
• Environmental Feedback