Augmenting the Intelligence of Large Language Model-Based Agents with Graphs: A Survey

Large language models (LLMs) have achieved remarkable performance, prompting the development of LLM-based agents to tackle complex, open-world tasks. Despite these advances, autonomous agents still face critical bottlenecks in environmental perception, multi-step reasoning, and long-term opera-tional consistency. Graphs provide a systematic solution to these challenges by explicitly modeling entities, relationships, and dependencies, thereby establishing a rigorous foundation for reasoning and interaction. While recent literature explores this integration, existing surveys primarily focus on operational workflows and architectures, lacking a systematic analysis of how graphs fundamentally elevate agent intelligence. To bridge this gap, this survey presents a comprehensive review of Graph-Augmented LLM-based Agents (GLAs). We systematically decompose agent architecture to distill five core capabilities required for advanced intelligence: perception, reasoning, memorization, generaliza-tion, and interaction. We explicitly address why graphs are uniquely suited to bolster these capabilities and how they are practically integrated, proposing a novel taxonomy that organizes existing studies along these five dimensions. Furthermore, we review representative real-world applications of GLAs across diverse domains and outline key challenges and promising directions for future research. A curated repository containing the literature reviewed in this survey, along with the latest advancements in this field, is maintained and regularly updated at https://github.com/sqs17/GLA.