Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often limited under noisy or sparsely supervised scenarios. Recently, large language models (LLMs) have been introduced to EA and achieved notable improvements by leveraging rich semantic knowledge. However, existing LLM-based EA approaches typically treat LLMs as black-box decision makers, resulting in limited interpretability, and the direct use of large-scale triples substantially increases inference cost. To address these challenges, we propose \textbf{EA-Agent}, a reasoning-driven agent for EA. EA-Agent formulates EA as a structured reasoning process with multi-step planning and execution, enabling interpretable alignment decisions. Within this process, it introduces attribute and relation triple selectors to filter redundant triples before feeding them into the LLM, effectively addressing efficiency challenges. Experimental results on three benchmark datasets demonstrate that EA-Agent consistently outperforms existing EA methods and achieves state-of-the-art performance. The source code is available at https://github.com/YXNan0110/EA-Agent.

翻译：实体对齐（Entity Alignment, EA）旨在跨不同知识图谱（Knowledge Graphs, KGs）识别指向同一真实世界实体的对象，在知识融合与集成中起关键作用。传统EA方法主要依赖知识表示学习，但在噪声或稀疏监督场景下性能常受限。近期，大语言模型（Large Language Models, LLMs）被引入EA领域，通过利用丰富的语义知识取得了显著改进。然而，现有基于LLM的EA方法通常将LLM视为黑盒决策器，导致可解释性有限，且直接使用大规模三元组显著增加推理成本。为应对这些挑战，我们提出\textbf{EA-Agent}——一种面向EA的推理驱动型智能体。EA-Agent将EA形式化为包含多步规划与执行的结构化推理过程，实现可解释的对齐决策。在此过程中，它引入属性与关系三元组选择器，在将三元组输入LLM前过滤冗余信息，有效解决效率难题。三个基准数据集上的实验结果表明，EA-Agent始终优于现有EA方法，并达到最先进性能。源代码见https://github.com/YXNan0110/EA-Agent。