Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat external information as unstructured text and fail to leverage the topological dependencies inherent in real-world data. To bridge this gap, we introduce Agentic Graph Learning (AGL), a paradigm that reframes graph learning as an interleaved process of topology-aware navigation and LLM-based inference. Specifically, we propose AgentGL, the first reinforcement learning (RL)-driven framework for AGL. AgentGL equips an LLM agent with graph-native tools for multi-scale exploration, regulates tool usage via search-constrained thinking to balance accuracy and efficiency, and employs a graph-conditioned curriculum RL strategy to stabilize long-horizon policy learning without step-wise supervision. Across diverse Text-Attributed Graph (TAG) benchmarks and multiple LLM backbones, AgentGL substantially outperforms strong GraphLLMs and GraphRAG baselines, achieving absolute improvements of up to 17.5% in node classification and 28.4% in link prediction. These results demonstrate that AGL is a promising frontier for enabling LLMs to autonomously navigate and reason over complex relational environments. The code is publicly available at https://github.com/sunyuanfu/AgentGL.

翻译：大语言模型（LLMs）日益依赖智能体能力——迭代检索、工具使用与决策制定——以克服静态参数化知识的局限。然而现有智能体框架将外部信息视为非结构化文本，未能利用真实数据中固有的拓扑依赖关系。为弥合这一鸿沟，我们提出智能体图学习（AGL）范式，该范式将图学习重构为拓扑感知导航与大语言模型推理的交替过程。具体而言，我们提出AgentGL——首个面向AGL的强化学习驱动框架。AgentGL为LLM智能体配备图原生工具以支持多尺度探索，通过搜索约束思维调控工具使用以平衡精度与效率，并采用图条件课程强化学习策略在无逐步骤监督的情况下稳定长程策略学习。在多个文本属性图（TAG）基准测试与多种LLM主干网络上，AgentGL显著超越强基线GraphLLM与GraphRAG方法，在节点分类与链接预测任务上分别实现最高17.5%与28.4%的绝对性能提升。这些结果表明AGL是推动LLM在复杂关系环境中自主导航与推理的前沿方向。代码已开源至https://github.com/sunyuanfu/AgentGL。