AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
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.
69d75f25c6ed9968ac5c6b0b
Yuanfu Sun
claimed_verified
2026-04-09T18:50:46.184Z
659d4de1354c910bbe5b79fa
https://cdn-avatars.huggingface.co/v1/production/uploads/659d4de1354c910bbe5b79fa/gmQ6h7OvUiWOFr17m1-rO.png
Yuanfu Sun
Travissun
user
Travissun
69d75f25c6ed9968ac5c6b0c
Kang Li
69d75f25c6ed9968ac5c6b0d
Dongzhe Fan
69d75f25c6ed9968ac5c6b0e
Jiajin Liu
69d75f25c6ed9968ac5c6b0f
Qiaoyu Tan
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
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.
AgentGL is a reinforcement learning-driven framework that enables large language models to navigate and reason over complex relational data by integrating graph-native tools and curriculum learning strategies.
agentic capabilities graph learning reinforcement learning graph-native tools multi-scale exploration search-constrained thinking graph-conditioned curriculum long-horizon policy learning Text-Attributed Graph node classification link prediction