  Graph embedding methods such as Graph Neural Networks (GNNs) and Graph
Transformers have contributed to the development of graph reasoning algorithms
for various tasks on knowledge graphs. However, the lack of interpretability
and explainability of graph embedding methods has limited their applicability
in scenarios requiring explicit reasoning. In this paper, we introduce the
Graph Agent (GA), an intelligent agent methodology of leveraging large language
models (LLMs), inductive-deductive reasoning modules, and long-term memory for
knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning
and existing graph embedding methods to provide an innovative approach for
complex graph reasoning tasks. By converting graph structures into textual
data, GA enables LLMs to process, reason, and provide predictions alongside
human-interpretable explanations. The effectiveness of the GA was evaluated on
node classification and link prediction tasks. Results showed that GA reached
state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and
89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to
existing GNN and transformer models, GA offered advantages of explicit
reasoning ability, free-of-training, easy adaption to various graph reasoning
tasks
