The paper introduces LLaGA (Large Language and Graph Assistant), an innovative framework that integrates Large Language Models (LLMs) to handle graph-structured data. LLaGA addresses the challenges of translating graph structures into a format compatible with LLMs by reorganizing graph nodes into structured sequences and mapping these sequences into token embedding spaces using a versatile projector. This approach ensures versatility, generalizability, and interpretability, allowing LLaGA to perform well across various datasets and tasks, including supervised and zero-shot scenarios. Extensive experiments on popular graph benchmarks show that LLaGA outperforms state-of-the-art graph models, demonstrating its effectiveness in both supervised and zero-shot learning. The framework's performance is robust across different base LLMs and text encoding methods, further highlighting its flexibility and generalization capabilities.The paper introduces LLaGA (Large Language and Graph Assistant), an innovative framework that integrates Large Language Models (LLMs) to handle graph-structured data. LLaGA addresses the challenges of translating graph structures into a format compatible with LLMs by reorganizing graph nodes into structured sequences and mapping these sequences into token embedding spaces using a versatile projector. This approach ensures versatility, generalizability, and interpretability, allowing LLaGA to perform well across various datasets and tasks, including supervised and zero-shot scenarios. Extensive experiments on popular graph benchmarks show that LLaGA outperforms state-of-the-art graph models, demonstrating its effectiveness in both supervised and zero-shot learning. The framework's performance is robust across different base LLMs and text encoding methods, further highlighting its flexibility and generalization capabilities.