LLaGA is a novel framework that integrates Large Language Models (LLMs) with graph data to handle complex graph-structured tasks. It addresses the challenge of translating graph structures into a format compatible with LLMs by reorganizing graph nodes into structured sequences and mapping them into token embedding spaces using a versatile projector. LLaGA excels in versatility, generalizability, and interpretability, allowing it to perform consistently across various datasets and tasks, extend its ability to unseen data, and provide explanations for graph structures. Extensive experiments on popular graph benchmarks show that LLaGA outperforms state-of-the-art graph models in both supervised and zero-shot scenarios, achieving strong performance across four datasets and three tasks with a single model. LLaGA's unique approach involves encoding graph data into node sequences without converting structural information into natural language, preserving the original structure and enabling efficient alignment with LLMs. The framework uses two templates: the Neighborhood Detail Template for detailed node and neighbor information, and the Hop-Field Overview Template for broader neighborhood summaries. These templates, combined with a versatile projector, enable LLaGA to handle multiple tasks and generalize well to new datasets. The model's ability to generate interpretable explanations for node embeddings further enhances its practical utility. LLaGA's performance is validated through comprehensive experiments, demonstrating its effectiveness in both supervised and zero-shot learning scenarios. The framework's success lies in its ability to maintain LLMs' general-purpose capabilities while effectively handling graph data.LLaGA is a novel framework that integrates Large Language Models (LLMs) with graph data to handle complex graph-structured tasks. It addresses the challenge of translating graph structures into a format compatible with LLMs by reorganizing graph nodes into structured sequences and mapping them into token embedding spaces using a versatile projector. LLaGA excels in versatility, generalizability, and interpretability, allowing it to perform consistently across various datasets and tasks, extend its ability to unseen data, and provide explanations for graph structures. Extensive experiments on popular graph benchmarks show that LLaGA outperforms state-of-the-art graph models in both supervised and zero-shot scenarios, achieving strong performance across four datasets and three tasks with a single model. LLaGA's unique approach involves encoding graph data into node sequences without converting structural information into natural language, preserving the original structure and enabling efficient alignment with LLMs. The framework uses two templates: the Neighborhood Detail Template for detailed node and neighbor information, and the Hop-Field Overview Template for broader neighborhood summaries. These templates, combined with a versatile projector, enable LLaGA to handle multiple tasks and generalize well to new datasets. The model's ability to generate interpretable explanations for node embeddings further enhances its practical utility. LLaGA's performance is validated through comprehensive experiments, demonstrating its effectiveness in both supervised and zero-shot learning scenarios. The framework's success lies in its ability to maintain LLMs' general-purpose capabilities while effectively handling graph data.