In Vietnam, accessing accurate and personalized legal advice remains a significant challenge due to the complexity of legal documents and limited public legal literacy. This paper introduces an intelligent legal consulting system that integrates three key components: the Legal-Onto knowledge representation model, the VietnamLegalText-SBERT embedding model, and a ReAct-based multi-agent framework. Legal-Onto organizes legal knowledge into semantic graphs of legal concepts, capturing internal relationships, definitions, and key attributes from legal texts. Each node in the knowledge graph is embedded using VietnamLegalText-SBERT—a BERT-based model fine-tuned on Vietnamese legal corpora—and stored in Elasticsearch to enable efficient vector-based retrieval. A multi-agent architecture facilitates step-by-step reasoning, query decomposition, document retrieval, legal interpretation, and output validation. The system is evaluated on real-world legal queries collected from public sources, showing superior performance over a general-purpose GPT-4o baseline in terms of answer accuracy, citation correctness, and contextual relevance. The results demonstrate that combining domain-specific semantic modeling with reasoning agents yields interpretable, reliable, and context-aware legal support, paving the way for broader public access to legal guidance in Vietnam.