Lifestyle medicine offers a proactive and behavior-centric approach to managing chronic diseases, yet scalable, personalized coaching remains a significant challenge. We present LIFE-CRAFT (Lifestyle Intervention Facilitation Engine-Conversational RAG Framework with Traceability), a novel multi-agentic framework that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for personalized, lifestyle medicine related guidance. Central to LIFE-CRAFT is an orchestrator agent that interprets user queries and dynamically routes them to specialized supporting agents with expertise in prominent subdomains of lifestyle medicine. LIFE-CRAFT distinguishes itself from prior LLM-based assistants by introducing adaptive RAG pipeline with specialized sub-domain expert agents alongside dynamic domain routing and iterative feedback-based self-correcting refinement mechanisms for fine-grained context traceability as each agent’s response is grounded in semantically relevant document chunks retrieved from a well-curated, expert-reviewed PDF corpus of over 7,000 PubMed articles. For every generated response, the system logs the source document context that contributed to the response, providing transparency and explainability crucial for health-related decision support. The framework is evaluated across 864 GPT-4–generated lifestyle case scenarios using both LLM-based and human-in-the-loop evaluation. Initial results indicate that LIFE-CRAFT achieves higher context alignment and perceived trustworthiness, owing to its modular architecture, agentic decision-making, and transparent evidence grounding. This work demonstrates the feasibility and promise of multi-agentic, traceable RAG systems in supporting scalable, explainable, and personalized digital lifestyle medicine coaching—laying the foundation for future health AI systems that blend reasoning, retrieval, and real-world relevance. We intend to release the code in a GitHub repository upon acceptance of the paper to promote transparency and reproducibility.