In this demo paper, we present SPARK (Semantic Planning with Augmented Retrieval and Knowledge), an AI-driven web application that generates personalized itineraries with natural language input. By addressing the limitations of traditional routing systems and machine learning models that lack adaptability to user intent and real-time contexts, SPARK integrates a Neo4j [6] graph that built on OpenStreetMap [8] data with semantic analysis, custom route optimization, and retrieval-augmented generation. By using large language models (LLM), the system outputs travel planning with real-time and enriched data via external APIs. Also, SPARK dynamically adapts to personalized user queries and optimizes routes based on contextual relevance. As demonstrated in an urban scenario, this demo paper highlights the potential of combining graph-based retrieval and LLMs to deliver flexible and context-aware route planning. Meanwhile, our demo shows that the proposed system is scalable and deployable for travel guidance.