Numerous studies have shown that integrating scents into movies enhances viewer engagement and immersion. However, creating such olfactory experiences often requires professional perfumers to match scents, limiting their widespread use. To address this, we propose OdorAgent which combines a LLM with a text-image model to automate video-odor matching. The generation framework is in four dimensions: subject matter, emotion, space, and time. We applied it to a specific movie and conducted user studies to evaluate and compare the effectiveness of different system elements. The results indicate that OdorAgent possesses significant scene adaptability and enables inexperienced individuals to design odor experiences for video and images.