VideoRAG is an innovative retrieval-augmented generation framework specifically developed for understanding and processing videos with very long contexts. It intelligently combines graph-driven textual knowledge anchoring with hierarchical multimodal context encoding, enabling comprehension of videos of unrestricted lengths. The framework dynamically builds knowledge graphs, maintains semantic coherence across multiple video contexts, and enhances retrieval efficiency through adaptive multimodal fusion mechanisms. Key advantages of VideoRAG include efficient processing of long-context videos, structured video knowledge indexing, and multimodal retrieval capabilities, allowing it to provide comprehensive answers to complex queries. This framework holds significant technical value and application prospects in the field of long video understanding.