  Recent advancements in Large Language Models (LLMs) have expanded their
capabilities to multimodal contexts, including comprehensive video
understanding. However, processing extensive videos such as 24-hour CCTV
footage or full-length films presents significant challenges due to the vast
data and processing demands. Traditional methods, like extracting key frames or
converting frames to text, often result in substantial information loss. To
address these shortcomings, we develop OmAgent, efficiently stores and
retrieves relevant video frames for specific queries, preserving the detailed
content of videos. Additionally, it features an Divide-and-Conquer Loop capable
of autonomous reasoning, dynamically invoking APIs and tools to enhance query
processing and accuracy. This approach ensures robust video understanding,
significantly reducing information loss. Experimental results affirm OmAgent's
efficacy in handling various types of videos and complex tasks. Moreover, we
have endowed it with greater autonomy and a robust tool-calling system,
enabling it to accomplish even more intricate tasks.
