WorFBench is a comprehensive open-source framework designed to assess the capabilities of AI agents built on large language models. It offers a diverse suite of tasks—from itinerary planning to code generation workflows—each with clearly defined goals and evaluation metrics. Users can configure custom agent strategies, integrate external tools via standardized APIs, and run automated evaluations that record performance on decomposition, planning depth, tool invocation accuracy, and final output quality. Built‐in visualization dashboards help trace each agent’s decision path, making it easy to identify strengths and weaknesses. WorFBench’s modular design enables rapid extension with new tasks or models, fostering reproducible research and comparative studies.