  In recent years, conversational recommender system (CRS) has received much
attention in the research community. However, existing studies on CRS vary in
scenarios, goals and techniques, lacking unified, standardized implementation
or comparison. To tackle this challenge, we propose an open-source CRS toolkit
CRSLab, which provides a unified and extensible framework with highly-decoupled
modules to develop CRSs. Based on this framework, we collect 6 commonly-used
human-annotated CRS datasets and implement 18 models that include recent
techniques such as graph neural network and pre-training models. Besides, our
toolkit provides a series of automatic evaluation protocols and a human-machine
interaction interface to test and compare different CRS methods. The project
and documents are released at https://github.com/RUCAIBox/CRSLab.
