Few-shot Learning (FSL) targets at bridging the gap between artificial intelligence (AI) and human learning. It can learn new tasks containing only a few examples with supervised information by incorporating prior knowledge. Besides acting as a test-bed for AI, FSL makes the learning of rare cases possible, such as predicting new molecular property given a few labeled molecules in drug discovery. It also helps to relieve the burden of collecting large-scale supervised date in industrial applications, where large-scale unlabeled data exists but high-quality labeled data is costly to acquire. In this talk, we will introduce a development toolkit for few-shot learning called PaddleFSL which builds upon PaddlePaddle. Currently, it provides reliable implementations of popularly used FSL methods in many classic applications such as image classification and relation extraction. It is also easy to customize PaddleFSL for other applications. We hope PaddleFSL can contribute to helping users from both academia and industry to easily conduct FSL.