Zero-shot learning (ZSL) focuses on annotating texts with entities or relations that have never been seen before during training. This task has a lot of applications in practice due to the lacking labeled data in real-world situations within specific domains. Recent advances in machine learning with large pretrained language models demonstrate significant results in zero-shot learning with numerous novel methods. It is very high demand both in the industry and the research community to have a frame work where people with different backgrounds can easily access the latest ZSL methods or pretrained models. In this work, we create a new ZSL framework called Zshot. The main goal of our work is to provide researchers with a frame work where they can quickly benchmark and compare different state-of-the-art ZRL methods with standard benchmark datasets included in the framework. Moreover, it is designed to support the industry with ready APIs for production under the standard Spacy NLP pipeline. Our API is extendible and evaluable, moreover, we include numerous enhancements such as automatic description generation, boosting the accuracy with pipeline ensembling, and visualization utilities available as a SpaCy extension.