During past years, several frameworks for (Neural) Information Retrieval have been proposed. However, while they allow reproducing already published results, it is still very hard to re-use some parts of the learning pipelines, such as for instance the pre-training, sampling strategy, or a loss in newly developed models. It is also difficult to use new training techniques with old models, which makes it more difficult to assess the usefulness of ideas on various neural IR models. This slows the adoption of new techniques, and in turn, the development of the IR field. In this paper, we present XpmIR, a Python library defining a reusable set of experimental components. The library already contains state-of-the-art models and indexing techniques and is integrated with the HuggingFace hub.