Deep learning offers powerful tools for enhancing image quality and acquisition speed of MR images. Standard frameworks such as TensorFlow and PyTorch provide simple access to deep learning methods. However, they lack MRI specific operations and make reproducible research and code reuse more difficult due to fast changing APIs and complicated dependencies. By integrating deep learning into the MRI reconstruction toolbox BART, we have created a reliable framework combining state-of-the-art MRI reconstruction methods with neural networks. For demonstration, we reimplemented the Variational Network and MoDL. Both implementations achieve similar performance as implementations using TensorFlow.