The EMLE method was implemented in Python with the PyTorch 22 machine learning framework used for the calculation of energies and gradients. A simple TCP (Transmission Control Protocol) socket server is used to handle requests for calculations from the client. This allows for a long-running process with access to the CPU/GPU resources throughout, thus avoiding the start-up cost of re-running an executable, importing modules, etc., each time a calculation is required. In this case, the client is a "fake" orca executable that is used to intercept the external call by a QM/MM MD engine with an existing ORCA interface, then send a message to the EMLE server instruct it to perform a calculation. It is possible to use a single server to handle calculations from multiple clients, e.g., in the case of limited resources, or to use a dedicated server for each client. The calculator used in the server supports the use of multiple backends for the calculation of in vacuo energies and gradients. Currently, we include support for the TorchANI, DeepMD 25 and rascal ML backends. We also support several QM backends (XTB, SQM, ORCA) and sander as MM backend, that can be combined with ML backends allowing for Δ-learning potential. For instance, in this work, sander with rascal Δ-learned correction was used. Support is also provided for user-defined external backends via an Atomic Simulation Environment calculator interface.