Using a combination of gradient boosting with LSTM for classification?
I'm not aware of any streamlined way of doing this without writing the proper code to connect the two models.  It should be fairly straight forward to run XGBoost, grab the feature importance and use this as a way to identify the features to use for the LSTM.  This part should be not so hard, just have a list of features with some criterion of whether to keep or drop it.  Once you have the list of features you want to use for the LSTM, just filter the whole dataset, and build out the LSTM model using that set.