If your data doesn't require preprocessing ... Very often you need to prepare data to use xgboost/lightgbm. You need to fill missing values, convert categoricals/dates/text to numeric, do feature engineering.
What is more, MLJAR AutoML is checking much simpler algorithms for you, like Dummy Models (average response or majority vote), linear models, simple decision trees - because very often you don't need Machine Learning. The xgboost/lightgbm cant do this :)
You do not need to fill missing values or treat categorical with LightGBM.
You can pass the name of the columns that are categoricals when constructing the Booster or Data, and LightGBM will work with them under the hood treating them as a 1 hot encoded.
LightGBM also has a way of automatically treat missing values as either zeros, their own category, or the sample average (I might be mistaken on the last one)
All in all, you still need to do feature engineering and the like, but LightGBM removes a lot of the hassle from Xgboost.
What is more, MLJAR AutoML is checking much simpler algorithms for you, like Dummy Models (average response or majority vote), linear models, simple decision trees - because very often you don't need Machine Learning. The xgboost/lightgbm cant do this :)