During the last few years, social activities over the internet especially on social media platforms increased drastically, but unfortunately, social networks have also become the place for hate speech proliferation by which most people’s social lives are disturbed because of hate speech posts and conflicts triggered by those posts. Studies confirm that online hate speech has different offline consequences. Even though there are a lot of researches on automated hate speech detection most of them are for other language and there is a scarcity of labeled data to apply automated analysis and detection methods on Amharic dataset. Therefore the research on automatic detection of hate speech posts attracted our attention. As a solution to those problems, this research aimed to prepare a labeled huge Amharic dataset by collecting posts and comments from selected Facebook pages of activists that participated actively. Those Facebook data sets are labeled manually as hate and free based on the guidelines given from researcher and pre-processed by applying data cleaning and normalization techniques. In this research the recurrent neural network models for automated hate speech posts detection from Amharic posts on Facebook is developed by using Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) with word n-grams for feature extraction and word2vec to represent each unique word by vector representation. The experiment conducted on those two models by using 80% of the data set for training and 10% for validation to train the model and to select the best hyper-parameters combination for automated hate speech posts detection. The remaining 10% of the dataset used for testing the model after training. As a result LSTM based RNN of Batch size 128, and learning rate 0.001 with RMSProp optimizer and 0.5 dropout achieves an accuracy of 97.9% to detect posts as hate speech or free by training with 100 epochs. Which is assured by testing the models using models performance test and inference on user-generated data.