We present an automated tool for real-time detection of anomalous behaviors while a DJI drone is executing a flight mission. The tool takes sensor data logged by drone at fixed time intervals and performs anomaly detection using a Bi-LSTM model. The model is trained on flight logs produced by a baseline drone after conducting a successful mission physically or via a simulator. Essentially, the model learns the sequential patterns of time series reference sensor data. The tool has two modules — the first module is designed to be embedded in the drone’s control program, which is responsible for sending the log data to the remote controller station, and the second module is run as a service in the remote controller station powered by the Bi-LSTM model, which receives the log data and produces visual graphs showing the real-time flight anomaly statuses with respect to various sensor readings on a dashboard. We have successfully evaluated the tool on three datasets including industrial test scenarios. DronLomaly is released as an open- source tool on GitHub [1], and the demo video can be found at https://www.youtube.com/watch?v=LLHbhqEhLCA.