Recent large-scale community effort has produced comprehensive mapping of neurons and chemical synapses in both the Drosophila larva and adult brains (‘connectomes’). In addition, the neurotransmitter expression of each neuron can be predicted using a machine learning algorithm. However, interpreting connectomics data has proven challenging: most neurons connect to tens/hundreds of upstream/downstream neurons, and removing weak connections eliminates most connections in the brain. This could lead to biassed usage of connectomics data, favouring neurons previously studied, or those accessible with genetic tools. Furthermore, understanding the relationship between distant neurons, such as how sensory inputs map to motor outputs, is particularly difficult. We present Connectome Interpreter, an open-source Python package, to facilitate unbiased and efficient exploration of connectomics data. It offers functionalities such as excitatory/inhibitory effective connectivity calculation and rapid path-finding between source and target neurons, to allow identification of the most relevant sub-circuit from the whole brain data. We further incorporate experimental data, mapping from stimulus space (e.g. odour) to neuron activation space, enabling users to easily generate connectome-based, falsifiable and testable hypotheses: for instance, given the experimental data and the connectome, which odour should activate the neurons of interest? In parallel, methods from explainable AI, successful in interpreting artificial neural networks, have been incorporated into our tool to identify receptive fields and optimal stimuli for any neuron in the brain. By leveraging these advances, Connectome Interpreter bridges the gap between raw connectomic data and functional insights, enhancing our understanding of neural circuits for both neuroscience and neuro-inspired systems.