This is the TACAS 24 artifact to NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of neural and symbolic approaches to solving the reactive synthesis problem. To ensure soundness, the neural engine is coupled with model checkers verifying the predictions of the underlying neural models. This open-source implementation provides an integration framework for reactive synthesis in which new neural and state-of-the-art symbolic approaches can be seamlessly integrated. Extensive experiments demonstrate its efficacy in handling challenging specifications, enhancing the state-of-the-art reactive synthesis solvers, with NeuroSynt contributing novel solves in the current SYNTCOMP benchmarks.The artifact contains the open-source code, models, datasets, and experimental results. The neural solver is implemented in Tensorflow for Python. The model-checker and symbolic solver are given as docker images. We provide multiple notebooks to reproduce results and figures from the paper.