NeuralGCM is a Python library for building and running neural general circulation models (GCMs) that combine physics-based spectral dynamical cores with learned neural network parameterizations for weather and climate simulation. It provides a high-level inference API (`PressureLevelModel`) for loading pre-trained checkpoints and running forecasts, as well as a flexible framework for constructing custom hybrid models using JAX/Flax. The codebase supports deterministic and stochastic forecasting, multi-scale physics-ML coupling (ocean, land, atmosphere), and integrates with Earth system data (ERA5, WeatherBench). The library is actively developed by Google Research and includes both a legacy Haiku/gin-based codebase and a modern Flax NNX-based refactoring, reflecting ongoing architectural evolution.