AlphaGenome is a Python client library and SDK for interacting with Google DeepMind's AlphaGenome cloud service, a deep learning model that predicts molecular properties (gene expression, splicing, chromatin accessibility, histone marks, contact maps) from DNA sequence. The library provides gRPC-based clients for sequence, interval, and variant prediction, along with precomputed variant scores via the Atlas service. It includes rich data containers (TrackData, JunctionData), modular visualization components (tracks, transcripts, sashimi plots), and interpretation tools like in-silico mutagenesis. The repository also contains a set of Colab notebooks demonstrating end-to-end workflows for variant scoring, haplotype analysis, splicing quantification, and batch processing. The architecture is a thin client layer over a remote API, with protobuf definitions for service contracts and tensor serialization. Core modules handle genomic data types (Interval, Variant, Transcript), gene annotation parsing, ontology mapping, and efficient tensor packing. The variant scoring framework is extensible via dataclass-based scorers (center mask, gene mask LFC, splicing, polyadenylation) and includes utilities to tidy AnnData results into pandas DataFrames. Visualization is component-based, allowing composition of tracks, contact maps, and annotations into publication-quality figures. While primarily an application library, the repo has a strong data research character due to its focus on genomic analysis and the included notebooks. The code is well-structured, with extensive unit tests and CI/CD pipelines. However, it is tightly coupled to the AlphaGenome API and protobuf definitions, limiting standalone reuse. The most reusable components are the data containers (TrackData, JunctionData), tensor packing utilities, and the variant scorer dataclass pattern, which could be extracted for other genomic scoring tasks.