AlphaFold3 is Google DeepMind's official command-line application for running AlphaFold 3 biomolecular structure prediction inference. The dominant character is a production-grade scientific application, though it is heavily intertwined with research data pipelines: it ingests a JSON job specification describing protein, RNA, DNA, and ligand chains, runs Jackhmmer/Nhmmer searches over large genetic databases, builds multiple sequence alignments, searches structural templates with HMMER, featurizes the input into JAX/Haiku tensors, samples structures with a diffusion model, and writes predicted structures as mmCIF files plus confidence JSON (pLDDT, PAE, pTM, ipTM). The codebase is not a training repository; it provides inference only and requires separately downloaded model parameters under restrictive legal terms. Architecturally, the system is a multi-stage pipeline driven by the `run_alphafold.py` entry point. Core modules include the data pipeline (`src/alphafold3/data/pipeline.py`), MSA/template tools (`src/alphafold3/data/tools/`), a structure representation layer (`src/alphafold3/structure/`), JAX geometric primitives (`src/alphafold3/jax/geometry/`), the neural network model (`src/alphafold3/model/`), and performance-critical C++ pybind11 extensions for mmCIF parsing, MSA feature extraction, and string/array operations. It depends on external binaries (Jackhmmer, Nhmmer, HMMER suite), RDKit for ligand conformer generation, JAX/Haiku for model execution, and ~630 GB of downloadable sequence and structure databases. The repository is actively maintained, with recent commits and CI running CPU-only data tests. Technical drift is low for a research release: dependencies are pinned via `pyproject.toml` and `uv.lock`, and active maintenance keeps it aligned with its ecosystem, though the C++ build, GPU-specific requirements, and external database URLs remain fragility points.