PyMC is a production-grade Bayesian modeling and probabilistic programming library built on PyTensor. It exposes a model-building API (pm.Model, pm.Normal, pm.sample), automatic log-probability derivation through graph rewriting, MCMC samplers (NUTS, HMC, Metropolis, Slice, DEMetropolis), Sequential Monte Carlo, variational inference (ADVI, FullRankADVI, SVGD), Gaussian processes, ODE support, and data containers (pm.Data, Minibatch). Users construct symbolic random-variable graphs using PyTensor; PyMC rewrites those graphs into measurable IR, computes log-probabilities and transforms, compiles sampling or variational objective functions, and returns posterior traces as xarray/ArviZ DataTree objects. Although the library primarily serves research and statistical workflows, the repository itself is an application/library, not a data-research pipeline. Architecturally, this is a library, not a service: there are no daemons or network endpoints. The entry points are Python APIs in pymc/model/core.py, pymc/sampling/mcmc.py, pymc/distributions/, pymc/step_methods/, and pymc/variational/. The core data flow is model specification -> PyTensor graph -> fgraph_from_model -> logprob rewrites -> compiled sampling functions -> trace backends (ndarray, zarr) -> to_inference_data for posterior analysis. External dependencies include NumPy, SciPy, PyTensor, ArviZ, and optional JAX/BlackJAX/NumPyro, Numba, Zarr, Rich, and Marimo. The codebase has an extensive test suite and CI, with some experimental areas such as pymc.dims and ongoing deprecations. Maintenance is active, with recent commits and rigorous CI. Technical drift is medium: most of the stack tracks current Python, but the Zarr backend is not yet ported to Zarr 3 and several optional integrations carry compatibility shims. Overall craftsmanship is strong but not exceptional: the code is deeply engineered, though its size and internal coupling make individual components less trivially extractable, and some deprecated APIs and experimental modules add friction.