Tangermeme is a PyTorch library for post-training interpretability of sequence-based deep learning models in regulatory genomics. It provides a comprehensive suite of functions for analyzing what a trained model has learned about cis-regulatory elements, including attribution methods (DeepLIFT/SHAP, saturation mutagenesis, PISA), sequence perturbation experiments (marginalization, ablation, variant effect, spacing analysis), motif discovery via seqlet calling (recursive and TF-MoDISco-style), and sequence design (greedy/beam search substitution). The library is designed to be assumption-free, extensible to any alphabet, and works with any PyTorch model that operates on one-hot encoded sequences. Key entry points are the `deep_lift_shap`, `predict`, `ersatz`, `marginalize`, `ablate`, `seqlet`, `annotate`, and `design` subpackages, all exposed through a consistent API that accepts a `func=` argument for composability. The library includes a built-in Claude Code skill installer for AI-assisted development, extensive unit tests with toy models, and documentation with tutorials and vignettes. Tangermeme is actively developed (semantic versioning, regular releases) and targets bioinformaticians working with models like BPNet, Enformer, Beluga, or ChromBPNet.