Timeloop is a framework for modeling, mapping, and design-space exploration of dense and sparse tensor algebra workloads on hardware accelerators. It combines a polyhedral analysis engine (based on the Integer Set Library, ISL) with a configurable architecture model to evaluate mappings (tiling, permutation, datatype bypass) in terms of energy, latency, and area. The primary application is the `timeloop-mapper` executable, which searches over a customizable mapping space using strategies like exhaustive, random, hybrid, or pruned search. A companion `timeloop-model` evaluates a single mapping, and `timeloop-metrics` computes detailed statistics. The codebase is predominantly C++ with Python scripts for automation and post-processing. It also includes subprojects: **Tenssella** (a polyhedral-based emulation and code generation framework) and **Orojenesis** (a Python tool for deriving operational intensity bounds of fused tensor algorithms). While the core is a robust software tool for accelerator architects, the presence of experimental subdirectories, multiple code generations, and research-oriented features (sparse tensor compression, density models, fused workloads) gives it a hybrid character—part production tool, part research prototype. The architecture is modular, with clear separation between mapping representation, loop analysis, hardware modeling, and search algorithms. Key entry points include `src/applications/mapper/mapper.cpp` and `src/applications/model/model.cpp`. External dependencies include ISL, barvinok, Boost, yaml-cpp, and libconfig.