xtensor-stack/xtensor
C++ tensors with broadcasting and lazy computing observed · 2026-08-28
Health v2 · maintenance only
74/100
- Activity 92
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3594
- days_rel: n/a
- days_push: 49
- n_releases_24m: 0
Adoption not part of the score
3762 stars · 441 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A header-only C++ library for numerical analysis with multi-dimensional array expressions, offering lazy broadcasting and a NumPy-inspired API. It integrates with Python, Julia, and R arrays via buffer protocol bindings.
Use cases
- perform numpy-style tensor math in C++
- lazy broadcasting of multidimensional arrays
- process NumPy arrays in place from C++
- vectorized numerical computations with SIMD
- build scientific computing tools in C++
When to choose
- you need NumPy-like array semantics in C++
- you want lazy evaluation to avoid temporary allocations
- you need cross-language interop with Python/Julia/R arrays
When to avoid
- you need GPU tensor operations like PyTorch or TensorFlow
- you only need simple linear algebra where Eigen suffices
- your project cannot use C++17/20 compilers
Facets
library · maturity active
math data-science machine-learning data-science performance cpp cross-platform python tensors multidimensional-arrays numpy lazy-evaluation broadcasting header-only numerical-computing xsimd algorithms
1 source
- readme: https://github.com/xtensor-stack/xtensor · fetched 2026-08-28 · e15a883c658e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| xtensor-stack/xtensor | main | 74 |
For agents
markdown · JSON · MCP: product_card(name="xtensor-stack/xtensor")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem