# owlbarn/owl

Owl - OCaml Scientific Computing @ https://ocaml.xyz

Repository: https://github.com/owlbarn/owl
Canonical: https://ross.abutalabs.com/products/owlbarn-owl
Language: OCaml
License: MIT
License Family: permissive
Topics: matrix, linear-algebra, ndarray, statistical-functions, topic-modeling, regression, maths, gsl, plotting, sparse-linear-systems, scientific-computing, numerical-calculations, statistics, mcmc, optimization, autograd, algorithmic-differentation, automatic-differentiation, machine-learning, neural-network
Last push: 2026-08-05T09:10:17+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 8, longevity 100
- inputs: {"age_days": 3726, "days_push": 28, "days_rel": 617, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1351, forks 131 (observed 2026-08-28T04:04:28.149099+00:00)

## What it is
Owl is an OCaml library for scientific and engineering computing, providing n-dimensional arrays, linear algebra, statistics, optimization, automatic differentiation, and neural networks. It aims to be the de-facto numerical computing toolkit for the OCaml ecosystem.

## Use cases
- perform linear algebra and matrix computations in OCaml
- run automatic differentiation for gradient-based optimization
- build and train neural networks in OCaml
- compute statistics, regressions, and MCMC sampling
- solve ODEs and do signal processing with FFTs
- plot and visualize numerical data

## When to choose
- you need scientific computing or numerical analysis in OCaml
- you want ndarray/tensor operations with broadcasting and slicing
- you need autodiff and optimization in a typed functional language

## When to avoid
- you need a large ecosystem of pretrained models like PyTorch or TensorFlow
- you are not working in OCaml
- you need GPU-heavy deep learning at scale

## Facets
- artifact type: library
- maturity: active
- function: math, machine-learning, data-science, data-visualization, deep-learning
- domain: data-science, mathematics, machine-learning
- platform: cross-platform
- tags: ocaml, ndarray, linear-algebra, automatic-differentiation, scientific-computing, statistics, optimization, mcmc, neural-networks, numerical-computing, algorithms, linux, macos, docker

## Member repositories
- owlbarn/owl (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:28.149099+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:42:13.595268+00:00, confidence not recorded.
  - readme: https://github.com/owlbarn/owl (fetched 2026-08-28T04:04:28.149099+00:00, sha 88f01949a65f)
- Data as of 2026-08-30T08:39:29.467469+00:00.