# blackjax-devs/blackjax

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

Repository: https://github.com/blackjax-devs/blackjax
Canonical: https://ross.abutalabs.com/products/blackjax
Homepage: https://blackjax-devs.github.io/blackjax/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: probabilistic-programming, bayesian-inference, sampling-methods, hamiltonian-monte-carlo
Last push: 2026-08-31T10:35:26+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 100, release rhythm 69, longevity 100
- inputs: {"age_days": 2093, "days_push": 2, "days_rel": 48, "gap_med": 98, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1119, forks 150 (observed 2026-09-01T02:13:59.221600+00:00)

## What it is
BlackJAX is a library of fast, modular MCMC samplers (NUTS, HMC, and more) built on JAX, running on CPU, GPU, and TPU. It is not a full probabilistic programming language but integrates with any PPL that can supply a JAX-compatible log-probability density function.

## Use cases
- sample from a posterior given a logpdf
- run MCMC sampling on GPU
- build a custom sampling algorithm from modular kernels
- use NUTS or HMC with a probabilistic programming language
- learn how MCMC sampling algorithms work internally
- speed up Bayesian inference with JIT-compiled samplers

## When to choose
- you already have a JAX-compatible log-density and just need a sampler
- you need GPU/TPU-accelerated sampling
- you are a researcher building or studying new sampling algorithms
- you are developing a PPL and need robust sampler building blocks

## When to avoid
- you want a full probabilistic programming language with model syntax and inference automation
- you need a one-liner black-box inference framework like PyMC or Stan
- your project does not use JAX or cannot provide a JAX-compatible logpdf

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, math
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: mcmc, bayesian-inference, jax, sampling, hamiltonian-monte-carlo, nuts, probabilistic-programming, statistics, algorithms, gpu

## Member repositories
- blackjax-devs/blackjax (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:59.221600+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 2026-08-30T06:41:57.151799+00:00, confidence not recorded.
  - readme: https://github.com/blackjax-devs/blackjax (fetched 2026-09-01T02:13:59.221600+00:00, sha eace34e130c0)
  - homepage: https://blackjax-devs.github.io/blackjax/ (fetched 2026-08-29T12:45:57.692133+00:00, sha b66d15dc3edb)
  - registry_pypi: https://pypi.org/pypi/blackjax/json (fetched 2026-08-29T12:45:57.695107+00:00, sha fc9b31d4c9af)
- Data as of 2026-08-30T08:39:29.467469+00:00.