diffrax
GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX.
Decision gist · record as of 2026-08-14
Yes, if you work with differential equations in JAX or need autodiff through a solver. Low install friction, active maintenance, permissive license, and no known vulnerabilities. The Alpha status and tight coupling to the JAX ecosystem mean it's best suited for research and projects where you can tolerate API changes; for production systems requiring long-term stability, verify that the specific solver and adjoint method you need are stable.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11+; JAX must be installed and functional (may require GPU/TPU drivers for hardware acceleration).
- Low friction install with a pure-Python wheel.
- Requires Python 3.11+ and depends on established JAX ecosystem packages (equinox, jax, jaxtyping, lineax, optimistix).
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions—you must include a copy of the license and note any modifications, but there are no copyleft obligations.
last release 2026-02-18 (177 days) · last repo commit 2026-06-21 · 2,084 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 357,141 downloads/mo, #7,275 on PyPI
Alternatives
Verify before relying
pip install diffrax
from diffrax import diffeqsolve, ODETerm, Dopri5
import jax.numpy as jnp
def f(t, y, args):
return -y
term = ODETerm(f)
solver = Dopri5()
y0 = jnp.array([2., 3.])
solution = diffeqsolve(term, solver, t0=0, t1=1, dt0=0.1, y0=y0)- Whether all solver types (Tsit5, Dopri8, symplectic, implicit) are production-ready or still experimental given Alpha status.
- Performance characteristics and numerical accuracy compared to other differential equation libraries.
- Specific GPU/TPU compatibility and performance gains in practice.
What it is and what it does
Diffrax is a JAX-native library for solving differential equations—ODEs, SDEs, and CDEs—with full automatic differentiation and GPU support. It unifies the treatment of different equation types under a single internal architecture, making it compact and composable. The library is designed for researchers and practitioners working with neural differential equations, dynamical systems, and scientific computing where gradient-based optimization through the solver is needed.
The package integrates tightly with the JAX ecosystem (equinox, jaxtyping, lineax, optimistix) and supports advanced features like vmappable solvers, PyTree state representations, dense solutions, and multiple adjoint methods for backpropagation. It includes a range of solvers from standard choices like Dopri5 to specialized symplectic and implicit methods, all callable from a unified interface.
Use it for
- Training neural differential equations where gradients flow through the solver during backpropagation.
- Solving systems of ODEs/SDEs on GPU for large-scale scientific simulations with automatic differentiation.
- Implementing controlled differential equations for sequence modeling and time-series tasks.
- Research prototyping of dynamical systems where vmappable solvers enable batched integration over parameter ranges.
- Combining differential equation solving with JAX's functional programming model in end-to-end differentiable pipelines.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with differential equations in JAX or need autodiff through a solver.
Low install friction, active maintenance, permissive license, and no known vulnerabilities. The Alpha status and tight coupling to the JAX ecosystem mean it's best suited for research and projects where you can tolerate API changes; for production systems requiring long-term stability, verify that the specific solver and adjoint method you need are stable.
Install
diffrax on PyPI
Before you install
Low friction install with a pure-Python wheel. Requires Python 3.11+ and depends on established JAX ecosystem packages (equinox, jax, jaxtyping, lineax, optimistix). Repository is active with recent commits and 2084 stars.
Requires Python 3.11+; JAX must be installed and functional (may require GPU/TPU drivers for hardware acceleration).
License in practice
Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions—you must include a copy of the license and note any modifications, but there are no copyleft obligations.
Quickstart
pip install diffrax
from diffrax import diffeqsolve, ODETerm, Dopri5
import jax.numpy as jnp
def f(t, y, args):
return -y
term = ODETerm(f)
solver = Dopri5()
y0 = jnp.array([2., 3.])
solution = diffeqsolve(term, solver, t0=0, t1=1, dt0=0.1, y0=y0)Verify before relying
- Whether all solver types (Tsit5, Dopri8, symplectic, implicit) are production-ready or still experimental given Alpha status.
- Performance characteristics and numerical accuracy compared to other differential equation libraries.
- Specific GPU/TPU compatibility and performance gains in practice.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesequinoxjaxjaxtypinglineaxoptimistixtyping-extensionswadler-lindig |
| Maintenance | Actively maintained 177 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 357,141 / month, #7,275 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Mathematics |
Evidence: diffrax-0.7.2-py3-none-any.whl
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