autograd
Efficiently computes derivatives of NumPy code.
Decision gist · record as of 2026-08-14
Yes. Autograd is actively maintained, has no known vulnerabilities, installs with low friction, and is permissively licensed. It is a solid choice if you need automatic differentiation for NumPy-based code and prefer a lightweight, pure-Python solution over heavier frameworks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with a single runtime dependency (numpy).
- Actively maintained with recent releases; last commit 2026-08-10.
- Supports current Python versions (3.10–3.14).
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely in proprietary and open-source projects with minimal restrictions.
last release 2026-07-02 (43 days) · last repo commit 2026-08-10 · 7,523 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,606,457 downloads/mo, #2,063 on PyPI
Alternatives
Verify before relying
pip install autograd
import autograd.numpy as np
from autograd import grad
def f(x):
return x**3
grad_f = grad(f)
print(grad_f(2.0)) # Computes df/dx at x=2.0- Whether SciPy integration (optional dependency) is required for your use case or only for specific features.
- Performance characteristics compared to other autodiff frameworks for your problem scale.
What it is and what it does
Autograd is a library that automatically computes derivatives of Python and NumPy functions without requiring you to write gradient code by hand. It handles a large subset of Python's features—loops, conditionals, recursion, closures—and can differentiate functions multiple times over. The library supports both reverse-mode differentiation (efficient for scalar-valued functions with many inputs, as in neural networks) and forward-mode differentiation, and these can be composed arbitrarily.
The main use case is gradient-based optimization: you define a function in NumPy, wrap it with autograd's grad function, and get back a function that computes its gradient. This is particularly useful for training neural networks, Bayesian inference, and other machine learning tasks where you need gradients but don't want to manually implement backpropagation. Autograd provides a thin wrapper around NumPy that tracks operations for differentiation.
Use it for
- Training neural networks by computing gradients for backpropagation without manual derivative code.
- Implementing Bayesian inference methods like HMC or variational inference that require gradients of log-probability functions.
- Optimizing functions where analytical gradients are tedious or error-prone to derive by hand.
- Computing higher-order derivatives (e.g., Hessians) for second-order optimization methods.
- Prototyping machine learning models in pure Python/NumPy with automatic gradient computation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Autograd is actively maintained, has no known vulnerabilities, installs with low friction, and is permissively licensed. It is a solid choice if you need automatic differentiation for NumPy-based code and prefer a lightweight, pure-Python solution over heavier frameworks.
Install
autograd on PyPI
Before you install
Low friction install with a single runtime dependency (numpy). Actively maintained with recent releases; last commit 2026-08-10. Supports current Python versions (3.10–3.14).
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely in proprietary and open-source projects with minimal restrictions.
Quickstart
pip install autograd
import autograd.numpy as np
from autograd import grad
def f(x):
return x**3
grad_f = grad(f)
print(grad_f(2.0)) # Computes df/dx at x=2.0Verify before relying
- Whether SciPy integration (optional dependency) is required for your use case or only for specific features.
- Performance characteristics compared to other autodiff frameworks for your problem scale.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,606,457 / month, #2,063 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: autograd-1.9.1-py3-none-any.whl
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See also autograd-gamma · numdifftools · torch · findiff · pytorch_revgrad · jax · torchdiffeq · better-optimize · tinygrad · sparsediffpy