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Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

A fast and hackable meta-tensor library in Python

FeaturesGet StartedInstallGet helpContribute



  • A hackable, pure-Python codebase
  • Extensible graph framework suitable for rapid development of custom operators and symbolic optimizations
  • Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba.
  • Based on one of the most widely-used Python tensor libraries: Theano.

Aesara Overview Diagram: A graph linking the different components of Aesara. From left to right: Numpy API->Symbolic Graph<->Rewrites->Optimize/Stabilize->[C, Jax, Numba]

Get started

import aesara
from aesara import tensor as at

# Declare two symbolic floating-point scalars
a = at.dscalar("a")
b = at.dscalar("b")

# Create a simple example expression
c = a + b

# Convert the expression into a callable object that takes `(a, b)`
# values as input and computes the value of `c`.
f_c = aesara.function([a, b], c)

assert f_c(1.5, 2.5) == 4.0

# Compute the gradient of the example expression with respect to `a`
dc = aesara.grad(c, a)

f_dc = aesara.function([a, b], dc)

assert f_dc(1.5, 2.5) == 1.0

# Compiling functions with `aesara.function` also optimizes
# expression graphs by removing unnecessary operations and
# replacing computations with more efficient ones.

v = at.vector("v")
M = at.matrix("M")

d = a/a + (M + a).dot(v)

# Elemwise{add,no_inplace} [id A] ''
#  |InplaceDimShuffle{x} [id B] ''
#  | |Elemwise{true_divide,no_inplace} [id C] ''
#  |   |a [id D]
#  |   |a [id D]
#  |dot [id E] ''
#    |Elemwise{add,no_inplace} [id F] ''
#    | |M [id G]
#    | |InplaceDimShuffle{x,x} [id H] ''
#    |   |a [id D]
#    |v [id I]

f_d = aesara.function([a, v, M], d)

# `a/a` -> `1` and the dot product is replaced with a BLAS function
# (i.e. CGemv)
# Elemwise{Add}[(0, 1)] [id A] ''   5
#  |TensorConstant{(1,) of 1.0} [id B]
#  |CGemv{inplace} [id C] ''   4
#    |AllocEmpty{dtype='float64'} [id D] ''   3
#    | |Shape_i{0} [id E] ''   2
#    |   |M [id F]
#    |TensorConstant{1.0} [id G]
#    |Elemwise{add,no_inplace} [id H] ''   1
#    | |M [id F]
#    | |InplaceDimShuffle{x,x} [id I] ''   0
#    |   |a [id J]
#    |v [id K]
#    |TensorConstant{0.0} [id L]

See the Aesara documentation for in-depth tutorials.


The latest release of Aesara can be installed from PyPI using pip:

pip install aesara

Or via conda-forge:

conda install -c conda-forge aesara

The current development branch of Aesara can be installed from GitHub, also using pip:

pip install git+

Get help

Report bugs by opening an issue. If you have a question regarding the usage of Aesara, start a discussion. For real-time feedback or more general chat about Aesara use our Discord server, or Gitter.


We welcome bug reports and fixes and improvements to the documentation.

For more information on contributing, please see the contributing guide and the Aesara Mission Statement.

A good place to start contributing is by looking through the issues.


Special thanks to Bram Timmer for the logo.