# aesara-devs/aesara

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

Repository: https://github.com/aesara-devs/aesara
Canonical: https://ross.abutalabs.com/products/aesara
Homepage: https://aesara.readthedocs.io
Language: Python
License: NOASSERTION
License Family: other
Topics: symbolic-computation, tensors, theano, automatic-differentiation, transpiler, aesara, term-rewriting-system, optimizing-compiler, optimizing-compilers
Archived: true
Last push: 2024-11-15T03:40:56+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2486, "days_push": 656, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1219, forks 152 (observed 2026-08-28T04:04:01.662523+00:00)

## What it is
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions over multi-dimensional arrays, forked from Theano. It provides a hackable symbolic graph framework with automatic differentiation and transpilation to C, JAX, and Numba.

## Use cases
- define and optimize symbolic tensor expressions in python
- compute gradients of mathematical expressions automatically
- compile numpy-like expressions to fast C, JAX, or Numba code
- build custom operators with graph rewrites and optimizations
- probabilistic programming backend for tensor computation

## When to choose
- you need a hackable pure-Python symbolic tensor framework
- you want Theano-style graph optimization and autodiff
- you need to transpile symbolic graphs to multiple backends

## When to avoid
- you need an actively maintained project - Aesara is no longer maintained
- you want deep-learning training at scale - use PyTorch or JAX instead
- starting a new project - consider its successor PyTensor

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, compiler, math, serialization
- domain: machine-learning, performance, data-science
- platform: python, cross-platform
- tags: symbolic-computation, automatic-differentiation, tensor-library, theano-fork, term-rewriting, transpilation, numba, jax, algorithms

## Member repositories
- aesara-devs/aesara (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.662523+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 2026-08-30T06:16:12.152305+00:00, confidence not recorded.
  - readme: https://github.com/aesara-devs/aesara (fetched 2026-08-28T04:04:01.662523+00:00, sha ac384253fd76)
  - registry_pypi: https://pypi.org/pypi/aesara/json (fetched 2026-08-29T12:25:08.982015+00:00, sha ad8c74aa0acf)
- Data as of 2026-08-30T08:39:29.467469+00:00.