einops-exts
Einops Extensions
Decision gist · record as of 2026-08-14
No. While the package has low install friction and a permissive license, it is abandoned and has not been maintained since 2023-01-05. Without active support, it poses a risk for compatibility issues with updated dependencies and Python versions. Use einops directly or seek actively maintained alternatives instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a single dependency on einops.
- However, the package is abandoned—last release was 2023-01-05, with no ongoing maintenance since then.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, requiring only attribution and inclusion of the license notice.
last release 2023-01-05 (1317 days) · last repo commit 2023-01-05 · 57 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 207,744 downloads/mo, #9,542 on PyPI
Alternatives
Verify before relying
pip install einops-exts
import einops_exts
# Use extended helper functions alongside einops for tensor operations- What specific helper functions are provided and how they extend einops beyond its core API.
- Whether the package remains compatible with recent versions of einops and Python.
- Active usage patterns and whether the community has adopted alternatives or forks.
What it is and what it does
Einops-exts is a small utility library that adds helper functions on top of einops, a popular tensor manipulation library. It's designed for developers working with deep learning and tensor operations who want additional convenience functions built on einops' Einstein-like notation syntax.
The package depends solely on einops and installs with low friction. However, it has been abandoned since 2023-01-05 with no active maintenance, meaning it may not receive updates for compatibility with newer versions of its dependency or Python itself. The MIT license permits free use and modification.
Use it for
- Extend einops with custom tensor manipulation helpers for research or prototyping.
- Reduce boilerplate in deep learning code by using pre-built helper functions.
- Build on top of einops notation for specialized tensor operations in your own library.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
While the package has low install friction and a permissive license, it is abandoned and has not been maintained since 2023-01-05. Without active support, it poses a risk for compatibility issues with updated dependencies and Python versions. Use einops directly or seek actively maintained alternatives instead.
Install
einops-exts on PyPI
Before you install
Low install friction with a single dependency on einops. However, the package is abandoned—last release was 2023-01-05, with no ongoing maintenance since then.
License in practice
MIT license permits commercial and private use with minimal restrictions, requiring only attribution and inclusion of the license notice.
Quickstart
pip install einops-exts
import einops_exts
# Use extended helper functions alongside einops for tensor operationsVerify before relying
- What specific helper functions are provided and how they extend einops beyond its core API.
- Whether the package remains compatible with recent versions of einops and Python.
- Active usage patterns and whether the community has adopted alternatives or forks.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageeinops |
| Maintenance | Abandoned 1,317 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 207,744 / month, #9,542 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 :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: einops_exts-0.0.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “einops extensions”
- einops-extsProvides helper functions that extend Einops, a tensor manipulation…
- torch-einops-utilsProvides utility functions for PyTorch and einops to accelerate…
- xarray-einstatsProvides xarray-friendly wrappers around NumPy linear algebra, SciPy…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also einops · torch-einops-utils · einx · cotengra · einshape · axial-positional-embedding · safetensors · xarray-einstats · tensorly · unfoldNd