#1818th of 22 in ML frameworks and libraries
spacy
Python and Cython NLP library with pretrained pipelines for 70+ languages
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- Stars
- 34k
- License
- MIT
- Last commit
- Sep 2026
- Last release
- Aug 2026
- Language
- Python
33,166 on 25 Mar 202633,962 on 14 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
spaCy is a Python/Cython library for tokenization, tagging, dependency parsing, named entity recognition, text classification, lemmatization and entity linking. It ships pretrained pipelines installed as Python packages and a config-driven training system, and can use transformers such as BERT or custom PyTorch and TensorFlow models. LLMs can be added to pipelines through a documented integration.
Who it is for: Python developers building production NLP pipelines (NER, classification)
Strengths
- Pretrained pipelines install as ordinary Python packages via `python -m spacy download`
- Tokenization and training support for 70+ languages
- Built-in training system, model packaging, and syntax and NER visualizers
- Extensible with custom components; accepts PyTorch and TensorFlow models
Weaknesses
- README lists Python >=3.7, <3.13 only, 64-bit
- Models may need re-downloading or retraining after spaCy upgrades
- Library only; no model server or HTTP API included
- Support is public only (GitHub, Stack Overflow); no individual email support
What it needs
- GPU optional
- Needs Python >=3.7,<3.13, pip or conda
- Models: spaCy pretrained pipelines, transformers (BERT), PyTorch, TensorFlow, word vectors
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