We make a suite of AI developer tools that emphasize usability, performance and data privacy. We’re proud to be part of the best-in-class Python data science ecosystem. Most of our software is open-source, and the components that aren’t are just as privacy-conscious and developer-friendly. Unlike most AI companies, we don’t want your data: it never has to leave your servers if you don’t want it to.

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. It’s designed specifically for production use and helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning.

    27k+GitHub stars

spaCy website spaCy on GitHub

Prodigy is a modern annotation tool for creating training data for machine learning models. It’s so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Whether you’re working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster.


Prodigy website Live demo

Other open-source software

🪐 projectsProject templates for end-to-end NLP workflows
🔮 thincLightweight deep learning library powering spaCy
🦙 spacy-llmIntegrating LLMs into structured NLP pipelines
🛸 spacy-transformersspaCy pipelines for pre-trained BERT and other transformers
👩‍🏫 spacy-courseAdvanced NLP with spaCy: A free online course
🦆 sense2vecContextually-keyed word vectors
👑 spacy-streamlitspaCy building blocks and visualizers for Streamlit apps
💥 spacy-stanzaUse the latest Stanza (StanfordNLP) research models directly in spaCy
🦉 srslyModern high-performance serialization utilities for Python
💥 cython-blisFast matrix-multiplication as a self-contained Python library – no system dependencies!
🍳 prodigy-recipesRecipes for the Prodigy annotation tool
👩‍💻 spacy-vscodeVisual Studio Code extension for spaCy
👩‍💻 prodigy-vscodeVisual Studio Code extension for Prodigy
🧬 jupyterlab-prodigyA JupyterLab extension for annotating data with Prodigy
💥 cymemCython memory pool for RAII-style memory management
💥 preshedCython hash tables that assume keys are pre-hashed
📙 catalogueSuper lightweight function registries for your library
🍬 confectionThe sweetest config system for Python
🕊️ radicliRadically lightweight command-line interfaces


Demos and visualizations aren’t just eye candy — they’re an essential part of explaining and exploring AI technologies, especially during development. A good visualization lets you understand your model’s behavior and catch obvious problems early. Our demos include visualizations for spaCy’s dependency trees, entity recognition and similarity models.


    displaCy Dependency Visualizer

    Visualize spaCy’s guess at the syntactic structure of a sentence. Arrows point from children to heads, and are labelled by their relation type.

    displacy ent

    displaCy Named Entity Visualizer

    Visualize spaCy’s guess at the named entities in the document. You can filter the displayed types, to only show the annotations you’re interested in.

    spacy streamlit

    spaCy v3.0 Trained Pipeline Explorer

    Test and compare spaCy’s trained pipelines interactively with widgets for their components, powered by our Streamlit add-on, which you can use to build your own spaCy apps.


    Rule-based Matcher Explorer

    Test spaCy’s rule-based Matcher by creating token patterns interactively and running them over your text. Explore how spaCy processes your text – and why your pattern matches, or doesn’t.


    sense2vec: Semantic Analysis of the Reddit Hivemind

    We parsed every comment posted to Reddit in 2015 and 2019, and trained different word2vec models for each year.


    Prodigy Annotation tool

    Whether you’re working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster.