A natural language processor powered by plugins that transforms and analyzes text using syntax trees.
Retext is a natural language processor that transforms and analyzes text using a plugin-based architecture built on the unified framework. It converts prose into concrete syntax trees (CSTs), enabling developers to perform tasks like spell checking, readability analysis, and smart punctuation correction through composable plugins.
JavaScript developers working on text processing applications, content management systems, or linguistic analysis tools who need programmable control over natural language.
Developers choose Retext for its modular plugin ecosystem, interoperability with the unified collective, and ability to handle text as structured data, making it ideal for building custom NLP pipelines without reinventing core parsing logic.
natural language processor powered by plugins part of the @unifiedjs collective
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Offers a wide range of community and official plugins for tasks like spell checking and readability analysis, enabling rapid development without rebuilding core NLP logic.
Uses natural language concrete syntax trees (nlcst) to represent prose as machine-readable data, allowing for precise inspection and transformation of text.
Runs on Node.js, browsers, Deno, and other JavaScript environments, providing flexibility for diverse deployment scenarios as highlighted in the README.
Fully typed with comprehensive TypeScript definitions and @types/nlcst support, ensuring robust type safety and developer experience.
Primarily focused on English and Dutch via dedicated parsers, with only Latin-script languages supported through retext-latin, excluding many global languages.
Relies on community plugins that can vary in maintenance and reliability, requiring careful assessment as advised in the README, which adds risk.
Requires composing multiple plugins with unified, leading to a steeper learning curve for simple tasks compared to all-in-one NLP libraries.