Compromise
Modest natural-language processing in JavaScript
Compromise is a clever, lightweight library for when you need quick and dirty NLP without server costs. It lacks the power and accuracy of cloud services, but for privacy-conscious projects or simple text processing, it's a solid pick.
- JavaScript developers building privacy-first apps
- Data journalists cleaning and analyzing text
- Frontend engineers adding lightweight NLP to static sites
- Rapid prototyping and proof-of-concept NLP features
- Enterprise-grade production NLP with high accuracy requirements
- Non-English language processing (English only)
- Large-scale corpus analysis (performance degrades with huge inputs)
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In short
Compromise — Modest natural-language processing in JavaScript. Best for JavaScript developers building privacy-first apps, Data journalists cleaning and analyzing text, Frontend engineers adding lightweight NLP to static sites. Free to use.
Viability Score
How likely is Compromise to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Part-of-speech tagging
- Named entity recognition
- Verb conjugation and normalization
- Noun pluralization and singularization
- Sentence boundary detection
- Text cleaning (e.g., removal of punctuation)
- Basic sentiment analysis
- Tokenization and word counting
- Match patterns (e.g., find all adjectives)
- JSON output for structured data
About Compromise
Compromise is a lightweight, client-side JavaScript library for natural language processing (NLP) that runs directly in the browser or Node.js. It provides a simple, intuitive API for parsing and manipulating English text—identifying parts of speech, extracting named entities, normalizing verb tenses, and more. Unlike heavyweight NLP services that require cloud calls or GPU infrastructure, Compromise operates entirely offline, making it ideal for privacy-sensitive applications and rapid prototyping. Targeted at JavaScript developers, data journalists, and frontend engineers, Compromise allows you to quickly analyze text without setting up external APIs or databases. It works by building a parse tree from the input sentence, then offering chainable methods to filter, transform, or count words. For example, you can select all nouns, conjugate verbs to past tense, or find sentence boundaries with just a few lines of code. What sets Compromise apart is its minimal footprint (~200KB gzipped) and zero dependencies. It operates without a server, meaning no costs for API calls and no data leaving the user's machine. However, this also means it is not as accurate or comprehensive as cloud-based NLP platforms like Google Cloud Natural Language or spaCy. Compromise is best suited for moderate-sized texts and common NLP tasks, not for deep semantic parsing or large-scale analysis. The project is open-source (MIT license) and has a small but dedicated community. It includes built-in methods for named-entity recognition (NER), sentiment analysis (basic), and quick text cleaning. While it may not handle complex linguistic phenomena, its ease of use and speed make it a go-to for quick text processing in static websites, browser extensions, or Node scripts.
Behind the Verdict
Compromise is a great tool for developers who want to add basic natural language processing to their JavaScript projects without relying on external APIs. Its small size and offline capability make it ideal for browser extensions, Progressive Web Apps, and tools that must respect user privacy by not sending data to the cloud. However, if you need high accuracy, support for multiple languages, or advanced capabilities like dependency parsing or sentiment analysis beyond simple word lists, you'll likely outgrow Compromise quickly. For those jobs, consider spaCy (Python) or the Google Cloud Natural Language API. Compromise shines in its niche: quick, easy, free, and local. It's not a competitor to industrial-strength NLP, but a delightful tool for modest needs.
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Use Cases
- Extract all person names from a news article.
- Convert a paragraph of verbs from past tense to present tense.
- Tokenize a document and count word frequencies.
- Find and remove all hashtags from social media posts.
- Identify sentences and split a text into individual statements.
- Normalize irregular verbs in a user-submitted text.
Limitations
- Compromise is limited to English text and its accuracy is lower than cloud-based NLP services, especially on complex or ambiguous sentences.
- It performs poorly on very large texts and does not support deep learning models for contextual understanding.
- The library also lacks built-in support for training custom models or domains.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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