Compromise
Fast, offline JavaScript NLP library for text parsing
Compromise remains a top pick for client-side NLP when you need speed, privacy, and a tiny footprint. It won't replace spaCy for heavy lifting, but for quick phrase matching, NER, and verb normalization in JavaScript, the zero-dependency convenience is unmatched. If you need deep semantics or multilingual support, look at Natural or Google Cloud NLP.
Verified 4d ago · liveness 61/100 · cite: rightaichoice.com/tools/compromise
- Developers building privacy-first, offline browser apps
- Frontend engineers adding NLP to static sites or extensions
- Rapid prototyping of text processing in JavaScript
- Data journalists cleaning small-to-medium texts
- Enterprise NLP needing high accuracy and deep semantics
- Non-English text processing (only English currently)
- Large-scale corpus analysis (performance degrades)
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Skip Compromise if you need high-accuracy semantic understanding, multilingual support, or large-scale corpus processing—it's that simple.
Compromise is free and open source (MIT), so it costs nothing to adopt. Unlike cloud NLP services that charge per request, Compromise runs on the client, eliminating API costs. If you need heavy NLP, you'd pay for spaCy (free but heavier) or cloud APIs (usage-based), so Compromise is the budget-friendly pick for JavaScript.
In short
Compromise — Fast, offline JavaScript NLP library for text parsing. Best for Developers building privacy-first, offline browser apps, Frontend engineers adding NLP to static sites or extensions, Rapid prototyping of text processing in JavaScript. Free to use.
What people actually say about Compromise — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
54 mentions across 4 sources (Hacker News, App Store, GitHub, Lemmy) · researched Jul 3, 2026.
- +Zero-dependency, ~200KB gzipped — runs anywhere JavaScript runs.
- +Simple, chainable API for common NLP tasks in few lines.
- +Works offline, no cloud calls — perfect for privacy-sensitive apps.
- +Fast tokenization and part-of-speech tagging for moderate texts.
- +Open-source (MIT) with 12k+ GitHub stars and active maintenance.
- −Accuracy suffers on complex sentences and unusual phrasing.
- −No built-in support for languages other than English.
- −Missing official TypeScript definitions despite years of requests.
- −Gender pronoun guessing via heuristics is controversial.
- −Coreference resolution is absent — no entity tracking across sentences.
- • No hidden costs — it's free and open-source, but be prepared to invest time in writing additional code for advanced features like TypeScript types or language extensions.
Viability Score
How well maintained and how widely used is Compromise? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Part-of-speech tagging
- Named entity recognition
- Verb conjugation
- Noun pluralization
- Sentence boundary detection
- Tokenization
- Match patterns
- Number parsing
- Date parsing
- Redaction
- Syllable parsing
- TF-IDF computation
- Wikipedia topic extraction
- Sentiment analysis
- JSON output
About Compromise
Compromise is an open-source natural language processing library for JavaScript that runs entirely in the browser or Node.js. It gives developers fast, private, offline text analysis without any cloud dependencies. The library is known for its tiny footprint — roughly 200KB gzipped — and zero dependencies, making it a practical choice for frontend engineers who want to add parts-of-speech tagging, named entity extraction, and verb conjugation directly to their apps. It processes ~800KB of text per second and tokenizes a sentence in under a millisecond, so it's built for speed and efficiency on modest hardware. Compromise's API is chainable and uses match patterns that feel like regular expressions for words. You can parse English text into parts of speech, extract names, places, dates, and money, conjugate verbs, pluralize nouns, split sentences, and more. It also includes built-in sentiment analysis and TF-IDF for text ranking. Because it's modular, you can load only what you need: `compromise/one` tokenizes at about 40KB, `compromise/two` adds grammar at roughly 130KB, and `compromise/three` handles chunking. This modular approach lets you keep bundle sizes minimal, which matters for performance-sensitive web apps. Compromise is English-only. Community versions for Spanish, German, French, and Italian are in progress, but production multilingual use isn't available yet. That limits its reach for global products. However, for developers who need a privacy-first, client-side NLP solution with minimal footprint, Compromise is a solid pick. It's especially useful for static sites, browser extensions, or any app where you don't want to send text to a remote server for analysis. Compared to heavier NLP frameworks like spaCy or cloud-based APIs, Compromise trades depth for speed and simplicity. It won't give you deep semantic reasoning or SOTA accuracy, but it's remarkably convenient for quick phrase matching, NER, and verb normalization in JavaScript. If your
Behind the Verdict
Compromise earns its place in your toolkit if you're building a client-side app that must process text without phoning home. Running NLP entirely in the browser is a privacy win that few libraries offer at this speed and size. We'd reach for it when building static sites, extensions, or any environment where a server call is undesirable. Where it bites: you're limited to English, and the accuracy isn't at the level of a serious NLP pipeline. For production multi-language support or deep context understanding, you'll want something like spaCy or a cloud API. Compromise is a tool for pragmatic, lightweight extraction — not for solving hard linguistics problems. Compared to Natural, another JS NLP library, Compromise feels more modern and faster for basic tasks. Natural has a wider range of algorithms but a clunkier API. Compromise's chainable patterns and low overhead make it a more pleasant everyday choice for quick text cleanup. In practice, watch out for corpus size. The docs note performance degrades on large-scale analysis, so if you're processing millions of documents, consider batching or moving to a server-side tool. For most web apps, though, the speed is plenty. The modular loading is a real plus — you can keep bundle sizes down by importing only the level of grammar you need. That's a thoughtful touch for frontend performance budgets. If you're a data journalist or educator prototyping text processing without a backend, this is a great sandbox. You get sentiment, TF-IDF, and entity extraction in a few lines of code, and it runs anywhere JS runs. But don't expect magic. For accurate, nuanced analysis of messy real-world text, you'll need to pair it with other tools. Compromise is a sharp, fast knife — not a full kitchen.
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Real-world workflow fit
Concrete scenarios for the personas Compromise actually fits — and what changes day-one when you adopt it.
You install compromise in your React app and use `compromise(text).people().out('array')` to flag potential personal names in user comments.
Outcome: Within minutes you have a privacy-friendly, offline name extraction feature that never sends user data to a server.
You have a CSV of article snippets and use a Node script with compromise to extract all dates and normalize verb tense across the text.
Outcome: You transform messy text into structured data (dates, normalized verbs) without any external API dependencies or costs.
You feed social media post text into compromise's built-in sentiment analysis to gauge overall tone.
Outcome: You get a quick, offline sentiment score on a corpus, giving you a rough read without the overhead of a cloud sentiment API.
Use Cases
- Extract all person names from a news article.
- Convert verbs in a paragraph from past to present tense.
- Tokenize a document and count word frequencies.
- Find and remove hashtags from social media posts.
- Split a text into individual sentences.
- Normalize irregular verbs in user-submitted text.
Limitations
- Compromise is English-only and its accuracy is lower than cloud NLP on complex or ambiguous sentences.
- It handles small to medium texts well but degrades on very large inputs.
- It lacks deep learning models for contextual understanding, and you can't train custom models or domains.
as of 2026-08-21
Verification history
We have re-verified Compromise 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Compromise tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Any developer or organization that wants a free, offline NLP library for JavaScript without usage limits or licensing fees.
What this tier adds
This is the sole tier—free and open source with full access to all features, zero dependencies, and community support.
Where the pricing makes sense
The company stage and team size where Compromise's pricing actually pencils out — and where peers do it cheaper.
Compromise is free and open source (MIT), so it costs nothing to adopt. Unlike cloud NLP services that charge per request, Compromise runs on the client, eliminating API costs. If you need heavy NLP, you'd pay for spaCy (free but heavier) or cloud APIs (usage-based), so Compromise is the budget-friendly pick for JavaScript.
Setup time & first value
How long it actually takes to get something useful out of Compromise — broken out by persona, not the marketing-page minute.
For a developer, first value in under 10 minutes: `npm install compromise` and run `compromise('your text').sentences().out()`. For a non-developer, expect up to an hour if you're adapting existing JavaScript, but no server setup is needed.
Switching to or from Compromise
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Natural (Node.js NLP): replace Natural's tokenizer and POS tagger with Compromise's chainable API for a smaller, faster, offline solution.
- →From cloud NLP (Google/AWS/Azure): switch to Compromise to eliminate API costs and latency, but be ready for lower accuracy on complex text.
- ↗To spaCy: if you need higher accuracy, deep learning models, or non-English support, migrate to spaCy (Python) and heavier dependencies.
- ↗To a cloud NLP API: if you need deep semantics, multilingual, or large-scale processing, moving to a cloud provider gives you more power at a cost.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Compromise
Common stack mates teams adopt alongside Compromise, with the specific reason each pairing earns its keep.
SpaCy
Industrial-strength NLP library for production-scale text processing in Python.
Nltk
Free, open-source Python NLP library for tokenization, POS tagging, parsing, and 50+ corpora—built for learning and teaching.
Wink Nlp
Fast, developer-friendly JavaScript NLP library for Node.js and browsers, no dependencies
Featured Head-to-Head Comparisons
Compromise vs Geologicai
These tools serve completely different domains and should not be compared directly. GeologicAI is an enterprise AI platform for mining companies that need rapid, accurate core analysis; it justifies premium pricing with end-to-end scanning and modeling. Compromise is a free, lightweight NLP library for JavaScript developers who want offline text processing. Choose GeologicAI if you are in critical minerals mining, otherwise Compromise may be irrelevant to your needs.
Compromise vs Screenplayiq
ScreenplayIQ is a niche, paid AI for film professionals needing financial predictions, while Compromise is a free JavaScript NLP library for developers. Choose ScreenplayIQ if you're evaluating script marketability; pick Compromise for lightweight text parsing in apps.
Compromise vs Versatile
Choose Versatile if you're a steel erector needing real-time crane productivity data without crew changes; it's purpose-built hardware-backed intelligence. Choose Compromise only for lightweight client-side NLP in JavaScript, but beware of its latest security news regarding compromised packages—evaluate risk carefully before using in production.
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