Compromise
Zero-dependency JavaScript NLP library for offline POS tagging, named entity recognition, and verb conjugation in the browser or Node.js.
Compromise wins when the text is English, the answer has to come back instantly, and the content can't leave the machine. It loses on semantics, multilingual text, and volume. If you're weighing spaCy or a cloud NLP API, this is the option you pick for convenience and bundle discipline — not for accuracy records. Reach for it when you need person names, dates, or verb normalization inside a browser extension or static site and don't want a network call in the loop.
Last checked 2d ago · cite: rightaichoice.com/tools/compromise
- Frontend engineers adding English NLP to browser apps without a backend
- Privacy-first products that cannot send user text to a remote API
- Static sites, browser extensions, and internal tools needing quick text parsing
- Prototyping phrase matching, NER, or verb normalization in JavaScript
- Non-English text — the library is English-only today
- Large-scale corpus analysis where single-threaded browser execution bottlenecks
- Tasks needing deep semantic reasoning or complex syntax disambiguation
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Skip Compromise if your product is multilingual or needs contextual, semantic-grade accuracy — it is English-only and trades model depth for speed, privacy, and a zero-dependency install.
As an MIT-licensed library, Compromise costs nothing to adopt regardless of team size, and it inverts the usual trade-off: you save the per-call bill and the data-handling review that a cloud NLP API would require, but you spend engineer hours building the rules and handling text that falls outside English. Teams that need multilingual or semantic-grade results will pay for a hosted API or a Python NLP stack instead.
In short
Compromise — Zero-dependency JavaScript NLP library for offline POS tagging, named entity recognition, and verb conjugation in the browser or Node.js. Best for Frontend engineers adding English NLP to browser apps without a backend, Privacy-first products that cannot send user text to a remote API, Static sites, browser extensions, and internal tools needing quick text parsing. 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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Part-of-speech tagging for English sentences
- Named entity recognition for people, places, dates, and money
- Match patterns that work like regular expressions for words
- Verb conjugation and noun pluralization
- Sentence boundary detection and tokenization
- Number and date parsing from free text
- Text redaction for privacy-sensitive fields
- Syllable parsing for readability work
- TF-IDF computation for text ranking
- Wikipedia topic extraction
- Built-in sentiment analysis
- JSON output for parsed documents
- Runs fully offline in the browser or Node.js
- Zero runtime dependencies
- Layered builds (compromise/one, two, three) to control bundle size
About Compromise
Compromise is an open-source JavaScript natural language processing library that parses, tags, and analyzes English text wherever JavaScript runs — in the browser or in Node.js. Text stays on the client, which is why it turns up in browser extensions, static sites, and internal tools where shipping user content to a third-party NLP API was never on the table. Frontend engineers get parts-of-speech tagging, named entity extraction, verb conjugation, and match patterns that read like regular expressions for words, all through a chainable API. Install it with `npm install compromise` or drop it in from unpkg with a single script tag. Footprint drives most of the adoption. The library installs with zero runtime dependencies and splits into loadable layers — compromise/one handles tokenization, compromise/two adds grammar, compromise/three adds chunking — so you only pay for the depth you actually call. Beyond tagging, it covers number and date parsing, sentence boundary detection, redaction, syllable parsing, TF-IDF for text ranking, Wikipedia topic extraction, sentiment analysis, and JSON output. It is English-only today, with Spanish, German, French, and Italian work listed as separate ongoing efforts. Position it against spaCy and cloud NLP APIs. Compromise trades depth, model accuracy, and semantic reasoning for speed, privacy, and a dependency-free install — and for quick phrase matching, NER, and verb normalization inside JavaScript, few options are this convenient to drop in.
Behind the Verdict
The pitch here is unusually honest for an NLP tool: Compromise does not try to be a language model. It is a rule-and-lexicon engine that tokenizes, tags parts of speech, pulls named entities, conjugates verbs, and runs match patterns that feel like regular expressions for words. Everything happens locally — in a browser tab or a Node process — with zero runtime dependencies, and the library splits into compromise/one, compromise/two and compromise/three so you only load the depth you call. Tokenizing a sentence lands under a millisecond, which is the real reason it shows up in interactive UIs. The breadth beyond tagging is easy to overlook: number and date parsing from free text, sentence boundary detection, redaction for privacy-sensitive fields, syllable parsing for readability work, TF-IDF for text ranking, Wikipedia topic extraction, sentiment analysis, and JSON output of parsed documents. The homepage also points to Spanish, German, French and Italian efforts alongside a "meet next weekend" placeholder — real work, but not the shipped English core. The honest weaknesses are scope and depth. It is English-only, accuracy trails cloud NLP on ambiguous or complex sentences, it degrades on very large inputs, and there is no deep-learning layer for contextual understanding — you cannot train custom models or domains. Compared with spaCy you give up model accuracy and semantic reasoning; compared with a cloud API you give up nothing in privacy but everything in volume headroom, since single-threaded browser execution bottlenecks on corpus-scale work. The fit is narrow and real: frontend engineers adding English NLP to a browser app without standing up a backend, privacy-first products that cannot send user text to a remote service, static sites, extensions, internal tools, and anyone prototyping phrase matching or NER in JavaScript. If you need multilingual support or state-of-the-art accuracy, this is not the tool — and the project's own framing admits as much.
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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 need to highlight every person and place name in the page the user is reading, without sending the page text anywhere. You add compromise from unpkg via a script tag, load compromise/two for grammar, and run a match pattern for #Person and #Place.
Outcome: Entity highlighting runs locally in the extension with no network call and no runtime dependency to bundle, so the text never leaves the user's machine.
Your app accepts free-text support tickets and must strip names, dates, and money amounts before anything is displayed in a shared dashboard. You call the library's redaction and number-parsing helpers on the way into the view layer.
Outcome: Sensitive fields are removed or normalized client-side, so the raw user text never has to be shipped to a third-party NLP endpoint.
You have a folder of English press releases and want verb tense normalized and sentence boundaries split before you paste quotes into a story. You run tokenization and sentence segmentation in a short Node script.
Outcome: Clean, split sentences come back in milliseconds with no model download and no API key, which is fast enough for deadline work on small-to-medium English corpora.
Use Cases
- Extract all person names from a news article in the browser.
- 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.
- Pull dates and money amounts out of free text fields.
- Redact names before logging or displaying user content.
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.
- Community language efforts for Spanish, German, French and Italian are listed on the project site but are not part of the shipped English core.
as of 2026-10-15
Verification history
We have re-verified Compromise 10 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-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
Showing the 6 most recent of 10 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
Individual developers, frontend teams, and privacy-first products that need English NLP running locally with no per-call cost.
What this tier adds
Starting tier — MIT-licensed, zero runtime dependencies, usable in the browser and Node.js with no vendor account.
Where the pricing makes sense
The company stage and team size where Compromise's pricing actually pencils out — and where peers do it cheaper.
As an MIT-licensed library, Compromise costs nothing to adopt regardless of team size, and it inverts the usual trade-off: you save the per-call bill and the data-handling review that a cloud NLP API would require, but you spend engineer hours building the rules and handling text that falls outside English. Teams that need multilingual or semantic-grade results will pay for a hosted API or a Python NLP stack instead.
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.
If you already work in JavaScript, first value is minutes: add the script tag from unpkg or run `npm install compromise`, call it on a string, and you get tags back immediately — no API key, no model download, no backend. Choosing the compromise/one, /two or /three build is the only real decision. Expect longer if you are writing custom match patterns or lexicon additions.
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 spaCy: port the specific tagging, NER, or verb-normalization calls you actually use, then re-accept lower accuracy on ambiguous sentences.
- →From a cloud NLP API: replace the network call with a local library call, then verify that your English-only traffic still passes before cutting over.
- →From a regex-only pipeline: swap the fragile phrase regexes for match patterns that operate on tagged words.
- ↗To spaCy: move the same tasks into a Python service once you need semantic accuracy, custom models, or non-English text.
- ↗To a cloud NLP API: reintroduce a network call when you need multilingual coverage or accuracy beyond rule-based tagging.
Resources & Guides
Tutorials & Learning
YouTube returned 1 video for “Compromise”, and we withheld 1: 1 could not be judged, because “Compromise” is a single word that other videos use for other things. We are showing none, because we could not prove any of them is about Compromise.
Official links
Tools that pair well with Compromise
Common stack mates teams adopt alongside Compromise, with the specific reason each pairing earns its keep.
Nltk
NLTK is the free Python library for tokenization, POS tagging, parsing, and 50+ linguistic corpora.
SpaCy
spaCy is the open-source Python NLP library for fast, production-grade text processing and information extraction at scale.
Simplemma
Simplemma is a pure-Python multilingual lemmatizer that returns dictionary base forms across 35 languages with zero 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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