MonkeyLearn

Machine learning text analysis platform with REST APIs for sentiment analysis, keyword extraction, topic classification, and custom ML model training on text data. Now part of Medallia's experience management platform.

MonkeyLearn is listed with 1 API on the APIs.io network. Tagged areas include Text Analysis, Machine Learning, Sentiment Analysis, Natural Language Processing, and Text Classification.

MonkeyLearn’s developer surface includes documentation, engineering blog, pricing, and 9 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

15.4/100 emerging ▬ flat Agent 16/100 agent aware Front door AI 5/6 prominent Heartbeat 100/100 alive saas Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

AccessFreemium

1 API

Text Analysis Machine Learning Sentiment Analysis Natural Language Processing Text Classification Keyword Extraction Artificial Intelligence

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 15.4/100 · emerging

Contract Quality 5.1 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 2.5 / 17

Operational Transparency 1.3 / 11

Contract Governance 0.0 / 10

Discoverability 5.6 / 9

Regulatory Posture 0.0 / 15

Agent readiness — 16/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship Ask about this →
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author Ask about this →
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey) Ask about this →
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented) Ask about this →
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes Ask about this →
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0 Ask about this →

11 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Developer Ergonomics scores 2.4 — up to +19.5 composite points Ask about this →
Contract Quality scores 24.0 — up to +19.0 composite points Ask about this →
Access Clarity scores 14.9 — up to +17.0 composite points Ask about this →

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Not sure how this applies to your stack? Ask about any one item. Kin Lane reads every question, looks at your profile and your situation, and replies with what a fix would take — scoped to that one area, priced up front, yours whether you ship it or not. Ask a question →

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/monkeylearn/apis.yml: open an issue on github.com/api-evangelist/monkeylearn to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

OpenAPI

measured — observed by APIs.io (published)

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

MonkeyLearn API

REST API for text analysis including sentiment analysis, keyword extraction, topic classification, and custom ML model training. Supports classifiers and extractors with Token-b...

MonkeyLearn API pricing

Not yet checked against MonkeyLearn's own pricing page. Compiled by API Evangelist; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Free	Free
Team	$299
Business	—

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Plans

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Rate Limits

0 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Finops

FINOPS

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

MonkeyLearn Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrg

GitHubOrg

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

StatusPage

StatusPage

RateLimits

RateLimits

Commercial 3

Pricing, plans, and the legal terms of use

Pricing

Pricing

Plans

Plans

FinOps

FinOps

Company 3

The organization behind the API

Website

Website

LinkedIn

Blog

Blog

Other 1

Properties that don't map to a standard resource type

Source (apis.yml)

aid: monkeylearn
name: MonkeyLearn
description: Machine learning text analysis platform with REST APIs for sentiment analysis, keyword extraction, topic classification,
  and custom ML model training on text data. Now part of Medallia's experience management platform.
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: low
  source:
  - plans
  - security
  - url: https://monkeylearn.com/
    status: 301
    note: declared website redirects to https://www.medallia.com/ — a different registrable domain (monkeylearn.com -> medallia.com),
      possible rename or acquisition (probed 2026-09-03, roadmap#169)
  generated: '2026-11-13'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/monkeylearn.png
url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/apis.yml
created: '2026-06-13'
modified: '2026-06-13'
specificationVersion: '0.23'
tags:
- Text Analysis
- Machine Learning
- Sentiment Analysis
- Natural Language Processing
- Text Classification
- Keyword Extraction
- Artificial Intelligence
tags_raw:
- Text Analysis
- Machine Learning
- Sentiment Analysis
- Natural Language Processing
- Text Classification
- Keyword Extraction
- Artificial Intelligence
- Machine-Learning
apis:
- aid: monkeylearn:monkeylearn-api
  name: MonkeyLearn API
  description: REST API for text analysis including sentiment analysis, keyword extraction, topic classification, and custom
    ML model training. Supports classifiers and extractors with Token-based authentication.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://monkeylearn.com/api/
  baseURL: https://api.monkeylearn.com/v3/
  tags:
  - Text Classification
  - Named Entity Recognition
  - Sentiment Analysis
  - Keyword Extraction
  - Machine Learning
  tags_raw:
  - Text Classification
  - Named Entity Recognition
  - Sentiment Analysis
  - Keyword Extraction
  - Machine Learning
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://monkeylearn.com/api/
  - type: OpenAPI
    url: https://new.monkeylearn.com/api/v3/
  - type: Plans
    url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/plans/plans.yml
  - type: RateLimits
    url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/rate-limits/rate-limits.yml
  - type: FinOps
    url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/finops/finops.yml
common:
- type: DomainSecurity
  url: security/monkeylearn-domain-security.yml
- type: Website
  url: https://monkeylearn.com/
- type: Documentation
  url: https://monkeylearn.com/api/
- type: GitHubOrg
  url: https://github.com/monkeylearn
- type: LinkedIn
  url: https://www.linkedin.com/company/monkeylearn
- type: Blog
  url: https://monkeylearn.com/blog/
- type: Pricing
  url: https://monkeylearn.com/pricing/
- type: StatusPage
  url: https://status.monkeylearn.com/
- type: X
  url: https://twitter.com/monkeylearn
- type: Plans
  url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/plans/plans.yml
- type: RateLimits
  url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/rate-limits/rate-limits.yml
- type: FinOps
  url: https://raw.githubusercontent.com/api-evangelist/monkeylearn/refs/heads/main/finops/finops.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

MonkeyLearn API — frequently asked questions

Does MonkeyLearn have an API?

Yes. APIs.io lists 1 API from MonkeyLearn, including MonkeyLearn API.

Is there an OpenAPI specification for the MonkeyLearn API?

Yes. APIs.io indexes 1 OpenAPI specification for MonkeyLearn, each linked for download from this page.

How much does the MonkeyLearn API cost?

APIs.io lists 3 pricing plans for MonkeyLearn, with each plan's price, limits and inclusions under Pricing on this page.

Is the MonkeyLearn API ready for AI agents?

MonkeyLearn has a Kin Score of 15.4 out of 100 (emerging) and an Agent Readiness score of 15.5 (agent-aware), scored by API Evangelist on 2026-11-13.

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