Adaption website screenshot

Adaption

Adaption (Adaption Labs) is a San Francisco AI research company building adaptive, continuously-learning AI systems rather than relying on ever-larger static models. Founded in 2026 by former Cohere leaders Sara Hooker and Sudip Roy, it exited stealth with a $50M seed round led by Emergence Capital. Its first product, Adaptive Data, exposes a REST API and official Python SDK to ingest, adapt, evaluate, and export model-ready training datasets — folding data-optimization techniques usually reserved for frontier labs into a self-serve workflow for everyday teams.

Adaption publishes 2 APIs on the APIs.io network: Datasets API and Upload API. Tagged areas include Company, Artificial Intelligence, Machine Learning, Training Data, and Datasets.

Adaption’s developer surface includes documentation, API reference, getting-started guide, engineering blog, signup flow, support, and 6 more developer resources.

🌐 Visit website 📡 Source on GitHub
36.3/100 thin ▬ flat Agent 22/100 agent aware saas Full breakdown ↓
scored 2026-09-25 · rubric v0.23.0
1 APIs
CompanyArtificial IntelligenceMachine LearningTraining DataDatasetsData AugmentationLLMModel TrainingDeveloper Tools

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-25 · rubric v0.23.0
Composite quality — 36.3/100 · thin
Contract Quality 9.1 / 19
Developer Ergonomics 6.8 / 15
Access Clarity 5.1 / 15
Operational Transparency 0.0 / 10
Contract Governance 0.0 / 9
Discoverability 5.0 / 8
Regulatory Posture 1.9 / 15
Create-or-Update Ergonomics 2.8 / 10
Agent readiness — 22/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 0 / 10
Documented Reversibility 0 / 6
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 9 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 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
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 applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.
The six quality facets above are damped to 75 points between them, because the conditional facet above carries the other 25. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
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 github.com/api-evangelist/adaption: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 2

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

Adaption Datasets API

Create, list, run, evaluate, download, and publish adaptive datasets.

Adaption Upload API

Pre-signed direct-to-S3 upload lifecycle for file-sourced datasets.

Open Collections 3

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

API Collection

OPEN COLLECTION

Adaption Adaptive Data Datasets API

OPEN COLLECTION

Adaption Adaptive Data Datasets Upload API

OPEN COLLECTION

Security Posture 1

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

Adaption Domain Security

TLSv1.2 · DMARC

SECURITY

Resources

Get Started 4

Portal, sign-up, and the first successful call

DeveloperPortal
DeveloperPortal
 GettingStarted
GettingStarted
 SignUp
SignUp
 Login
Login

Documentation 2

Reference material describing how the API behaves

Documentation
Documentation
 APIReference
APIReference

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support
Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService
TermsOfService
 PrivacyPolicy
PrivacyPolicy

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: adaption
name: Adaption
description: Adaption (Adaption Labs) is a San Francisco AI research company building adaptive, continuously-learning AI systems
  rather than relying on ever-larger static models. Founded in 2026 by former Cohere leaders Sara Hooker and Sudip Roy, it
  exited stealth with a $50M seed round led by Emergence Capital. Its first product, Adaptive Data, exposes a REST API and
  official Python SDK to ingest, adapt, evaluate, and export model-ready training datasets — folding data-optimization techniques
  usually reserved for frontier labs into a self-serve workflow for everyday teams.
url: https://raw.githubusercontent.com/api-evangelist/adaption/refs/heads/main/apis.yml
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: true
  label: Hosted service · you call their endpoint
  confidence: high
  source:
  - openapi
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://adaptionlabs.ai/opengraph-image.jpg?opengraph-image.28ce77cd.jpg
x-type: company
x-source: vc-portfolio
x-backed-by:
- emergence-capital
x-tier: profiled
x-tier-reason: enriched-from-portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-17'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Training Data
- Datasets
- Data Augmentation
- LLM
- Model Training
- Developer Tools
tags_raw:
- Company
- Ai
- Artificial Intelligence
- Machine Learning
- Training Data
- Datasets
- Data Augmentation
- LLM
- Model Training
- Developer Tools
- Machine-Learning
apis:
- aid: adaption:adaption-datasets-api
  name: Adaption Datasets API
  description: Create, list, run, evaluate, download, and publish adaptive datasets.
  humanURL: https://docs.adaptionlabs.ai/
  baseURL: https://api.prod.adaptionlabs.ai
  tags:
  - Datasets
  properties:
  - type: OpenAPI
    url: openapi/adaption-datasets-api-openapi.yml
  - type: Documentation
    url: https://docs.adaptionlabs.ai/
  - type: APIReference
    url: https://docs.adaptionlabs.ai/api
  - type: GettingStarted
    url: https://docs.adaptionlabs.ai/introduction/getting-started
- aid: adaption:adaption-upload-api
  name: Adaption Upload API
  description: Pre-signed direct-to-S3 upload lifecycle for file-sourced datasets.
  humanURL: https://docs.adaptionlabs.ai/
  baseURL: https://api.prod.adaptionlabs.ai
  tags:
  - Upload
  properties:
  - type: OpenAPI
    url: openapi/adaption-upload-api-openapi.yml
  - type: Documentation
    url: https://docs.adaptionlabs.ai/
  - type: APIReference
    url: https://docs.adaptionlabs.ai/api
  - type: GettingStarted
    url: https://docs.adaptionlabs.ai/introduction/getting-started
common:
- type: Website
  url: https://www.adaptionlabs.ai/
- type: DomainSecurity
  url: security/adaption-domain-security.yml
- type: DeveloperPortal
  url: https://docs.adaptionlabs.ai/
- type: Documentation
  url: https://docs.adaptionlabs.ai/
- type: APIReference
  url: https://docs.adaptionlabs.ai/api
- type: GettingStarted
  url: https://docs.adaptionlabs.ai/introduction/getting-started
- type: Blog
  url: https://adaptionlabs.ai/blog
- type: SignUp
  url: https://www.adaptionlabs.ai/app/auth
- type: Login
  url: https://www.adaptionlabs.ai/app/auth
- type: Support
  url: https://discord.gg/sHhG8kwVav
- type: TermsOfService
  url: https://adaptionlabs.ai/terms-of-service
- type: PrivacyPolicy
  url: https://adaptionlabs.ai/privacy-policy
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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