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

38.3/100 thin ▬ flat Agent 16/100 agent aware saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

1 APIs

Company Artificial Intelligence Machine-Learning Training Data DatasetsData AugmentationLLM Model Training Developer Tools

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 38.3/100 · thin

Contract Quality 12.2 / 23

Developer Ergonomics 8.1 / 18

Access Clarity 6.2 / 18

Operational Transparency 0.0 / 12

Contract Governance 0.0 / 11

Discoverability 6.2 / 9

Create-or-Update Ergonomics 2.8 / 10

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 10 / 10

Idempotency 0 / 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

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.

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

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)

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
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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