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

The Adaption catalog on APIs.io includes 1 Spectral governance ruleset.

Adaption’s developer surface includes documentation, API reference, getting-started guide, engineering blog, signup flow, support, and 7 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

38.0/100 thin ▬ flat Agent 24/100 agent aware Heartbeat 85/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

1 published contract

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

What this lets a business do 1

Business capabilities this provider's published APIs can perform, derived from its own contracts. Browse all capabilities →

Information & Data Management

BC-610

Datasets

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 38.0/100 · thin

Contract Quality 9.1 / 19

Developer Ergonomics 6.8 / 15

Access Clarity 5.1 / 15

Operational Transparency 0.0 / 10

Contract Governance 1.2 / 9

Discoverability 5.0 / 8

Regulatory Posture 1.9 / 15

Create-or-Update Ergonomics 2.8 / 10

Agent readiness — 24/100 · 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

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 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/.

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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
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented)
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link

10 more unmet checks in the Agent Readiness table above.

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

Access Clarity scores 34.2 — up to +13.2 composite points
Operational Transparency scores 0.0 — up to +13.0 composite points
Contract Quality scores 48.7 — up to +12.8 composite points

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.

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/adaption/apis.yml: open an issue on github.com/api-evangelist/adaption 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 →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Hugging Face Hub Organisations	builds-on	17.2 (thin)	-	-

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

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 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 2

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

Adaption Adaptive Data Datasets API

OPEN COLLECTION

Adaption Adaptive Data Datasets Upload API

OPEN COLLECTION

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

Adaption API Rules

58 rules · 55 errors 2 warnings 1 info

SPECTRAL

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

Other 1

Properties that don't map to a standard resource type

CapabilityMap

CapabilityMap

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:
  - security
  generated: '2026-11-06'
  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: CapabilityMap
  url: capabilities/adaption-capability-edges.yml
  name: Adaption Business Capability Map
- 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

Adaption API — frequently asked questions

Does Adaption have an API?

Yes. APIs.io lists 2 APIs from Adaption, including Adaption Datasets API and Adaption Upload API.

Is there an OpenAPI specification for the Adaption API?

Yes. APIs.io indexes 2 OpenAPI specifications for Adaption, each linked for download from this page.

How do I authenticate with the Adaption API?

Adaption uses bearer-token authentication.

Is the Adaption API ready for AI agents?

Adaption has a Kin Score of 38.0 out of 100 (thin) and an Agent Readiness score of 23.6 (agent-aware), scored by API Evangelist on 2026-11-06.

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