DataLoop

Dataloop is an enterprise AI data platform for the full unstructured-data lifecycle — ingesting images, video, audio, text and LiDAR from cloud or on-premises storage, labeling and reviewing it through managed annotation tasks and recipes, orchestrating it with pipelines and serverless functions (FaaS), and turning it into governed, model-ready datasets. The platform is programmable end to end: a JWT-authenticated REST API at gate.dataloop.ai/api/v1, a first-party Python SDK and `dlp` CLI (dtlpy), a JavaScript SDK, and a Vue component library for building in-platform applications. Founded in Israel in 2017, Dataloop was acquired by Dell Technologies in December 2025 and the platform is now marketed as the Dell Data Orchestration Engine (DDOE).

DataLoop is listed with 1 API on the APIs.io network. Tagged areas include Artificial Intelligence, Machine Learning, Data Management, Data Annotation, and MLOps.

The DataLoop catalog on APIs.io includes 1 event-driven AsyncAPI specification.

DataLoop’s developer surface includes documentation, API reference, getting-started guide, support, engineering blog, signup flow, changelog, and 25 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

50.8/100 developing ▬ flat Agent 24/100 agent aware Front door AI 6/6 dominant Heartbeat 100/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

1 API 1 MCP Servers

Artificial Intelligence Machine Learning Data Management Data Annotation MLOps Computer Vision Generative AI Data Labeling Pipelines Serverless Enterprise Software Company

Kin Score

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

Composite quality — 50.8/100 · developing

Contract Quality 8.3 / 21

Developer Ergonomics 12.1 / 17

Access Clarity 6.3 / 17

Operational Transparency 6.7 / 11

Contract Governance 1.9 / 10

Discoverability 6.1 / 9

Regulatory Posture 4.9 / 15

Agent readiness — 24/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 8 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 6 / 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 AI & Biometrics 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):

Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
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)
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
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document

8 more unmet checks in the Agent Readiness table above.

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

Contract Quality scores 39.0 — up to +15.3 composite points
Access Clarity scores 36.8 — up to +12.6 composite points
Contract Governance scores 18.2 — up to +9.8 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/dataloop/apis.yml: open an issue on github.com/api-evangelist/dataloop 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
MLOps and AI/ML Infrastructure (LF AI & Data, MLPerf, Kubeflow)	distribution	19.2 (thin)	-	-
ONVIF Video Analytics (video surveillance, edge cameras, computer vision)	distribution	19.2 (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.

Model Context Protocol (MCP)

measured — observed by APIs.io (documented)

APIs 1

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

Dataloop Platform API

The Dataloop (Dell Data Orchestration Engine) platform REST API. Every part of the platform — projects, datasets, items, annotations, recipes and ontologies, tasks and assignmen...

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Dataloop MCP Server

Remote MCP server over HTTP; 1 tool listed.

MCP SERVER

Event Specifications 1

AsyncAPI definitions for this provider's event-driven and streaming APIs.

Dataloop Platform Events

ASYNCAPI

Security Posture 4

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

Dataloop Authentication

http/oauth2/apiKey · 5 schemes

SECURITY

Dataloop Domain Security

TLSv1.3 · DMARC

SECURITY

Dataloop Vulnerability Disclosure

Hackerone · contact published

SECURITY

Dataloop Trust Center

SOC 2 Type 2, ISO/IEC 27001:2022, ISO/IEC 27701, ISO/IEC 27017:2015, ISO/IEC 27018:2019, GDPR, AWS Qualified Software

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

LLMsTxt

LLMsTxt

Design & Contract 7

Pagination, idempotency, versioning, errors, and events

Lifecycle

Conventions

ErrorCatalog

Conformance

Components

DataModel

Webhooks

Scroll for all 7

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 6

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Security

Security

VulnerabilityDisclosure

VulnerabilityDisclosure

Compliance

Compliance

TrustCenter

TrustCenter

Authentication

Authentication

Operate 4

Status, limits, changes, and where to get help

Support

StatusPage

Deprecation

ChangeLog

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: dataloop
name: DataLoop
description: 'Dataloop is an enterprise AI data platform for the full unstructured-data lifecycle — ingesting images, video,
  audio, text and LiDAR from cloud or on-premises storage, labeling and reviewing it through managed annotation tasks and
  recipes, orchestrating it with pipelines and serverless functions (FaaS), and turning it into governed, model-ready datasets.
  The platform is programmable end to end: a JWT-authenticated REST API at gate.dataloop.ai/api/v1, a first-party Python SDK
  and `dlp` CLI (dtlpy), a JavaScript SDK, and a Vue component library for building in-platform applications. Founded in Israel
  in 2017, Dataloop was acquired by Dell Technologies in December 2025 and the platform is now marketed as the Dell Data Orchestration
  Engine (DDOE).'
url: https://raw.githubusercontent.com/api-evangelist/dataloop/refs/heads/main/apis.yml
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: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://dataloop.ai/wp-content/uploads/2024/02/dataloop-logo-main.svg
x-type: company
x-source: harvest:secondary-market
specificationVersion: '0.23'
created: '2026-08-04'
modified: '2026-08-04'
x-docs-platform: redocly
x-docs-platform-confidence: high
x-docs-platform-checked: 2026-09-25
tags:
- Artificial Intelligence
- Machine Learning
- Data Management
- Data Annotation
- MLOps
- Computer Vision
- Generative AI
- Data Labeling
- Pipelines
- Serverless
- Enterprise Software
- Company
tags_raw:
- Artificial Intelligence
- Machine Learning
- Data Management
- Data Annotation
- MLOps
- Computer Vision
- Generative AI
- Data Labeling
- Pipelines
- Serverless
- Enterprise Software
- Company
- Machine-Learning
- Computer-Vision
apis:
- aid: dataloop:platform-api
  name: Dataloop Platform API
  description: The Dataloop (Dell Data Orchestration Engine) platform REST API. Every part of the platform — projects, datasets,
    items, annotations, recipes and ontologies, tasks and assignments, packages, services, triggers, executions and pipelines
    — can be created, read, updated and deleted over HTTP. Requests are authenticated with a JSON Web Token supplied as an
    Authorization bearer token, obtained either from a platform API key or from the /token endpoint. Dataloop does not publish
    a public OpenAPI definition; the interactive Swagger UI at gate.dataloop.ai/api/v1/docs/ requires an authenticated browser
    session.
  humanURL: https://docs.dataloop.ai/docs/rest-api-connection
  baseURL: https://gate.dataloop.ai/api/v1
  tags:
  - Data Management
  - Annotation
  - Pipelines
  - Machine Learning
  tags_raw:
  - Data Management
  - Annotation
  - Pipelines
  - Machine Learning
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://docs.dataloop.ai/docs/rest-api-connection
  - type: Authentication
    url: authentication/dataloop-authentication.yml
  - type: ErrorCatalog
    url: errors/dataloop-problem-types.yml
  - type: Conventions
    url: conventions/dataloop-conventions.yml
  - type: DataModel
    url: data-model/dataloop-data-model.yml
  - type: Webhooks
    url: asyncapi/dataloop-platform-events.yml
common:
- type: DomainSecurity
  url: security/dataloop-domain-security.yml
- type: Security
  url: https://dataloop.ai/security/
- type: VulnerabilityDisclosure
  url: security/dataloop-vulnerability-disclosure.yml
- type: Website
  url: https://dataloop.ai/
- type: DeveloperPortal
  url: https://developers.dataloop.ai/resources
- type: Documentation
  url: https://docs.dataloop.ai/docs
- type: APIReference
  url: https://sdk-docs.dataloop.ai/en/latest/
- type: GettingStarted
  url: https://developers.dataloop.ai/onboarding/onboarding
- type: Support
  url: https://dataloop.ai/contact/
- type: Blog
  url: https://dataloop.ai/blog/
- type: GitHubOrganization
  url: https://github.com/dataloop-ai
- type: SignUp
  url: https://console.dataloop.ai/welcome
- type: TermsOfService
  url: https://dataloop.ai/terms/
- type: PrivacyPolicy
  url: https://dataloop.ai/privacy-policy/
- type: Compliance
  url: https://docs.dataloop.ai/docs/compliance
- type: TrustCenter
  url: security/dataloop-trust-center.yml
- type: StatusPage
  url: https://status.dataloop.ai/
- type: Deprecation
  url: https://docs.dataloop.ai/docs/deprecations
- type: ChangeLog
  url: changelog/dataloop-changelog.yml
- type: Lifecycle
  url: lifecycle/dataloop-lifecycle.yml
- type: Authentication
  url: authentication/dataloop-authentication.yml
- type: Conventions
  url: conventions/dataloop-conventions.yml
- type: ErrorCatalog
  url: errors/dataloop-problem-types.yml
- type: Conformance
  url: conformance/dataloop-conformance.yml
- type: Packages
  url: packages/dataloop-packages.yml
- type: SDKs
  url: packages/dataloop-packages.yml
- type: CLI
  url: cli/dataloop-cli.yml
- type: Components
  url: components/dataloop-components.yml
- type: DataModel
  url: data-model/dataloop-data-model.yml
- type: MCPServer
  url: mcp/dataloop-mcp.yml
- type: LLMsTxt
  url: llms/dataloop-llms.txt
- type: Webhooks
  url: asyncapi/dataloop-platform-events.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-08-04'
  status: enriched
  artifacts_added: 15
  pass: local-v1

DataLoop API — frequently asked questions

Does DataLoop have an API?

Yes. APIs.io lists 1 API from DataLoop, including Dataloop Platform API.

How do I authenticate with the DataLoop API?

DataLoop uses bearer-token authentication (http/oauth2/apiKey · 5 schemes). The details are profiled on APIs.io as Dataloop Authentication.

Does DataLoop have an MCP server?

Yes. APIs.io lists 1 MCP server for DataLoop: Remote MCP server over HTTP; 1 tool listed.

Is the DataLoop API ready for AI agents?

DataLoop has a Kin Score of 50.8 out of 100 (developing) and an Agent Readiness score of 24.0 (agent-aware), scored by API Evangelist on 2026-11-06.

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