Ray

Ray is an open-source unified compute framework, stewarded by Anyscale, that scales Python and AI workloads from a laptop to a cluster. It consists of Ray Core (a distributed runtime) and a set of AI libraries (Ray Train, Ray Data, Ray Tune, Ray Serve, RLlib) for training, batch inference, hyperparameter search, and model serving. Ray clusters expose a Dashboard and Jobs REST API on the head node (default port 8265) for submitting jobs, inspecting actors and tasks, and serving deployed applications via Ray Serve HTTP endpoints.

Ray publishes 5 API contracts indexed on the APIs.io network, including Jobs API, Version API, and 3 more. Tagged areas include Distributed Computing, Machine Learning, AI Infrastructure, Python, and Model Serving.

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

Ray’s developer surface includes documentation, engineering blog, and 15 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

32.4/100 thin ▬ flat Agent 17/100 agent aware Front door AI 6/6 dominant self hosted · Apache-2.0 Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

1 published contract

Distributed Computing Machine Learning AI Infrastructure Python Model Serving Open Source Compute

Kin Score

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

Composite quality — 32.4/100 · thin

Contract Quality 6.2 / 16

Developer Ergonomics 4.0 / 13

Access Clarity 0.8 / 13

Operational Transparency 2.4 / 8

Contract Governance 1.1 / 8

Discoverability 4.3 / 7

Regulatory Posture 0.8 / 15

Open Source Surface 8.5 / 10

Create-or-Update Ergonomics 0.0 / 10

Agent readiness — 17/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 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 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.

Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against 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 65 points between them, because both conditional facets apply and carry 35 points together. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 65% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Show this score on your site.

It updates itself when the score changes. Paste this where you link to your API docs:

<a href="https://apis.io/providers/ray/"><img src="https://apis.io/badge/ray/card.svg" alt="Ray on APIs.io — Kin Score by API Evangelist"></a>

Other styles, a README pill and a shields.io endpoint are on the badge page.

Raise your score.

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 →
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 →
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented) 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):

Access Clarity scores 6.4 — up to +18.7 composite points Ask about this →
Contract Quality scores 38.0 — up to +15.5 composite points Ask about this →
Developer Ergonomics scores 31.0 — up to +13.8 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/ray/apis.yml: open an issue on github.com/api-evangelist/ray 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)	standard	23.6 (thin)	-	1
OpenSSF Best Practices Badge Holders	standard	23.6 (thin)	-	1

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 5

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

Ray Jobs REST API

REST API on the Ray head node for submitting, listing, inspecting, and stopping Ray jobs, plus streaming logs. Default base URL is http://:8265/api/jobs/. Open-source clusters a...

Ray Dashboard API

Internal REST API powering the Ray Dashboard, exposing endpoints for nodes, actors, tasks, placement groups, runtime environments, and cluster events. Same base URL as the Jobs ...

Ray Serve HTTP API

HTTP interface for invoking models and applications deployed via Ray Serve. Each deployed application is exposed as an HTTP endpoint on the Serve HTTP proxy (default port 8000);...

Ray Jobs API

The Jobs API from Ray — 4 operation(s) for jobs.

Ray Version API

The Version API from Ray — 1 operation(s) for version.

Open Collections 3

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

Ray REST Jobs API

OPEN COLLECTION

Ray REST Jobs Version API

OPEN COLLECTION

Ray Jobs REST API

OPEN COLLECTION

Terms of Service 1

What its API terms permit and prohibit, quoted from its own page.

Ray

2 quoted statements, read from Ray's own page on 2026-11-10.

TERMS

Data License 1

How its data and outputs may be reused, quoted from its own terms.

Ray

2 quoted statements, read from Ray's own page on 2026-11-10.

LICENSE

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs. Counts are the severity each rule declares, not results of running them.

Ray API Rules

38 rules · 36 error-severity 1 warn-severity 1 info-severity

SPECTRAL

Security Posture 1

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

Ray Domain Security

TLSv1.3 · DMARC

SECURITY

Agentic Access (API Evangelist recommendations) 1

Recommended x-agentic-access execution contracts for AI agents.

Ray Agentic Access

6 operations · 2 acting · 1 human-in-the-loop

6 operations · 2 acting

AGENTIC

Resources

Documentation 2

Reference material describing how the API behaves

ContributionGuide

ContributionGuide

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Build 2

SDKs, sample code, and the tooling you integrate with

GitHubRepository

GitHubRepository

GitHubOrganization

GitHubOrganization

Access & Security 2

Authentication, authorization, and security posture

SecurityPolicy

SecurityPolicy

DomainSecurity

DomainSecurity

Operate 4

Status, limits, changes, and where to get help

Releases

Slack

Forums

Issues

Commercial 3

Pricing, plans, and the legal terms of use

License

License

UsageTerms

UsageTerms

DataLicense

DataLicense

Company 2

The organization behind the API

Website

Website

Blog

Blog

Other 1

Properties that don't map to a standard resource type

Anyscale

Anyscale

Source (apis.yml)

aid: ray
name: Ray
description: Ray is an open-source unified compute framework, stewarded by Anyscale, that scales Python and AI workloads from
  a laptop to a cluster. It consists of Ray Core (a distributed runtime) and a set of AI libraries (Ray Train, Ray Data, Ray
  Tune, Ray Serve, RLlib) for training, batch inference, hyperparameter search, and model serving. Ray clusters expose a Dashboard
  and Jobs REST API on the head node (default port 8265) for submitting jobs, inspecting actors and tasks, and serving deployed
  applications via Ray Serve HTTP endpoints.
type: Index
deliveryModel:
  model: self-hosted
  license: Apache-2.0
  license_evidence:
    repo: ray-project/ray
    spdx: Apache-2.0
    stars: 43923
  open_source: true
  commercial: false
  callable_host: false
  label: Self-hosted open source · you run it yourself
  confidence: medium
  source:
  - openapi
  - repository-license
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  try_now_blocked_by: loopback
  source:
  - security
  generated: '2026-11-13'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/ray.png
x-docs-platform: readthedocs
x-docs-platform-confidence: high
x-docs-platform-checked: 2026-10-04
tags:
- Distributed Computing
- Machine Learning
- AI Infrastructure
- Python
- Model Serving
- Open Source
- Compute
tags_raw:
- Distributed Computing
- Machine Learning
- AI Infrastructure
- Python
- Model Serving
- Open Source
- Compute
- Machine-Learning
- Open-Source
url: https://raw.githubusercontent.com/api-evangelist/ray/refs/heads/main/apis.yml
created: '2026-05-11'
modified: '2026-05-11'
specificationVersion: '0.23'
apis:
- aid: ray:jobs-api
  name: Ray Jobs REST API
  description: REST API on the Ray head node for submitting, listing, inspecting, and stopping Ray jobs, plus streaming logs.
    Default base URL is http://<head-node>:8265/api/jobs/. Open-source clusters are typically unauthenticated; production
    deployments rely on network controls or Anyscale-managed authentication.
  humanURL: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/rest.html
  baseURL: http://127.0.0.1:8265/api
  tags:
  - Job
  - Cluster
  - Submission
  - Logs
  tags_raw:
  - Jobs
  - Cluster
  - Submission
  - Logs
  properties:
  - type: Documentation
    url: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/rest.html
  - type: Python SDK
    url: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/sdk.html
  - type: CLI
    url: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/cli.html
- aid: ray:dashboard-api
  name: Ray Dashboard API
  description: Internal REST API powering the Ray Dashboard, exposing endpoints for nodes, actors, tasks, placement groups,
    runtime environments, and cluster events. Same base URL as the Jobs API (http://<head>:8265).
  humanURL: https://docs.ray.io/en/latest/ray-observability/getting-started.html
  baseURL: http://127.0.0.1:8265/api
  tags:
  - Observability
  - Dashboards
  - Cluster State
  - Actors
  tags_raw:
  - Observability
  - Dashboard
  - Cluster State
  - Actors
  properties:
  - type: Documentation
    url: https://docs.ray.io/en/latest/ray-observability/getting-started.html
- aid: ray:serve-api
  name: Ray Serve HTTP API
  description: HTTP interface for invoking models and applications deployed via Ray Serve. Each deployed application is exposed
    as an HTTP endpoint on the Serve HTTP proxy (default port 8000); authentication and routing are configured per deployment.
  humanURL: https://docs.ray.io/en/latest/serve/index.html
  baseURL: http://127.0.0.1:8000
  tags:
  - Model Serving
  - Inference
  - HTTP
  properties:
  - type: Documentation
    url: https://docs.ray.io/en/latest/serve/index.html
  - type: Production Guide
    url: https://docs.ray.io/en/latest/serve/production-guide/index.html
- aid: ray:ray-jobs-api
  name: Ray Jobs API
  description: The Jobs API from Ray — 4 operation(s) for jobs.
  humanURL: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/rest.html
  baseURL: http://127.0.0.1:8265/api
  tags:
  - Job
  tags_raw:
  - Jobs
  properties:
  - type: OpenAPI
    url: openapi/ray-jobs-api-openapi.yml
- aid: ray:ray-version-api
  name: Ray Version API
  description: The Version API from Ray — 1 operation(s) for version.
  humanURL: https://docs.ray.io/en/latest/cluster/running-applications/job-submission/rest.html
  baseURL: http://127.0.0.1:8265/api
  tags:
  - Version
  properties:
  - type: OpenAPI
    url: openapi/ray-version-api-openapi.yml
common:
- type: Releases
  url: https://github.com/ray-project/ray/releases
- type: SecurityPolicy
  url: https://github.com/ray-project/ray/blob/master/SECURITY.md
- type: ContributionGuide
  url: https://github.com/ray-project/ray/blob/master/CONTRIBUTING.rst
- type: AgenticAccess
  url: agentic-access/ray-agentic-access.yml
- type: DomainSecurity
  url: security/ray-domain-security.yml
- type: Website
  url: https://www.ray.io
- type: Documentation
  url: https://docs.ray.io
- type: GitHubRepository
  url: https://github.com/ray-project/ray
- type: GitHubOrganization
  url: https://github.com/ray-project
- type: Anyscale
  url: https://www.anyscale.com
- type: Slack
  url: https://www.ray.io/community
- type: Forums
  url: https://discuss.ray.io
- type: Blog
  url: https://www.anyscale.com/blog
- type: Issues
  url: https://github.com/ray-project/ray/issues
- type: License
  url: https://github.com/ray-project/ray/blob/master/LICENSE
- type: UsageTerms
  url: terms/ray-terms-of-service.yml
- type: DataLicense
  url: data-license/ray-data-license.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

Ray API — frequently asked questions

Does Ray have an API?

Yes. APIs.io lists 5 APIs from Ray, including Ray Jobs REST API, Ray Dashboard API and Ray Serve HTTP API, and 2 more.

Is there an OpenAPI specification for the Ray API?

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

Is the Ray API ready for AI agents?

Ray has a Kin Score of 32.4 out of 100 (thin) and an Agent Readiness score of 16.5 (agent-aware), scored by API Evangelist on 2026-11-13.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

All 92 tools →

Call it yourself

curl for this page

This provider

curl "https://apis.io/api/v1/providers/ray"

All providers

curl "https://apis.io/api/v1/providers?limit=25"

Every operation they expose

curl "https://apis.io/api/v1/providers/ray/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/ray/evidence"

Calls without X-API-Key need no key. Calls with it need a paid plan — get a key.

Get an API key

Free tier, no form to fill in. Signing in shares your email address with us — we store it to create your key and to recognise you if you sign in with another provider. See our Privacy Policy and Terms.

GitHub Google LinkedIn

A second provider on the same verified email joins the account you already have.