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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.

agent aware

More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.

Kin Score

API Evangelist profiles Ray the way a machine reads it — 8 machine-readable artifacts across 5 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.

Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Ray scores 24.1/100 (emerging), with a separate agent-readiness read of 32/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 24.1/100 · emerging
Contract Quality 11.3 / 25
Developer Ergonomics 2.2 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 0.7 / 13
Governance 0.0 / 12
Discoverability 10.0 / 10
Agent readiness — 32/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 15 / 15
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

How we profile Ray

Each block below is one kind of artifact we hold for Ray. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

APIs 5

Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.

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...

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 1

Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.

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

Ray Jobs REST API

OPEN COLLECTION

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.

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

Ray Domain Security

TLSv1.3 · DMARC

SECURITY

Agentic Access 1

An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.

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

Every other property we hold for Ray — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

Build 2

SDKs, sample code, and the tooling you integrate with

Access & Security 1

Authentication, authorization, and security posture

Operate 3

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Company 2

The organization behind the API

Other 1

Properties that don't map to a standard resource type

← All providers · Data indexed from github.com/api-evangelist/ray · machine-readable index on apis.io