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ZenML

ZenML is an open-source MLOps and LLMOps framework that unifies machine learning and generative AI workflows through a single orchestration, versioning, and governance layer. It provides a Python SDK, CLI, REST API, and server for managing pipelines, stacks, artifacts, models, and deployments across any infrastructure backend, with 60+ integrations spanning orchestrators, ML frameworks, GenAI tools, cloud storage, and experiment tracking platforms.

agent ready

Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.

Kin Score

API Evangelist profiles ZenML the way a machine reads it — 48 machine-readable artifacts across 14 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 — ZenML scores 69.4/100 (strong), with a separate agent-readiness read of 60/100 (agent ready). 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 — 69.4/100 · strong
Contract Quality 16.5 / 25
Developer Ergonomics 12.2 / 20
Commercial Clarity 14.2 / 20
Operational Transparency 8.9 / 13
Governance 8.8 / 12
Discoverability 8.8 / 10
Agent readiness — 60/100 · agent ready
Machine-Readable Contract 18 / 18
Agentic Access Contract 15 / 15
MCP Server 12 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile ZenML

Each block below is one kind of artifact we hold for ZenML. 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 14

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.

ZenML Pro REST API

The ZenML Pro REST API extends the OSS API with managed control-plane features for teams, including organization and tenant management, role-based access control, audit logs, an...

ZenML Artifacts API

Artifact metadata and versions produced by pipeline runs

ZenML Auth API

Authentication and token management

ZenML Deployments API

Pipeline deployments

ZenML Models API

Registered models and their versions

ZenML Pipeline Runs API

Pipeline run instances and their steps

ZenML Pipelines API

ML pipeline definitions

ZenML Projects API

Project workspaces

ZenML Schedules API

Scheduled pipeline runs

ZenML Secrets API

Encrypted secret storage

ZenML Service Connectors API

Connectors to external infrastructure providers

ZenML Stack Components API

Individual stack components such as orchestrators, artifact stores, and experiment trackers

ZenML Stacks API

ZenML stacks and their components

ZenML Users API

User accounts

Scroll within the panel for all 14 ·

Postman Collections 1

A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.

Ready-to-run Postman collections for exercising this provider's APIs.

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).

ZenML OSS REST API

OPEN COLLECTION

Arazzo Workflows 12

Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.

Multi-step API workflows described with the Arazzo specification.

ZenML Audit Pipeline Runs

Walk from a named pipeline to its run history and drill into the most recent run.

ARAZZO

ZenML Authenticate and List Pipelines

Exchange credentials for a token, confirm the session identity, and list pipelines.

ARAZZO

ZenML Bootstrap Project Pipeline

Identify the caller, resolve a project workspace, register a pipeline, and confirm it.

ARAZZO

ZenML Inspect Run Artifacts

Select a pipeline run, confirm it succeeded, and inspect an artifact produced in the deployment.

ARAZZO

ZenML Inspect Stack Topology

Pick a stack, read its component wiring, and cross-reference the component catalog.

ARAZZO

ZenML Monitor Pipeline Run

Find the latest run of a pipeline, poll its status to completion, and branch on success or failure.

ARAZZO

ZenML Provision Pipeline

Resolve a project, register a new pipeline in it, and confirm the pipeline was created.

ARAZZO

ZenML Provision Secret

Confirm the caller identity, create a scoped secret, and confirm it appears in the secret store.

ARAZZO

ZenML Register Model

Register a new model in the model control plane and enumerate its versions.

ARAZZO

ZenML Register Stack

Discover available stack components, assemble them into a new stack, and confirm the stack was created.

ARAZZO

ZenML Trace Deployment Runs

Select a pipeline deployment, resolve its pipeline, and read the latest run it produced.

ARAZZO

ZenML Track Scheduled Pipeline

Resolve a schedule, find the run it produced for its pipeline, and read that run.

ARAZZO

Scroll within the panel for all 12 ·

MCP Servers 1

Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.

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

MCP Server

MCP SERVER

Pricing Plans 1

Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.

Published pricing tiers and plan structures.

Zenml Plans Pricing

3 plans

PLANS

Rate Limits 1

Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.

Documented rate limits and quota policies.

Zenml Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.

Cost, billing, and metering signals for API financial operations.

Zenml Finops

FINOPS

Semantic Vocabularies 1

JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.

JSON-LD contexts and semantic vocabularies used across these APIs.

Zenml Context

23 classes · 3 properties

JSON-LD

Spectral Rules 2

Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.

ZenML API Rules

5 rules · 4 warnings

SPECTRAL

ZenML API Rules

6 rules · 2 errors · 3 warnings

SPECTRAL

JSON Schema 5

Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.

Standalone JSON Schema definitions for this provider's data models.

ZenML Artifact

8 properties

JSON SCHEMA

ZenML Model

10 properties

JSON SCHEMA

ZenML Pipeline Run

9 properties

JSON SCHEMA

ZenML Pipeline

9 properties

JSON SCHEMA

ZenML Stack

6 properties

JSON SCHEMA

JSON Structure 2

JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.

JSON Structure definitions describing this provider's data shapes.

Zenml Pipeline Run Structure

7 properties

JSON STRUCTURE

Zenml Pipeline Structure

8 properties

JSON STRUCTURE

Examples 3

Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.

Example request and response payloads for these APIs.

Security Posture 2

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.

Zenml Authentication

http · 1 scheme

SECURITY

Zenml Domain Security

TLSv1.3 · HSTS · 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.

Zenml Agentic Access

25 operations · 7 acting

25 operations · 7 acting

AGENTIC

Resources

Every other property we hold for ZenML — 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.

Get Started 2

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

Access & Security 2

Authentication, authorization, and security posture

Operate 3

Status, limits, changes, and where to get help

Commercial 3

Pricing, plans, and the legal terms of use

Company 2

The organization behind the API

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