LF AI and Data
The LF AI & Data Foundation is a Linux Foundation umbrella that advances open source artificial intelligence, machine learning, and data projects. It hosts 80+ projects spanning graduated, incubation, and sandbox stages, including ONNX, Milvus, Horovod, Flyte, Kedro, Pyro, Egeria, OpenLineage, Marquez, and the Adversarial Robustness Toolbox, fostering scalable, trustworthy, and interoperable AI and data solutions.
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.
API Evangelist profiles LF AI and Data the way a machine reads it — 15 machine-readable artifacts across 11 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 — LF AI and Data scores 23.6/100 (emerging), with a separate agent-readiness read of 7/100 (human only). 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
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile LF AI and Data
Each block below is one kind of artifact we hold for LF AI and Data. 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 11
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.
ONNX
Open Neural Network Exchange (ONNX) is an open format for representing deep learning models, enabling interoperability between AI frameworks.
Milvus
Milvus is an open source vector database built for scalable similarity search, supporting embedding-based AI applications.
Horovod
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Flyte
Flyte is a production-grade, cloud-native workflow orchestration platform for data and machine learning processes.
Kedro
Kedro is a Python framework for creating reproducible, maintainable, and modular data science code.
OpenLineage
OpenLineage is an open standard and API for collecting lineage metadata across data pipelines.
Marquez
Marquez is an open source metadata service for the collection, aggregation, and visualization of a data ecosystem's metadata.
Egeria
Egeria is the world's first open source metadata standard for enterprise data management, enabling unified governance and discovery.
Adversarial Robustness Toolbox
ART (Adversarial Robustness Toolbox) provides tools for evaluating and defending machine learning models against adversarial threats.
Delta Lake
Delta Lake is an open source storage layer that brings ACID transactions and reliability to data lakes.
Feast
Feast is an open source feature store for machine learning, providing consistent feature serving across training and inference.
Scroll within the panel for all 11 ·
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.
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.
Lf Ai And Data Rate Limits
RATE LIMITSFinOps 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.
Lf Ai And Data Finops
FINOPSSecurity 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.
Resources
Every other property we hold for LF AI and Data — 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
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Company 3
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/lf-ai-and-data · machine-readable index on apis.io