Flower
Flower Labs builds infrastructure for collaborative, decentralized AI. Its flagship open-source project, Flower (the `flwr` framework), is a friendly, framework-agnostic federated AI framework that lets organizations train and fine-tune machine-learning models on distributed, privacy-sensitive data without moving it across organizational boundaries — with support for PyTorch, TensorFlow, Hugging Face, scikit-learn, JAX, XGBoost, and more. Flower Intelligence extends this to on-device inference (TypeScript/JavaScript, Kotlin, and Swift SDKs) that can hand off to a confidential remote-compute service when extra capacity is needed. Flower also runs SuperGrid (federated AI networks), Flower Hub, and a hosted control plane accessed via the flwr CLI. Flower Labs is backed by Felicis, Northzone, and Y Combinator.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Flower the way a machine reads it — 3 machine-readable artifacts, 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 — Flower scores 31.0/100 (thin), with a separate agent-readiness read of 10/100 (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 Flower
Each block below is one kind of artifact we hold for Flower. 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.
Security Posture 3
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 Flower — 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 3
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 4
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/flower · machine-readable index on apis.io