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.

Flower is profiled on the APIs.io network. Tagged areas include Company, Federated Learning, Federated AI, Machine-Learning, and Artificial Intelligence.

Flower’s developer surface includes documentation, API reference, getting-started guide, engineering blog, support, signup flow, CLI, and 13 more developer resources.

🌐 Visit website 📡 Source on GitHub

30.8/100 thin ▬ flat Agent 3/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Federated LearningFederated AIMachine-Learning Artificial Intelligence Privacy SDK On-Device AI Confidential Computing Open-Source

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 30.8/100 · thin

Contract Quality 0.0 / 25

Developer Ergonomics 14.3 / 20

Access Clarity 8.4 / 20

Operational Transparency 2.4 / 13

Contract Governance 0.0 / 12

Discoverability 5.7 / 10

Agent readiness — 3/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 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

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

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 github.com/api-evangelist/flower: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 3

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

Flower Authentication

apiKey · 2 schemes

SECURITY

Flower Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Flower Trust Center

trust center published

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 3

Authentication, authorization, and security posture

TrustCenter

TrustCenter

Authentication

Authentication

DomainSecurity

DomainSecurity

Operate 3

Status, limits, changes, and where to get help

Support

Support

Community

Community

ChangeLog

ChangeLog

Commercial 2

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

TermsOfService

TermsOfService

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: flower
name: Flower
description: 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.
url: https://raw.githubusercontent.com/api-evangelist/flower/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- felicis
- northzone
x-tier: stub
x-tier-reason: portfolio-lead
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-09-03'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
image: https://flower.ai/static/images/icon/apple-touch-icon.png
tags:
- Company
- Federated Learning
- Federated AI
- Machine-Learning
- Artificial Intelligence
- Privacy
- SDK
- On-Device AI
- Confidential Computing
- Open-Source
tags_raw:
- Company
- Federated Learning
- Federated AI
- Machine Learning
- Artificial Intelligence
- Privacy
- SDK
- On-Device AI
- Confidential Computing
- Open Source
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://flower.ai
- type: DeveloperPortal
  url: https://flower.ai/docs/
- type: Documentation
  url: https://flower.ai/docs/
- type: APIReference
  url: https://flower.ai/docs/framework/ref-api/flwr.html
- type: GettingStarted
  url: https://flower.ai/docs/framework/
- type: GitHubOrganization
  url: https://github.com/flwrlabs
- type: Blog
  url: https://flower.ai/blog
- type: Support
  url: https://discuss.flower.ai
- type: Community
  url: https://flower.ai/join-slack
- type: SignUp
  url: https://flower.ai/intelligence
- type: PrivacyPolicy
  url: https://flower.ai/privacy
- type: TermsOfService
  url: https://flower.ai/legal/terms-of-service
- type: TrustCenter
  url: security/flower-trust-center.yml
- type: Packages
  url: packages/flower-packages.yml
- type: SDKs
  url: packages/flower-packages.yml
- type: CLI
  url: cli/flower-cli.yml
- type: ChangeLog
  url: changelog/flower-changelog.yml
- type: Authentication
  url: authentication/flower-authentication.yml
- type: DomainSecurity
  url: security/flower-domain-security.yml
- type: LLMsTxt
  url: llms/flower-llms.txt
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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