# Flower

**Canonical:** https://apis.io/providers/flower/  
**Website:** https://flower.ai  
**APIs profiled:** 0

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.

## Kin Score — 30.8 / 100 (thin)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 30.8).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 18.4 |
| Developer Ergonomics | 71.4 |
| Commercial Clarity | 42.1 |
| Access Clarity | 42.1 |

## Agent readiness — 8.5 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | yes |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Self-serve signup — onboarding: self-serve, pricing: unknown, trial: no (confidence: medium).

## Security (3)

- **Flower Authentication** — apiKey · 2 schemes
- **Flower Domain Security** — TLSv1.3 · HSTS · DMARC
- **Flower Trust Center** — trust center published

## Tags

Company, Federated Learning, Federated AI, Machine-Learning, Artificial Intelligence, Privacy, SDK, On-Device AI, Confidential Computing, Open-Source

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/flower/). Scores are computed from the provider's own public artifacts under a published rubric.