RRepoGEO

REPOGEO REPORT · LITE

flwrlabs/flower

Default branch main · commit 4e7318e5 · scanned 6/23/2026, 6:07:02 AM

GitHub: 6,999 stars · 1,212 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
90 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface flwrlabs/flower, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening statement to clarify Flower's unique positioning

    Why:

    CURRENT
    Flower (`flwr`) is a framework for building federated AI systems. The design of Flower is based on a few guiding principles: Customizable, Extendable, Framework-agnostic...
    COPY-PASTE FIX
    Flower (`flwr`) is the leading framework for building federated AI systems, uniquely designed to be framework-agnostic and highly customizable for privacy-preserving machine learning across decentralized data. Unlike general distributed task queues or monitoring tools, Flower is purpose-built for federated learning and analytics.
  • mediumabout#2
    Expand the repository description to include a key differentiator

    Why:

    CURRENT
    Flower: A Friendly Federated AI Framework
    COPY-PASTE FIX
    Flower: A Friendly Federated AI Framework. Build privacy-preserving, framework-agnostic machine learning systems with PyTorch, TensorFlow, JAX, and more, across diverse client devices.
  • lowreadme#3
    Add explicit mention of diverse client support in README

    Why:

    COPY-PASTE FIX
    Flower enables federated learning across a wide range of client devices, including mobile (Android, iOS), edge devices (Raspberry Pi), and traditional servers.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface flwrlabs/flower
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
17%
Of all named tools, what % are you?
Top rival
tensorflow/federated
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/federated · recommended 1×
  2. OpenMined/PySyft · recommended 1×
  3. FedML-AI/FedML · recommended 1×
  4. intel/openfl · recommended 1×
  5. FederatedAI/FATE · recommended 1×
  • CATEGORY QUERY
    How can I implement a distributed machine learning model training across multiple client devices?
    you: #3
    AI recommended (in order):
    1. TensorFlow Federated (TFF) (tensorflow/federated)
    2. PySyft (OpenMined) (OpenMined/PySyft)
    3. Flower (adap/flower) ← you
    4. FedML (FedML-AI/FedML)
    5. Intel OpenFL (intel/openfl)
    6. FATE (Federated AI Technology Enabler) (FederatedAI/FATE)
    7. Ray (ray-project/ray)
    Show full AI answer
  • CATEGORY QUERY
    What framework supports federated deep learning with PyTorch, TensorFlow, and mobile clients?
    you: #1
    AI recommended (in order):
    1. Flower ← you
    2. FedML
    3. TensorFlow Federated (TFF)
    4. PySyft (OpenMined)
    5. LEAF (Learning in Federated Settings)
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of flwrlabs/flower?
    pass
    AI named flwrlabs/flower explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts flwrlabs/flower in production, what risks or prerequisites should they evaluate first?
    pass
    AI named flwrlabs/flower explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo flwrlabs/flower solve, and who is the primary audience?
    pass
    AI named flwrlabs/flower explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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flwrlabs/flower — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite