RRepoGEO

REPOGEO REPORT · LITE

google-ai-edge/LiteRT

Default branch main · commit 426843c1 · scanned 5/8/2026, 12:21:25 PM

GitHub: 2,359 stars · 304 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 google-ai-edge/LiteRT, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    machine-learning, genai, on-device-ai, edge-ai, tensorflow-lite-successor, ml-deployment, iot, mobile-ai, embedded-systems, high-performance-ml
  • mediumreadme#2
    Add a 'Why LiteRT?' or 'LiteRT vs. X' section to the README

    Why:

    COPY-PASTE FIX
    Add a section like: `## Why LiteRT?` or `## LiteRT vs. Alternatives` that clearly outlines how LiteRT improves upon or differs from TensorFlow Lite, PyTorch Mobile, ONNX Runtime, and Core ML, especially regarding its modularity and unified runtime for GenAI on edge.
  • lowreadme#3
    Add 'successor to TensorFlow Lite' to the README's opening paragraph

    Why:

    CURRENT
    Google's on-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
    COPY-PASTE FIX
    LiteRT, successor to TensorFlow Lite, is Google's on-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization.

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
0 / 2
0% of queries surface google-ai-edge/LiteRT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 2×
  2. PyTorch Mobile · recommended 2×
  3. ONNX Runtime · recommended 2×
  4. Core ML · recommended 2×
  5. OpenVINO Toolkit · recommended 1×
  • CATEGORY QUERY
    What framework helps deploy high-performance machine learning models on edge devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. OpenVINO Toolkit
    5. Core ML
    6. Apache TVM

    AI recommended 6 alternatives but never named google-ai-edge/LiteRT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an optimized framework for running generative AI models directly on mobile or IoT devices.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. Core ML
    5. MediaPipe
    6. Edge Impulse

    AI recommended 6 alternatives but never named google-ai-edge/LiteRT. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 google-ai-edge/LiteRT?
    pass
    AI named google-ai-edge/LiteRT explicitly

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

  • If a team adopts google-ai-edge/LiteRT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google-ai-edge/LiteRT 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 google-ai-edge/LiteRT solve, and who is the primary audience?
    pass
    AI named google-ai-edge/LiteRT explicitly

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

Embed your GEO score

Drop this badge into the README of google-ai-edge/LiteRT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/google-ai-edge/LiteRT.svg)](https://repogeo.com/en/r/google-ai-edge/LiteRT)
HTML
<a href="https://repogeo.com/en/r/google-ai-edge/LiteRT"><img src="https://repogeo.com/badge/google-ai-edge/LiteRT.svg" alt="RepoGEO" /></a>
Pro

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google-ai-edge/LiteRT — 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