RRepoGEO

REPOGEO REPORT · LITE

pytorch/executorch

Default branch main · commit e03f777c · scanned 6/25/2026, 2:41:45 AM

GitHub: 4,755 stars · 1,044 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)

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

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 pytorch/executorch, 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
    Reposition the README's opening statement to clarify ExecuTorch's unique role

    Why:

    CURRENT
    ExecuTorch is PyTorch's unified solution for deploying AI models on-device—from smartphones to microcontrollers—built for privacy, performance, and portability.
    COPY-PASTE FIX
    ExecuTorch is PyTorch's **official and unified solution** for deploying AI models on-device—from smartphones to microcontrollers—built for privacy, performance, and portability. It is the **successor to PyTorch Mobile and TorchScript** for on-device inference, offering extreme modularity and minimal footprint for even the most resource-constrained edge devices.
  • mediumtopics#2
    Add more specific topics to improve category visibility for edge and tiny ML

    Why:

    CURRENT
    deep-learning, embedded, gpu, machine-learning, mobile, neural-network, tensor
    COPY-PASTE FIX
    deep-learning, embedded, gpu, machine-learning, mobile, neural-network, tensor, microcontrollers, edge-ai, on-device-ai, tiny-ml
  • lowlicense#3
    Add a clear statement about the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ExecuTorch is licensed under [insert specific license name(s) here, e.g., Apache 2.0 and MIT]. Please see the LICENSE file for full details.

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 pytorch/executorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. PyTorch Mobile · recommended 2×
  3. TensorFlow Lite · recommended 2×
  4. ONNX · recommended 1×
  5. TorchScript · recommended 1×
  • CATEGORY QUERY
    How to deploy PyTorch deep learning models efficiently on edge devices?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. ONNX
    3. PyTorch Mobile
    4. TorchScript
    5. TensorFlow Lite
    6. NVIDIA TensorRT
    7. OpenVINO
    8. Apache TVM

    AI recommended 8 alternatives but never named pytorch/executorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable running machine learning models on resource-constrained mobile hardware?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. Core ML
    4. ML Kit
    5. ONNX Runtime
    6. MediaPipe

    AI recommended 6 alternatives but never named pytorch/executorch. This is the gap to close.

    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 pytorch/executorch?
    pass
    AI named pytorch/executorch explicitly

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

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

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

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pytorch/executorch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite