RRepoGEO

REPOGEO REPORT · LITE

pykeio/ort

Default branch main · commit 5f997386 · scanned 7/1/2026, 2:46:31 AM

GitHub: 2,370 stars · 249 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
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 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 pykeio/ort, 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
    Clarify that `ort` is a pure Rust library, not Python bindings

    Why:

    CURRENT
    `ort` is a Rust interface for performing hardware-accelerated inference & training on machine learning models in the Open Neural Network Exchange (ONNX) format.
    COPY-PASTE FIX
    `ort` is a pure Rust library providing a high-performance interface for hardware-accelerated inference & training on machine learning models in the Open Neural Network Exchange (ONNX) format.
  • hightopics#2
    Add 'deployment' and 'application-development' topics

    Why:

    CURRENT
    ai, ai-training, fine-tuning, inference, machine-learning, onnx, onnxruntime, rust
    COPY-PASTE FIX
    ai, ai-training, fine-tuning, inference, machine-learning, onnx, onnxruntime, rust, deployment, application-development
  • mediumreadme#3
    Explicitly state `ort` is the successor to `onnxruntime-rs`

    Why:

    CURRENT
    Based on the now-inactive `onnxruntime-rs` crate, `ort` is primarily a wrapper for Microsoft's ONNX Runtime library, but offers support for other pure-Rust runtimes.
    COPY-PASTE FIX
    As the official successor to the now-inactive `onnxruntime-rs` crate, `ort` is primarily a wrapper for Microsoft's ONNX Runtime library, but offers support for other pure-Rust runtimes.

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
1 / 2
50% of queries surface pykeio/ort
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
LaurentMazare/tch-rs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LaurentMazare/tch-rs · recommended 1×
  2. pykeio/onnxruntime-rs · recommended 1×
  3. rust-tflite/tflite · recommended 1×
  4. tracel-ai/burn · recommended 1×
  5. huggingface/candle · recommended 1×
  • CATEGORY QUERY
    How to deploy pre-trained machine learning models efficiently in a Rust application?
    you: not recommended
    AI recommended (in order):
    1. Tch-rs (LaurentMazare/tch-rs)
    2. ONNX Runtime (pykeio/onnxruntime-rs)
    3. TensorFlow Lite (rust-tflite/tflite)
    4. burn (tracel-ai/burn)
    5. candle (huggingface/candle)
    6. tract (sonos/tract)
    7. ndarray (rust-ndarray/ndarray)

    AI recommended 7 alternatives but never named pykeio/ort. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Rust library for fast, hardware-accelerated ONNX model inference and training.
    you: #3
    AI recommended (in order):
    1. tract
    2. candle
    3. ort ← you
    4. tch-rs
    5. rust-bert
    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 pykeio/ort?
    pass
    AI named pykeio/ort explicitly

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

  • If a team adopts pykeio/ort in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pykeio/ort 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 pykeio/ort solve, and who is the primary audience?
    pass
    AI named pykeio/ort 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 pykeio/ort. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/pykeio/ort.svg)](https://repogeo.com/en/r/pykeio/ort)
HTML
<a href="https://repogeo.com/en/r/pykeio/ort"><img src="https://repogeo.com/badge/pykeio/ort.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

pykeio/ort — 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