RRepoGEO

REPOGEO REPORT · LITE

onnx/onnx-mlir

Default branch main · commit 75d60b24 · scanned 5/28/2026, 7:37:13 PM

GitHub: 1,023 stars · 428 forks

AI VISIBILITY SCORE
66 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 onnx/onnx-mlir, 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 README's opening to clarify its role as an ONNX compiler for efficient deployment using MLIR

    Why:

    CURRENT
    This project (https://onnx.ai/onnx-mlir/) provides compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime support. It implements the ONNX standard and is based on the underlying LLVM/MLIR compiler technology.
    COPY-PASTE FIX
    ONNX-MLIR is a compiler project that transforms Open Neural Network Exchange (ONNX) graphs into highly optimized, hardware-specific code using the LLVM/MLIR compiler infrastructure. It enables efficient, low-runtime deployment of ONNX models across diverse targets, serving as a powerful alternative to general ONNX runtimes for performance-critical applications.
  • hightopics#2
    Add relevant topics to improve categorization and searchability

    Why:

    COPY-PASTE FIX
    onnx, mlir, compiler, deep-learning, machine-learning, neural-networks, code-generation, llvm, ai, optimization
  • highhomepage#3
    Add the project's official homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://onnx.ai/onnx-mlir/

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 onnx/onnx-mlir
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
microsoft/onnxruntime
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/onnxruntime · recommended 1×
  2. NVIDIA/TensorRT · recommended 1×
  3. openvinotoolkit/openvino · recommended 1×
  4. apache/tvm · recommended 1×
  5. Tencent/ncnn · recommended 1×
  • CATEGORY QUERY
    How to compile ONNX neural network models for efficient deployment?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime (microsoft/onnxruntime)
    2. TensorRT (NVIDIA/TensorRT)
    3. OpenVINO Toolkit (openvinotoolkit/openvino)
    4. Apache TVM (apache/tvm)
    5. NCNN (Tencent/ncnn)
    6. MNN (alibaba/MNN)

    AI recommended 6 alternatives but never named onnx/onnx-mlir. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools can convert ONNX graphs into optimized code using MLIR?
    you: #2
    AI recommended (in order):
    1. IREE
    2. ONNX-MLIR ← you
    3. LLVM-MLIR
    4. Apache TVM
    5. PlaidML
    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 onnx/onnx-mlir?
    pass
    AI named onnx/onnx-mlir explicitly

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

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

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

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onnx/onnx-mlir — RepoGEO report