RRepoGEO

REPOGEO REPORT · LITE

lmstudio-ai/mlx-engine

Default branch main · commit aea09111 · scanned 5/10/2026, 9:22:47 AM

GitHub: 1,040 stars · 106 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 lmstudio-ai/mlx-engine, 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 comprehensive topics for LLM inference on Apple Silicon

    Why:

    CURRENT
    mlx, python
    COPY-PASTE FIX
    mlx, python, llm, inference, apple-silicon, macos, machine-learning, deep-learning, local-llm, mlx-engine, lm-studio
  • mediumabout#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://lmstudio.ai/
  • mediumabout#3
    Enhance the repository description for better keyword matching

    Why:

    CURRENT
    LM Studio Apple MLX engine
    COPY-PASTE FIX
    Apple MLX engine for efficient local LLM inference on Apple Silicon Macs, integrated with LM Studio.

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 lmstudio-ai/mlx-engine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. LM Studio · recommended 2×
  3. llama-cpp-python · recommended 2×
  4. MLX · recommended 2×
  5. bitsandbytes · recommended 2×
  • CATEGORY QUERY
    How to run large language models locally on Apple Silicon for fast inference?
    you: not recommended
    AI recommended (in order):
    1. Metal Performance Shaders (MPS)
    2. Ollama
    3. LM Studio
    4. Jan
    5. llama.cpp
    6. llama-cpp-python
    7. Hugging Face
    8. MLX
    9. Hugging Face `transformers`
    10. bitsandbytes

    AI recommended 10 alternatives but never named lmstudio-ai/mlx-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python-based engine for local LLM inference on macOS, what are the options?
    you: not recommended
    AI recommended (in order):
    1. llama-cpp-python
    2. Ollama
    3. Transformers
    4. bitsandbytes
    5. flash-attention
    6. MLX
    7. mlx-lm
    8. ctransformers
    9. GPTQ-for-LLaMa
    10. GGML
    11. LM Studio

    AI recommended 11 alternatives but never named lmstudio-ai/mlx-engine. 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 lmstudio-ai/mlx-engine?
    pass
    AI named lmstudio-ai/mlx-engine explicitly

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

  • If a team adopts lmstudio-ai/mlx-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lmstudio-ai/mlx-engine 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 lmstudio-ai/mlx-engine solve, and who is the primary audience?
    pass
    AI named lmstudio-ai/mlx-engine 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 lmstudio-ai/mlx-engine. 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/lmstudio-ai/mlx-engine.svg)](https://repogeo.com/en/r/lmstudio-ai/mlx-engine)
HTML
<a href="https://repogeo.com/en/r/lmstudio-ai/mlx-engine"><img src="https://repogeo.com/badge/lmstudio-ai/mlx-engine.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

lmstudio-ai/mlx-engine — 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