RRepoGEO

REPOGEO REPORT · LITE

lmstudio-ai/mlx-engine

Default branch main · commit ae24add9 · scanned 6/20/2026, 7:52:58 AM

GitHub: 1,096 stars · 117 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
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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Expand repository topics with relevant keywords

    Why:

    CURRENT
    mlx, python
    COPY-PASTE FIX
    mlx, python, llm, inference, apple-silicon, macos, local-inference, structured-output, machine-learning, deep-learning, ai
  • mediumhomepage#2
    Add the LM Studio website as the repository homepage

    Why:

    COPY-PASTE FIX
    https://lmstudio.ai/

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
llama.cpp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 1×
  2. MLX · recommended 1×
  3. Ollama · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Core ML · recommended 1×
  • CATEGORY QUERY
    How to run large language models efficiently on Apple Silicon hardware?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. MLX
    3. Ollama
    4. Hugging Face Transformers
    5. Core ML
    6. TensorFlow Lite

    AI recommended 6 alternatives but never named lmstudio-ai/mlx-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries enable local LLM inference with structured output on macOS?
    you: not recommended
    AI recommended (in order):
    1. llama-cpp-python (abetlen/llama-cpp-python)
    2. Ollama (ollama/ollama)
    3. Transformers (huggingface/transformers)
    4. Pydantic (pydantic/pydantic)
    5. Guidance (microsoft/guidance)
    6. MLX (ml-explore/mlx)
    7. LiteLLM (BerriAI/litellm)

    AI recommended 7 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?

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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