RRepoGEO

REPOGEO REPORT · LITE

ggml-org/Llama

Default branch main · commit 17e014e4 · scanned 6/16/2026, 10:06:41 PM

GitHub: 1,325 stars · 76 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 ggml-org/Llama, 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 H1 to specify category and competitors

    Why:

    CURRENT
    # Llama

Llama is a macOS menu bar app for running local LLMs.
    COPY-PASTE FIX
    # Llama

Llama is a macOS menu bar app that hosts local LLMs and provides an OpenAI-compatible API endpoint, making it an easy-to-use alternative to tools like Ollama or LM Studio.
  • hightopics#2
    Add specific topics for local LLM hosting and API compatibility

    Why:

    CURRENT
    ai, llama-cpp, llms, macos, swift
    COPY-PASTE FIX
    ai, llama-cpp, llms, macos, swift, local-llm-server, openai-api, ollama-alternative, lm-studio-alternative
  • mediumhomepage#3
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://llama.app

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 ggml-org/Llama
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. Jan · recommended 2×
  4. LocalAI · recommended 2×
  5. llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    What's the easiest way to run open-source large language models locally on macOS?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. LocalAI
    5. llama-cpp-python
    6. MLC LLM

    AI recommended 6 alternatives but never named ggml-org/Llama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I host local LLMs on my Mac with an OpenAI-compatible API endpoint?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. LocalAI
    4. text-generation-webui (oobabooga/text-generation-webui)
    5. Jan
    6. llama.cpp

    AI recommended 6 alternatives but never named ggml-org/Llama. 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 ggml-org/Llama?
    pass
    AI named ggml-org/Llama explicitly

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

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

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

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ggml-org/Llama — 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