RRepoGEO

REPOGEO REPORT · LITE

Andyyyy64/whichllm

Default branch main · commit 16347b40 · scanned 6/21/2026, 11:36:49 PM

GitHub: 5,101 stars · 275 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
28 /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
2 / 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 Andyyyy64/whichllm, 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 sentence to clarify tool's role

    Why:

    CURRENT
    **Find the best local LLM that actually runs on your hardware.**
    COPY-PASTE FIX
    **`whichllm` is a command-line tool to find the best local LLM that actually runs and performs best on your hardware.**
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/whichllm/
  • mediumtopics#3
    Expand repository topics with more specific LLM recommendation terms

    Why:

    CURRENT
    ai, apple-silicon, benchmarks, cli, command-line-tool, gguf, gpu, huggingface, inference, llm, local-llm, ollama, python, vram
    COPY-PASTE FIX
    ai, apple-silicon, benchmarks, cli, command-line-tool, gguf, gpu, huggingface, inference, llm, local-llm, ollama, python, vram, llm-recommendation, llm-discovery, hardware-compatibility

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 Andyyyy64/whichllm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. Mistral 7B Instruct · recommended 1×
  3. Llama 2 7B Chat · recommended 1×
  4. Phi-2 · recommended 1×
  5. OpenHermes 2.5 Mistral 7B · recommended 1×
  • CATEGORY QUERY
    What's the best local LLM to run on my machine's specific hardware?
    you: not recommended
    AI recommended (in order):
    1. Mistral 7B Instruct
    2. Llama 2 7B Chat
    3. Phi-2
    4. OpenHermes 2.5 Mistral 7B
    5. Zephyr 7B Beta
    6. ollama (ollama/ollama)
    7. LM Studio
    8. Jan (janhq/jan)
    9. llama.cpp (ggerganov/llama.cpp)

    AI recommended 9 alternatives but never named Andyyyy64/whichllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I benchmark different local LLMs to find the fastest one for my GPU?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. text-generation-webui
    4. llama.cpp
    5. Hugging Face Transformers
    6. accelerate
    7. bitsandbytes

    AI recommended 7 alternatives but never named Andyyyy64/whichllm. 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 Andyyyy64/whichllm?
    pass
    AI named Andyyyy64/whichllm explicitly

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

  • If a team adopts Andyyyy64/whichllm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Andyyyy64/whichllm 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 Andyyyy64/whichllm solve, and who is the primary audience?
    pass
    AI did not name Andyyyy64/whichllm — likely talking about a different project

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

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Andyyyy64/whichllm — 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