RRepoGEO

REPOGEO REPORT · LITE

huggingface/llm-ls

Default branch main · commit 1c52b94f · scanned 6/7/2026, 5:36:42 AM

GitHub: 873 stars · 70 forks

AI VISIBILITY SCORE
57 /100
Needs work
Category recall
1 / 2
Avg rank #5.0 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 huggingface/llm-ls, 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's opening paragraph to highlight AI code completion for IDEs

    Why:

    CURRENT
    **llm-ls** is a LSP server leveraging LLMs to make your development experience smoother and more efficient.
    COPY-PASTE FIX
    **llm-ls** is an open-source, LLM-agnostic Language Server Protocol (LSP) server that brings powerful AI-powered code completion and smart suggestions directly into your favorite IDE.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/huggingface/llm-ls
  • lowtopics#3
    Add 'code-completion' to the repository topics

    Why:

    CURRENT
    ai, code-generation, huggingface, ide, llamacpp, llm, lsp, lsp-server, openai, self-hosted
    COPY-PASTE FIX
    ai, code-completion, code-generation, huggingface, ide, llamacpp, llm, lsp, lsp-server, openai, self-hosted

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 huggingface/llm-ls
Avg rank
#5.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 1×
  2. Tabnine · recommended 1×
  3. CodeWhisperer · recommended 1×
  4. Jedi · recommended 1×
  5. Kite · recommended 1×
  • CATEGORY QUERY
    How can I integrate AI-powered code completion directly into my development environment?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. CodeWhisperer
    4. Jedi
    5. Kite
    6. IntelliCode
    7. YouCode

    AI recommended 7 alternatives but never named huggingface/llm-ls. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a language server protocol tool for smart code suggestions with local LLMs.
    you: #5
    AI recommended (in order):
    1. Tabby (TabbyML/tabby)
    2. Continue (Continue-team/continue)
    3. Code Llama
    4. Ollama (ollama/ollama)
    5. llm-ls (huggingface/llm-ls) ← you
    6. FauxPilot (fauxpilot/fauxpilot)
    7. LM Studio
    8. LocalAI (mudler/LocalAI)
    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 huggingface/llm-ls?
    pass
    AI named huggingface/llm-ls explicitly

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

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

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

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huggingface/llm-ls — 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