RRepoGEO

REPOGEO REPORT · LITE

ray-project/ray-llm

Default branch master · commit 3f8f4da8 · scanned 5/15/2026, 11:51:34 PM

GitHub: 1,267 stars · 91 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 ray-project/ray-llm, 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
  • highabout#1
    Update repository description to clearly state archived status and redirect

    Why:

    CURRENT
    RayLLM - LLMs on Ray (Archived). Read README for more info.
    COPY-PASTE FIX
    ARCHIVED: RayLLM APIs are now upstreamed into Ray Core. For current LLM serving, see `ray.serve.llm` and `ray.data.llm` in the main Ray documentation: https://docs.ray.io/en/latest/serve/llm/index.html
  • highreadme#2
    Add a section to README clarifying license status

    Why:

    COPY-PASTE FIX
    ## License
    This repository is archived and does not have an explicit license file. For licensing information regarding the actively maintained `ray.serve.llm` and `ray.data.llm` APIs, please refer to the main Ray project's licensing.
  • mediumtopics#3
    Add 'archived' topic to signal repository status

    Why:

    CURRENT
    llm, llm-serving, ray
    COPY-PASTE FIX
    llm, llm-serving, ray, archived

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 ray-project/ray-llm
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. Ray Serve · recommended 2×
  3. DeepSpeed-MII · recommended 2×
  4. TensorRT-LLM · recommended 2×
  5. NVIDIA Triton Inference Server · recommended 1×
  • CATEGORY QUERY
    How to deploy and serve large language models efficiently in a distributed environment?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. vLLM
    3. Ray Serve
    4. KServe
    5. OpenVINO Model Server
    6. DeepSpeed-MII
    7. TensorRT-LLM

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

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for scalable LLM inference using a distributed computing framework?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. DeepSpeed-MII
    4. Ray Serve
    5. TensorRT-LLM
    6. OpenVINO
    7. TorchServe

    AI recommended 7 alternatives but never named ray-project/ray-llm. 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 ray-project/ray-llm?
    pass
    AI named ray-project/ray-llm explicitly

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

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

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

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ray-project/ray-llm — 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