RRepoGEO

REPOGEO REPORT · LITE

ray-project/llmperf

Default branch main · commit f1d6bed4 · scanned 5/19/2026, 2:27:27 AM

GitHub: 1,116 stars · 205 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
62 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 ray-project/llmperf, 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
  • hightopics#1
    Add specific topics for LLM benchmarking

    Why:

    COPY-PASTE FIX
    llm, llms, large-language-models, benchmarking, performance-testing, evaluation, ray, distributed-systems, mlops, machine-learning
  • highreadme#2
    Refine README H1 to emphasize LLM serving system benchmarking

    Why:

    CURRENT
    # LLMPerf

A Tool for evaulation the performance of LLM APIs.
    COPY-PASTE FIX
    # LLMPerf

A distributed tool for benchmarking and evaluating the performance of LLM serving systems and APIs.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/ray-project/llmperf

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 ray-project/llmperf
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
psf/requests
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. psf/requests · recommended 1×
  2. curl/curl · recommended 1×
  3. python/cpython · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude API · recommended 1×
  • CATEGORY QUERY
    How can I measure the throughput and latency of different large language model APIs?
    you: not recommended
    AI recommended (in order):
    1. requests (psf/requests)
    2. curl (curl/curl)
    3. asyncio (python/cpython)
    4. OpenAI API
    5. Anthropic Claude API
    6. Google Gemini API
    7. Cohere API
    8. Mistral AI API
    9. Locust (locustio/locust)
    10. JMeter (apache/jmeter)
    11. k6 (grafana/k6)
    12. Artillery (artilleryio/artillery)
    13. AWS Distributed Load Testing
    14. Google Cloud Load Testing

    AI recommended 14 alternatives but never named ray-project/llmperf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help compare the performance and correctness of various LLM inference endpoints?
    you: #1
    AI recommended (in order):
    1. LLMPerf ← you
    2. Arize AI Phoenix
    3. Weights & Biases Prompts
    4. LangChain Evaluation
    5. OpenAI Evals
    6. requests
    7. time
    8. pandas
    9. fuzzywuzzy
    10. nltk
    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/llmperf?
    pass
    AI did not name ray-project/llmperf — 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?

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