RRepoGEO

REPOGEO REPORT · LITE

VibeBench/VibeSearchBench

Default branch main · commit a310d345 · scanned 6/6/2026, 1:27:56 AM

GitHub: 780 stars · 9 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 VibeBench/VibeSearchBench, 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 to explicitly state the repo's category

    Why:

    CURRENT
    # VibeSearchBench

> <span style="color:#dc2626;background:rgba(220,38,38,0.12);padding:0.15em 0.4em;border-radius:4px;font-weight:800;box-shadow:inset 0 -2px 0 rgba(220,38,38,0.45)">Hardest</span> — vague multi-turn proactive search in the wild.
    COPY-PASTE FIX
    # VibeSearchBench

The definitive benchmark for evaluating LLM-powered, multi-turn, proactive search agents.

> <span style="color:#dc2626;background:rgba(220,38,38,0.12);padding:0.15em 0.4em;border-radius:4px;font-weight:800;box-shadow:inset 0 -2px 0 rgba(220,38,38,0.45)">Hardest</span> — vague multi-turn proactive search in the wild.
  • mediumtopics#2
    Add more specific topics related to LLM evaluation and agent benchmarking

    Why:

    CURRENT
    agentic-ai, benchmark, llm, proactive-agent, search, search-agent
    COPY-PASTE FIX
    agentic-ai, benchmark, llm, proactive-agent, search, search-agent, llm-evaluation, search-evaluation, agent-benchmark, knowledge-graph-evaluation
  • lowreadme#3
    Clarify the 'No vibes' statement in the README

    Why:

    COPY-PASTE FIX
    Despite the name, VibeSearchBench evaluates search performance objectively using triplet F1, not subjective 'vibes' or qualitative aspects. Our focus is on verifiable, schema-free knowledge graph evaluation.

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 VibeBench/VibeSearchBench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Appen
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Appen · recommended 1×
  2. Amazon Mechanical Turk · recommended 1×
  3. RasaHQ/rasa · recommended 1×
  4. Rasa Enterprise · recommended 1×
  5. Botium Box · recommended 1×
  • CATEGORY QUERY
    How to benchmark the performance of multi-turn proactive search agents?
    you: not recommended
    AI recommended (in order):
    1. Appen
    2. Amazon Mechanical Turk
    3. Rasa X (RasaHQ/rasa)
    4. Rasa Enterprise
    5. Botium Box
    6. Botium Core (botium/botium-core)
    7. OpenAI Gym (Farama-Foundation/Gym)
    8. GPT-4
    9. Claude Opus

    AI recommended 9 alternatives but never named VibeBench/VibeSearchBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a challenging benchmark for evaluating LLM-powered search with knowledge graph metrics.
    you: not recommended
    AI recommended (in order):
    1. WebQuestionsSP (WQSP)
    2. MetaQA
    3. GrailQA
    4. LC-QUAD 2.0
    5. FreebaseQA
    6. QALD (Question Answering over Linked Data) benchmarks

    AI recommended 6 alternatives but never named VibeBench/VibeSearchBench. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 VibeBench/VibeSearchBench?
    pass
    AI named VibeBench/VibeSearchBench explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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