RRepoGEO

REPOGEO REPORT · LITE

benchflow-ai/awesome-evals

Default branch main · commit 54a392b6 · scanned 7/1/2026, 2:57:40 PM

GitHub: 612 stars · 43 forks

AI VISIBILITY SCORE
22 /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
1 / 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 benchflow-ai/awesome-evals, 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
    Clarify repo description to emphasize 'awesome list' format

    Why:

    CURRENT
    A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.
    COPY-PASTE FIX
    A curated, non-BS **awesome list** of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/benchflow-ai/awesome-evals
  • mediumreadme#3
    Add a clear statement about the existing license to the README

    Why:

    COPY-PASTE FIX
    Add the following line to your README, replacing `[Your License Name(s) Here]` with the actual license(s) that apply: `This project is licensed under [Your License Name(s) Here]. See the [LICENSE](LICENSE) file for full details.`

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 benchflow-ai/awesome-evals
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Hub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Hub · recommended 1×
  2. OpenAI Documentation & Blog · recommended 1×
  3. LangChain Documentation & Ecosystem · recommended 1×
  4. Awesome-LLM-Agents · recommended 1×
  5. Papers With Code · recommended 1×
  • CATEGORY QUERY
    Where can I find curated resources for building and evaluating AI agents and large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. OpenAI Documentation & Blog
    3. LangChain Documentation & Ecosystem
    4. Awesome-LLM-Agents
    5. Papers With Code
    6. DeepLearning.AI Courses
    7. Google AI Blog & Research

    AI recommended 7 alternatives but never named benchflow-ai/awesome-evals. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools and benchmarks for assessing AI agent performance and reliability?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. LangSmith
    3. Scale AI
    4. Appen
    5. Surge AI
    6. HELM
    7. AgentBench
    8. MLflow
    9. Pytest
    10. Jest

    AI recommended 10 alternatives but never named benchflow-ai/awesome-evals. 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 benchflow-ai/awesome-evals?
    pass
    AI did not name benchflow-ai/awesome-evals — 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 benchflow-ai/awesome-evals in production, what risks or prerequisites should they evaluate first?
    pass
    AI named benchflow-ai/awesome-evals 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 benchflow-ai/awesome-evals solve, and who is the primary audience?
    pass
    AI did not name benchflow-ai/awesome-evals — 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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benchflow-ai/awesome-evals — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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