RRepoGEO

REPOGEO REPORT · LITE

openai/simple-evals

Default branch main · commit 652c89d0 · scanned 5/20/2026, 6:58:15 PM

GitHub: 4,490 stars · 487 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 openai/simple-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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to state project purpose before deprecation

    Why:

    CURRENT
    # ⚠️ Deprecation Notice
    COPY-PASTE FIX
    # simple-evals: A Lightweight Library for Language Model Evaluation

⚠️ Deprecation Notice

**July 2025**: `simple-evals` will no longer be updated for new models or benchmark results. The repo will continue to host reference implementations for **HealthBench**, **BrowseComp**, and **SimpleQA**.
  • highabout#2
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A lightweight, extensible library for evaluating language models, providing a framework for researchers and developers to assess model performance.

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 openai/simple-evals
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers & Datasets Ecosystem
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers & Datasets Ecosystem · recommended 1×
  2. Hugging Face `evaluate` library · recommended 1×
  3. Hugging Face `datasets` library · recommended 1×
  4. Hugging Face `transformers` library · recommended 1×
  5. Hugging Face Transformers & Datasets · recommended 1×
  • CATEGORY QUERY
    How to evaluate and benchmark the performance of large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Datasets Ecosystem
    2. Hugging Face `evaluate` library
    3. Hugging Face `datasets` library
    4. Hugging Face `transformers` library

    AI recommended 4 alternatives but never named openai/simple-evals. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist for comparing language model accuracy on common NLP benchmarks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Datasets
    2. EleutherAI's LM Evaluation Harness
    3. OpenAI Evals
    4. Papers With Code
    5. AllenNLP
    6. TensorFlow Datasets (TFDS)
    7. PyTorch-NLP

    AI recommended 7 alternatives but never named openai/simple-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
    fail

    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 openai/simple-evals?
    pass
    AI named openai/simple-evals explicitly

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

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

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

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openai/simple-evals — 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