RRepoGEO

REPOGEO REPORT · LITE

WeOpenML/PandaLM

Default branch main · commit 3871806e · scanned 6/1/2026, 1:22:52 AM

GitHub: 923 stars · 65 forks

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 WeOpenML/PandaLM, 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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    PandaLM provides a reproducible and automated benchmark for evaluating and comparing Large Language Models (LLMs) using an LLM-as-a-judge methodology.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    Add the URL for the 'PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization' paper or a dedicated project page.

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 WeOpenML/PandaLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
EleutherAI/lm-evaluation-harness
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. EleutherAI/lm-evaluation-harness · recommended 1×
  2. huggingface/evaluate · recommended 1×
  3. openai/evals · recommended 1×
  4. google/BIG-bench · recommended 1×
  5. stanford-crfm/helm · recommended 1×
  • CATEGORY QUERY
    How can I automatically assess the performance of different language models?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI's LM Evaluation Harness (lm-eval) (EleutherAI/lm-evaluation-harness)
    2. Hugging Face Evaluate Library (huggingface/evaluate)
    3. OpenAI Evals (openai/evals)
    4. BigBench (Beyond the Imitation Game Benchmark) (google/BIG-bench)
    5. HELM (Holistic Evaluation of Language Models) (stanford-crfm/helm)
    6. Microsoft's DeepSpeed-MII (Model Inference and Intelligence) (microsoft/DeepSpeed-MII)
    7. NLTK (nltk/nltk)
    8. spaCy (explosion/spaCy)
    9. scikit-learn (scikit-learn/scikit-learn)

    AI recommended 9 alternatives but never named WeOpenML/PandaLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide reproducible and automated evaluation for large language models?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B) Prompts
    2. MLflow (mlflow/mlflow)
    3. LangChain Evaluation (langchain-ai/langchain)
    4. DeepEval (confident-ai/deepeval)
    5. Ragas (explodinggradients/ragas)
    6. Galileo (by Arize AI)

    AI recommended 6 alternatives but never named WeOpenML/PandaLM. 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 WeOpenML/PandaLM?
    pass
    AI named WeOpenML/PandaLM explicitly

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

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

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

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WeOpenML/PandaLM — 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