RRepoGEO

REPOGEO REPORT · LITE

PacificAI/langtest

Default branch main · commit 45a32bdc · scanned 6/3/2026, 6:32:10 PM

GitHub: 559 stars · 49 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 PacificAI/langtest, 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
    Add a concise problem-solution statement immediately after the H1

    Why:

    CURRENT
    The README excerpt shows the H1 followed by badges and navigation links.
    COPY-PASTE FIX
    LangTest is an open-source framework designed to systematically test and evaluate Large Language Models (LLMs) and NLP models for quality, robustness, safety, bias, and other responsible AI aspects. It helps developers and ML engineers ensure their models are safe, effective, and trustworthy in production.
  • mediumabout#2
    Expand the 'About' description to clearly state its function as a testing/evaluation tool

    Why:

    CURRENT
    Deliver safe & effective language models
    COPY-PASTE FIX
    An open-source framework for systematically testing and evaluating Large Language Models (LLMs) and NLP models for quality, robustness, safety, bias, and responsible AI aspects.
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

LangTest differentiates itself from tools like Microsoft Responsible AI Toolkit, Giskard, and Hugging Face Evaluate by offering a comprehensive, automated framework for proactively identifying vulnerabilities and quality issues in LLM applications through diverse test case generation across dimensions like robustness, fairness, bias, and toxicity.

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 PacificAI/langtest
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Microsoft Responsible AI Toolkit (RAI Toolkit)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Microsoft Responsible AI Toolkit (RAI Toolkit) · recommended 2×
  2. Giskard · recommended 1×
  3. Hugging Face Evaluate · recommended 1×
  4. DeepMind's Ethical AI Framework · recommended 1×
  5. OpenAI Evals · recommended 1×
  • CATEGORY QUERY
    How to test large language models for safety, bias, and performance issues?
    you: not recommended
    AI recommended (in order):
    1. Giskard
    2. Microsoft Responsible AI Toolkit (RAI Toolkit)
    3. Hugging Face Evaluate
    4. DeepMind's Ethical AI Framework
    5. OpenAI Evals
    6. Snorkel AI
    7. Scale AI
    8. Appen
    9. Surge AI

    AI recommended 9 alternatives but never named PacificAI/langtest. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate and benchmark large language models for responsible AI development?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Evaluate Library
    2. IBM AI Fairness 360 (AIF360)
    3. Microsoft Responsible AI Toolkit (RAI Toolkit)
    4. Google's What-If Tool (WIT)
    5. Robustness Gym
    6. Fiddler AI
    7. TruLens

    AI recommended 7 alternatives but never named PacificAI/langtest. 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 PacificAI/langtest?
    pass
    AI named PacificAI/langtest explicitly

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

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