RRepoGEO

REPOGEO REPORT · LITE

evidentlyai/evidently

Default branch main · commit a4aa4c2b · scanned 5/23/2026, 4:37:06 AM

GitHub: 7,525 stars · 850 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
33 /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
2 / 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 evidentlyai/evidently, 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
  • highhomepage#1
    Update homepage URL to main website

    Why:

    CURRENT
    https://discord.gg/xZjKRaNp8b
    COPY-PASTE FIX
    https://evidentlyai.com
  • mediumtopics#2
    Add more specific LLM evaluation topics

    Why:

    CURRENT
    data-drift, data-quality, data-science, data-validation, generative-ai, hacktoberfest, html-report, jupyter-notebook, llm, llmops, machine-learning, mlops, model-monitoring, pandas-dataframe
    COPY-PASTE FIX
    data-drift, data-quality, data-science, data-validation, generative-ai, hacktoberfest, html-report, jupyter-notebook, llm, llmops, machine-learning, mlops, model-monitoring, pandas-dataframe, llm-evaluation, rag-evaluation, ai-observability

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 evidentlyai/evidently
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Datadog
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Datadog · recommended 1×
  2. Arize AI · recommended 1×
  3. Sagemaker Model Monitor · recommended 1×
  4. MLflow · recommended 1×
  5. Grafana · recommended 1×
  • CATEGORY QUERY
    How can I monitor data drift and model performance for my deployed machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Datadog
    2. Arize AI
    3. Sagemaker Model Monitor
    4. MLflow
    5. Grafana
    6. Fiddler AI
    7. Evidently AI

    AI recommended 7 alternatives but never named evidentlyai/evidently. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate and test generative AI models and LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Phoenix
    3. W&B Prompts
    4. DeepEval
    5. Galileo Evaluate
    6. Humanloop
    7. Ragas

    AI recommended 7 alternatives but never named evidentlyai/evidently. 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 evidentlyai/evidently?
    pass
    AI did not name evidentlyai/evidently — 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 evidentlyai/evidently in production, what risks or prerequisites should they evaluate first?
    pass
    AI named evidentlyai/evidently 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 evidentlyai/evidently solve, and who is the primary audience?
    pass
    AI named evidentlyai/evidently explicitly

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

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evidentlyai/evidently — 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