RRepoGEO

REPOGEO REPORT · LITE

argilla-io/argilla

Default branch develop · commit 5338519a · scanned 6/21/2026, 7:47:00 AM

GitHub: 5,011 stars · 492 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
74 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 argilla-io/argilla, 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
    Reorder README to place value proposition before maintenance notice

    Why:

    CURRENT
    The README currently starts with the `[!IMPORTANT]` block, followed by the H1/H3 and project description.
    COPY-PASTE FIX
    Reorder the README content so that the H1, H3, and the initial descriptive paragraph ('Argilla is a collaboration tool for AI engineers...') appear first, followed by the `[!IMPORTANT]` block.
  • mediumabout#2
    Refine the 'About' description for more specificity

    Why:

    CURRENT
    Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
    COPY-PASTE FIX
    Argilla is a human-in-the-loop collaboration platform for AI engineers and domain experts to build high-quality datasets for LLMs and machine learning models, focusing on feedback, active learning, and data curation.
  • lowreadme#3
    Update README H1 and H3 for clearer positioning

    Why:

    CURRENT
    <h1 align="center">Argilla</h1> <h3 align="center">Build high quality datasets for your AI models</h3>
    COPY-PASTE FIX
    <h1 align="center">Argilla: Human-in-the-Loop Platform for LLM & ML Datasets</h1> <h3 align="center">Collaboratively build high-quality datasets with active learning and human feedback</h3>

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
1 / 2
50% of queries surface argilla-io/argilla
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Labelbox
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Labelbox · recommended 1×
  2. Scale AI · recommended 1×
  3. Superb AI Suite · recommended 1×
  4. V7 · recommended 1×
  5. iterative/dvc · recommended 1×
  • CATEGORY QUERY
    What tools help AI teams collaboratively build high-quality datasets for machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Labelbox
    2. Scale AI
    3. Superb AI Suite
    4. V7
    5. DVC (iterative/dvc)
    6. FiftyOne (voxel51/fiftyone)
    7. Snorkel AI

    AI recommended 7 alternatives but never named argilla-io/argilla. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for human-in-the-loop annotation platforms to improve NLP models with active learning.
    you: #1
    AI recommended (in order):
    1. Argilla (argilla-io/argilla) ← you
    2. Prodigy
    3. Label Studio (heartexlabs/label-studio)
    4. Snorkel Flow
    5. LightTag
    6. DataLoop
    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 argilla-io/argilla?
    pass
    AI named argilla-io/argilla explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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