RRepoGEO

REPOGEO REPORT · LITE

opendatalab/LabelLLM

Default branch main · commit 11f2a221 · scanned 6/20/2026, 2:02:31 PM

GitHub: 1,241 stars · 128 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 opendatalab/LabelLLM, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    data-annotation, llm, large-language-models, machine-learning, open-source, multimodal, data-labeling, nlp, computer-vision
  • mediumabout#2
    Refine the repository's 'About' description for clarity and keywords

    Why:

    CURRENT
    The Open-Source Data Annotation Platform
    COPY-PASTE FIX
    An open-source platform for efficient, multimodal data annotation specifically designed for Large Language Models (LLMs).
  • lowreadme#3
    Emphasize LLM focus in the README's main heading

    Why:

    CURRENT
    # LabelLLM: The Open-Source Data Annotation Platform
    COPY-PASTE FIX
    # LabelLLM: The Open-Source LLM Data Annotation Platform

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 opendatalab/LabelLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
heartexlabs/label-studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. heartexlabs/label-studio · recommended 2×
  2. doccano/doccano · recommended 2×
  3. argilla-io/argilla · recommended 1×
  4. Prodigy · recommended 1×
  5. inception-project/inception · recommended 1×
  • CATEGORY QUERY
    Need an open-source platform for annotating data to train large language models efficiently.
    you: not recommended
    AI recommended (in order):
    1. Label Studio (heartexlabs/label-studio)
    2. Argilla (argilla-io/argilla)
    3. Doccano (doccano/doccano)
    4. Prodigy
    5. INCEpTION (inception-project/inception)
    6. UBIAI

    AI recommended 6 alternatives but never named opendatalab/LabelLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good open-source tools for multimodal data annotation and task management?
    you: not recommended
    AI recommended (in order):
    1. CVAT (opencv/cvat)
    2. Label Studio (heartexlabs/label-studio)
    3. Doccano (doccano/doccano)
    4. Supervisely (supervisely/supervisely)
    5. VGG Image Annotator (VIA) (vgg/via)
    6. Animo (animo-project/animo)

    AI recommended 6 alternatives but never named opendatalab/LabelLLM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    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 opendatalab/LabelLLM?
    pass
    AI named opendatalab/LabelLLM explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of opendatalab/LabelLLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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opendatalab/LabelLLM — 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