REPOGEO REPORT · LITE

opendatalab/LabelLLM

Default branch main · commit 11f2a221 · scanned 5/10/2026, 2:58:20 PM

GitHub: 1,221 stars · 126 forks

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, multimodal, open-source, machine-learning, ai-tools, data-labeling

mediumreadme#2
Clarify the LLM-centric and multimodal positioning in the README's H1
Why:
CURRENT
```
# LabelLLM: The Open-Source Data Annotation Platform
```
COPY-PASTE FIX
```
# LabelLLM: The Open-Source Multimodal Data Annotation Platform for LLMs
```
lowabout#3
Update the repository description for better LLM and multimodal context
Why:
CURRENT
```
The Open-Source Data Annotation Platform
```
COPY-PASTE FIX
```
The Open-Source Multimodal Data Annotation Platform for Large Language Models (LLMs)
```

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

Of all named tools, what % are you?

Top rival

argilla-io/argilla

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

argilla-io/argilla · recommended 1×
heartexlabs/label-studio · recommended 1×
doccano/doccano · recommended 1×
Prodigy · recommended 1×
UBIAI Community Edition · recommended 1×

CATEGORY QUERY
What open-source platforms are available for efficient data annotation for large language models?
you: not recommended
AI recommended (in order):
1. Argilla (argilla-io/argilla)
2. Label Studio (heartexlabs/label-studio)
3. Doccano (doccano/doccano)
4. Prodigy
5. UBIAI Community Edition
AI recommended 5 alternatives but never named opendatalab/LabelLLM. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a tool for managing multimodal data annotation tasks for LLM training.
you: not recommended
AI recommended (in order):
1. Labelbox
2. Scale AI
3. Superb AI Suite
4. V7
5. DataLoop
6. CVAT (opencv/cvat)
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?

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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/opendatalab/LabelLLM.svg)](https://repogeo.com/en/r/opendatalab/LabelLLM)

HTML

<a href="https://repogeo.com/en/r/opendatalab/LabelLLM"><img src="https://repogeo.com/badge/opendatalab/LabelLLM.svg" alt="RepoGEO" /></a>

Pro

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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