REPOGEO REPORT · LITE

datawhalechina/hugging-multi-agent

Default branch main · commit 9ccd34a8 · scanned 5/16/2026, 4:02:55 PM

GitHub: 1,391 stars · 161 forks

AI VISIBILITY SCORE

22 /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

1 / 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 datawhalechina/hugging-multi-agent, 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
Reposition the README H1 to specify category and purpose
Why:
CURRENT
```
# Hugging Multi-Agent
```
COPY-PASTE FIX
```
# Hugging Multi-Agent: A MetaGPT-based Tutorial for AI Agent System Development
```
hightopics#2
Add relevant topics to improve categorization
Why:
COPY-PASTE FIX
```
multi-agent-systems, metagpt, ai-agents, llm-agents, tutorial, datawhale, python
```
highlicense#3
Add a LICENSE file to clarify usage rights
Why:
COPY-PASTE FIX
```
Add a LICENSE file (e.g., MIT License) to the repository root.
```

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 datawhalechina/hugging-multi-agent

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Stable Baselines3

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Stable Baselines3 · recommended 1×
OpenAI Gym / Gymnasium · recommended 1×
Hugging Face Transformers · recommended 1×
TensorFlow · recommended 1×
PyTorch · recommended 1×

CATEGORY QUERY
Seeking a practical guide for developing AI agent systems from scratch.
you: not recommended
AI recommended (in order):
1. Stable Baselines3
2. OpenAI Gym / Gymnasium
3. Hugging Face Transformers
4. TensorFlow
5. PyTorch
6. Scikit-learn
7. Python
8. NumPy
AI recommended 8 alternatives but never named datawhalechina/hugging-multi-agent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to implement custom multi-agent solutions using advanced frameworks?
you: not recommended
AI recommended (in order):
1. LangChain
2. AutoGen
3. CrewAI
4. Haystack
5. LlamaIndex
6. Mirasol
AI recommended 6 alternatives but never named datawhalechina/hugging-multi-agent. 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 datawhalechina/hugging-multi-agent?
pass
AI did not name datawhalechina/hugging-multi-agent — 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 datawhalechina/hugging-multi-agent in production, what risks or prerequisites should they evaluate first?
pass
AI named datawhalechina/hugging-multi-agent 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 datawhalechina/hugging-multi-agent solve, and who is the primary audience?
pass
AI did not name datawhalechina/hugging-multi-agent — 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?

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MARKDOWN (README)

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datawhalechina/hugging-multi-agent — 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