RRepoGEO

REPOGEO REPORT · LITE

microsoft/MM-REACT

Default branch main · commit b8f29af7 · scanned 5/30/2026, 2:36:54 PM

GitHub: 965 stars · 68 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 microsoft/MM-REACT, 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
  • highabout#1
    Update the repository's 'About' description

    Why:

    CURRENT
    Official repo for MM-REACT
    COPY-PASTE FIX
    MM-REACT is an orchestration system that integrates large language models (LLMs) like ChatGPT with a pool of vision experts to achieve multimodal reasoning and action.
  • mediumreadme#2
    Refine the README's main heading to emphasize 'orchestration system'

    Why:

    CURRENT
    # MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
    COPY-PASTE FIX
    # MM-REACT: An Orchestration System for Multimodal Reasoning and Action with LLMs and Vision Experts

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 microsoft/MM-REACT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. LangChain · recommended 2×
  3. OpenAI GPT-4V (Vision) · recommended 1×
  4. Google Gemini (Pro Vision) · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    How can I combine large language models with image analysis for complex tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google Gemini (Pro Vision)
    3. Llama 3
    4. CLIP (Contrastive Language-Image Pre-training)
    5. BLIP-2 (Bootstrapping Language-Image Pre-training)
    6. Hugging Face Transformers
    7. Vision Transformers (ViT)
    8. DETR (DEtection TRansformer)
    9. YOLO (You Only Look Once)
    10. LangChain
    11. OpenCV
    12. PyTorch
    13. TensorFlow
    14. Google Cloud Vision API
    15. AWS Rekognition
    16. Microsoft Azure AI Vision
    17. Azure OpenAI Service

    AI recommended 17 alternatives but never named microsoft/MM-REACT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks allow language models to leverage external vision tools for visual understanding?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Function Calling
    4. Hugging Face Transformers
    5. Microsoft Semantic Kernel

    AI recommended 5 alternatives but never named microsoft/MM-REACT. 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 microsoft/MM-REACT?
    pass
    AI named microsoft/MM-REACT explicitly

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

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

    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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microsoft/MM-REACT — 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