RRepoGEO

REPOGEO REPORT · LITE

Visual-Agent/DeepEyes

Default branch main · commit 11d20c6b · scanned 5/15/2026, 4:38:13 PM

GitHub: 1,210 stars · 77 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 Visual-Agent/DeepEyes, 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
  • highabout#1
    Add a concise "About" description

    Why:

    COPY-PASTE FIX
    DeepEyes is an agentic multimodal model that learns to "think with images" via end-to-end reinforcement learning, enabling visual reasoning for embodied AI and robotics.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    multimodal-ai, reinforcement-learning, visual-reasoning, agentic-ai, vlm, embodied-ai, deep-learning, computer-vision
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://visual-agent.github.io/

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 Visual-Agent/DeepEyes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 1×
  2. pytorch/vision · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. keras-team/keras · recommended 1×
  • CATEGORY QUERY
    How to build an AI model that can reason directly from visual inputs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. torchvision (pytorch/vision)
    3. transformers library (huggingface/transformers)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. TensorFlow Hub (tensorflow/hub)
    7. Diffusers (huggingface/diffusers)
    8. OpenCV (opencv/opencv)
    9. Detectron2 (facebookresearch/detectron2)
    10. MMDetection (open-mmlab/mmdetection)
    11. MMSegmentation (open-mmlab/mmsegmentation)

    AI recommended 11 alternatives but never named Visual-Agent/DeepEyes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an agentic multimodal model capable of visual reasoning through reinforcement learning.
    you: not recommended
    AI recommended (in order):
    1. DeepMind's Perceiver IO / Perceiver AR
    2. Stable Baselines3
    3. Ray RLlib
    4. OpenAI's CLIP
    5. Google's PaLM-E
    6. Meta's Data2vec
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named Visual-Agent/DeepEyes. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Visual-Agent/DeepEyes?
    pass
    AI did not name Visual-Agent/DeepEyes — 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 Visual-Agent/DeepEyes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Visual-Agent/DeepEyes 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 Visual-Agent/DeepEyes solve, and who is the primary audience?
    pass
    AI named Visual-Agent/DeepEyes explicitly

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

Embed your GEO score

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Visual-Agent/DeepEyes — 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