RRepoGEO

REPOGEO REPORT · LITE

Visual-Agent/DeepEyesV2

Default branch main · commit 62575437 · scanned 6/2/2026, 7:58:38 AM

GitHub: 605 stars · 57 forks

AI VISIBILITY SCORE
30 /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
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 Visual-Agent/DeepEyesV2, 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 description to the repository

    Why:

    COPY-PASTE FIX
    DeepEyesV2 is an agentic multimodal model that unifies code execution and web search within a single reasoning loop, enabling reliable and complex reasoning.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    agentic-ai, multimodal-llm, code-execution, web-search, visual-reasoning, large-language-models, ai-agents, open-source-ai
  • mediumreadme#3
    Refine the README's opening to immediately state the core problem and solution

    Why:

    CURRENT
    The README currently starts with a large div and H1, with the core description under 'What's new?'.
    COPY-PASTE FIX
    Add this text directly after the main H1: 'DeepEyesV2 is an agentic multimodal model designed to unify code execution and web search within a single reasoning loop. It enables reliable and complex reasoning by integrating visual information directly into the AI's thought process, addressing the challenge of autonomous interaction with digital environments.'

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/DeepEyesV2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. OpenAI API · recommended 1×
  3. Google Gemini API · recommended 1×
  4. SerpApi · recommended 1×
  5. Google Custom Search API · recommended 1×
  • CATEGORY QUERY
    How to build an agentic multimodal system for complex reasoning and unified web search?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. OpenAI API
    3. Google Gemini API
    4. SerpApi
    5. Google Custom Search API
    6. FAISS (facebookresearch/faiss)
    7. Pinecone
    8. Weaviate (weaviate/weaviate)
    9. Beautiful Soup (crummy/BeautifulSoup4)
    10. Playwright (microsoft/playwright)
    11. Selenium (SeleniumHQ/selenium)
    12. Hugging Face Transformers (huggingface/transformers)
    13. Hugging Face Diffusers (huggingface/diffusers)

    AI recommended 13 alternatives but never named Visual-Agent/DeepEyesV2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a model to integrate visual data into reasoning chains with code execution.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o / GPT-4V
    2. Google Gemini
    3. Llama 3 / LLaVA (llava-vl/llava)
    4. Microsoft Copilot
    5. Azure OpenAI Service

    AI recommended 5 alternatives but never named Visual-Agent/DeepEyesV2. 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/DeepEyesV2?
    pass
    AI named Visual-Agent/DeepEyesV2 explicitly

    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/DeepEyesV2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Visual-Agent/DeepEyesV2 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/DeepEyesV2 solve, and who is the primary audience?
    pass
    AI named Visual-Agent/DeepEyesV2 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 Visual-Agent/DeepEyesV2. 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)
[![RepoGEO](https://repogeo.com/badge/Visual-Agent/DeepEyesV2.svg)](https://repogeo.com/en/r/Visual-Agent/DeepEyesV2)
HTML
<a href="https://repogeo.com/en/r/Visual-Agent/DeepEyesV2"><img src="https://repogeo.com/badge/Visual-Agent/DeepEyesV2.svg" alt="RepoGEO" /></a>
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Visual-Agent/DeepEyesV2 — 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