RRepoGEO

REPOGEO REPORT · LITE

jmerelnyc/Photo-agents

Default branch main · commit 6c9d6d63 · scanned 6/3/2026, 9:33:38 AM

GitHub: 954 stars · 21 forks

AI VISIBILITY SCORE
20 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 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 jmerelnyc/Photo-agents, 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 README H1 to emphasize computer operation

    Why:

    CURRENT
    # Photo Agents

Autonomous self-evolving **Photo Agents**. A perceive / reason / act framework for photo-aware agents that operate your computer the way you do.
    COPY-PASTE FIX
    # Photo Agents: Autonomous LLM Agents for Computer Control

Autonomous self-evolving **Photo Agents**. A perceive / reason / act framework for vision-grounded agents that operate your computer's UI the way you do.
  • mediumtopics#2
    Add specific topics for UI automation and computer control

    Why:

    CURRENT
    agent-memory, ai-agents, autonomous-agents, computer-use, llm, photo-agents, photographic-memory, python, self-evolving-agents, vision-agents
    COPY-PASTE FIX
    agent-memory, ai-agents, autonomous-agents, computer-use, llm, photo-agents, photographic-memory, python, self-evolving-agents, vision-agents, ui-automation, desktop-automation, computer-vision-agents, screen-perception
  • lowreadme#3
    Clarify the 'About' section to differentiate from generic image tasks or frameworks

    Why:

    CURRENT
    Photo Agents is building the next generation of LLM-driven agents that ground in what they actually see on screen. Instead of dumping longer chat transcripts into a model and hoping for the best we treat memory the way biology does. Vision in. Bound observations stored in layers. Skills written by the agent itself from real success.
    COPY-PASTE FIX
    Photo Agents is building the next generation of LLM-driven agents that ground in what they actually see on screen to *control your computer's user interface*. Instead of dumping longer chat transcripts into a model and hoping for the best we treat memory the way biology does. Vision in. Bound observations stored in layers. Skills written by the agent itself from real success. This is not just an image processing library or a generic LangChain wrapper; it's a runtime for agents that truly operate your desktop.

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 jmerelnyc/Photo-agents
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4V (Vision)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4V (Vision) · recommended 1×
  2. PyAutoGUI · recommended 1×
  3. Selenium · recommended 1×
  4. Microsoft AutoGen · recommended 1×
  5. Appian RPA · recommended 1×
  • CATEGORY QUERY
    How to create an AI agent that operates a computer using visual screen perception?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. PyAutoGUI
    3. Selenium
    4. Microsoft AutoGen
    5. Appian RPA
    6. UiPath Studio
    7. Automation Anywhere
    8. Playwright
    9. OpenCV
    10. Pillow
    11. Tesseract
    12. SikuliX

    AI recommended 12 alternatives but never named jmerelnyc/Photo-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for autonomous LLM agents with self-evolving skills and layered memory for task execution?
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. BabyAGI
    3. LangChain
    4. LlamaIndex
    5. AgentVerse
    6. Open Interpreter

    AI recommended 6 alternatives but never named jmerelnyc/Photo-agents. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 jmerelnyc/Photo-agents?
    pass
    AI did not name jmerelnyc/Photo-agents — 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 jmerelnyc/Photo-agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name jmerelnyc/Photo-agents — 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?

  • In one sentence, what problem does the repo jmerelnyc/Photo-agents solve, and who is the primary audience?
    pass
    AI did not name jmerelnyc/Photo-agents — 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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jmerelnyc/Photo-agents — RepoGEO report