RRepoGEO

REPOGEO REPORT · LITE

liweiphys/layra

Default branch main · commit 63c9c9be · scanned 6/9/2026, 10:47:54 AM

GitHub: 902 stars · 99 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 liweiphys/layra, 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
    Refine the 'About' description for visual document processing

    Why:

    CURRENT
    LAYRA—an enterprise-ready, out-of-the-box solution—unlocks next-generation intelligent systems powered by visual RAG and limitless visual multi-step agent workflow orchestration.
    COPY-PASTE FIX
    LAYRA is an enterprise-ready, visual-native AI agent engine for document RAG and multi-step workflow orchestration, enabling intelligent systems that see, understand, and act on visual documents.
  • highhomepage#2
    Add homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://liweiphys.github.io/layra
  • mediumreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 LAYRA vs. Other Solutions

Unlike general-purpose LLM frameworks (e.g., LangChain, Semantic Kernel) or cloud-native document AI services (e.g., Azure AI Document Intelligence, Google Cloud Document AI, Amazon Textract, OpenAI GPT-4V), LAYRA is purpose-built as a visual-native AI agent engine. It uniquely combines advanced visual RAG capabilities with flexible, multi-step agent workflow orchestration, allowing it to 'see' and process complex documents like a human, preserving layout and graphical context, which is critical for enterprise-grade automation beyond simple text extraction.

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 liweiphys/layra
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure AI Document Intelligence
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure AI Document Intelligence · recommended 1×
  2. Google Cloud Document AI · recommended 1×
  3. Amazon Textract · recommended 1×
  4. OpenAI GPT-4V (Vision) · recommended 1×
  5. PaddleOCR · recommended 1×
  • CATEGORY QUERY
    How to build an AI system that understands visual documents for RAG and information extraction?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Document Intelligence
    2. Google Cloud Document AI
    3. Amazon Textract
    4. OpenAI GPT-4V (Vision)
    5. PaddleOCR
    6. Donut (Document Understanding Transformer)
    7. LayoutLMv3

    AI recommended 7 alternatives but never named liweiphys/layra. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools support multi-step AI agent workflow orchestration for complex visual tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    3. OpenAI Assistants API
    4. Apache Airflow (apache/airflow)
    5. Kubeflow Pipelines (kubeflow/pipelines)
    6. Prefect (PrefectHQ/prefect)

    AI recommended 6 alternatives but never named liweiphys/layra. 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 liweiphys/layra?
    pass
    AI named liweiphys/layra explicitly

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

  • If a team adopts liweiphys/layra in production, what risks or prerequisites should they evaluate first?
    pass
    AI named liweiphys/layra 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 liweiphys/layra solve, and who is the primary audience?
    pass
    AI named liweiphys/layra 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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liweiphys/layra — 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