RRepoGEO

REPOGEO REPORT · LITE

lavague-ai/LaVague

Default branch main · commit 9024bb83 · scanned 5/23/2026, 6:27:46 AM

GitHub: 6,353 stars · 574 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /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
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 lavague-ai/LaVague, 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
    Strengthen README's opening to highlight LLM-driven code generation

    Why:

    CURRENT
    A Large Action Model framework for developing AI Web Agents
    COPY-PASTE FIX
    A Large Action Model framework for developing AI Web Agents. Unlike traditional browser automation tools, LaVague leverages Large Language Models to generate executable Playwright/Selenium scripts directly from natural language instructions, enabling true AI Web Agents.
  • mediumcomparison#2
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## LaVague vs. Traditional Browser Automation

Explain how LaVague builds upon or differs from tools like Playwright and Selenium by adding an LLM-driven agent layer for natural language automation.
  • lowtopics#3
    Expand repository topics for finer categorization

    Why:

    CURRENT
    ai, browser, large-action-model, llm, oss, rag
    COPY-PASTE FIX
    ai, browser, large-action-model, llm, oss, rag, web-automation-agents, natural-language-automation, agentic-ai

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 lavague-ai/LaVague
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/playwright
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/playwright · recommended 1×
  2. SeleniumHQ/selenium · recommended 1×
  3. puppeteer/puppeteer · recommended 1×
  4. Browserless · recommended 1×
  5. crossbario/autobahn-python · recommended 1×
  • CATEGORY QUERY
    How can I build AI agents to automate web browser interactions for user objectives?
    you: not recommended
    AI recommended (in order):
    1. Playwright (microsoft/playwright)
    2. Selenium WebDriver (SeleniumHQ/selenium)
    3. Puppeteer (puppeteer/puppeteer)
    4. Browserless
    5. Autobahn|Python (crossbario/autobahn-python)
    6. LangChain (langchain-ai/langchain)
    7. Robocorp

    AI recommended 7 alternatives but never named lavague-ai/LaVague. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source frameworks exist for developing LLM-driven web automation agents?
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. LangChain
    3. LlamaIndex
    4. AutoGPT
    5. Puppeteer
    6. Selenium
    7. AgentVerse

    AI recommended 7 alternatives but never named lavague-ai/LaVague. 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 lavague-ai/LaVague?
    pass
    AI named lavague-ai/LaVague explicitly

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

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

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

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lavague-ai/LaVague — 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