RRepoGEO

REPOGEO REPORT · LITE

raznem/parsera

Default branch main · commit 865d7772 · scanned 6/21/2026, 5:03:08 PM

GitHub: 1,279 stars · 73 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 raznem/parsera, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumcomparison#1
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Comparison with Traditional Scrapers') explaining how Parsera leverages LLMs for intelligent data extraction, differentiating it from tools like Scrapy, Playwright, or Beautiful Soup.
  • lowreadme#2
    Expand 'Why Parsera?' to emphasize LLM benefits

    Why:

    CURRENT
    ## Why Parsera?
Because it's simple and lightweight. With interface as simple as:
    COPY-PASTE FIX
    ## Why Parsera?
Parsera stands out by integrating Large Language Models (LLMs) directly into the web scraping process, making data extraction intelligent and flexible. It's simple, lightweight, and designed for scenarios where traditional selectors fall short, offering an interface as straightforward as:

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 raznem/parsera
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Playwright
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Playwright · recommended 1×
  2. Beautiful Soup 4 · recommended 1×
  3. Scrapy · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude · recommended 1×
  • CATEGORY QUERY
    How can I scrape website content and extract specific data using LLMs?
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. Beautiful Soup 4
    3. Scrapy
    4. OpenAI API
    5. Anthropic Claude
    6. Google Gemini API
    7. Mistral AI
    8. LlamaIndex
    9. LangChain

    AI recommended 9 alternatives but never named raznem/parsera. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for AI-driven web scraping and structured data extraction.
    you: not recommended
    AI recommended (in order):
    1. Beautiful Soup 4 (crummy/BeautifulSoup)
    2. OpenAI API (openai/openai-python)
    3. Scrapy (scrapy/scrapy)
    4. Playwright (microsoft/playwright-python)
    5. Selenium (SeleniumHQ/selenium)
    6. Requests-HTML (psf/requests-html)
    7. Trafilatura (adbar/trafilatura)

    AI recommended 7 alternatives but never named raznem/parsera. 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 raznem/parsera?
    pass
    AI named raznem/parsera explicitly

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

  • If a team adopts raznem/parsera in production, what risks or prerequisites should they evaluate first?
    pass
    AI named raznem/parsera 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 raznem/parsera solve, and who is the primary audience?
    pass
    AI named raznem/parsera 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 raznem/parsera. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/raznem/parsera.svg)](https://repogeo.com/en/r/raznem/parsera)
HTML
<a href="https://repogeo.com/en/r/raznem/parsera"><img src="https://repogeo.com/badge/raznem/parsera.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

raznem/parsera — 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