RRepoGEO

REPOGEO REPORT · LITE

daveebbelaar/python-whatsapp-bot

Default branch main · commit ef27cb06 · scanned 5/25/2026, 11:52:49 PM

GitHub: 1,491 stars · 844 forks

AI VISIBILITY SCORE
27 /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
1 / 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 daveebbelaar/python-whatsapp-bot, 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
    Reinforce Meta Cloud API usage in README's opening sentence

    Why:

    CURRENT
    This guide will walk you through the process of creating a WhatsApp bot using the Meta (formerly Facebook) Cloud API with pure Python, and Flask particular.
    COPY-PASTE FIX
    This guide walks you through building an AI WhatsApp bot using the **Meta Cloud API** (formerly Facebook) with pure Python and Flask. It focuses on integrating webhook events for real-time messages and leveraging OpenAI for AI responses.
  • hightopics#2
    Add specific topics for Meta Cloud API and chatbot development

    Why:

    CURRENT
    ai, flask, llm, openai, python, whatsapp
    COPY-PASTE FIX
    ai, flask, llm, openai, python, whatsapp, whatsapp-api, meta-cloud-api, chatbot, bot-development
  • mediumreadme#3
    Add a concise 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features

- **WhatsApp Bot Development:** Build interactive bots using the official Meta Cloud API.
- **AI Integration:** Seamlessly connect with OpenAI for intelligent, generative responses.
- **Python & Flask:** A pure Python solution built on the Flask web framework.
- **Real-time Messaging:** Configure webhooks to handle incoming messages instantly.

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 daveebbelaar/python-whatsapp-bot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Twilio API for WhatsApp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Twilio API for WhatsApp · recommended 1×
  2. OpenAI GPT-4 · recommended 1×
  3. OpenAI GPT-3.5 Turbo · recommended 1×
  4. openai · recommended 1×
  5. Meta's WhatsApp Business Platform API · recommended 1×
  • CATEGORY QUERY
    How to build an AI-driven chatbot for WhatsApp using Python?
    you: not recommended
    AI recommended (in order):
    1. Twilio API for WhatsApp
    2. OpenAI GPT-4
    3. OpenAI GPT-3.5 Turbo
    4. openai
    5. Meta's WhatsApp Business Platform API
    6. Google Gemini Pro
    7. google-generativeai
    8. Hugging Face Transformers
    9. Rasa
    10. Flask
    11. FastAPI

    AI recommended 11 alternatives but never named daveebbelaar/python-whatsapp-bot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to integrate large language models into a Python Flask messaging bot?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI Python Library (openai/openai-python)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. Haystack (deepset-ai/haystack)
    6. LiteLLM (BerriAI/litellm)

    AI recommended 6 alternatives but never named daveebbelaar/python-whatsapp-bot. 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 daveebbelaar/python-whatsapp-bot?
    pass
    AI did not name daveebbelaar/python-whatsapp-bot — 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 daveebbelaar/python-whatsapp-bot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named daveebbelaar/python-whatsapp-bot 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 daveebbelaar/python-whatsapp-bot solve, and who is the primary audience?
    pass
    AI did not name daveebbelaar/python-whatsapp-bot — 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?

Embed your GEO score

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