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

Of all named tools, what % are you?

Top rival

Twilio API for WhatsApp

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Twilio API for WhatsApp · recommended 1×
OpenAI GPT-4 · recommended 1×
OpenAI GPT-3.5 Turbo · recommended 1×
openai · recommended 1×
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

Drop this badge into the README of daveebbelaar/python-whatsapp-bot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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daveebbelaar/python-whatsapp-bot — 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