Building a WhatsApp AI Agent for Small Businesses with Python

Building a WhatsApp AI Agent for small businesses sounds complicated — but with Python, the WhatsApp Business API, and a modern LLM, you can have a working agent in an afternoon. This guide walks through the real implementation: webhook handling, conversation state, lead capture, and production error handling. We built this for AI Buddy , our AI automation company serving Israeli small businesses. If you want a fully managed version without writing code, check out ClawBud . Architecture Overview The system has four main pieces: WhatsApp webhook — receives messages from Meta's API State manager — tracks conversation context per user LLM integration — generates intelligent responses Lead capture — extracts and stores contact info WhatsApp → Webhook (Flask) → State Manager → LLM → Response → WhatsApp ↓ Lead Store (DB) Setting Up the Webhook Meta requires a webhook endpoint that handles both verification and message events. Here's a clean Flask implementation: import os import hmac import