Tutorial: WhatsApp AI Support Agent with RAG & Memory (PydanticAI, LangGraph, Supabase, FastAPI)

Learn how to build an AI customer support agent for WhatsApp. This tutorial guides you through creating an agent that solves customer problems using a knowledge base, remembers conversation history (memory), and includes a monitoring system. We use Retrieval Augmented Generation (RAG) to provide relevant answers from your business data. Discover how to transform a simple CSV file of common problems and solutions into a vector database using Supabase and pgvector, enabling your AI agent to perform efficient semantic searches and provide step-by-step guidance. This video covers the entire process, from setting up the database and AI agent logic with Pydantic AI and LangGraph, configuring a Meta App webhook for WhatsApp integration, and monitoring performance with Logfire. *Timestamps:* * *03:19* Outline * *04:37* Setting up Supabase * *5:04* Converting a CSV knowledge base into embeddings and storing them in Supabase. * *12:07* Creating core agent with Pydantic AI. * *15:07* Implementing Memory with LangGraph * *22:26* Server Setup with FastAPI * *25:45* WhatsApp Integration * *29:16* Live demo * *29:54* Monitoring dashboard *Technologies Used:* * Pydantic AI * LangGraph (for memory management) * Supabase (with pgvector extension for vector database) * FastAPI (Python web framework) * Logfire (Monitoring) * OpenAI API (for embeddings & LLM) * Meta Developer Portal (for WhatsApp API) * Ngrok (for exposing local server) 🦾 Automate your business operations: https://flowhog.com/?utm_source=youtube&utm_medium=video&utm_campaign=whatsapp_agent&utm_content=video_desc_whatsapp_agent 🚀Follow me on LinkedIn: https://www.linkedin.com/in/daniel-fuya-espinar/ 🔗 Code: https://danifuya.com/resources/2 #WhatsApp #AIChatbot #CustomerSupport #Automation #RAG #LangGraph #Supabase #FastAPI #PydanticAI #LLM #GenerativeAI #Python #Tutorial #VectorDatabase #AIagent