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ava whatsapp agent course

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Basic Information

This repository is a course project and complete reference implementation to build "Ava", a production-oriented WhatsApp agent. It provides lessons, example code and deployment guidance so engineers can create an agent that receives and sends WhatsApp messages, understands voice input, recognizes and generates images, returns voice notes, maintains chat history and long-term memory, and connects to the WhatsApp API. The material covers LangGraph workflows, a vector database for memory, model choices for text, vision and speech, and instructions to run locally or deploy to Google Cloud Run. The README organizes a multi-lesson syllabus with written and video content aimed at helping Software, ML and AI Engineers build end-to-end conversational agents.

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1452
App URL
https://github.com/neural-maze/ava-whatsapp-agent-course
Github Repository
https://github.com/neural-maze/ava-whatsapp-agent-course/blob/main/README.md

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App Details

Features
The repo documents and demonstrates an end-to-end tech stack and lesson plan. Key components include LangGraph workflow examples, long-term memory using Qdrant as a vector database, Groq model usage including Llama and vision variants, Whisper for speech-to-text, ElevenLabs for text-to-speech, FLUX diffusion models for image generation, VLM models for image understanding, Chainlit for chat interfaces, and deployment guidance to Google Cloud Run. The syllabus lists six lessons covering architecture, memory, voice pipelines, vision pipelines, WhatsApp integration and deployment. The project includes code, videos and setup instructions to reproduce the system and experiment with free-tier services.
Use Cases
The course helps practitioners move from concept to a runnable conversational agent by combining architectural explanation, hands-on code and narrated video lessons. Learners get practical experience wiring together speech, vision, generation and memory subsystems, implementing STT and TTS, building vector-based long-term memory, and integrating models suitable for production. The materials emphasize cost-aware experimentation with free tiers for core services and give explicit deployment steps for Cloud Run, enabling developers to run locally or in the cloud. The README targets engineers who want to level up their ability to build robust, multi-modal WhatsApp agents and agentic applications.

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