Local AI Assistant for WhatsApp · Live · April 26

Build a Local AI Assistant for WhatsApp — Running on Your Own Machine

A local AI assistant for WhatsApp means your AI model runs on your own hardware, your conversations stay private, and you access it through the WhatsApp app you already use. This live workshop builds exactly this using OpenClaw and Docker Model Runner in 4 hours.

Sunday, April 26 9am to 1pm EDT

4 Hours Hands-on coding

Live Online Interactive

Workshop Details

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Date and Time

Sunday, April 26, 2026

9:00am to 1:00pm EDT

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Duration

4 Hours · Hands-on

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Format

Live Online · Interactive

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Includes

Certificate of Completion

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Privacy

100% Local · No Cloud Required

By Packt Publishing · Refunds up to 10 days before

About This Workshop

Why a Local AI Assistant for WhatsApp Outperforms Cloud Alternatives

Cloud AI assistants for WhatsApp charge per message and send your data to external servers. A local AI assistant for WhatsApp runs on your own machine with no per-message cost and complete data privacy — and this workshop shows you how to build one.

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What is OpenClaw?

OpenClaw is the open-source personal AI assistant that went viral in early 2026 with 200K+ GitHub stars. It runs on your own devices and connects to WhatsApp, Telegram, Slack and more. No subscription. No data leaving your machine.

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What is Docker Model Runner?

Docker Model Runner is Docker's native feature for running large language models locally on your machine. It gives you an OpenAI-compatible API that OpenClaw uses as its AI brain — complete data privacy, no cloud costs.

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Why Combine OpenClaw and Docker?

OpenClaw gives you the assistant interface and messaging integrations. Docker Model Runner gives you the AI brain running privately on your machine. Together they create a production grade private AI assistant you fully own.

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Why Attend as a Live Workshop?

Setting this up from scattered documentation takes days of debugging. This live workshop gives you a complete guided build in 4 hours with a live instructor answering your questions. Packt has delivered 108 workshops worldwide.

Workshop Curriculum

How to Build a Local AI Assistant for WhatsApp

Six modules. From local AI setup to a working private AI assistant in your WhatsApp.

How OpenClaw Works

Understand the Gateway, channels, and skills architecture. Set up and configure OpenClaw locally from scratch.

Docker Model Runner Setup

Run and manage local LLMs using Docker Model Runner. Pull models, configure memory, and understand the OpenAI-compatible API.

Security and Privacy

Configure DM pairing, allowlists, sandbox mode, and proper access controls for your local AI deployment.

Connect to WhatsApp or Telegram

Deploy your AI assistant to real messaging platforms without sending data to any third party cloud service.

Scalable Architecture

Design an extensible assistant architecture. Add skills, configure personality, and set up proactive automation.

Production Deployment

Deploy your OpenClaw and Docker setup to a VPS for always-on availability running 24 hours a day.

What You Walk Away With

By the End of This Workshop You Will Have

A fully working local AI assistant connected to your WhatsApp — private and free to run.

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A fully functional local AI assistant running on your machine

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Docker Model Runner configured with your chosen LLM model

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OpenClaw connected to WhatsApp or Telegram

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Security and privacy configuration you can trust

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A reusable architecture for future AI assistant projects

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Certificate of completion from Packt Publishing

Your Instructor

Learn Local WhatsApp AI Development From a Real Expert

Rami Krispin has deployed local AI assistants for WhatsApp in production environments.

Rami Krispin

Workshop Instructor · April 26, 2026

Rami is a Senior Manager of Data Science and Engineering, Docker Captain, and LinkedIn Learning Instructor with deep expertise in building and deploying production AI systems. He guides you step by step from a blank terminal to a fully deployed private AI assistant — answering your questions live throughout the 4-hour session.

Frequently Asked Questions

Common Questions About Local AI Assistants for WhatsApp

Everything you need to know about building a local WhatsApp AI assistant.

How does a local AI assistant connect to WhatsApp? +

OpenClaw connects to WhatsApp through its built-in channel integration. Your WhatsApp account is linked to your OpenClaw instance through a QR code pairing process. From that point, messages sent to your connected number in WhatsApp are routed to your local AI model via Docker Model Runner, and responses are sent back to WhatsApp automatically.

Can I use my existing WhatsApp number for the local AI assistant? +

Yes. The OpenClaw WhatsApp integration works with an existing personal WhatsApp account. You can use a secondary SIM or WhatsApp account if you prefer to keep your personal and AI assistant contacts separate. The instructor covers the setup options during the workshop.

Will my WhatsApp contacts know they are talking to an AI? +

Your WhatsApp contacts see messages from your connected WhatsApp number. They interact with your local AI assistant as if they were messaging you. The instructor covers the ethical and practical considerations of disclosing AI use to contacts during the workshop.

Can my local AI assistant for WhatsApp send proactive messages? +

Yes. OpenClaw supports proactive messaging — your AI assistant can send scheduled messages or notifications to your WhatsApp contacts based on configured triggers. The instructor covers proactive messaging configuration in the skills module of the workshop.

What happens to WhatsApp message history when using a local AI assistant? +

Your WhatsApp message history is stored normally in WhatsApp's own storage. OpenClaw maintains its own conversation context for AI processing. The instructor covers how to configure context window length and data retention for your local AI assistant during the workshop.

Does a local AI assistant for WhatsApp work with WhatsApp groups? +

OpenClaw's WhatsApp integration primarily focuses on direct message conversations. Group chat integration is more complex and has different behaviour. The instructor covers what group chat support is available and its limitations during the workshop.