Build a WhatsApp Bot Powered by a Local LLM — Not the OpenAI API

When you send a WhatsApp message to your bot, OpenClaw receives it through its WhatsApp channel integration. It passes the message to Docker Model Runner's local API which processes it using your locally running open weight LLM. The response comes back to OpenClaw which sends it to your WhatsApp. The entire AI processing chain runs on your own hardware.

Yes — local LLM inference is generally slower than the OpenAI API for typical hardware configurations. On a laptop with 16GB RAM you can expect responses in 5 to 20 seconds for most messages. This is acceptable for personal assistant use where you are not expecting instant responses. VPS deployment with more powerful hardware improves response times.

Yes. OpenClaw's allowlist system lets you add multiple authorised WhatsApp contacts who can all interact with your local LLM WhatsApp bot. Each contact sends messages to the same connected WhatsApp number and receives AI responses powered by your local model.

For WhatsApp bot use cases where response time matters, smaller and faster models like Phi-3 Mini (3.8B) or Mistral 7B work well. For higher response quality at the cost of speed, Llama 3 8B is an excellent choice. The instructor covers model selection and performance trade-offs during the workshop.

OpenClaw maintains conversation context within a session. The instructor covers how OpenClaw manages context and what options are available for configuring context window length and memory retention for your local LLM WhatsApp bot during the workshop.

The final module of this workshop covers deploying your OpenClaw setup to a VPS server for always-on availability. Once deployed to a VPS, your local LLM WhatsApp bot runs 24 hours a day — responding to messages even when your laptop is off.