How to Revive Legacy Smart Home Gear with LLM Agents

An LLM agent just saved a decade-old Belkin WiFi switch from the landfill in about five minutes—a task that would have realistically taken me a full day of manual research and coding. The manufacturer killed the official app, effectively bricking the device's configuration settings, but the hardware itself was still humming along on my local network.

The problem was a leftover Christmas timer that was shutting off my mosquito repellent too early, ruining my sleep. Since the app was dead, I couldn't change the schedule. Instead of manually hunting for documentation, I treated this as a deployment challenge for my AI agent.

The Prompt and Execution

I didn't give the agent a complex technical roadmap; I just gave it the goal and the environment. I used the following prompt:

Find the wifi switch in my local network. The app is end of life. Try to connect to it and change the timer to 22:00 ON until 8am

The agent's workflow was impressively systematic. First, it scanned my local network to identify the device's IP and discovered an open protocol that allowed for basic on/off toggling. However, the goal was a persistent timer, not a momentary trigger.

The agent then performed a deep dive into available libraries and discovered pywemo, an open-source Python library specifically designed for Wemo devices. Here is the logic the agent followed to resolve the issue:

1. Discovery: Identified the device on the LAN.
2. Research: Found the pywemo library to interface with the hardware.
3. Safety First: The agent automatically read the existing timer settings and created a backup before making changes.
4. Implementation: It pushed the new schedule (22:00 to 08:00) directly to the switch's onboard memory.

Because the timer is stored locally on the device, the switch now operates autonomously. It doesn't need a constant connection to a computer or a cloud server to trigger the power—it just works.

Technical Takeaways for AI Workflows

This is a perfect example of how an LLM agent shifts the developer's role from "implementer" to "orchestrator." As a programmer, I know how to use Python and how to read API docs, but the "time to resolution" is the real metric here. The agent handled the discovery, the library selection, and the backup process in a fraction of the time it would take to manually set up a virtual environment and test the library.

For those looking to build a similar AI workflow, the key is giving the agent access to your local network environment and the ability to execute shell commands or Python scripts.

I've now integrated this setup with my Hermes agent on Discord. Instead of relying on a proprietary (and fragile) corporate app, I can just send a chat message to control my hardware. It's a much cleaner user experience and completely removes the vendor lock-in.