River_Wang
Getting Started
Adafruit Playground is a wonderful and safe place to share your interests with Adafruit's vibrant community of makers and doers. Have a cool project you are working on? Have a bit of code that you think others will find useful? Want to show off your electronics workbench? You have come to the right place.
The goal of Adafruit Playground is to make it as simple as possible to share your work. On the Adafruit Playground users can create Notes. A note is a single-page space where you can document your topic using Adafruit's easy-to-use editor. Notes are like Guides on the Adafruit Learning System but guides are high-fidelity content curated and maintained by Adafuit. Notes are whatever you want them to be. Have fun and be kind.
Click here to learn more about Adafruit Playground and how to get started.
-
Coding CircuitPython with AI Agent: The Easiest Setup
A tutorial on a full AI agent workflow with CircuitPython Online IDE
Disclosure: The author of this post is the developer of CircuitPython Online IDE.
Background: Current State of AI Agent workflow for CircuitPython
The trend of AI-assisted coding is already affecting how we code for microcontrollers. It is easy to ask LLM (Large Language Model) chatbots questions about CircuitPython and copy the code snippets to the microcontroller. And if there are any errors, we naturally paste the error message from the REPL back to the chatbot. Some chat environments went one step further and can interpret CircuitPython code, but they are still blind to real hardware such as sensors and motors.
AI coding agents, on the other hand, have changed how software gets written. As CircuitPython lead developer Scott Shawcroft put it in his #CircuitPython2026 post, LLMs used by a client-side agent that "can run commands and relay back the result in a loop" are game changing. He then envisioned the upcoming trend for the CircuitPython workflow: "we need to create the 'agentic' feedback loop for best results. We let the LLM auto-load code and give it the serial output back. We should also give it context about recent CircuitPython changes and API references so it can correct its knowledge."
The full agent workflow Scott described isn't new. Experts have been giving agents real hardware access for a while (ohararp-g's Reddit post is one example), typically through the Model Context Protocol (MCP). But that power comes with setup work: installing and configuring MCP servers for code, the REPL, libraries, and more. That's a fine trade for a professional dev environment. It's a steep ask for learners and hobbyists.
What people have been asking for is an easy way to get access to the coding agent for CircuitPython in seconds. That's the gap closed by the AI Agent Bridge feature of CircuitPython Online IDE (https://circuitpy.dev).
What is AI Agent Bridge?
To be clear, the IDE itself doesn't contain any AI. Instead, it exposes a set of tools, implemented as JavaScript functions, that an AI agent can call. These functions serve as the bridge between the IDE and the agent.
This means the agent also needs to live inside the browser. The agent we're going to use is Claude in Chrome.
Prerequisites
- Google Chrome with the Claude in Chrome extension installed.
- A Claude subscription that includes Claude in Chrome.
- A CircuitPython-compatible board.
Note that everything runs in the browser, so there is nothing to install or configure on your local computer.
Example 1: Night Light on Breadboard
In this example, I'm going to show how to use the AI Agent Bridge feature from start to finish to build a simple night light project.
