OpenClaw was designed to run on your laptop or a VPS. That is still the typical setup. But in early 2026, a corner of the maker community asked a different question: what is the minimum viable hardware for an OpenClaw-compatible AI agent? The answer turned out to be a $5 microchip — and it spawned two separate projects, each taking a different approach to running an intelligent agent on hardware smaller than a credit card.
MimiClaw: Bare-Metal AI on an ESP32-S3
MimiClaw is the more radical of the two. It runs on an ESP32-S3 microcontroller — a chip that costs between $5 and $8 — with no Linux, no Node.js, no operating system of any kind. The entire agent stack is compiled C code running bare-metal, which means no background services, no containers, and nothing humming away in a server room. The firmware is roughly 1MB. Power consumption sits around 0.5W.
Despite those constraints, MimiClaw is a functional AI agent. It connects over Wi-Fi, interfaces through Telegram (send a message, the chip fetches it, forwards it to OpenAI or Anthropic, returns the response), supports tool calling for both providers, and implements the ReAct pattern — meaning the LLM can call tools mid-conversation, loop until a task is complete, and behave like an agent rather than a simple chatbot. It also maintains persistent local memory, stored in the chip’s flash storage, OpenClaw workspace-style.
The project went viral in maker circles because it collapsed the usual assumptions about what AI agent hardware looks like. No Mac mini, no Raspberry Pi, no VPS subscription. A chip you can buy in bulk for less than a cup of coffee, running continuously on less electricity than a LED bulb.
PycoClaw: MicroPython, More Features, More Hardware
PycoClaw takes a different approach. Rather than bare-metal C, it is built on MicroPython — which trades some of MimiClaw’s efficiency for significantly expanded capabilities and broader hardware support. It runs on ESP32-S3 (8MB+ flash and PSRAM required), ESP32-P4, and has Raspberry Pi RP2350 support planned.
The feature list is more expansive: multiple LLM providers (OpenAI, Gemini, and Ollama for fully local inference), interfaces through Telegram, Scripto Studio, and WebRTC, GPIO and LVGL display support for hardware integration, OTA updates, battery-optimized operation, and a hybrid memory system combining TF-IDF and vector approaches. It also supports recursive tool calling, sub-agents, and a full dual-loop agent architecture — capabilities that push well past what most people would expect from a microcontroller.
Installation is browser-based one-click flashing, which removes most of the embedded development friction for non-specialists. One caveat worth noting: despite being described as MIT-licensed open source, the firmware source code has not been publicly released — only the website source appears on GitHub, which is worth factoring into any trust assessment.
MimiClaw vs PycoClaw: Which to Choose
The two projects occupy slightly different positions. MimiClaw is leaner, more power-efficient, fully open (compiled C, bare-metal), and better suited for always-on, battery-powered deployments where you want maximum reliability and minimum resource usage. PycoClaw offers more features, broader hardware compatibility, display and GPIO integration, and a friendlier development environment — at the cost of a larger firmware footprint and the closed-source firmware concern.
For a simple persistent Telegram-accessible agent that runs indefinitely on a battery pack, MimiClaw is the cleaner choice. For a more capable agent that needs to drive a display, control GPIO pins, or connect through WebRTC, PycoClaw covers that ground.
Why This Matters Beyond the Novelty
The obvious angle is the “wow, AI on a $5 chip” story. But the deeper implication is about the direction of the ecosystem. OpenClaw’s local-first philosophy was always partly philosophical — a stance against cloud dependency and data centralization. MimiClaw and PycoClaw take that philosophy to its logical extreme: an AI agent that requires no cloud infrastructure at all for its runtime, can be physically carried in a pocket, costs less than a monthly streaming subscription to set up, and runs indefinitely on minimal power.
These projects also open up use cases that a laptop or VPS agent simply cannot cover: embedded home automation controllers, wearable agents, field devices with Telegram interfaces, agricultural or industrial sensors with conversational interfaces. The OpenClaw workspace model — memory in flat files, skills as markdown — turns out to translate reasonably well to flash storage on a microcontroller.
The ecosystem that started as “a self-hosted WhatsApp agent on your laptop” is now running on chips you can buy by the dozen for the price of a sandwich. That is a strange and interesting thing to have happened in under six months.
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