Run a modular agent runtime on ESP32-S3 that manages LLMs, tools, memory, and channels for efficient message processing.

EmbedClaw

[中文]

Decouple LLM, Tools, Agent, and Channels—then pack them onto a single ESP32-S3.

EmbedClaw is not just “a chatbot on an MCU.”
It’s an Agent Runtime on a microcontroller: messages enter via Channels, the Agent orchestrates, the LLM decides, Tools execute, Memory is persisted, Skills supply task-level knowledge, and results go back out through Channels.

Origins

This project draws on the ideas and direction of:

• OpenClaw
• MimiClaw

EmbedClaw keeps the goal of running a full AI Agent on low-power hardware but focuses the architecture on decoupling LLM, Tools, Agent, and Channels.
That means you can add new models, new channels, new tools, or new Skills without rewriting the rest of the system.

Why EmbedClaw

1. Decoupled, not feature-bloated

The main idea is not “it can chat,” but that the parts that usually get tangled are separated:

• Channel only handles how messages are received and sent; it doesn’t care how the LLM reasons.
• Agent only handles task orchestration, context building, and the tool loop; it doesn’t care about transport.
• LLM only adapts model request/response; it doesn’t care whether the message came from Feishu or WebSocket.
• Tools only expose capabilities and JSON sche