# PicoClaw — Tiny Deployable AI Assistant for Any Device

> A minimal, fast AI personal assistant that runs on low-power hardware including microcontrollers and edge devices with no OS dependency.

## Install

Save in your project root:

# PicoClaw — Tiny Deployable AI Assistant for Any Device

## Quick Use
```bash
git clone https://github.com/sipeed/picoclaw.git
cd picoclaw
make build TARGET=linux
./picoclaw --model local
```

## Introduction
PicoClaw is an ultra-lightweight AI assistant designed to run on resource-constrained hardware. Written in Go, it compiles to a small binary that can execute on everything from a Raspberry Pi to embedded Linux boards, bringing AI agent capabilities to edge devices and IoT environments without requiring cloud connectivity.

## What PicoClaw Does
- Runs AI assistant workflows on devices with as little as 256 MB RAM
- Supports local model inference for fully offline operation
- Automates routine tasks like file management, monitoring, and scripting
- Provides a minimal terminal interface and optional HTTP API
- Connects to cloud LLM providers when network is available for advanced tasks

## Architecture Overview
PicoClaw uses a layered architecture with a tiny core runtime that handles task scheduling and tool execution. The inference layer abstracts between local GGUF models and remote API providers. A plugin system allows adding custom tools as simple shell scripts or Go modules. The entire binary compiles to under 15 MB with no external dependencies.

## Self-Hosting & Configuration
- Build from source with Go 1.22+ or download prebuilt binaries for ARM/x86
- Configure the model backend (local GGUF, Ollama, or cloud API) in config.yaml
- Set up custom tool definitions in the plugins directory
- Enable the HTTP API for remote management and integration
- Run as a systemd service for always-on operation on headless devices

## Key Features
- Runs on hardware as small as Raspberry Pi Zero with local inference
- Single static binary with zero runtime dependencies
- Hybrid inference supporting local models and cloud fallback
- Built-in tools for file operations, system monitoring, and shell automation
- Sub-second startup time for responsive interactive use

## Comparison with Similar Tools
- **OpenClaw** — full-featured personal AI assistant; PicoClaw targets minimal embedded deployments
- **Ollama** — model server only; PicoClaw includes agent workflows and tool use
- **LocalAI** — heavier runtime requiring more resources; PicoClaw optimized for constrained hardware
- **llama.cpp** — inference library; PicoClaw wraps inference with a complete agent runtime

## FAQ
**Q: What is the minimum hardware to run PicoClaw?**
A: A device with a single ARM core and 256 MB RAM can run PicoClaw with a small quantized model.

**Q: Can it work completely offline?**
A: Yes. With a local GGUF model loaded, no network connection is needed.

**Q: Does it support tool calling?**
A: Yes. PicoClaw implements tool calling with both local and cloud models.

**Q: Can I extend it with custom tools?**
A: Yes. Drop shell scripts or Go plugins into the plugins directory and they become available as agent tools.

## Sources
- https://github.com/sipeed/picoclaw
- https://picoclaw.dev

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Source: https://tokrepo.com/en/workflows/asset-d394b29d
Author: AI Open Source