
PicoClaw
An ultra-lightweight AI agent framework that runs on $10 hardware with under 10MB of memory

Tool details
PicoClaw is an ultra-lightweight open-source AI agent framework from Sipeed, built from scratch in Go and capable of running on RISC-V development boards costing as little as $10. With memory usage under 10MB and startup in under 1 second, it is built for edge devices and resource-constrained environments.
Core Capabilities
- Extreme lightness: Memory footprint under 10MB—99% less than OpenClaw—with cold start within 1 second
- All-architecture coverage: A single binary supports RISC-V, ARM, x86, MIPS, and nearly every other CPU architecture
- Native MCP integration: Supports the Model Context Protocol for flexible extension of the agent toolchain
- Multi-platform chat: Connects to Telegram, Discord, WhatsApp, QQ, DingTalk, and 10+ other chat platforms
- Multi-model support: Compatible with mainstream LLM providers including OpenRouter, Anthropic, OpenAI, and Ollama
- Offline-first: Task queues persist to SQLite, so local tasks keep running even when disconnected
Use Cases Embedded/IoT developers, edge computing engineers, Raspberry Pi hobbyists, and self-hosting enthusiasts. If you want to run an AI assistant at the lowest possible cost, this is the answer.
Unique Advantages Software made by hardware vendor Sipeed, naturally fitted to low-cost edge devices. Where OpenClaw needs 1GB of memory and $400 hardware, PicoClaw runs on 10MB and $10.
Editor's Review It gathered 25K+ GitHub Stars within two months of release—community momentum is fierce. In the lightweight AI agent lane it currently has no rival. Still, it is early-stage (v0.2.3), the team does not yet recommend production use, and it is wise to wait for v1.0 before serious deployment. Recommendation: 4/5.
Pricing
FreeFree (MIT license)Completely free and open source, no feature restrictions
LicheeRV-Nano RISC-V development board
Additional notes
Recommended hardware is a Raspberry Pi Zero 2 W or Pi 5; it can also run on any Linux/macOS/Windows computer. LLM API costs are self-covered; Ollama local models are supported for a completely free offline setup.
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