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@ruvector/edge

Run browser-based AI swarms with P2P networking and vector search.

1|Updated Feb 8, 2026
Search Tags:#vector search#decentralized#neural networks#edge ai#browser ai#p2p networking
One-click install
npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-edge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: @ruvector/edge
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/ruvector-edge
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-edge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the creation of powerful AI applications that run directly in the user's browser, eliminating the need for server infrastructure and offering enhanced privacy and offline capabilities.

Core Features & Use Cases

  • Client-Side AI: Run neural networks, vector search, and P2P networking entirely within the browser.
  • Decentralized Coordination: Build swarms of AI agents that communicate and collaborate peer-to-peer.
  • Use Case: Develop a collaborative research tool where users can share and search vast datasets using AI, all without uploading data to a central server.

Quick Start

Initialize a new EdgeSwarm with 4 workers and enable P2P networking.

Frequently Asked Questions about @ruvector/edge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run AI inference directly in the browser without a backend server?▼

You can run client-side AI workloads entirely in the browser by utilizing Web Workers, ONNX Runtime (WASM) for neural network inference, and HNSW (WASM) for vector search, eliminating server dependencies.

Can I build decentralized AI agent swarms using P2P networking in a web app?▼

Yes, you can build decentralized AI agent swarms by leveraging WebRTC for peer-to-peer networking, which allows browser-based agents to communicate and collaborate directly without uploading data to a central server.

Does browser-based vector search work offline and maintain user data privacy?▼

Browser-based vector search maintains data privacy by executing HNSW (WASM) locally on the client side. Because no server infrastructure is required, it also enables full offline capabilities for AI workloads after initial loading.

What is the best way to coordinate edge-deployed AI agents across multiple clients?▼

The best way to coordinate edge-deployed AI agents is using a decentralized swarm architecture. By initializing an EdgeSwarm with Web Workers and enabling P2P networking via WebRTC, clients coordinate peer-to-peer without server dependencies.

Do I need server infrastructure to perform neural network inference on the edge?▼

No server infrastructure is needed to perform neural network inference on the edge. The workload runs entirely client-side using ONNX Runtime (WASM) within the browser, offering enhanced privacy and offline capabilities.

Are there limitations to running decentralized AI swarms entirely on the client side?▼

Running decentralized AI swarms client-side relies heavily on browser resources and Web Workers. While it removes server dependencies and enhances privacy, performance is constrained by the user's local device capabilities and WebRTC network conditions.

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