ruby-llm

Unify chat, tools, embeddings, and streaming behind a single Ruby API.

32|1|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/faqndo97/ai-skills --skill ruby-llm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ruby-llm
Source: https://github.com/faqndo97/ai-skills/tree/main/ruby-llm
Command: npx skills add https://github.com/faqndo97/ai-skills --skill ruby-llm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ruby developers often face fragmentation when integrating AI capabilities across providers. This Skill provides a single, coherent interface to build AI-powered Ruby applications with chat, tools (function calling), streaming, embeddings, and Rails-ready persistence.

Core Features & Use Cases

  • Unified Ruby API across providers for chat, tools, and embeddings, enabling consistent development patterns.
  • Streaming, tool calls, and persistence to build interactive experiences in Rails apps with automatic history and tool invocation.
  • Production-ready workflows including multi-provider model access, background processing, and optional structured outputs for UI integration.

Quick Start

Set RubyLLM to power a Rails-ready chat with persistent conversations, tool calls, streaming, and embeddings.

Frequently Asked Questions about ruby-llm

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

FAQPage Schema
How do I build a Rails app with streaming chat and tool calls?▼

A unified Ruby API for AI apps solves this by managing chat, tool calls, and streaming behind a single interface. It enables interactive multi-provider LLM experiences with automatic history persistence and tool invocation within Rails services.

What's the best way to integrate multiple LLM providers in a Ruby application?▼

Using a unified Ruby API to integrate multiple LLM providers ensures consistent development patterns for chat, embeddings, and tools. This approach abstracts provider-specific fragmentation and supports production-ready workflows in Ruby services.

Does this Ruby LLM integration approach support background jobs and structured outputs?▼

Yes, this Ruby LLM integration approach supports background job processing and optional structured output configurations. It requires proper Ruby environment setup, provider keys, and background job support to manage multi-provider model access and UI integration.

How do I generate and persist embeddings for AI workflows in Ruby?▼

You generate and persist embeddings for AI workflows in Ruby by using a unified API that combines embeddings with chat and tools. This provides Rails-ready persistence and automatic history management for end-to-end AI pipelines.

Why do I need a unified API for chat, tools, and embeddings in Ruby?▼

A unified API for chat, tools, and embeddings in Ruby solves the fragmentation of integrating AI capabilities across different providers. A single coherent interface streamlines development and enables production-ready workflows with streaming and persistence.