RubyLLM: A single, beautiful Ruby framework for all major AI providers

RubyLLM, a unified Ruby framework for multiple AI providers, launched to simplify building chatbots, agents, and AI workflows. The framework supports OpenAI, Anthropic, Google, and others with a single interface, reducing dependency bloat. It offers features like chat, vision, audio, document analysis, image generation, and tool integration.

A single, beautiful Ruby framework for all major AI providers. Easily build chatbots, AI agents, RAG applications, content generators, and every AI workflow you can think of. Battle tested at https://chatwithwork.com - Fully private work AI Build a working Ruby AI chat in two minutes Using RubyLLM? Share your story https://tally.so/r/3Na02p Takes 5 minutes. Why RubyLLM? Every AI provider ships their own bloated client. Different APIs. Different response formats. Different conventions. It’s exhausting. RubyLLM gives you one beautiful framework for all of them. Same interface whether you’re using GPT, Claude, or your local Ollama. Just three dependencies: Faraday, Zeitwerk, and Marcel. That’s it. Show me the code Just ask questions chat = RubyLLM.chat chat.ask "What's the best way to learn Ruby?" Analyze any file type chat.ask "What's in this image?", with: "ruby conf.jpg" chat.ask "What's happening in this video?", with: "video.mp4" chat.ask "Describe this meeting", with: "meeting.wav" chat.ask "Summarize this document", with: "contract.pdf" chat.ask "Explain this code", with: "app.rb" Multiple files at once chat.ask "Analyze these files", with: "diagram.png", "report.pdf", "notes.txt" Stream responses chat.ask "Tell me a story about Ruby" do |chunk| print chunk.content end Generate images RubyLLM.paint "a sunset over mountains in watercolor style" Create embeddings RubyLLM.embed "Ruby is elegant and expressive" Transcribe audio to text RubyLLM.transcribe "meeting.wav" Moderate content for safety RubyLLM.moderate "Check if this text is safe" Let AI use your code class Weather < RubyLLM::Tool desc "Get current weather" def execute latitude:, longitude: url = "https://api.open-meteo.com/v1/forecast?latitude= {latitude}&longitude= {longitude}&current=temperature 2m,wind speed 10m" JSON.parse Faraday.get url .body end end chat.with tool Weather .ask "What's the weather in Berlin?" Define an agent with instructions + tools class WeatherAssistant < RubyLLM::Agent model "gpt-5-nano" instructions "Be concise and always use tools for weather." tools Weather end WeatherAssistant.new.ask "What's the weather in Berlin?" Get structured output class ProductSchema < RubyLLM::Schema string :name number :price array :features do string end end response = chat.with schema ProductSchema .ask "Analyze this product", with: "product.txt" Features Chat: Conversational AI with RubyLLM.chat Vision: Analyze images and videos Audio: Transcribe and understand speech with RubyLLM.transcribe Documents: Extract from PDFs, CSVs, JSON, any file type Image generation: Create images with RubyLLM.paint Embeddings: Generate embeddings with RubyLLM.embed Moderation: Content safety with RubyLLM.moderate Tools: Let AI call your Ruby methods Agents: Reusable assistants with RubyLLM::Agent Structured output: JSON schemas that just work Streaming: Real-time responses with blocks Rails: ActiveRecord integration with acts as chat Async: Fiber-based concurrency Model registry: 800+ models with capability detection and pricing Extended thinking: Control, view, and persist model deliberation Providers: OpenAI, xAI, Anthropic, Gemini, VertexAI, Bedrock, DeepSeek, Mistral, Ollama, OpenRouter, Perplexity, GPUStack, and any OpenAI-compatible API Installation Add to your Gemfile: gem 'ruby llm' Then bundle install . Configure your API keys: config/initializers/ruby llm.rb RubyLLM.configure do |config| config.openai api key = ENV 'OPENAI API KEY' end Rails Install Rails Integration bin/rails generate ruby llm:install bin/rails db:migrate bin/rails ruby llm:load models v1.13+ Add Chat UI optional bin/rails generate ruby llm:chat ui class Chat < ApplicationRecord acts as chat end chat = Chat.create model: "claude-sonnet-4" chat.ask "What's in this file?", with: "report.pdf" Visit http://localhost:3000/chats for a ready-to-use chat interface