RubyLLM

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 - Fully private work AI Build a working Ruby AI chat in two minutes Using RubyLLM? Share your story! 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!