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:

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!

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