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}¤t=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!