One beautiful Ruby API for GPT, Claude, Gemini, and more.

Battle tested at — Claude Code for your documents

Note

Using RubyLLM in production? Share your story! Takes 5 minutes.

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Build chatbots, AI agents, RAG applications. Works with OpenAI, Anthropic, Google, AWS, local models, and any OpenAI-compatible API.

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 API 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"

# Moderate content for safety
RubyLLM.moderate("Check if this text is safe").flagged? # => false

# Let AI use your code
class Weather < RubyLLM::Tool
  description "Get current weather"
  param :latitude
  param :longitude

  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?"

# 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
• Documents: Extract from PDFs, CSVs, JSON, any file type
• Image generation: Create images with RubyLLM.paint
• Embeddings: Vector search with RubyLLM.embed
• Moderation: Content safety with RubyLLM.moderate
• Tools: Let AI call your Ruby methods
• 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: 500+ models with capability detection and pricing
• Providers: OpenAI, 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 database models
rails generate ruby_llm:install

# Add chat UI (optional)
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!

Documentation

rubyllm.com

Contributing

See CONTRIBUTING.md.

License

Released under the MIT License.