
ruby_llm
One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code.
Install with your AI
Paste into Claude Code, Cursor, or any agent — it reads the repo and wires the tool into your project.
Install and set up ruby_llm (release-binary project) into my current project. Found on https://claudeers.com/rubyllm Repo: https://github.com/crmne/ruby_llm Homepage/docs: https://rubyllm.com/ Detected install method: release-binary → inspect the README Category: rag. Platforms: api. Read the repo's README for exact setup and env vars, then install it and wire it into my project. Claudeers Health Verdict: active; community-verified: false. Confirm the source before running anything.
Grab the latest release asset from GitHub.
# download a build from https://github.com/crmne/ruby_llm/releases
git clone https://github.com/crmne/ruby_llm
// compatibility
| Platforms | api |
|---|---|
| Operating systems | — |
| AI compatibility | claude |
| License | MIT |
| Pricing | open-source |
| Language | Ruby |
One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code.
[!NOTE] Using RubyLLM? Share your story! Takes 5 minutes.
Build chatbots, AI agents, RAG applications. Works with OpenAI, xAI, Anthropic, Google, AWS, local models, and any OpenAI-compatible API.
Build a working Ruby AI chat in two minutes
https://github.com/user-attachments/assets/65422091-9338-47da-a303-92b918bd1345
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"
# Turn text into speech
speech = RubyLLM.speak "Hello, welcome to RubyLLM!"
speech.save "welcome.mp3"
# 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 speech with
RubyLLM.transcribeand generate it withRubyLLM.speak - 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
- Citations: Normalized source citations from documents, search, and grounding
- Batches: Provider-side batch processing at half price with
RubyLLM.batch - 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!
Documentation
Contributing
See CONTRIBUTING.md.
License
Released under the MIT License.
// faq
What is ruby_llm?
One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code.. It is open-source on GitHub.
Is ruby_llm free to use?
ruby_llm is open-source under the MIT license, so it is free to use.
What category does ruby_llm belong to?
ruby_llm is listed under automation in the Claudeers registry of Claude-compatible tools.
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