base on One beautiful Ruby API for OpenAI, Anthropic, Gemini, Bedrock, OpenRouter, DeepSeek, Ollama, VertexAI, Perplexity, Mistral, GPUStack & OpenAI compatible APIs. Chat, Vision, Audio, PDF, Images, Embeddings, Tools, Streaming & Rails integration. <div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="/docs/assets/images/logotype_dark.svg"> <img src="/docs/assets/images/logotype.svg" alt="RubyLLM" height="120" width="250"> </picture> <strong>One *beautiful* Ruby API for GPT, Claude, Gemini, and more.</strong> Battle tested at [<picture><source media="(prefers-color-scheme: dark)" srcset="https://chatwithwork.com/logotype-dark.svg"><img src="https://chatwithwork.com/logotype.svg" alt="Chat with Work" height="30" align="absmiddle"></picture>](https://chatwithwork.com) — *Claude Code for your documents* [](https://badge.fury.io/rb/ruby_llm) [](https://github.com/testdouble/standard) [](https://rubygems.org/gems/ruby_llm) [](https://codecov.io/gh/crmne/ruby_llm) <a href="https://trendshift.io/repositories/13640" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13640" alt="crmne%2Fruby_llm | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div> > [!NOTE] > Using RubyLLM in production? [Share your story](https://tally.so/r/3Na02p)! Takes 5 minutes. --- 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 ```ruby # Just ask questions chat = RubyLLM.chat chat.ask "What's the best way to learn Ruby?" ``` ```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" ``` ```ruby # Multiple files at once chat.ask "Analyze these files", with: ["diagram.png", "report.pdf", "notes.txt"] ``` ```ruby # Stream responses chat.ask "Tell me a story about Ruby" do |chunk| print chunk.content end ``` ```ruby # Generate images RubyLLM.paint "a sunset over mountains in watercolor style" ``` ```ruby # Create embeddings RubyLLM.embed "Ruby is elegant and expressive" ``` ```ruby # Moderate content for safety RubyLLM.moderate("Check if this text is safe").flagged? # => false ``` ```ruby # 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?" ``` ```ruby # 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: ```ruby gem 'ruby_llm' ``` Then `bundle install`. Configure your API keys: ```ruby # config/initializers/ruby_llm.rb RubyLLM.configure do |config| config.openai_api_key = ENV['OPENAI_API_KEY'] end ``` ## Rails ```bash # Install database models rails generate ruby_llm:install # Add chat UI (optional) rails generate ruby_llm:chat_ui ``` ```ruby 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](https://rubyllm.com) ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md). ## License Released under the MIT License. ", Assign "at most 3 tags" to the expected json: {"id":"13640","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"