# Template agent — structured input via .call class SearchIntentAgent < ApplicationAgent model "gpt-4o" temperature 0.0 system "You are a search intent analyzer. Extract structured data from queries." user "Extract search intent from: {query}" assistant '{"refined_query":' # Force JSON output returns do string :refined_query, description: "Cleaned search query" array :filters, of: :string, description: "Extracted filters" end end result = SearchIntentAgent.call(query: "red summer dress under $50") result.content # => { refined_query: "red dress", filters: ["color:red", "price:<50"] } result.total_cost # => 0.00025 result.total_tokens # => 150 result.duration_ms # => 850

# Conversational agent — freeform input via .ask class RubyExpert < ApplicationAgent model "claude-sonnet-4-5-20250929" system "You are a senior Ruby developer with 20 years of experience." end result = RubyExpert.ask("What's the difference between proc and lambda?") puts result.content # Stream the response RubyExpert.ask("Explain metaprogramming") { |chunk| print chunk.content }

# Resilient agents with automatic retries and fallbacks class ReliableAgent < ApplicationAgent model "gpt-4o" user "{query}" on_failure do retries times: 3, backoff: :exponential fallback to: ["gpt-4o-mini", "claude-3-5-sonnet"] circuit_breaker after: 10, within: 60, cooldown: 5.minutes timeout 30 end end

# Vector embeddings for semantic search and RAG # app/agents/embedders/document_embedder.rb module Embedders class DocumentEmbedder < ApplicationEmbedder model "text-embedding-3-small" dimensions 512 cache_for 1.week end end result = Embedders::DocumentEmbedder.call(text: "Hello world") result.vector # => [0.123, -0.456, ...] result.dimensions # => 512 # Batch embedding result = Embedders::DocumentEmbedder.call(texts: ["Hello", "World", "Ruby"]) result.vectors # => [[...], [...], [...]]

# Message classification and routing class SupportRouter < ApplicationAgent include RubyLLM::Agents::Routing model "gpt-4o-mini" temperature 0.0 cache_for 1.hour route :billing, "Billing, charges, refunds, payments" route :technical, "Bugs, errors, crashes, technical issues" route :sales, "Pricing, plans, upgrades, discounts" default_route :general end result = SupportRouter.call(message: "I was charged twice") result.route # => :billing result.total_cost # => 0.00008

# Text-to-speech and speech-to-text # app/agents/audio/podcast_speaker.rb module Audio class PodcastSpeaker < ApplicationSpeaker model "tts-1" voice "onyx" speed 0.95 output_format :aac streaming true end end result = Audio::PodcastSpeaker.call(text: "Welcome to the show!") result.audio # => Binary audio data result.duration # => 1.5 result.save_to("episode.aac") # Speech-to-text transcription result = Audio::MeetingTranscriber.call(audio: "standup.mp3") result.text # => "Good morning everyone..." result.word_count # => 5432

# Image generation, analysis, and pipelines # app/agents/images/logo_generator.rb module Images class LogoGenerator < ApplicationImageGenerator model "gpt-image-1" size "1024x1024" quality "hd" style "vivid" template "Professional logo design: {prompt}. Minimalist, scalable." end end result = Images::LogoGenerator.call(prompt: "tech startup logo") result.url # => "https://..." result.save("logo.png")

# Evaluate agent quality with built-in scoring class SupportRouter::Eval < RubyLLM::Agents::Eval::EvalSuite agent SupportRouter test_case "billing", input: { message: "charged twice" }, expected: "billing" test_case "technical", input: { message: "500 error" }, expected: "technical" test_case "greeting", input: { message: "hello" }, expected: "general" end run = SupportRouter::Eval.run! puts run.summary # SupportRouter eval: 3/3 passed (score: 1.0)

# Gemfile gem "ruby_llm-agents"

RubyLLM::Agents.configure do |config| config.openai_api_key = ENV["OPENAI_API_KEY"] end

# config/routes.rb mount RubyLLM::Agents::Engine => "/agents"