Top 5 Ruby Gems to Build AI-Powered Rails Apps in 2025

When I first started experimenting with AI inside my Ruby on Rails apps, the landscape was wildly fragmented. I had to write custom wrappers, deal with strange API formats, and manage external machine learning models without proper support. Fast forward to 2025, and building AI-powered applications with Ruby on Rails is not only possible—it’s easier and more exciting than ever.

Thanks to the evolution of Large Language Models (LLMs) like OpenAI's GPT-4-turbo, Anthropic's Claude, Meta’s Llama 3, and Google Gemini, plus accessible GPU-backed cloud platforms, Rails developers now have a wide range of tools to plug directly into this AI revolution.

AI is no longer a “nice to have” for Rails developers—it’s becoming core to how we build smarter, faster, more personalized applications. Whether you're integrating GPT-4, building an internal Claude-based assistant, or embedding an AI copilot in your product, Ruby on Rails is more than capable of handling modern AI workflows.

In this post, I’ll break down the top 5 Ruby gems for building AI applications with Rails in 2025. I’ve worked with all of them in production, and each plays a different role—from natural language generation to document Q&A and self-hosted models.

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1. 🧠 ruby-openai: The OpenAI API Client for Ruby

One of the most powerful gems in the Rails + AI ecosystem is ruby-openai, a clean wrapper around the OpenAI API.

Why I Use It

This gem lets me integrate OpenAI’s GPT models like gpt-4-turbo, gpt-3.5, and even image models like DALL·E 3 directly into my Ruby on Rails app. I’ve used it to generate summaries, write emails, build chatbots, and even build a Rails-based app that can convert support tickets into SQL queries.

Code Example

client = OpenAI::Client.new(access_token: ENV["OPENAI_API_KEY"])

response = client.chat(
  parameters: {
    model: "gpt-4-turbo",
    messages: [{ role: "user", content: "Explain Ruby on Rails in one sentence" }],
    temperature: 0.7
  }
)

puts response.dig("choices", 0, "message", "content")

Best For

• Chatbots

• Auto-generated content

• LLM integrations

• AI assistants inside your Rails app

• Code Generation

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2. 🤖 langchainrb: The LangChain Ruby Port

The langchainrb gem brings the popular LangChain architecture to the Ruby world. Originally developed for Python, LangChain is 🤖 langchainrb: The LangChain Ruby Porta framework for chaining LLM calls together in a structured, logical workflow.

Why I Use It

I wanted to move beyond simple one-off prompts and build multi-step reasoning AI agents inside my Rails apps. langchainrb allowed me to easily create an AI that could read a PDF, summarize it, and answer questions about it—all inside a Rails controller.

Code Example

llm = Langchain::LLM::OpenAI.new(api_key: ENV["OPENAI_API_KEY"])
agent = Langchain::Agent::Conversational.new(llm: llm)

agent.run("Summarize the Ruby on Rails framework in 3 sentences.")

Best For

• Building AI workflows

• Semantic search with AI

• Chat with your documents (PDF, Notion, etc.)

• Rails-based AI chat interfaces

• Agent-based workflows (task runners, AutoGPT clones)

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3. 🧩 hugging-face: Hugging Face API for Ruby

As a Rails dev, I was thrilled when someone created an API gem for Hugging Face Transformers, the hub of open-source AI models like BERT, LLaMA, and Whisper.

Why I Use It

Whether it’s for text generation, audio transcription, or zero-shot classification, this gem lets me call Hugging Face-hosted models from my Rails app. I use it for more specialized LLM use-cases where OpenAI feels like overkill—or when I want full transparency.

Code Example

client = HuggingFace::Client.new(api_key: ENV["HF_API_KEY"])

result = client.text_generation("Once upon a time in Ruby on Rails...")
puts result

Best For

• Custom LLMs like Llama 3

• Access 1000s of community models without leaving Rails

• Text classification and Sentiment Analysis

• Transcription

• Lightweight AI integrations

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4. 🧠 llm.rb – Universal LLM Client Wrapper

This is a newer gem that launched in late 2024 and exploded in popularity in early 2025. It wraps LLMs from OpenAI, Claude (Anthropic), Gemini (Google), and Mistral under a single unified interface.

Why I Use It

It’s my go-to gem when I want to experiment with different LLMs without rewriting code. It supports Claude 3, Gemini Pro 1.5, and even self-hosted LLaMA 3 models via Ollama.

Code Example

llm = LLM::Client.new(provider: :anthropic, api_key: ENV["CLAUDE_API_KEY"])
response = llm.chat("Summarize this Rails migration in simple words.")

puts response.text

Best For

• Rapid LLM prototyping

• Multi-model support (Claude, Gemini, Llama)

• Ideal for plug-and-play model switching

• Unified AI interface for your Rails app

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5. Self-hosted Models via Ollama + Rails

Not a gem per se, but worth including. With Ollama, you can run LLaMA 3, Mistral, and other open-source models locally and call them from Rails via simple HTTP POST requests.

# Example HTTP call from Rails
response = HTTP.post("http://localhost:11434/api/generate", json: {
  model: "llama3",
  prompt: "Hello AI",
})

Why use it:

• Full control over data and privacy

• No API cost

• Great for edge or on-prem deployments

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Honorable Mentions

• whisper-ruby – For speech-to-text using OpenAI Whisper

• llama_cpp.rb – Interact with LLaMA 3 models locally or via Ollama

• vector_search – Lightweight vector database in ActiveRecord

• chatbot_rails – Scaffold complete chatbots in your Rails apps

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Final Thoughts

As a long-time Ruby on Rails developer, I’m incredibly excited about how RubyonRails and AI are converging in 2025. With the rise of LLMs, GPU-powered inference APIs, and a vibrant open-source gem ecosystem, Rails is becoming a serious player in the AI and machine learning development world.

You don’t need to know TensorFlow or PyTorch to build intelligent features. With these gems, you can enhance your Rails apps with AI content generation, chatbots, smart workflows, and custom ML integrations in a few lines of Ruby code.

If you're looking to start building AI-powered apps with Rails, just pick any of these gems, plug in your API key, and let the machine do the thinking!

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Visit RubyGems website and find all gems that help you build AI applications in Ruby, Ruby on Rails, and other RubyLang ecosystem.

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🤖 How to Build a GPT Chatbot in Rails with ruby-openai

Here’s a step-by-step guide to building a simple GPT chatbot inside a Rails app using ruby-openai.

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✅ Step 1: Add the gem

gem "ruby-openai"
gem "dotenv-rails"

bundle install

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✅ Step 2: Set up API keys

In .env:

OPENAI_API_KEY=your_key_here

In config/application.rb:

Dotenv::Railtie.load

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✅ Step 3: Create a Chat controller

rails generate controller Chat index create

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✅ Step 4: Add routes

# config/routes.rb
root "chat#index"
post "/chat", to: "chat#create"

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✅ Step 5: Controller Logic

# app/controllers/chat_controller.rb
class ChatController < ApplicationController
  def index; end

  def create
    client = OpenAI::Client.new(access_token: ENV["OPENAI_API_KEY"])
    response = client.chat(
      parameters: {
        model: "gpt-4-turbo",
        messages: [
          { role: "system", content: "You are a helpful assistant." },
          { role: "user", content: params[:message] }
        ],
        temperature: 0.7
      }
    )
    @reply = response.dig("choices", 0, "message", "content")
    respond_to do |format|
      format.turbo_stream
    end
  end
end

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✅ Step 6: Frontend with Turbo + Hotwire

<!-- app/views/chat/index.html.erb -->
<%= form_with url: "/chat", method: :post, data: { turbo_stream: true } do |f| %>
  <%= f.text_field :message, placeholder: "Ask something...", class: "border p-2 w-full mb-2" %>
  <%= f.submit "Send", class: "bg-blue-600 text-white px-4 py-2 rounded" %>
<% end %>

<div id="chat-replies"></div>

<!-- app/views/chat/create.turbo_stream.erb -->
<turbo-stream action="append" target="chat-replies">
  <template>
    <p><strong>AI:</strong> <%= @reply %></p>
  </template>
</turbo-stream>

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🙋 Frequently Asked Questions (2025)

1. Can I use Rails alone to build AI apps in 2025?

Yes, modern Ruby gems allow full integration with AI models like GPT-4, Claude, Gemini, and LLaMA without needing Python or TensorFlow.

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2. What’s the fastest way to integrate GPT-4 into Rails?

Use the ruby-openai gem. You can build a working chatbot in less than 15 minutes using a basic controller and Hotwire frontend.

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3. Which Ruby gem supports Claude 3 or Gemini?

Use langchainrb or llm.rb. Both offer APIs to work with Anthropic’s Claude models and Google’s Gemini Pro/Ultra via API keys.

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4. Can I use open-source models like LLaMA 3 or Mistral with Rails?

Yes—by running the models locally with Ollama and making HTTP requests to your local server.

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5. How do I add document Q&A ("chat with your files") in Rails?

Use langchainrb, which supports PDF, Notion, Markdown, and web content loaders. Combine it with a vector store like Qdrant or pgvector.

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6. How do I manage token limits or costs with OpenAI in production?

Track usage per request using the metadata returned by the API (usage.total_tokens) and log it to your database for billing or alerts.

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7. Which is better for AI workflows in Rails—langchainrb or llm.rb?

Use llm.rb if you want a simple wrapper across models. Choose langchainrb for more advanced pipelines (e.g. tools, agents, memory, vector search).

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8. Can I use Rails to build a full RAG system (Retrieval-Augmented Generation)?

Yes. Use langchainrb + vector search (Qdrant, pgvector, Pinecone) to retrieve context chunks and feed them to an LLM.

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9. What is the best Rails gem for using Hugging Face models?

The hugging-face gem is simple and works well for calling Hugging Face's hosted inference endpoints from Rails.

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10. How do I deploy a Rails + AI app with local GPU inference?

Run models with Ollama or LM Studio on your local GPU server and connect your Rails app via HTTP to the local inference endpoint.