Last Updated on July 3, 2025

When we talk about Artificial Intelligence, the conversation can go in a hundred different directions by consuming OpenAI’s API, actually building your own AI models, or even people with little to no programming experience using AI-powered editors to develop their own app ideas.

In this post, I’ll focus on something more grounded: why Ruby and Rails and AI are a surprisingly effective duo and how you can start exploring that combo today!

Oh, and by the way, if you’re curious about how AI works under the hood, I highly recommend this blog post.

Why Rails Is Built for This Moment

One of the things I love (and hate) most about Rails is that it follows the convention over configuration paradigm. This essentially means that we avoid a lot of bikeshedding by using established conventions, so we don’t have to worry about trivial matters, which is great!

"But if this is great, why do you also hate it?"

Well, this sounds great when you already know how to use the framework and most of its features but when you’re learning it for the first time, and most things are done automagically, it can make the learning curve steeper.

This stops becoming a problem when you have enough time to practice and learn though. Ruby on Rails has been around for 20 years, so there’s A LOT of knowledge and content about the framework, good practices, bad practices, tips and tricks.

And if you know the basics about LLMs (Large Language Models) and how AI "learns" things, you probably already see where I’m heading with this. If you don’t, I might have to ruin the magic for you by saying that… Artificial Intelligence is not truly intelligent. It becomes "smarter" by being trained on existing content and data. This is why Rails works well with AI – it’s predictable, well-documented, and rich with patterns that AI can easily learn from and work with.

Another big reason Rails pairs so well with AI is Ruby’s readability. Ruby’s syntax is clean, expressive, and designed to be easy for humans to read and write. For beginners, this means getting up to speed quickly without wrestling with confusing syntax or boilerplate.

But beyond benefiting from generative AI to build regular apps due to them being reliable and future-proof, Rails is also a fantastic tool for developing AI-driven features. Its batteries-included philosophy gives you everything you need to get an idea off the ground quickly, from REST APIs and background jobs to real-time updates with ActionCable. Need to build a chatbot? Rails makes it easy to handle webhooks, manage sessions, and stream responses. Want to wrap an LLM-powered feature in a simple web app or internal tool? You can go from idea to working prototype in an afternoon. That combined with its rich ecosystem of gems, and you’ve got yourself a powerful, developer-friendly foundation for building AI products without reinventing the wheel.

Rails AI Tools You Can Use Right Now

AI tools, libraries, and gems evolve fast, so rather than giving you a long list that might be outdated by next week, I’ll highlight a few solid, well-supported options that have stuck around and then show you how you can find new tools on your own, so you’re never short of shiny new things to explore!

1. ruby-openai

What it does:

A mature, reliable wrapper for the OpenAI API, giving you access to:
- Completions & Chat – Generate text or hold multi-turn conversations.
- Embeddings – Convert text into vectors for similarity/search tasks.
- Edits, Images, Audio – Access to DALL·E and Whisper APIs.

Use cases:

Build chatbots, content generators, or tools that summarize, rewrite, or classify text.
Add image generation, audio transcription, or embedding search to your apps.

The best choice when you’re focused solely on OpenAI’s platform and want maximum control.

2. langchainrb

What it does:

A high-level framework for building AI-driven features using any major LLM provider (OpenAI, Anthropic, Google Gemini, etc.).
Supports:
- Prompt templates and few-shot prompting
- Tool calling and chaining
- RAG (Retrieval-Augmented Generation)
- Evaluation tools (via Ragas)

Use cases:

Build advanced assistants, RAG pipelines, or multi-step workflows.
Great for AI prototypes that may evolve or switch providers later.

If you’re looking for something simpler and more lightweight, ruby_llm is another great gem. It offers a unified LLM interface and tool calling without the full framework overhead.

3. raix-rails

What it does:

Brings Raix and the Model Context Protocol (MCP) to Rails.
Helps you define AI behaviors as "agents" using prompts, structured inputs/outputs, and streamable sessions.
Supports multiple backends and transports (HTTP, STDIO, SSE).

Use cases:

Build structured, runtime-integrated AI components that act like microservices.
Ideal for internal tools, assistants, or backend AI services with business logic.

Think of it as prompt-driven architecture baked into Rails.

4. torch.rb

What it does:

Ruby bindings for LibTorch, enabling deep learning in native Ruby.
You can define, train, and run models without leaving the Ruby ecosystem.

Use cases:

Train your own models for classification, prediction, etc.
Integrate ML directly into your app without Python overhead.

Note: It’s more low-level than other tools here and great for folks exploring custom AI solutions or ML research in Ruby.

5. sublayer

What it does:

A model-agnostic Ruby framework for building AI-powered applications.
Provides base classes for creating Generators, Actions, Tasks, and Agents, enabling the development of complex AI workflows.
Designed to integrate smoothly with Rails and other Ruby frameworks.

Use cases:

Automated Code Generation: Create systems that generate code snippets or entire files based on user input or predefined templates.
Task Automation: Build agents that handle specific, repeatable tasks like data processing, code generation, or report creation triggered by events or schedules. A great example is the RSpecAgent, which watches for file changes, runs tests automatically, and if any fail, rewrites the code to make them pass.
Custom AI Workflows: Develop tailored AI workflows by combining generators and actions to meet specific business needs.

Why it’s useful:

Model-Agnostic: Supports integration with various AI models, allowing flexibility in choosing the best provider for your needs.
Rails Integration: Works seamlessly within Rails applications, leveraging familiar conventions and tools.
Extensible: Easily extendable to accommodate custom generators, actions, and agents, providing a high degree of customization.

You can check out a lot more tools in this post (although, like we said before, some might already be obsolete).

So, a solution to this problem is… teaching you how to find them on your own! Here’s how:

🔍 1. Search through GitHub effectively!

Use advanced search filters to find trending or recently updated Ruby AI tools.

Try this GitHub search for #ruby #ai
Sort by stars, filter by recent activity, and check READMEs for maturity or roadmap info.

📰 2. Follow Blogs & Newsletters

Codeminer42 Blog – Shameless plug, I know.
daily.dev – A developer-focused news aggregator and browser extension that helps you stay up-to-date with the latest tech news, tutorials, blog posts, and open-source projects.

💬 3. Watch the Community

AI-themed RailsConf and RubyConf talks also highlight tools before they hit critical mass!

Looking Ahead: The Future of Rails + AI

Indeed, some tools in the Ruby ecosystem are still early in their development. Compared to other languages like Python or JavaScript, Ruby’s AI tooling isn’t always the first to receive cutting-edge support. But that’s changing = fast.

There’s a growing wave of gems, frameworks, and experimental projects that are bringing powerful AI capabilities into the Rails world. From agent-based systems to task automation and LLM-driven workflows, we’re seeing more and more examples of what’s possible when Rails and AI work together. As these tools mature and adoption grows, Rails could play a key role in shaping the next generation of AI-enabled developer experiences and applications.

If you’re a Rails developer, this is your invitation: don’t wait for AI to "come to Ruby", it’s already here! The real question is what you will build with it.

The Overlooked Power of Rails in the Age of AI

Why Rails Is Built for This Moment