How to Build AI Agents with Ruby

Introduction

Artificial intelligence agents represent autonomous programs that analyze their surroundings to make decisions and perform actions toward achieving specific objectives. This tutorial will teach you how to develop AI agents with Ruby through the OpenAI API.

It will help you to set up your development environment, build a basic chatbot, and explore advanced development techniques.

Upon completion, you will understand how to integrate Ruby with OpenAI’s GPT models, apply few-shot learning, and manage prompts and token limits to avoid common pitfalls in AI agent development.

Why Use Ruby for Building AI Agents?

AI agent development benefits from multiple features provided by Ruby:

• Readability: Ruby’s clean, expressive syntax accelerates prototyping.
• Integration: Ruby provides seamless connections to REST APIs (like OpenAI, Cohere, Anthropic) while integrating with existing Ruby on Rails applications.
• Community: Though the Ruby ecosystem is smaller than Python, it provides gems for AI and machine learning development.
• Productivity: Agentic AI logic development benefits from rapid feedback cycles, which allow for efficient building and iteration.

Reactive vs. Autonomous AI Agents

Simple reactive agents respond to user queries, whereas advanced agents demonstrate autonomy by proactively gathering information and invoking tools to complete tasks. Common examples of AI agents include:

• Chatbots that engage in natural language interactions.
• Recommendation systems analyze user data to suggest products, content, or actions.
• Data processing agents automatically perform analysis and transformation tasks on datasets, such as sorting emails and detecting unusual patterns.

Prerequisites and Setup

• Basic Ruby knowledge will be beneficial.

• New Ruby users should review foundational concepts through the following tutorials:

  • Understanding Data Types in Ruby
  • How to Work with String Methods in Ruby
  • How to Write Your First Ruby Program.

• You must install Ruby on your system(the 3.x version is recommended to achieve optimal compatibility and performance).

• Register on the OpenAI Platform to get an API key. The Ruby program can use this key to access OpenAI’s GPT models.

Choosing Ruby AI Frameworks and Libraries

Ruby’s AI ecosystem is growing. The following are some of the leading Ruby AI libraries and frameworks available:

• ruby-openai: Official Ruby client for the OpenAI API. Ideal for GPT-3/4, DALL·E, and Whisper integration.
• rumale: It provides Ruby developers with a machine learning library that operates similarly to scikit-learn.
• ai4r: Classic AI algorithms (decision trees, neural networks).
• torch-rb: Ruby bindings for PyTorch (experimental).

The ruby-openai library is the best starting point for most modern agent-based AI applications.

Installing the Ruby OpenAI Gem

Developers can simplify the usage of OpenAI with Ruby through the official Ruby OpenAI gem. The library includes user-friendly functions to access OpenAI’s API capabilities, including text completion, chat services, and image generation, without handling raw HTTP requests. You can install the gem through the terminal interface by running the following command:

gem install ruby-openai

If using Bundler, you should insert the following line into your Gemfile:

gem "ruby-openai"

Then, execute:

bundle install

Now you can access the gem in your Ruby scripts by requiring it after installation:

require "openai"

The gem is the foundation of Ruby OpenAI integration. This package provides Ruby methods for interacting with GPT models while wrapping the REST API. After installation, you can set up an API client.

Setting Up OpenAI API Access

Accessing the OpenAI API requires submitting the API key to the Ruby client. The ruby-openai gem provides two methods for API key integration:

• Direct Initialization: Provide the API key as a parameter when you instantiate a client object.
• Global Configuration: Set up the OpenAI module with your API key, which is useful for Rails applications or large projects.

Option 1: Quick Initialization – good for scripts or quick tests:

client = OpenAI::Client.new(
  access_token: 'YOUR_OPENAI_API_KEY'
  log_errors: true # Recommended during development
)

Use your real secret key (within quotes) to replace “YOUR_OPENAI_API_KEY”. For example, if your key is abc123…, insert this string in that position. Although this method works properly, it is not recommended to hardcode the key in real projects; instead, you can use dotenv to securely pass the keys to your environment.

Option 2: Global Configuration – recommended for larger applications:

# require "openai"
# To use the OpenAI module, you must provide your credentials either in #an initializer(such as config/initializers/openai.rb in a Rails #project) or directly when initializing the client.
OpenAI.configure do |config|
  config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN")
  config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID", nil) #Org ID is optional
  config.log_errors = true # Recommended in development
end
# The client initialization process no longer requires you to pass the #token each time.
client = OpenAI::Client.new

We have used ENV.fetch(“OPENAI_ACCESS_TOKEN”) to load the key. A missing key will cause fetch to produce an error message reminding you to configure it. The organization_id setting is available for OpenAI accounts that manage multiple organizations; however, it remains optional.

Step 1: Define the Agent’s Role (Prompt Setup)

AI agents typically operate with predefined roles or goals established through user prompts. The OpenAI chat API allows users to supply a system message that determines the agent’s context or personality. We will build a prompt using a system message and test it through one question.

# Define the conversation context and test a single prompt
system_message = { role: "system", content: "You are a helpful Ruby programming assistant." }
user_message   = { role: "user", content: "How can I reverse a string in Ruby?" }

response = client.chat(
  parameters: {
    model: "gpt-3.5-turbo",                   # GPT model to use
    messages: [ system_message, user_message ],  # conversation history
    temperature: 0.7                           # some creativity
  }
)

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

In this code:

• The system message guides the AI to work as a Ruby programming assistant.
• We then provide a user message with a question.
• We call the client.chat with these messages. The OpenAI gem requires a model definition (here “gpt-3.5-turbo”) and an array containing message hashes. Setting the temperature to 0.7 introduces creativity into the output(range 0 to 1; 0.0 = strict, 1.0 = random).
• We use response.dig(“choices”, 0, “message”, “content”) to extract the assistant’s reply.
• Prints the answer (e.g., “You can reverse a string in Ruby using the ‘reverse’ method: ‘hello’.reverse # => ‘olleh’”).

The AI agent responds to a Ruby query. This shows how to use the Ruby programming language with the GPT API for basic single-turn interactions.

Step 2: Create an Interactive Agent Loop

We will transform this into an interactive agent that works as a simple chatbot. The system must allow users to ask several questions and receive answers until they decide to quit.

# Simple interactive chatbot loop
system_message = { role: "system", content: "You are a helpful Ruby programming assistant." }
messages = [ system_message ]
puts "Ask the Ruby AI agent anything. Type 'exit' to quit."
loop do
  print "You: "
  user_input = gets.chomp.strip  
  break if user_input.downcase == "exit" || user_input.downcase == "quit"
  # Append the new user message to the conversation
  messages << { role: "user", content: user_input }
  # Send the conversation to OpenAI
  response = client.chat(parameters: { model: "gpt-3.5-turbo", messages: messages })
  assistant_reply = response.dig("choices", 0, "message", "content")
  # Display the assistant's reply
  puts "Agent: #{assistant_reply}"
  # Append the assistant response to messages for context
  messages << { role: "assistant", content: assistant_reply }
end

How this works:

• The messages array starts with our system prompt, which establishes the agent’s role.
• We start a loop that requests input from the user.
• The loop terminates when the user inputs “exit” or “quit.”
• Otherwise, we take the user input and add it as a new message to the messages array.
• We call the API for processing using the complete message history (model: “gpt-3.5-turbo”, messages: messages).
• We retrieve the assistant’s response and print it.
• The assistant’s response is added to the messages array, maintaining the conversation context for the next round.

Try it out: Run this script in your terminal (make sure your API key is configured). For example, you may ask, “How do I read a file in Ruby?” or “What are Ruby’s data types?”

Step 3: Extending the Agent with Tools or Memory (Advanced)

The basic agent we built is extensible. In advanced applications, AI agents perform practical tasks such as conducting web searches and database queries. Python programmers benefit from frameworks such as LangChain to implement these functionalities, while Ruby developers are starting to develop similar concepts.

The Ruby AI development landscape has emerged with tools like langchainrb gem and Active Agent(an AI framework for Rails), which enable developers to build AI agents with advanced architecture. These frameworks let you specify available tools (functions agents can utilize), handle long-term memory storage, and chain together multiple prompts.

Few-Shot, One-Shot, and Zero-Shot Learning in Prompts

Modern language models such as GPT-4 demonstrate the powerful ability to complete tasks based on contextual information without additional training. By providing examples within the prompt, you can steer the model’s output.

Zero-Shot Learning

Zero-shot learning allows you to direct the model to perform tasks without supplying an example. For instance, “Translate the following sentence to French: The model performs the task by relying entirely on its existing knowledge base.

Zero-shot example (no examples provided):

english_text = "Good night"
prompt = "Translate the following text to French:\n#{english_text}"
response = client.chat(
  parameters: {
    model: "gpt-3.5-turbo",
    messages: [ { role: "user", content: prompt } ]
  }
)
puts response.dig("choices", 0, "message", "content")
# Expected output (approx): "Bonne nuit"

One-Shot Learning

One-shot learning entails providing one example of the task, which includes input and desired output in the prompt, before requesting the model to perform the task on new input data. One example enables the model to learn the required format or style.

One-shot example (with one example pair):

messages = [
  { role: "system", content: "You are a translation assistant. You translate English to French." },
  # One example interaction:
  { role: "user", content: "Translate to French: Hello, how are you?" },
  { role: "assistant", content: "Bonjour, comment allez-vous?" },
  # Now the actual query
  { role: "user", content: "Translate to French: Good night" }
]
response = client.chat(parameters: { model: "gpt-3.5-turbo", messages: messages })
puts response.dig("choices", 0, "message", "content")
# Expected output: "Bonne nuit"

Few-Shot Learning

Including multiple examples within the prompt before the query helps clarify the task through established patterns and often produces better results than zero- and one-shot prompting. The model conducts in-context learning by interpreting real-life examples without updating weights.

Few-shot example (multiple examples): Extend the above concept with more examples:

messages = [
  { role: "system", content: "Translate English to French." },
  { role: "user", content: "Weather: It is sunny." },
  { role: "assistant", content: "Météo : Il fait beau." },
  { role: "user", content: "Weather: It is raining." },
  { role: "assistant", content: "Météo : Il pleut." },
  { role: "user", content: "Weather: It is windy." }
]
# We provided two examples (sunny and raining). Now the model sees "windy"...
response = client.chat(parameters: { model: "gpt-3.5-turbo", messages: messages })
puts response.dig("choices", 0, "message", "content")
# Likely output: "Météo : Il y a du vent."

Best Practices for Prompt Engineering in Ruby

Prompt engineering is an effective way to communicate with AI systems. The underlying principles remain consistent across programming languages; however, the following table presents specific tips for designing prompts in Ruby.

Common Pitfalls When Building Ruby AI Agents and How to Avoid Them

Below, we explore the most frequent issues developers face and provide actionable strategies to overcome them.

Mismanagement of API Tokens: Protect Your Secrets

The improper management of API tokens is a serious security threat in AI agent development. Storing API keys within your source code or config files risks accidental exposure, particularly when your code repository is public or shared with other users.

Safeguard your credentials by storing sensitive information in environment variables. Gems like dotenv provide a straightforward solution for managing environment variables within Ruby applications. You must add files with sensitive data, such as .env, to your .gitignore to prevent them from being committed to version control.

Inefficient Asynchronous Handling: Keep Your Application Responsive

Synchronous handling of AI agent interactions that include network calls and complex computations can block the main thread of your application. Web applications that prioritize user experience may experience slowdowns, timeouts, or become unresponsive as these issues occur. Prevent these issues by offloading heavy operations to background job processors like Sidekiq or Resque, or Delayed Job. They allow your application to enqueue asynchronous tasks, freeing up the main thread to handle incoming requests without delay. Additionally, using asynchronous HTTP libraries such as Typhoeus allows developers to perform non-blocking API calls, which leads to better throughput and improved responsiveness. Using Sidekiq, you can create a background worker specifically designed to handle AI queries:

class AgentTaskWorker
  include Sidekiq::Worker

  def perform(question)
# Call OpenAI API and process the response here
  end
end

# Enqueue a job
AgentTaskWorker.perform_async("What is Ruby?")

This approach ensures your Ruby AI agent remains performant and scalable under load.

Overcomplicated Agent Logic: Embrace Simplicity and Modularity

Developers often feel inclined to build fully featured AI agents from the start. However, as codebases become complex, maintaining and debugging them become increasingly harder… Technical debt emerges from this approach, which consequently reduces development speed.

Start with a simple modular architecture that centers around the agent’s essential operations. Design the codebase by building distinct reusable classes and methods that follow the Single-Responsibility Principle. Regular refactoring of your agent’s codebase throughout its evolution will keep it clean and manageable.

Ignoring Token Limits and Prompt Optimization: Control Costs and Improve Reliability

AI language models such as OpenAI’s GPT series enforce restrictions on the number of tokens each request can contain. When requests go beyond established token limits, they may get truncated or cause API errors, and increase costs.

The best approach to handle API limits is to track the token consumption during each call. Remove unnecessary details to ensure your prompts remain brief and focused. To maintain token restriction, you can also remove or summarize older messages from the conversation history.

FAQ Section

What is an AI agent?
It’s a software that perceives its surroundings, makes decisions, and takes actions to reach certain goals. It often uses techniques like machine learning or natural language processing.

Why use Ruby for building AI agents?
Ruby enables quick application development, clean coding syntax, and robust web integration, making it ideal for prototyping and deploying AI-driven web applications.

How does few-shot learning work in AI agents?
Few-shot learning trains AI models by supplying them with a few examples through the prompt, which improves their generalization abilities across tasks.

Conclusion

This tutorial established the foundational skills to develop AI agents of varying complexities in Ruby through the OpenAI API. It taught how to install and configure the Ruby OpenAI gem, set up the environment, and create interactive chatbots using context management.

We have included advanced techniques such as few-shot, one-shot, and zero-shot learning to customize your agents, provide best practices for prompt engineering, and token management to maintain performance.

To see how AI agents can be seamlessly embedded into web applications, check out Integrating GenAI Agents into Your Website, and for a turnkey solution using DigitalOcean’s managed platform, see Build an AI Agent Chatbot with the GenAI Platform. With these skills and resources, you can develop and deploy Ruby AI agents that will handle multiple real-world applications.

Resources and References

• OpenAI Ruby API Library (ruby-openai)
• Ruby OpenAI Documentation
• Official documentation for the Chat Completions endpoint
• Package listing for the langchainrb gem