DSPy.rb

Build LLM apps like you build software. Type-safe, modular, testable.

DSPy.rb brings software engineering best practices to LLM development. Instead of tweaking prompts, define what you want with Ruby types and let DSPy handle the rest.

Overview

DSPy.rb is a Ruby framework for building language model applications with programmatic prompts. It provides:

• Type-safe signatures — Define inputs/outputs with Sorbet types
• Modular components — Compose and reuse LLM logic
• Automatic optimization — Use data to improve prompts, not guesswork
• Production-ready — Built-in observability, testing, and error handling

Core Concepts

1. Signatures

Define interfaces between your app and LLMs using Ruby types:

class EmailClassifier < DSPy::Signature
  description "Classify customer support emails by category and priority"

  class Priority < T::Enum
    enums do
      Low = new('low')
      Medium = new('medium')
      High = new('high')
      Urgent = new('urgent')
    end
  end

  input do
    const :email_content, String
    const :sender, String
  end

  output do
    const :category, String
    const :priority, Priority  # Type-safe enum with defined values
    const :confidence, Float
  end
end

2. Modules

Build complex workflows from simple building blocks:

• Predict — Basic LLM calls with signatures
• ChainOfThought — Step-by-step reasoning
• ReAct — Tool-using agents
• CodeAct — Dynamic code generation agents (install the dspy-code_act gem)

3. Tools & Toolsets

Create type-safe tools for agents with comprehensive Sorbet support:

# Enum-based tool with automatic type conversion
class CalculatorTool < DSPy::Tools::Base
  tool_name 'calculator'
  tool_description 'Performs arithmetic operations with type-safe enum inputs'

  class Operation < T::Enum
    enums do
      Add = new('add')
      Subtract = new('subtract')
      Multiply = new('multiply')
      Divide = new('divide')
    end
  end

  sig { params(operation: Operation, num1: Float, num2: Float).returns(T.any(Float, String)) }
  def call(operation:, num1:, num2:)
    case operation
    when Operation::Add then num1 + num2
    when Operation::Subtract then num1 - num2
    when Operation::Multiply then num1 * num2
    when Operation::Divide
      return "Error: Division by zero" if num2 == 0
      num1 / num2
    end
  end
end

# Multi-tool toolset with rich types
class DataToolset < DSPy::Tools::Toolset
  toolset_name "data_processing"

  class Format < T::Enum
    enums do
      JSON = new('json')
      CSV = new('csv')
      XML = new('xml')
    end
  end

  tool :convert, description: "Convert data between formats"
  tool :validate, description: "Validate data structure"

  sig { params(data: String, from: Format, to: Format).returns(String) }
  def convert(data:, from:, to:)
    "Converted from #{from.serialize} to #{to.serialize}"
  end

  sig { params(data: String, format: Format).returns(T::Hash[String, T.any(String, Integer, T::Boolean)]) }
  def validate(data:, format:)
    { valid: true, format: format.serialize, row_count: 42, message: "Data validation passed" }
  end
end

4. Type System & Discriminators

DSPy.rb uses sophisticated type discrimination for complex data structures:

• Automatic _type field injection — DSPy adds discriminator fields to structs for type safety
• Union type support — T.any() types automatically disambiguated by _type
• Reserved field name — Avoid defining your own _type fields in structs
• Recursive filtering — _type fields filtered during deserialization at all nesting levels

5. Optimization

Improve accuracy with real data:

• MIPROv2 — Advanced multi-prompt optimization with bootstrap sampling and Bayesian optimization
• GEPA — Genetic-Pareto Reflective Prompt Evolution with feedback maps, experiment tracking, and telemetry
• Evaluation — Comprehensive framework with built-in and custom metrics, error handling, and batch processing

Quick Start

# Install
gem 'dspy'

# Configure
DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY'])
end

# Define a task
class SentimentAnalysis < DSPy::Signature
  description "Analyze sentiment of text"

  input do
    const :text, String
  end

  output do
    const :sentiment, String  # positive, negative, neutral
    const :score, Float       # 0.0 to 1.0
  end
end

# Use it
analyzer = DSPy::Predict.new(SentimentAnalysis)
result = analyzer.call(text: "This product is amazing!")
puts result.sentiment  # => "positive"
puts result.score      # => 0.92

Provider Adapter Gems

Two strategies for connecting to LLM providers:

Per-provider adapters (direct SDK access)

# Gemfile
gem 'dspy'
gem 'dspy-openai'    # OpenAI, OpenRouter, Ollama
gem 'dspy-anthropic' # Claude
gem 'dspy-gemini'