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dspy-ruby

Builds type-safe LLM apps in Ruby with DSPy.rb structured signatures.

Updated Nov 11, 2023
Search Tags:#optimization#llm#type-safety#ruby#prompt-engineering#agent-design#dspy
One-click install
npx skills add https://github.com/michaelvolz/redmuffin.Blazor.StaticWeb --skill dspy-ruby-michaelvolz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/michaelvolz/redmuffin.Blazor.StaticWeb/tree/main/.opencode/skills/ce/dspy-ruby
Command: npx skills add https://github.com/michaelvolz/redmuffin.Blazor.StaticWeb --skill dspy-ruby-michaelvolz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

DSPy.rb prevents brittle prompt experimentation by letting Ruby developers define LLM interactions through type-safe signatures, modular components, and deterministic configurations so every output is predictable and testable.

Core Features & Use Cases

  • Type-safe Signatures: Declare inputs and outputs with Sorbet types, enums, and structs so LLM responses deserialize into strongly typed Ruby objects.
  • Modular Workflows: Compose Predict, ChainOfThought, ReAct, and CodeAct modules with lifecycle callbacks, fiber-local LM overrides, and configurable toolsets for complex agent scenarios.
  • Optimization & Observability: Integrate MIPROv2, GEPA, and Langfuse-ready telemetry plus structured evaluation metrics to improve prompts, trace runs, and report scores.
  • Use Case: Embed DSPy into a Rails app to classify support emails, route actions, rerank results, and optimize each predictor with real data and feature-flagged model selection.

Quick Start

Connect your Ruby project to DSPy by installing the gem, configuring DSPy::LM with RubyLLM or provider adapters, and invoking the predictor with DSPy::Predict to classify text.

Frequently Asked Questions about dspy-ruby

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build type-safe LLM applications in Ruby without ad-hoc prompt tinkering?▼

You can build type-safe LLM applications in Ruby by defining structured DSPy signatures with Sorbet types, enums, and structs, ensuring LLM responses deserialize into strongly typed Ruby objects for predictable outputs.

Can I use DSPy modules to compose complex agent workflows in a Rails app?▼

Yes, you can compose complex agent workflows in a Rails app by chaining Predict, ChainOfThought, ReAct, and CodeAct modules with lifecycle callbacks, fiber-local LM overrides, and configurable toolsets for routing and classifying actions.

What is the best way to optimize LLM prompts and trace runs in Ruby?▼

The best way to optimize LLM prompts in Ruby is using DSPy's integrated MIPROv2 and GEPA optimizers alongside Langfuse-ready telemetry and structured evaluation metrics to trace runs, report scores, and improve predictor performance with real data.

Does DSPy.rb require specific provider adapters for LLM configuration?▼

DSPy.rb requires configuring DSPy::LM with RubyLLM or other provider adapters to handle provider routing, ensuring your Ruby project connects to LLMs with telemetry-ready configurations and structured schemas.

Why should I use structured signatures instead of manual prompt engineering for LLM responses?▼

Structured signatures prevent brittle prompt experimentation by replacing manual prompt engineering with deterministic configurations and modular components, making every LLM output predictable, testable, and deserializable into typed Ruby objects.

Are there limitations to using DSPy.rb for agent orchestration in Ruby?▼

DSPy.rb is tailored for Ruby and Rails environments, so limitations arise if your stack lacks Sorbet type compatibility or if your agent orchestration requires real-time streaming outside its lifecycle callback and telemetry configuration scope.

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