It is a Ruby implementation of the DSPy framework, designed to build LLM applications using software engineering principles like types and modules instead of manual prompt tweaking.

Can I use this with Ruby on Rails?

Yes, it includes specific patterns for Rails integration, including recommended directory structures, initializers, and feature-flagged model selection.

Does this skill support Sorbet?

Yes, it utilizes Sorbet signatures for inputs and outputs, ensuring strict type safety and structured data handling throughout your AI application.

Which LLM providers are supported?

It supports major providers including Anthropic, OpenAI, and Google Gemini, either through direct adapters or the unified RubyLLM interface.

How does prompt optimization work in DSPy-Ruby?

It uses data-driven optimizers like MIPROv2 and GEPA to automatically refine instructions and prompts based on your specific evaluation metrics and ground-truth data.

DSPy.rb for Ruby

Name: DSPy.rb for Ruby
Author: aimi-so

byaimi-so

•

Data Science & ML

Builds type-safe, modular LLM applications in Ruby using programmatic prompt signatures and automated optimization techniques.

DSPy.rb brings software engineering rigor to LLM development by replacing brittle prompt engineering with declarative, type-safe signatures and composable modules. It enables developers to define desired outputs using Ruby types (Sorbet), implement sophisticated agentic workflows like ReAct, and optimize model performance through data-driven techniques like MIPROv2. This skill is essential for Ruby developers building predictable, production-grade AI features that require structured data, complex tool integration, and rigorous automated testing.

Key Features

01Type-safe signatures using Sorbet for structured LLM inputs and outputs

023 GitHub stars

03Automated optimization frameworks like MIPROv2 and GEPA for data-driven prompt tuning

04Multi-provider support for Anthropic, OpenAI, and Gemini with fiber-local context switching

05Comprehensive observability and Rails integration for enterprise AI applications

06Composable modules including ChainOfThought, ReAct agents, and Predictors

Use Cases

01Building structured AI agents that interact with existing Ruby toolsets and internal APIs

02Developing production-ready AI features with automated VCR testing and schema validation

03Creating reliable data extraction pipelines from unstructured text using typed signatures

Key Features

01Type-safe signatures using Sorbet for structured LLM inputs and outputs

023 GitHub stars

03Automated optimization frameworks like MIPROv2 and GEPA for data-driven prompt tuning

04Multi-provider support for Anthropic, OpenAI, and Gemini with fiber-local context switching

05Comprehensive observability and Rails integration for enterprise AI applications

06Composable modules including ChainOfThought, ReAct agents, and Predictors

Use Cases

01Building structured AI agents that interact with existing Ruby toolsets and internal APIs

02Developing production-ready AI features with automated VCR testing and schema validation

03Creating reliable data extraction pipelines from unstructured text using typed signatures