dspy-ruby

Type-safe LLM features in Ruby use DSPy.rb signatures and modules to define structured inputs and outputs for language model calls. Signatures enforce expected types, modules compose predictor logic, and multi-provider configuration routes requests to OpenAI, Anthropic, Gemini, or Ollama. This prevents type mismatches and enables predictable AI workflows in production Ruby applications.

DSPy.rb signatures define typed input and output fields instead of writing freeform prompts, enabling the framework to optimize and validate data automatically. Traditional prompting relies on string templates without type checking. Signatures make LLM tasks composable, testable, and easier to refactor across Ruby projects.

Yes, DSPy.rb supports multi-provider configuration for OpenAI, Anthropic, Gemini, and Ollama. You configure your provider once and swap implementations without changing module or signature code. This decouples your Ruby application logic from provider-specific APIs.

DSPy.rb modules include built-in testing and observability patterns. Define signatures with expected outputs, use predictor modules to structure your logic, and validate results against test cases. The type-safe design catches errors early, and the framework provides hooks for tracing and debugging LLM behavior.

No. DSPy.rb is the Ruby-native implementation of DSPy concepts adapted for Ruby idioms. The Skill includes a quick-start example showing minimal signatures and modules. Familiarity with Ruby and object-oriented design is sufficient to build LLM features with DSPy.rb.

Install the DSPy.rb gem in your Ruby environment and configure your LLM provider credentials. Add a SKILL.md frontmatter file to document your Skill metadata for consistency. Then define signatures and modules for your LLM tasks—no additional dependencies are required.