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' # Gemini

Each adapter gem pulls in the official SDK (openai , anthropic , gemini-ai ).

Unified adapter via RubyLLM (recommended for multi-provider)

Gemfile

gem 'dspy' gem 'dspy-ruby_llm' # Routes to any provider via ruby_llm gem 'ruby_llm'

RubyLLM handles provider routing based on the model name. Use the ruby_llm/ prefix:

DSPy.configure do |c| c.lm = DSPy::LM.new('ruby_llm/gemini-2.5-flash', structured_outputs: true)

c.lm = DSPy::LM.new('ruby_llm/claude-sonnet-4-20250514', structured_outputs: true)

c.lm = DSPy::LM.new('ruby_llm/gpt-4o-mini', structured_outputs: true)

end

Events System

DSPy.rb ships with a structured event bus for observing runtime behavior.

Module-Scoped Subscriptions (preferred for agents)

class MyAgent < DSPy::Module subscribe 'lm.tokens', :track_tokens, scope: :descendants

def track_tokens(_event, attrs) @total_tokens += attrs.fetch(:total_tokens, 0) end end

Global Subscriptions (for observability/integrations)

subscription_id = DSPy.events.subscribe('score.create') do |event, attrs| Langfuse.export_score(attrs) end

Wildcards supported

DSPy.events.subscribe('llm.*') { |name, attrs| puts "[#{name}] tokens=#{attrs[:total_tokens]}" }

Event names use dot-separated namespaces (llm.generate , react.iteration_complete ). Every event includes module metadata (module_path , module_leaf , module_scope.ancestry_token ) for filtering.

Lifecycle Callbacks

Rails-style lifecycle hooks ship with every DSPy::Module :

• before — Runs ahead of forward for setup (metrics, context loading)

• around — Wraps forward , calls yield , and lets you pair setup/teardown logic

• after — Fires after forward returns for cleanup or persistence

class InstrumentedModule < DSPy::Module before :setup_metrics around :manage_context after :log_metrics

def forward(question:) @predictor.call(question: question) end

private

def setup_metrics @start_time = Time.now end

def manage_context load_context result = yield save_context result end

def log_metrics duration = Time.now - @start_time Rails.logger.info "Prediction completed in #{duration}s" end end

Execution order: before → around (before yield) → forward → around (after yield) → after. Callbacks are inherited from parent classes and execute in registration order.

Fiber-Local LM Context

Override the language model temporarily using fiber-local storage:

fast_model = DSPy::LM.new("openai/gpt-4o-mini", api_key: ENV['OPENAI_API_KEY'])

DSPy.with_lm(fast_model) do result = classifier.call(text: "test") # Uses fast_model inside this block end

Back to global LM outside the block

LM resolution hierarchy: Instance-level LM → Fiber-local LM (DSPy.with_lm ) → Global LM (DSPy.configure ).

Use configure_predictor for fine-grained control over agent internals:

agent = DSPy::ReAct.new(MySignature, tools: tools) agent.configure { |c| c.lm = default_model } agent.configure_predictor('thought_generator') { |c| c.lm = powerful_model }

Evaluation Framework

Systematically test LLM application performance with DSPy::Evals :

metric = DSPy::Metrics.exact_match(field: :answer, case_sensitive: false) evaluator = DSPy::Evals.new(predictor, metric: metric) result = evaluator.evaluate(test_examples, display_table: true) puts "Pass Rate: #{(result.pass_rate * 100).round(1)}%"

Built-in metrics: exact_match , contains , numeric_difference , composite_and . Custom metrics return true /false or a DSPy::Prediction with score: and feedback: fields.

Use DSPy::Example for typed test data and export_scores: true to push results to Langfuse.

GEPA Optimization

GEPA (Genetic-Pareto Reflective Prompt Evolution) uses reflection-driven instruction rewrites:

gem 'dspy-gepa'

teleprompter = DSPy::Teleprompt::GEPA.new( metric: metric, reflection_lm: DSPy::ReflectionLM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY']), feedback_map: feedback_map, config: { max_metric_calls: 600, minibatch_size: 6 } )

result = teleprompter.compile(program, trainset: train, valset: val) optimized_program = result.optimized_program

The metric must return DSPy::Prediction.new(score:, feedback:) so the reflection model can reason about failures. Use feedback_map to target individual predictors in composite modules.

Typed Context Pattern

Replace opaque string context blobs with T::Struct inputs. Each field gets its own description: annotation in the JSON schema the LLM sees:

class NavigationContext < T::Struct const :workflow_hint, T.nilable(String), description: "Current workflow phase guidance for the agent" const :action_log, T::Array[String], default: [], description: "Compact one-line-per-action history of research steps taken" const :iterations_remaining, Integer, description: "Budget remaining. Each tool call costs 1 iteration." end

class ToolSelectionSignature < DSPy::Signature input do const :query, String const :context, NavigationContext # Structured, not an opaque string end

output do const :tool_name, String const :tool_args, String, description: "JSON-encoded arguments" end end

Benefits: type safety at compile time, per-field descriptions in the LLM schema, easy to test as value objects, extensible by adding const declarations.

Schema Formats (BAML / TOON)

Control how DSPy describes signature structure to the LLM:

• JSON Schema (default) — Standard format, works with structured_outputs: true

• BAML (schema_format: :baml ) — 84% token reduction for Enhanced Prompting mode. Requires sorbet-baml gem.

• TOON (schema_format: :toon, data_format: :toon ) — Table-oriented format for both schemas and data. Enhanced Prompting mode only.

BAML and TOON apply only when structured_outputs: false . With structured_outputs: true , the provider receives JSON Schema directly.

Storage System

Persist and reload optimized programs with DSPy::Storage::ProgramStorage :

storage = DSPy::Storage::ProgramStorage.new(storage_path: "./dspy_storage") storage.save_program(result.optimized_program, result, metadata: { optimizer: 'MIPROv2' })

Supports checkpoint management, optimization history tracking, and import/export between environments.

Rails Integration

Directory Structure

Organize DSPy components using Rails conventions:

app/ entities/ # T::Struct types shared across signatures signatures/ # DSPy::Signature definitions tools/ # DSPy::Tools::Base implementations concerns/ # Shared tool behaviors (error handling, etc.) modules/ # DSPy::Module orchestrators services/ # Plain Ruby services that compose DSPy modules config/ initializers/ dspy.rb # DSPy + provider configuration feature_flags.rb # Model selection per role spec/ signatures/ # Schema validation tests tools/ # Tool unit tests modules/ # Integration tests with VCR vcr_cassettes/ # Recorded HTTP interactions

Initializer

config/initializers/dspy.rb

Rails.application.config.after_initialize do next if Rails.env.test? && ENV["DSPY_ENABLE_IN_TEST"].blank?

RubyLLM.configure do |config| config.gemini_api_key = ENV["GEMINI_API_KEY"] if ENV["GEMINI_API_KEY"].present? config.anthropic_api_key = ENV["ANTHROPIC_API_KEY"] if ENV["ANTHROPIC_API_KEY"].present? config.openai_api_key = ENV["OPENAI_API_KEY"] if ENV["OPENAI_API_KEY"].present? end

model = ENV.fetch("DSPY_MODEL", "ruby_llm/gemini-2.5-flash") DSPy.configure do |config| config.lm = DSPy::LM.new(model, structured_outputs: true) config.logger = Rails.logger end

Langfuse observability (optional)

if ENV["LANGFUSE_PUBLIC_KEY"].present? && ENV["LANGFUSE_SECRET_KEY"].present? DSPy::Observability.configure! end end

Feature-Flagged Model Selection

Use different models for different roles (fast/cheap for classification, powerful for synthesis):

config/initializers/feature_flags.rb

module FeatureFlags SELECTOR_MODEL = ENV.fetch("DSPY_SELECTOR_MODEL", "ruby_llm/gemini-2.5-flash-lite") SYNTHESIZER_MODEL = ENV.fetch("DSPY_SYNTHESIZER_MODEL", "ruby_llm/gemini-2.5-flash") end

Then override per-tool or per-predictor:

class ClassifyTool < DSPy::Tools::Base def call(query:) predictor = DSPy::Predict.new(ClassifyQuery) predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SELECTOR_MODEL, structured_outputs: true) } predictor.call(query: query) end end

Schema-Driven Signatures

Prefer typed schemas over string descriptions. Let the type system communicate structure to the LLM rather than prose in the signature description.

Entities as Shared Types

Define reusable T::Struct and T::Enum types in app/entities/ and reference them across signatures:

app/entities/search_strategy.rb

class SearchStrategy < T::Enum enums do SingleSearch = new("single_search") DateDecomposition = new("date_decomposition") end end

app/entities/scored_item.rb

class ScoredItem < T::Struct const :id, String const :score, Float, description: "Relevance score 0.0-1.0" const :verdict, String, description: "relevant, maybe, or irrelevant" const :reason, String, default: "" end

Schema vs Description: When to Use Each

Use schemas (T::Struct/T::Enum) for:

• Multi-field outputs with specific types

• Enums with defined values the LLM must pick from

• Nested structures, arrays of typed objects

• Outputs consumed by code (not displayed to users)

Use string descriptions for:

• Simple single-field outputs where the type is String

• Natural language generation (summaries, answers)

• Fields where constraint guidance helps (e.g., description: "YYYY-MM-DD format" )

Rule of thumb: If you'd write a case statement on the output, it should be a T::Enum . If you'd call .each on it, it should be T::Array[SomeStruct] .

Tool Patterns

Tools That Wrap Predictions

A common pattern: tools encapsulate a DSPy prediction, adding error handling, model selection, and serialization:

class RerankTool < DSPy::Tools::Base tool_name "rerank" tool_description "Score and rank search results by relevance"

MAX_ITEMS = 200 MIN_ITEMS_FOR_LLM = 5

sig { params(query: String, items: T::Array[T::Hash[Symbol, T.untyped]]).returns(T::Hash[Symbol, T.untyped]) } def call(query:, items: []) return { scored_items: items, reranked: false } if items.size < MIN_ITEMS_FOR_LLM

capped_items = items.first(MAX_ITEMS)
predictor = DSPy::Predict.new(RerankSignature)
predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SYNTHESIZER_MODEL, structured_outputs: true) }

result = predictor.call(query: query, items: capped_items)
{ scored_items: result.scored_items, reranked: true }

rescue => e Rails.logger.warn "[RerankTool] LLM rerank failed: #{e.message}" { error: "Rerank failed: #{e.message}", scored_items: items, reranked: false } end end

Key patterns:

• Short-circuit LLM calls when unnecessary (small data, trivial cases)

• Cap input size to prevent token overflow

• Per-tool model selection via configure

• Graceful error handling with fallback data

Error Handling Concern

module ErrorHandling extend ActiveSupport::Concern

private

def safe_predict(signature_class, **inputs) predictor = DSPy::Predict.new(signature_class) yield predictor if block_given? predictor.call(**inputs) rescue Faraday::Error, Net::HTTPError => e Rails.logger.error "[#{self.class.name}] API error: #{e.message}" nil rescue JSON::ParserError => e Rails.logger.error "[#{self.class.name}] Invalid LLM output: #{e.message}" nil end end

Observability

Tracing with DSPy::Context

Wrap operations in spans for Langfuse/OpenTelemetry visibility:

result = DSPy::Context.with_span( operation: "tool_selector.select", "dspy.module" => "ToolSelector", "tool_selector.tools" => tool_names.join(",") ) do @predictor.call(query: query, context: context, available_tools: schemas) end

Setup for Langfuse

Gemfile

gem 'dspy-o11y' gem 'dspy-o11y-langfuse'

.env

LANGFUSE_PUBLIC_KEY=pk-... LANGFUSE_SECRET_KEY=sk-... DSPY_TELEMETRY_BATCH_SIZE=5

Every DSPy::Predict , DSPy::ReAct , and tool call is automatically traced when observability is configured.

Score Reporting

Report evaluation scores to Langfuse:

DSPy.score(name: "relevance", value: 0.85, trace_id: current_trace_id)

Testing

VCR Setup for Rails

VCR.configure do |config| config.cassette_library_dir = "spec/vcr_cassettes" config.hook_into :webmock config.configure_rspec_metadata! config.filter_sensitive_data('<GEMINI_API_KEY>') { ENV['GEMINI_API_KEY'] } config.filter_sensitive_data('<OPENAI_API_KEY>') { ENV['OPENAI_API_KEY'] } end

Signature Schema Tests

Test that signatures produce valid schemas without calling any LLM:

RSpec.describe ClassifyResearchQuery do it "has required input fields" do schema = described_class.input_json_schema expect(schema[:required]).to include("query") end

it "has typed output fields" do schema = described_class.output_json_schema expect(schema[:properties]).to have_key(:search_strategy) end end

Tool Tests with Mocked Predictions

RSpec.describe RerankTool do let(:tool) { described_class.new }

it "skips LLM for small result sets" do expect(DSPy::Predict).not_to receive(:new) result = tool.call(query: "test", items: [{ id: "1" }]) expect(result[:reranked]).to be false end

it "calls LLM for large result sets", :vcr do items = 10.times.map { |i| { id: i.to_s, title: "Item #{i}" } } result = tool.call(query: "relevant items", items: items) expect(result[:reranked]).to be true end end

Resources

• core-concepts.md — Signatures, modules, predictors, type system deep-dive

• toolsets.md — Tools::Base, Tools::Toolset DSL, type safety, testing

• providers.md — Provider adapters, RubyLLM, fiber-local LM context, compatibility matrix

• optimization.md — MIPROv2, GEPA, evaluation framework, storage system

• observability.md — Event system, dspy-o11y gems, Langfuse, score reporting

• signature-template.rb — Signature scaffold with T::Enum, Date/Time, defaults, union types

• module-template.rb — Module scaffold with .call(), lifecycle callbacks, fiber-local LM

• config-template.rb — Rails initializer with RubyLLM, observability, feature flags

Key URLs

• Homepage: https://oss.vicente.services/dspy.rb/

• GitHub: https://github.com/vicentereig/dspy.rb

• Documentation: https://oss.vicente.services/dspy.rb/getting-started/

Guidelines for Claude

When helping users with DSPy.rb:

• Schema over prose — Define output structure with T::Struct and T::Enum types, not string descriptions

• Entities in app/entities/ — Extract shared types so signatures stay thin

• Per-tool model selection — Use predictor.configure { |c| c.lm = ... } to pick the right model per task

• Short-circuit LLM calls — Skip the LLM for trivial cases (small data, cached results)

• Cap input sizes — Prevent token overflow by limiting array sizes before sending to LLM

• Test schemas without LLM — Validate input_json_schema and output_json_schema in unit tests

• VCR for integration tests — Record real HTTP interactions, never mock LLM responses by hand

• Trace with spans — Wrap tool calls in DSPy::Context.with_span for observability

• Graceful degradation — Always rescue LLM errors and return fallback data

Signature Best Practices

Keep description concise — The signature description should state the goal, not the field details:

Good — concise goal

class ParseOutline < DSPy::Signature description 'Extract block-level structure from HTML as a flat list of skeleton sections.'

input do const :html, String, description: 'Raw HTML to parse' end

output do const :sections, T::Array[Section], description: 'Block elements: headings, paragraphs, code blocks, lists' end end

Use defaults over nilable arrays — For OpenAI structured outputs compatibility:

Good — works with OpenAI structured outputs

class ASTNode < T::Struct const :children, T::Array[ASTNode], default: [] end

Recursive Types with $defs

DSPy.rb supports recursive types in structured outputs using JSON Schema $defs :

class TreeNode < T::Struct const :value, String const :children, T::Array[TreeNode], default: [] # Self-reference end

The schema generator automatically creates #/$defs/TreeNode references for recursive types, compatible with OpenAI and Gemini structured outputs.

Field Descriptions for T::Struct

DSPy.rb extends T::Struct to support field-level description: kwargs that flow to JSON Schema:

class ASTNode < T::Struct const :node_type, NodeType, description: 'The type of node (heading, paragraph, etc.)' const :text, String, default: "", description: 'Text content of the node' const :level, Integer, default: 0 # No description — field is self-explanatory const :children, T::Array[ASTNode], default: [] end

When to use field descriptions: complex field semantics, enum-like strings, constrained values, nested structs with ambiguous names. When to skip: self-explanatory fields like name , id , url , or boolean flags.

Version

Current: 0.34.3