Agent Skills: DSPy.rb

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:

1. Schema over prose — Define output structure with T::Struct and T::Enum types, not string descriptions
2. Entities in app/entities/ — Extract shared types so signatures stay thin
3. Per-tool model selection — Use predictor.configure { |c| c.lm = ... } to pick the right model per task
4. Short-circuit LLM calls — Skip the LLM for trivial cases (small data, cached results)
5. Cap input sizes — Prevent token overflow by limiting array sizes before sending to LLM
6. Test schemas without LLM — Validate input_json_schema and output_json_schema in unit tests
7. VCR for integration tests — Record real HTTP interactions, never mock LLM responses by hand
8. Trace with spans — Wrap tool calls in DSPy::Context.with_span for observability
9. 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