ruby-integration

This skill is for writing integrations. Claude acts as the Braintrust engineer implementing new integrations to the Ruby SDK.

Reference Integrations

Study existing integrations as examples:

• OpenAI: lib/braintrust/trace/contrib/openai.rb (tests: test/braintrust/trace/openai_test.rb, example: examples/openai.rb)
• Anthropic: lib/braintrust/trace/contrib/anthropic.rb (tests: test/braintrust/trace/anthropic_test.rb, example: examples/anthropic.rb)

Important Notes:

• Examine the library thoroughly - Study the library's documentation and source code to identify ALL critical methods that call LLMs/AI services. Plan to trace every method that makes API calls, not just the obvious ones.
• Some integrations (e.g. ruby-llm) support multiple providers (e.g. OpenAI and Anthropic). Test all supported providers.

Core Pattern: Module Prepending

# frozen_string_literal: true

module Braintrust
  module Trace
    module YourProvider
      def self.wrap(client = nil, tracer_provider: nil)
        tracer_provider ||= ::OpenTelemetry.tracer_provider

        # Idempotent wrapping: check if already wrapped
        return client if client && client.instance_variable_get(:@braintrust_wrapped)

        # Support class-level wrapping: wrap() with no args wraps class globally
        if client.nil?
          # Class wrapping: YourProvider.prepend(wrapper)
          # Instance wrapping: client.singleton_class.prepend(wrapper)
        end

        wrapper = Module.new do
          define_method(:your_api_method) do |**params|
            tracer = tracer_provider.tracer("braintrust")

            tracer.in_span("your_provider.operation") do |span|
              # IMPORTANT: Start span FIRST (before metadata extraction) for accurate timing
              # 1. Capture input
              set_json_attr(span, "braintrust.input_json", extract_input(params))

              # 2. Set metadata (provider, model, endpoint, all params)
              set_json_attr(span, "braintrust.metadata", {
                "provider" => "your_provider",
                "endpoint" => "/v1/endpoint",
                "model" => params[:model]
              }.compact)

              # 3. Call original
              response = super(**params)

              # 4. Capture output
              set_json_attr(span, "braintrust.output_json", extract_output(response))

              # 5. Capture metrics (normalized tokens)
              set_json_attr(span, "braintrust.metrics", parse_usage_tokens(response.usage))

              response
            end
          end
        end

        client.your_api.singleton_class.prepend(wrapper)
        client.instance_variable_set(:@braintrust_wrapped, true) if client
        client
      end

      ## Code Organization

      - Break large methods (>50 lines) into focused helpers
      - Separate streaming/non-streaming into distinct handler methods (e.g., `handle_streaming_request`, `handle_non_streaming_request`)
      - Extract metadata/input/output capture into helper methods (e.g., `extract_metadata`, `build_input_messages`, `capture_output`)

      private

      def self.set_json_attr(span, key, value)
        span.set_attribute(key, JSON.generate(value)) if value
      rescue => e
        warn "Failed to serialize #{key}: #{e.message}"
      end

      def self.parse_usage_tokens(usage)
        return {} unless usage
        {
          "prompt_tokens" => usage[:input_tokens] || usage[:prompt_tokens],
          "completion_tokens" => usage[:output_tokens] || usage[:completion_tokens],
          "tokens" => usage[:total_tokens]
        }.compact
      end
    end
  end
end

Streaming Pattern

define_method(:stream) do |**params|
  tracer = tracer_provider.tracer("braintrust")
  aggregated_chunks = []

  span = tracer.start_span("your_provider.operation.stream")
  set_json_attr(span, "braintrust.input_json", extract_input(params))
  set_json_attr(span, "braintrust.metadata", extract_metadata(params))

  stream = begin
    super(**params)
  rescue => e
    span.record_exception(e)
    span.status = ::OpenTelemetry::Trace::Status.error("Error: #{e.message}")
    span.finish
    raise
  end

  original_each = stream.method(:each)
  stream.define_singleton_method(:each) do |&block|
    original_each.call do |chunk|
      aggregated_chunks << chunk
      block&.call(chunk)
    end
  rescue => e
    span.record_exception(e)
    span.status = ::OpenTelemetry::Trace::Status.error("Streaming error: #{e.message}")
    raise
  ensure
    # CRITICAL: Always finish span even if stream partially consumed
    unless aggregated_chunks.empty?
      aggregated = aggregate_chunks(aggregated_chunks)
      set_json_attr(span, "braintrust.output_json", aggregated)
      set_json_attr(span, "braintrust.metrics", parse_usage_tokens(aggregated[:usage]))
    end
    span.finish
  end

  stream
end

Examples

Write two examples:

• Customer example (examples/your_provider.rb): Concise example demonstrating setup and basic usage
• Internal example (examples/internal/your_provider.rb): Comprehensive example using every library feature

Follow existing example patterns:

• Nest all API calls under a manual root span (see examples/openai.rb):

  tracer = OpenTelemetry.tracer_provider.tracer("your-provider-example")
  root_span = nil
  
  response = tracer.in_span("examples/your_provider.rb") do |span|
    root_span = span
    client.your_api.call(...)  # Automatically traced, nested under root_span
  end
  

• Use consistent nomenclature for spans and projects
• Print permalink at end: Braintrust::Trace.permalink(root_span)

Required Components

Do in this order:

• [ ] Appraisals FIRST: Add to Appraisals file (latest + 2 recent + uninstalled), run bundle exec appraisal generate
• [ ] Tests: test/braintrust/trace/your_provider_test.rb
• [ ] Integration: lib/braintrust/trace/contrib/your_provider.rb
• [ ] VCR cassettes: test/fixtures/vcr_cassettes/your_provider/ (record as you write tests)
• [ ] Auto-load: Add to lib/braintrust/trace.rb with begin/rescue LoadError
• [ ] Example: examples/your_provider.rb
• [ ] Example: examples/internal/your_provider.rb (comprehensive internal example)
• [ ] Env var: Add to .env.example if needed

Test Coverage (LLM Providers)

1. ✅ Non-streaming requests (basic + attributes + metrics)
2. ✅ Streaming requests (full consumption)
3. ✅ Early stream termination (partial consumption)
4. ✅ Error handling (exception recording)
5. ✅ All critical features - Test ALL provider capabilities:
   • Tool/function calling (if supported)
   • Images/vision (if supported)
   • System messages (if supported)
   • Multiple messages/chat history (if supported)
   • Any other provider-specific features
6. ✅ Token usage edge cases (cached, reasoning tokens)
7. ✅ Multiple APIs (if provider has multiple endpoints)
8. ✅ Verify we don't change the behaviour of the integration
9. ✅ LLM wrapper libraries - If tracing a library that wraps LLM providers (e.g., ruby_llm→OpenAI), verify traces match the underlying provider exactly (tools format, token format, output structure). Compare side-by-side with BRAINTRUST_ENABLE_TRACE_CONSOLE_LOG=1

Appraisal Configuration (Set up FIRST)

CRITICAL: Configure appraisal at the START, before writing tests. Test latest + 2 recent versions + uninstalled.

Step 1 - Add to Appraisals file:

# Appraisals file - ADD THIS FIRST
appraise "your_provider-latest" do
  gem "your_provider", ">= 2.0"
end

appraise "your_provider-1.5" do
  gem "your_provider", "~> 1.5.0"
end

appraise "your_provider-1.0" do
  gem "your_provider", "~> 1.0.0"
end

appraise "your_provider-uninstalled" do
  remove_gem "your_provider"
end

Step 2 - Generate gemfiles:

bundle exec appraisal generate

Step 3 - Use appraisal for ALL test runs:

bundle exec appraisal rake test   # Run all scenarios (use this in TDD cycle)

Determine versions: Check release history, focus on API changes, include customer-likely versions.

Testing Tools & Validation

Use multiple testing approaches to validate your integration:

1. Unit Tests (Primary)

• Location: test/braintrust/trace/your_provider_test.rb
• Purpose: Test all code paths, edge cases, and error handling
• Run: bundle exec appraisal rake test
• Coverage: Track with bundle exec rake coverage (>90% line, >80% branch)

2. Console Log Inspection

• Purpose: Quickly verify trace structure during development
• Usage:

  BRAINTRUST_ENABLE_TRACE_CONSOLE_LOG=true bundle exec ruby examples/your_provider.rb
  

• Verify: Check span hierarchy, attributes, and parent/child relationships

3. Braintrust MCP Server (Integration Testing)

• Purpose: Query and inspect traces in the Braintrust platform
• Setup: Should be auto-configured in Docker environment
• Commands:

  # List recent traces
  mcp__braintrust__list_recent_objects(object_type: "project_logs", limit: 10)
  
  # Inspect specific span
  mcp__braintrust__resolve_object(object_type: "project_logs", object_id: "span_id")
  
  # BTQL query
  mcp__braintrust__btql_query(query: "SELECT * FROM project_logs WHERE metadata.provider = 'your_provider'")
  

• Verify attributes: input, output, metadata, metrics, span_attributes.braintrust.parent, span_attributes.braintrust.org

4. Examples (Manual Testing)

• Customer example: bundle exec ruby examples/your_provider.rb
• Internal example: bundle exec ruby examples/internal/your_provider.rb
• Purpose: End-to-end validation of real API calls

Testing Workflow

1. TDD cycle: Write unit test → implement → run bundle exec appraisal rake test
2. Console log: Use BRAINTRUST_ENABLE_TRACE_CONSOLE_LOG=true to debug span structure
3. MCP validation: Query traces with Braintrust MCP server
4. Examples: Run examples to verify end-to-end behavior

TDD Workflow (CRITICAL)

After EVERY major change: test → lint → fix → commit cycle

1. Create todo list at start
2. Write one failing test
3. Implement minimal code to pass
4. Run tests with appraisal: bundle exec appraisal rake test
5. Lint: bundle exec rake lint (fix with rake lint:fix)
6. Verify with MCP tools
7. Refactor if needed
8. Repeat cycle for: basic → attributes → streaming → errors → tokens → multimodal

Defensive Coding

• ✅ Nil checks (return {} unless usage)
• ✅ Safe navigation (params[:model] || "unknown")
• ✅ Compact hashes (.compact)
• ✅ Error handling (begin/rescue/ensure)
• ✅ JSON safety (rescue in set_json_attr)
• ✅ Graceful gem loading (rescue LoadError)

StandardRB & CI

Lint after every change (part of TDD cycle):

bundle exec rake lint          # Check StandardRB
bundle exec rake lint:fix      # Auto-fix

Coverage target (check periodically):

bundle exec rake coverage      # >90% line, >80% branch

CI requirements: StandardRB + tests on Ruby 3.2/3.3/3.4 + Ubuntu/macOS + all appraisal scenarios

Token Normalization

Use shared TokenParser.parse_usage_tokens(usage) in lib/braintrust/trace/token_parser.rb to normalize tokens:

• prompt_tokens (input)
• completion_tokens (output)
• tokens (total, includes cache_creation_tokens)
• prompt_cached_tokens (if cached)
• prompt_cache_creation_tokens (if cache created)
• completion_reasoning_tokens (if reasoning)

VCR Cassettes

VCR_MODE=all bundle exec rake test           # Re-record all
VCR_MODE=new_episodes bundle exec rake test  # Record new only
VCR_OFF=true bundle exec rake test           # Skip VCR

Reference Files

• Integrations: lib/braintrust/trace/contrib/{openai,anthropic}.rb
• Tests: test/braintrust/trace/{openai,anthropic}_test.rb
• Test helpers: test/test_helper.rb
• Examples: examples/{openai,anthropic}.rb
• Config: Rakefile, Appraisals, .github/workflows/ci.yml