diff --git a/.env_integration_tests.example b/.env_integration_tests.example index 9c7b1a3b..1fa777f1 100644 --- a/.env_integration_tests.example +++ b/.env_integration_tests.example @@ -29,3 +29,11 @@ CLOUD_SDK_CFG_HANA_AGENT_MEMORY_DEFAULT_UAA='{"url":"https://your-auth-url","cli APPFND_CONHOS_LANDSCAPE=your-landscape-here TENANT_SUBDOMAIN=your-tenant-subdomain-here AGW_USER_TOKEN=your-user-jwt-here + +# AI Core — required for Traceloop/LangGraph integration tests +AICORE_CLIENT_ID=your-aicore-client-id-here +AICORE_CLIENT_SECRET=your-aicore-client-secret-here +AICORE_AUTH_URL=https://your-aicore-auth-url-here/oauth/token +AICORE_BASE_URL=https://your-aicore-api-url-here/v2 +AICORE_RESOURCE_GROUP=default +AICORE_MODEL=anthropic--claude-3-5-haiku diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index f8900d93..1dd5aec7 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -44,20 +44,20 @@ jobs: echo "Setting up environment variables for integration tests..." # Process GitHub secrets (all configuration stored as secrets) - echo '${{ toJSON(secrets) }}' | jq -r 'to_entries[] | select(.key | startswith("CLOUD_SDK_CFG_")) | "\(.key)=\(.value)"' | while read line; do + echo '${{ toJSON(secrets) }}' | jq -r 'to_entries[] | select(.key | startswith("CLOUD_SDK_CFG_") or startswith("AICORE_")) | "\(.key)=\(.value)"' | while read line; do echo "$line" >> $GITHUB_ENV var_name=$(echo "$line" | cut -d= -f1) echo "Set secret: $var_name" done # Process GitHub variables (all configuration stored as variables) - echo '${{ toJSON(vars) }}' | jq -r 'to_entries[] | select(.key | startswith("CLOUD_SDK_CFG_")) | "\(.key)=\(.value)"' | while read line; do + echo '${{ toJSON(vars) }}' | jq -r 'to_entries[] | select(.key | startswith("CLOUD_SDK_CFG_") or startswith("AICORE_")) | "\(.key)=\(.value)"' | while read line; do echo "$line" >> $GITHUB_ENV var_name=$(echo "$line" | cut -d= -f1) echo "Set variable: $var_name" done - echo "Environment setup complete - automatically configured all CLOUD_SDK_CFG_* environment variables and secrets" + echo "Environment setup complete - automatically configured all CLOUD_SDK_CFG_* and AICORE_* environment variables and secrets" - name: Run integration tests run: uv run pytest tests/*/integration/ -v --tb=short diff --git a/pyproject.toml b/pyproject.toml index bde51d83..401a762e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "sap-cloud-sdk" -version = "0.23.1" +version = "0.23.2" description = "SAP Cloud SDK for Python" readme = "README.md" license = "Apache-2.0" @@ -19,8 +19,9 @@ dependencies = [ "opentelemetry-exporter-otlp-proto-http~=1.42.1", "opentelemetry-processor-baggage~=0.61b0", "traceloop-sdk~=0.61.0", + "opentelemetry-instrumentation-langchain>=0.61.0", "httpx>=0.27.0", - "PyJWT~=2.12.1", + "PyJWT>=2.13.0", "protobuf>=4.25.0", "protovalidate>=0.13.0", "grpcio>=1.60.0", @@ -56,11 +57,16 @@ dev = [ "httpx>=0.27.0", "a2a-sdk>=0.2.0", "langchain-core>=1.2.7", + "langgraph>=0.2.0", + "langchain-community>=0.3.0", + "litellm>=1.40.0", + "langchain-litellm>=0.6.6", ] [tool.pytest.ini_options] markers = [ "integration: marks tests as integration tests (requires Docker)", + "aicore: marks tests that require AI Core credentials (AICORE_* env vars)", ] [tool.coverage.run] diff --git a/src/sap_cloud_sdk/core/telemetry/auto_instrument.py b/src/sap_cloud_sdk/core/telemetry/auto_instrument.py index 59f78e59..556b3598 100644 --- a/src/sap_cloud_sdk/core/telemetry/auto_instrument.py +++ b/src/sap_cloud_sdk/core/telemetry/auto_instrument.py @@ -82,6 +82,7 @@ def auto_instrument( _merge_resource_attrs_into_active_provider_if_wrapper_installed(resource) _set_baggage_processor() + _set_propagated_attributes_processor() if middlewares: _register_middleware_processors(middlewares) @@ -119,6 +120,15 @@ def _set_baggage_processor(): provider.add_span_processor(BaggageSpanProcessor(ALLOW_ALL_BAGGAGE_KEYS)) logger.info("Registered BaggageSpanProcessor for extension attribute propagation") + +def _set_propagated_attributes_processor(): + provider = trace.get_tracer_provider() + if not isinstance(provider, TracerProvider): + logger.warning( + "Unknown TracerProvider type. Skipping PropagatedAttributesSpanProcessor" + ) + return + provider.add_span_processor(PropagatedAttributesSpanProcessor()) logger.info( "Registered PropagatedAttributesSpanProcessor for ContextVar attribute propagation" diff --git a/tests/core/integration/telemetry/__init__.py b/tests/core/integration/telemetry/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/core/integration/telemetry/_agent.py b/tests/core/integration/telemetry/_agent.py new file mode 100644 index 00000000..df211f74 --- /dev/null +++ b/tests/core/integration/telemetry/_agent.py @@ -0,0 +1,41 @@ +"""LangGraph test agent used by the telemetry integration tests.""" + +import os +from dataclasses import dataclass +from typing import Annotated + +import pytest + +from langchain_core.messages import BaseMessage +from langgraph.graph.message import add_messages + + +@dataclass +class State: + messages: Annotated[list[BaseMessage], add_messages] + + +def build_langgraph_agent(): + """Build a minimal single-node LangGraph agent backed by LiteLLM via AI Core. + + Requires AICORE_MODEL env var (e.g. "anthropic--claude-3-5-haiku") in addition + to the AICORE_* credentials set by set_aicore_config(). LiteLLM uses the + "sap/" prefix to route through the SAP AI Core provider. + """ + try: + from langchain_litellm import ChatLiteLLM + from langgraph.graph import END, StateGraph + except ImportError: + pytest.skip("langchain-litellm or langgraph not installed") + + model_name = os.environ.get("AICORE_MODEL") or "anthropic--claude-4.5-sonnet" + llm = ChatLiteLLM(model=f"sap/{model_name}") + + def call_llm(state: State) -> State: + return State(messages=[llm.invoke(state.messages)]) + + graph = StateGraph(State) + graph.add_node("llm", call_llm) + graph.set_entry_point("llm") + graph.add_edge("llm", END) + return graph.compile() diff --git a/tests/core/integration/telemetry/conftest.py b/tests/core/integration/telemetry/conftest.py new file mode 100644 index 00000000..14eaaaa9 --- /dev/null +++ b/tests/core/integration/telemetry/conftest.py @@ -0,0 +1,74 @@ +"""Fixtures for telemetry integration tests.""" + +import os +from pathlib import Path +from unittest.mock import patch + +import pytest +from dotenv import load_dotenv +from opentelemetry import trace +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + +from sap_cloud_sdk.aicore import set_aicore_config +from sap_cloud_sdk.core.telemetry.auto_instrument import auto_instrument +from sap_cloud_sdk.core.telemetry.genai_attribute_transformer import ( + GenAIAttributeTransformer, +) + +_env_file = Path(__file__).parent.parent.parent.parent.parent / ".env_integration_tests" +if _env_file.exists(): + load_dotenv(_env_file, override=True) + + +@pytest.fixture(scope="session") +def memory_exporter() -> InMemorySpanExporter: + """Initialize auto_instrument once per session and inject an in-memory exporter. + + Uses OTEL_TRACES_EXPORTER=console so Traceloop.init runs for real without + needing a real collector endpoint. A second SimpleSpanProcessor backed by + InMemorySpanExporter is added afterward for test assertions on raw spans. + """ + raw_exporter = InMemorySpanExporter() + with patch.dict(os.environ, {"OTEL_TRACES_EXPORTER": "console"}, clear=False): + auto_instrument(disable_batch=True) + provider = trace.get_tracer_provider() + assert isinstance(provider, TracerProvider) + provider.add_span_processor(SimpleSpanProcessor(raw_exporter)) + return raw_exporter + + +@pytest.fixture(scope="session") +def transforming_exporter(memory_exporter: InMemorySpanExporter) -> InMemorySpanExporter: + """Inject a second in-memory exporter wrapped in GenAIAttributeTransformer. + + Used by traceloop.feature tests to assert on transformer output + (gen_ai.* attributes present, llm.usage.* and traceloop.* absent). + """ + transformed_sink = InMemorySpanExporter() + provider = trace.get_tracer_provider() + assert isinstance(provider, TracerProvider) + provider.add_span_processor( + SimpleSpanProcessor(GenAIAttributeTransformer(transformed_sink)) + ) + return transformed_sink + + +@pytest.fixture(autouse=True) +def clear_spans(memory_exporter: InMemorySpanExporter, transforming_exporter: InMemorySpanExporter): + """Clear both exporters before each test so spans don't bleed between scenarios.""" + memory_exporter.clear() + transforming_exporter.clear() + + +@pytest.fixture(scope="session") +def aicore_configured(): + """Call set_aicore_config() once per session. + + Skips if AICORE_BASE_URL is absent — tests depending on this fixture are + skipped automatically. + """ + if not os.environ.get("AICORE_BASE_URL"): + pytest.skip("AICORE_BASE_URL not set — skipping AI Core integration tests") + set_aicore_config() diff --git a/tests/core/integration/telemetry/telemetry.feature b/tests/core/integration/telemetry/telemetry.feature new file mode 100644 index 00000000..f09bad0b --- /dev/null +++ b/tests/core/integration/telemetry/telemetry.feature @@ -0,0 +1,84 @@ +Feature: SDK telemetry instrumentation + + Background: + Given auto_instrument is initialized + + Scenario: invoke_agent_span emits a span with required GenAI attributes + When I invoke an agent with provider "test" and name "bot" and conversation_id "c1" + Then a span named "invoke_agent bot" is recorded + And the span has attribute "gen_ai.operation.name" equal to "invoke_agent" + And the span has attribute "gen_ai.provider.name" equal to "test" + And the span has attribute "gen_ai.agent.name" equal to "bot" + And the span has attribute "gen_ai.conversation.id" equal to "c1" + + Scenario: invoke_agent_span records errors + When I invoke an agent that raises an exception + Then the span status is ERROR + And the span has an exception event + + Scenario: spans carry SDK resource attributes + When I invoke an agent with provider "test" and name "sdk-resource-test" + Then a span named "invoke_agent sdk-resource-test" is recorded + And the span resource has attribute "sap.cloud_sdk.name" equal to "SAP Cloud SDK for Python" + And the span resource has attribute "sap.cloud_sdk.language" equal to "python" + And the span resource has attribute "sap.cloud_sdk.version" set + + # Real LLM call scenarios — require AI Core credentials + + @aicore + Scenario: invoke_agent_span wrapping a real LLM call produces a complete trace + Given AI Core is configured via set_aicore_config + When I invoke an agent wrapping a direct LLM call + Then a span named "invoke_agent llm-agent" is recorded + And a span with operation "chat" is a child of "invoke_agent llm-agent" + And that span has attribute "gen_ai.usage.input_tokens" set + And that span has attribute "gen_ai.usage.output_tokens" set + And the span "invoke_agent llm-agent" has resource attribute "sap.cloud_sdk.name" equal to "SAP Cloud SDK for Python" + And the span "invoke_agent llm-agent" has resource attribute "sap.cloud_sdk.language" equal to "python" + And the span "invoke_agent llm-agent" has resource attribute "sap.cloud_sdk.version" set + + @aicore + Scenario: invoke_agent_span wrapping LLM call then tool produces a full agentic trace + Given AI Core is configured via set_aicore_config + When I invoke an agent that calls an LLM then executes a tool + Then a span named "invoke_agent agent-with-tool" is recorded + And a span with operation "chat" is a child of "invoke_agent agent-with-tool" + And that span has attribute "gen_ai.usage.input_tokens" set + And the span "execute_tool search" is a child of "invoke_agent agent-with-tool" + And the span "execute_tool search" has attribute "gen_ai.tool.name" equal to "search" + + @aicore + Scenario: propagate=True flows invoke_agent attributes to nested LLM span + Given AI Core is configured via set_aicore_config + When I invoke an agent with propagate=True wrapping a real LLM call + Then a span with operation "chat" is a child of "invoke_agent propagate-llm-agent" + And that span has attribute "custom.session" equal to "s42" + And that span has attribute "gen_ai.usage.input_tokens" set + + @aicore + Scenario: propagate=False does not leak invoke_agent attributes to nested LLM span + Given AI Core is configured via set_aicore_config + When I invoke an agent with propagate=False wrapping a real LLM call + Then a span with operation "chat" is a child of "invoke_agent no-propagate-llm-agent" + And that span does not have attribute "custom.session" + + @aicore + Scenario: baggage attributes propagate to Traceloop-instrumented LLM spans + Given baggage key "sap.extension.capabilityId" is set to "cap-traceloop" + And AI Core is configured via set_aicore_config + When I invoke an agent wrapping a direct LLM call with baggage + Then a span with operation "chat" is a child of "invoke_agent baggage-llm-agent" + And that span has attribute "sap.extension.capabilityId" equal to "cap-traceloop" + + @aicore + Scenario: LangGraph agent run produces an invoke_agent span with LangChain child spans + Given AI Core is configured via set_aicore_config + When I run a LangGraph agent with provider "sap-aicore" and name "test-agent" + Then a span named "invoke_agent test-agent" is recorded + And at least one descendant span with attribute "gen_ai.operation.name" equal to "chat" is recorded + And at least one descendant span has attribute "gen_ai.request.model" set + And at least one descendant span has attribute "gen_ai.usage.input_tokens" set + And at least one descendant span has attribute "gen_ai.usage.output_tokens" set + And no descendant span has an attribute starting with "llm.usage." + And no descendant span has attribute "traceloop.association.properties.ls_model_name" + And no descendant span has attribute "traceloop.association.properties.ls_provider" diff --git a/tests/core/integration/telemetry/test_telemetry_bdd.py b/tests/core/integration/telemetry/test_telemetry_bdd.py new file mode 100644 index 00000000..aecbc017 --- /dev/null +++ b/tests/core/integration/telemetry/test_telemetry_bdd.py @@ -0,0 +1,429 @@ +"""Step definitions for telemetry.feature.""" + +import logging +import os +from collections.abc import Sequence + +from langchain_core.messages import HumanMessage +from opentelemetry import baggage, context +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter +from opentelemetry.trace import StatusCode +from pytest_bdd import given, parsers, scenario, then, when +import pytest + +from sap_cloud_sdk.core.telemetry.tracer import ( + execute_tool_span, + invoke_agent_span, +) +from ._agent import build_langgraph_agent + +log = logging.getLogger(__name__) + +# --- scenario bindings --- + + +@scenario("telemetry.feature", "invoke_agent_span emits a span with required GenAI attributes") +def test_invoke_agent_required_attributes(): + pass + + +@scenario("telemetry.feature", "invoke_agent_span records errors") +def test_invoke_agent_records_errors(): + pass + + +@scenario("telemetry.feature", "spans carry SDK resource attributes") +def test_sdk_resource_attributes(): + pass + + +@scenario("telemetry.feature", "invoke_agent_span wrapping a real LLM call produces a complete trace") +def test_invoke_agent_wrapping_llm(): + pass + + +@scenario("telemetry.feature", "invoke_agent_span wrapping LLM call then tool produces a full agentic trace") +def test_invoke_agent_llm_then_tool(): + pass + + +@scenario("telemetry.feature", "propagate=True flows invoke_agent attributes to nested LLM span") +def test_propagate_true_to_llm_span(): + pass + + +@scenario("telemetry.feature", "propagate=False does not leak invoke_agent attributes to nested LLM span") +def test_propagate_false_to_llm_span(): + pass + + +@scenario("telemetry.feature", "baggage attributes propagate to Traceloop-instrumented LLM spans") +def test_baggage_propagates_to_llm_span(): + pass + + +@scenario("telemetry.feature", "LangGraph agent run produces an invoke_agent span with LangChain child spans") +def test_langgraph_auto_instrumentation(): + pass + + +# --- shared state --- + + +@pytest.fixture +def span_store(): + return {} + + +# --- given steps --- + + +@given("auto_instrument is initialized") +def auto_instrument_initialized(memory_exporter, transforming_exporter): + pass + + +@given("AI Core is configured via set_aicore_config") +def aicore_configured_step(aicore_configured): + pass + + +@given(parsers.parse('baggage key "{key}" is set to "{value}"')) +def set_baggage(key, value): + token = context.attach(baggage.set_baggage(key, value)) + yield + context.detach(token) + + +# --- when steps --- + + +@when(parsers.parse('I invoke an agent with provider "{provider}" and name "{name}" and conversation_id "{cid}"')) +def invoke_agent_full(provider, name, cid, memory_exporter, span_store): + with invoke_agent_span(provider=provider, agent_name=name, conversation_id=cid): + pass + span_store["last"] = _require_span(memory_exporter, f"invoke_agent {name}") + + +@when("I invoke an agent that raises an exception") +def invoke_agent_with_error(memory_exporter, span_store): + with pytest.raises(RuntimeError): + with invoke_agent_span(provider="test", agent_name="error-agent"): + raise RuntimeError("deliberate test error") + span_store["last"] = _require_span(memory_exporter, "invoke_agent error-agent") + + +@when(parsers.parse('I invoke an agent with provider "{provider}" and name "{name}"')) +def invoke_agent_named(provider, name, memory_exporter, span_store): + with invoke_agent_span(provider=provider, agent_name=name): + pass + span_store["last"] = _require_span(memory_exporter, f"invoke_agent {name}") + + +def _llm_call(): + """Make a real LLM call via AI Core using LangChain's LiteLLM integration. + + Uses ChatLiteLLM so Traceloop's LangChain instrumentor fires and emits a span + with gen_ai.usage.* token counts. + """ + from langchain_litellm import ChatLiteLLM + from langchain_core.messages import HumanMessage as LCHumanMessage + model_name = os.environ.get("AICORE_MODEL", "anthropic--claude-4.5-sonnet") + llm = ChatLiteLLM(model=f"sap/{model_name}") + llm.invoke([LCHumanMessage(content="Say hi in one word.")]) + + +@when("I invoke an agent wrapping a direct LLM call") +def invoke_agent_wrapping_llm(memory_exporter, transforming_exporter, span_store): + with invoke_agent_span(provider="sap-aicore", agent_name="llm-agent"): + _llm_call() + spans = transforming_exporter.get_finished_spans() + _log_spans(spans) + span_store["all_local_spans"] = spans + + +@when("I invoke an agent that calls an LLM then executes a tool") +def invoke_agent_llm_then_tool(memory_exporter, transforming_exporter, span_store): + with invoke_agent_span(provider="sap-aicore", agent_name="agent-with-tool"): + _llm_call() + with execute_tool_span(tool_name="search", tool_type="function"): + pass + spans = transforming_exporter.get_finished_spans() + _log_spans(spans) + span_store["all_local_spans"] = spans + + +@when("I invoke an agent with propagate=True wrapping a real LLM call") +def invoke_agent_propagate_to_llm(memory_exporter, transforming_exporter, span_store): + with invoke_agent_span( + provider="sap-aicore", + agent_name="propagate-llm-agent", + attributes={"custom.session": "s42"}, + propagate=True, + ): + _llm_call() + spans = transforming_exporter.get_finished_spans() + _log_spans(spans) + span_store["all_local_spans"] = spans + + +@when("I invoke an agent with propagate=False wrapping a real LLM call") +def invoke_agent_no_propagate_to_llm(memory_exporter, transforming_exporter, span_store): + with invoke_agent_span( + provider="sap-aicore", + agent_name="no-propagate-llm-agent", + attributes={"custom.session": "s42"}, + propagate=False, + ): + _llm_call() + spans = transforming_exporter.get_finished_spans() + _log_spans(spans) + span_store["all_local_spans"] = spans + + +@when("I invoke an agent wrapping a direct LLM call with baggage") +def invoke_agent_wrapping_llm_with_baggage(memory_exporter, transforming_exporter, span_store): + with invoke_agent_span(provider="sap-aicore", agent_name="baggage-llm-agent"): + _llm_call() + spans = transforming_exporter.get_finished_spans() + _log_spans(spans) + span_store["all_local_spans"] = spans + + +@when(parsers.parse('I run a LangGraph agent with provider "{provider}" and name "{name}"')) +def run_langgraph_agent(provider, name, transforming_exporter, span_store): + agent = build_langgraph_agent() + with invoke_agent_span(provider=provider, agent_name=name) as root_span: + ctx = root_span.get_span_context() + assert ctx is not None + span_store["root_span_id"] = ctx.span_id + agent.invoke({"messages": [HumanMessage(content="Say hello in one word.")]}) + all_spans = transforming_exporter.get_finished_spans() + _log_spans(all_spans) + span_store["all_spans"] = all_spans + span_store["last"] = _require_span_in(all_spans, f"invoke_agent {name}") + + +# --- then steps --- + + +@then(parsers.parse('a span named "{name}" is recorded')) +def span_named_is_recorded(name, memory_exporter, span_store): + spans = span_store.get("all_spans") or memory_exporter.get_finished_spans() + span = _find_span_in(spans, name) + assert span is not None, f"No span named '{name}' found. Available: {[s.name for s in spans]}" + span_store["last"] = span + + +@then(parsers.parse('the span has attribute "{key}" equal to "{value}"')) +def span_has_attribute(key, value, span_store): + span = span_store["last"] + actual = span.attributes.get(key) + assert actual == value, f"Expected span attribute '{key}' == '{value}', got {actual!r}" + + +@then("the span status is ERROR") +def span_status_is_error(span_store): + span = span_store["last"] + assert span.status.status_code == StatusCode.ERROR, ( + f"Expected ERROR status, got {span.status.status_code}" + ) + + +@then("the span has an exception event") +def span_has_exception_event(span_store): + span = span_store["last"] + event_names = [e.name for e in span.events] + assert "exception" in event_names, f"No exception event found. Events: {event_names}" + + +@then(parsers.parse('the span resource has attribute "{key}" equal to "{value}"')) +def span_resource_attr_equals(key, value, span_store): + span = span_store["last"] + actual = span.resource.attributes.get(key) + assert actual == value, f"Expected resource attribute '{key}' == '{value}', got {actual!r}" + + +@then(parsers.parse('the span resource has attribute "{key}" set')) +def span_resource_attr_set(key, span_store): + span = span_store["last"] + actual = span.resource.attributes.get(key) + assert actual, f"Expected resource attribute '{key}' to be set, got {actual!r}" + + +@then(parsers.parse('at least one descendant span with attribute "{key}" equal to "{value}" is recorded')) +def descendant_with_attribute_equals(key, value, span_store): + descendants = _get_descendants(span_store["all_spans"], span_store["root_span_id"]) + match = next((s for s in descendants if s.attributes and s.attributes.get(key) == value), None) + assert match is not None, ( + f"No descendant span with {key}={value!r}. " + f"Descendants: {[(s.name, dict(s.attributes or {})) for s in descendants]}" + ) + + +@then(parsers.parse('at least one descendant span has attribute "{key}" set')) +def descendant_has_attribute_set(key, span_store): + descendants = _get_descendants(span_store["all_spans"], span_store["root_span_id"]) + match = next((s for s in descendants if s.attributes and s.attributes.get(key)), None) + assert match is not None, ( + f"No descendant span with attribute '{key}' set. " + f"Descendants: {[(s.name, dict(s.attributes or {})) for s in descendants]}" + ) + + +@then(parsers.parse('no descendant span has an attribute starting with "{prefix}"')) +def no_descendant_has_attribute_prefix(prefix, span_store): + descendants = _get_descendants(span_store["all_spans"], span_store["root_span_id"]) + violations = [(s.name, k) for s in descendants for k in (s.attributes or {}) if k.startswith(prefix)] + assert not violations, f"Found raw attribute(s) with prefix '{prefix}' that should have been transformed: {violations}" + + +@then(parsers.parse('no descendant span has attribute "{key}"')) +def no_descendant_has_exact_attribute(key, span_store): + descendants = _get_descendants(span_store["all_spans"], span_store["root_span_id"]) + violations = [s.name for s in descendants if key in (s.attributes or {})] + assert not violations, f"Attribute '{key}' should have been transformed away but found on: {violations}" + + +@then(parsers.parse('the span "{name}" is a child of "{parent_name}"')) +def span_is_child_of(name, parent_name, memory_exporter, span_store): + spans = span_store.get("all_local_spans") or memory_exporter.get_finished_spans() + child = _find_span_in(spans, name) + parent = _find_span_in(spans, parent_name) + assert child is not None, f"Span '{name}' not found. Available: {[s.name for s in spans]}" + assert parent is not None, f"Span '{parent_name}' not found. Available: {[s.name for s in spans]}" + parent_id = _span_id(parent) + assert child.parent is not None and child.parent.span_id == parent_id, ( + f"Span '{name}' is not a child of '{parent_name}'. " + f"child.parent={child.parent}, expected span_id={parent_id}" + ) + + +@then(parsers.parse('the span "{name}" has attribute "{key}" equal to "{value}"')) +def named_span_has_attribute(name, key, value, memory_exporter, span_store): + spans = span_store.get("all_local_spans") or memory_exporter.get_finished_spans() + span = _find_span_in(spans, name) + assert span is not None, f"Span '{name}' not found. Available: {[s.name for s in spans]}" + actual = span.attributes.get(key) if span.attributes else None + assert str(actual) == value, f"Expected span '{name}' attribute '{key}' == '{value}', got {actual!r}" + + +@then(parsers.parse('a span with operation "{operation}" is a child of "{parent_name}"')) +def span_with_operation_is_child_of(operation, parent_name, memory_exporter, span_store): + spans = span_store.get("all_local_spans") or memory_exporter.get_finished_spans() + parent = _find_span_in(spans, parent_name) + assert parent is not None, f"Parent span '{parent_name}' not found. Available: {[s.name for s in spans]}" + parent_id = _span_id(parent) + child = next( + (s for s in spans + if (s.attributes or {}).get("gen_ai.operation.name") == operation + and s.parent is not None and s.parent.span_id == parent_id), + None, + ) + assert child is not None, ( + f"No span with gen_ai.operation.name='{operation}' found as child of '{parent_name}'. " + f"Spans: {[(s.name, (s.attributes or {}).get('gen_ai.operation.name')) for s in spans]}" + ) + span_store["op_span"] = child + + +@then(parsers.parse('that span has attribute "{key}" equal to "{value}"')) +def op_span_has_attribute_equals(key, value, span_store): + span = span_store["op_span"] + actual = (span.attributes or {}).get(key) + assert str(actual) == value, f"Expected attribute '{key}' == '{value}', got {actual!r}" + + +@then(parsers.parse('that span has attribute "{key}" set')) +def op_span_has_attribute_set(key, span_store): + span = span_store["op_span"] + actual = (span.attributes or {}).get(key) + assert actual is not None, f"Expected attribute '{key}' to be set on span '{span.name}', got None" + + +@then(parsers.parse('that span does not have attribute "{key}"')) +def op_span_lacks_attribute(key, span_store): + span = span_store["op_span"] + actual = (span.attributes or {}).get(key) + assert actual is None, f"Expected attribute '{key}' to be absent from '{span.name}', got {actual!r}" + + +@then(parsers.parse('the span "{name}" has resource attribute "{key}" equal to "{value}"')) +def named_span_resource_attr_equals(name, key, value, memory_exporter, span_store): + spans = span_store.get("all_local_spans") or memory_exporter.get_finished_spans() + span = _find_span_in(spans, name) + assert span is not None, f"Span '{name}' not found. Available: {[s.name for s in spans]}" + actual = span.resource.attributes.get(key) + assert actual == value, f"Expected span '{name}' resource attribute '{key}' == '{value}', got {actual!r}" + + +@then(parsers.parse('the span "{name}" has resource attribute "{key}" set')) +def named_span_resource_attr_set(name, key, memory_exporter, span_store): + spans = span_store.get("all_local_spans") or memory_exporter.get_finished_spans() + span = _find_span_in(spans, name) + assert span is not None, f"Span '{name}' not found. Available: {[s.name for s in spans]}" + actual = span.resource.attributes.get(key) + assert actual, f"Expected span '{name}' resource attribute '{key}' to be set, got {actual!r}" + + +# --- helpers --- + + +def _find_span(exporter: InMemorySpanExporter, name: str) -> ReadableSpan | None: + return _find_span_in(exporter.get_finished_spans(), name) + + +def _require_span(exporter: InMemorySpanExporter, name: str) -> ReadableSpan: + spans = exporter.get_finished_spans() + return _require_span_in(spans, name) + + +def _find_span_in(spans: Sequence[ReadableSpan], name: str) -> ReadableSpan | None: + return next((s for s in spans if s.name == name), None) + + +def _require_span_in(spans: Sequence[ReadableSpan], name: str) -> ReadableSpan: + span = _find_span_in(spans, name) + assert span is not None, ( + f"No span named '{name}' found.\n" + f"Recorded spans: {[s.name for s in spans]}" + ) + return span + + +def _log_spans(spans: Sequence[ReadableSpan]) -> None: + log.info("Collected %d span(s):", len(spans)) + for s in spans: + log.info(" [%s] attrs=%s", s.name, dict(s.attributes or {})) + + +def _get_descendants(all_spans: Sequence[ReadableSpan], root_span_id: int) -> list[ReadableSpan]: + return [ + s for s in all_spans + if _span_id(s) != root_span_id + and _is_descendant(s, root_span_id, all_spans) + ] + + +def _span_id(span: ReadableSpan) -> int: + ctx = span.get_span_context() + assert ctx is not None + return ctx.span_id + + +def _is_descendant(span: ReadableSpan, ancestor_id: int, all_spans: Sequence[ReadableSpan]) -> bool: + current: ReadableSpan | None = span + visited: set[int] = set() + while current is not None and current.parent is not None: + parent_id = current.parent.span_id + if parent_id in visited: + break + visited.add(parent_id) + if parent_id == ancestor_id: + return True + current = next( + (s for s in all_spans if _span_id(s) == parent_id), + None, + ) + return False diff --git a/uv.lock b/uv.lock index 1bb9da66..55b94ccd 100644 --- a/uv.lock 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