fix: pin opentelemetry-instrumentation-langchain and add telemetry integration tests

Author: LucasAlvesSoares

.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

.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

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]

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"

tests/core/integration/telemetry/__init__.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/<model>" 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()

tests/core/integration/telemetry/_agent.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()

tests/core/integration/telemetry/conftest.py

@@ -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"