fix: use standard gen_ai.conversation.id instead of microsoft.sessiomid in LangChain tracer

src/microsoft/opentelemetry/_genai/_langchain/_span_enricher.py

@@ -0,0 +1,59 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Span enricher for LangChain.
+
+Maps standard OTel GenAI attributes to A365-specific keys before export.
+Registered by LangChainInstrumentor when the A365 pipeline is available.
+"""
+
+from microsoft.opentelemetry.a365.core.constants import (
+    EXECUTE_TOOL_OPERATION_NAME,
+    GEN_AI_CONVERSATION_ID_KEY,
+    GEN_AI_INPUT_MESSAGES_KEY,
+    GEN_AI_OUTPUT_MESSAGES_KEY,
+    GEN_AI_TOOL_ARGS_KEY,
+    GEN_AI_TOOL_CALL_RESULT_KEY,
+    INVOKE_AGENT_OPERATION_NAME,
+    SESSION_ID_KEY,
+)
+from microsoft.opentelemetry.a365.core.exporters.enriched_span import EnrichedReadableSpan
+from opentelemetry.sdk.trace import ReadableSpan
+
+
+def enrich_langchain_span(span: ReadableSpan) -> ReadableSpan:
+    """Enricher function for LangChain spans.
+
+    Transforms standard OTel GenAI attributes to A365-specific keys:
+    - ``gen_ai.conversation.id`` → ``microsoft.session.id``
+    - For invoke_agent spans: extracts content from input/output messages
+    - For execute_tool spans: maps tool arguments and results
+    """
+    extra_attributes = {}
+    attributes = span.attributes or {}
+
+    # Map gen_ai.conversation.id → microsoft.session.id for A365 consumers
+    conversation_id = attributes.get(GEN_AI_CONVERSATION_ID_KEY)
+    if conversation_id and SESSION_ID_KEY not in attributes:
+        extra_attributes[SESSION_ID_KEY] = str(conversation_id)
+
+    if span.name.startswith(INVOKE_AGENT_OPERATION_NAME):
+        input_messages = attributes.get(GEN_AI_INPUT_MESSAGES_KEY)
+        if input_messages:
+            extra_attributes[GEN_AI_INPUT_MESSAGES_KEY] = str(input_messages)
+
+        output_messages = attributes.get(GEN_AI_OUTPUT_MESSAGES_KEY)
+        if output_messages:
+            extra_attributes[GEN_AI_OUTPUT_MESSAGES_KEY] = str(output_messages)
+
+    elif span.name.startswith(EXECUTE_TOOL_OPERATION_NAME):
+        if GEN_AI_TOOL_ARGS_KEY in attributes:
+            extra_attributes[GEN_AI_TOOL_ARGS_KEY] = str(attributes[GEN_AI_TOOL_ARGS_KEY])
+
+        if GEN_AI_TOOL_CALL_RESULT_KEY in attributes:
+            extra_attributes[GEN_AI_TOOL_CALL_RESULT_KEY] = str(attributes[GEN_AI_TOOL_CALL_RESULT_KEY])
+
+    if extra_attributes:
+        return EnrichedReadableSpan(span, extra_attributes)
+
+    return span

src/microsoft/opentelemetry/_genai/_langchain/_tracer_instrumentor.py

@@ -21,6 +21,12 @@
 from opentelemetry.instrumentation.instrumentor import BaseInstrumentor  # type: ignore[attr-defined]
 from opentelemetry.trace import Span
 
+from microsoft.opentelemetry.a365.core.exporters.enriching_span_processor import (
+    register_span_enricher,
+    unregister_span_enricher,
+)
+from microsoft.opentelemetry._genai._langchain._span_enricher import enrich_langchain_span
+
 logger = logging.getLogger(__name__)
 
 _INSTRUMENTS: str = "langchain-core >= 0.2.0"
@@ -51,6 +57,7 @@ def __init__(self) -> None:
         super().__init__()
         self._tracer: LangChainTracer | None = None
         self._original_cb_init: Callable[..., None] | None = None
+        self._owns_enricher: bool = False
 
     # ---- BaseInstrumentor API -------------------------------------------------
 
@@ -59,9 +66,7 @@ def instrumentation_dependencies(self) -> Collection[str]:
 
     def _instrument(self, **kwargs: Any) -> None:
         if not langchain_available:
-            logger.debug(
-                "Skipping LangChain instrumentation: langchain-core is not available."
-            )
+            logger.debug("Skipping LangChain instrumentation: langchain-core is not available.")
             return
         tracer_provider = kwargs.get("tracer_provider")
         tracer = trace_api.get_tracer(
@@ -98,9 +103,19 @@ def _instrument(self, **kwargs: Any) -> None:
             wrapper=_BaseCallbackManagerInit(self._tracer),
         )
 
+        # Register the A365 span enricher for LangChain.
+        try:
+            register_span_enricher(enrich_langchain_span)
+            self._owns_enricher = True
+        except RuntimeError:
+            logger.debug("A span enricher is already registered. Skipping LangChain enricher registration.")
+
     def _uninstrument(self, **kwargs: Any) -> None:
         if not langchain_available:
             return
+        if self._owns_enricher:
+            unregister_span_enricher()
+            self._owns_enricher = False
         if self._original_cb_init is not None:
             langchain_core.callbacks.BaseCallbackManager.__init__ = self._original_cb_init  # type: ignore[assignment]
         self._original_cb_init = None

src/microsoft/opentelemetry/_genai/_langchain/_utils.py

@@ -218,8 +218,6 @@ def __delitem__(self, key: K) -> None:
 GEN_AI_TOOL_DEFINITIONS_KEY = GEN_AI_TOOL_DEFINITIONS
 GEN_AI_AGENT_VERSION_KEY = GEN_AI_AGENT_VERSION
 
-SESSION_ID_KEY = "microsoft.session.id"
-
 
 # ---- Internal helpers --------------------------------------------------------
 
@@ -291,7 +289,7 @@ def metadata(run: Run) -> Iterator[tuple[str, str]]:
     if session_id := (
         meta.get(LANGCHAIN_SESSION_ID) or meta.get(LANGCHAIN_CONVERSATION_ID) or meta.get(LANGCHAIN_THREAD_ID)
     ):
-        yield SESSION_ID_KEY, session_id
+        yield GEN_AI_CONVERSATION_ID_KEY, session_id
 
 
 @stop_on_exception
@@ -921,15 +919,13 @@ def extract_agent_metadata(run: Run) -> Iterator[tuple[str, str]]:
 
 @stop_on_exception
 def extract_session_info(run: Run) -> Iterator[tuple[str, str]]:
-    """Extract session_id and conversation_id from run metadata."""
+    """Extract conversation ID from run metadata as gen_ai.conversation.id."""
     if not run.extra or not isinstance(run.extra, dict):
         return
     meta = run.extra.get("metadata")
     if not isinstance(meta, dict):
         return
     for key in (LANGCHAIN_SESSION_ID, LANGCHAIN_CONVERSATION_ID, LANGCHAIN_THREAD_ID):
         if sid := meta.get(key):
-            yield SESSION_ID_KEY, sid
+            yield GEN_AI_CONVERSATION_ID_KEY, sid
             break
-    if conv_id := meta.get(LANGCHAIN_CONVERSATION_ID):
-        yield GEN_AI_CONVERSATION_ID_KEY, conv_id

tests/langchain/test_span_enricher.py

@@ -0,0 +1,116 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Tests for LangChain span enricher."""
+
+import unittest
+from unittest.mock import Mock
+
+from microsoft.opentelemetry.a365.core.constants import (
+    GEN_AI_CONVERSATION_ID_KEY,
+    GEN_AI_INPUT_MESSAGES_KEY,
+    GEN_AI_OUTPUT_MESSAGES_KEY,
+    GEN_AI_TOOL_ARGS_KEY,
+    GEN_AI_TOOL_CALL_RESULT_KEY,
+    SESSION_ID_KEY,
+)
+from microsoft.opentelemetry._genai._langchain._span_enricher import enrich_langchain_span
+
+
+class TestLangChainSpanEnricher(unittest.TestCase):
+    """Test suite for enrich_langchain_span function."""
+
+    def test_maps_conversation_id_to_session_id(self):
+        """gen_ai.conversation.id is mapped to microsoft.session.id."""
+        span = Mock()
+        span.name = "chat gpt-4o"
+        span.attributes = {
+            GEN_AI_CONVERSATION_ID_KEY: "conv-123",
+        }
+
+        result = enrich_langchain_span(span)
+
+        assert result.attributes is not None
+        self.assertEqual(result.attributes[SESSION_ID_KEY], "conv-123")
+
+    def test_does_not_overwrite_existing_session_id(self):
+        """If microsoft.session.id is already present, don't overwrite."""
+        span = Mock()
+        span.name = "chat gpt-4o"
+        span.attributes = {
+            GEN_AI_CONVERSATION_ID_KEY: "conv-123",
+            SESSION_ID_KEY: "existing-session",
+        }
+
+        result = enrich_langchain_span(span)
+        # Should return original span unchanged
+        self.assertIs(result, span)
+
+    def test_invoke_agent_span_enrichment(self):
+        """invoke_agent spans pass through input/output messages."""
+        span = Mock()
+        span.name = "invoke_agent Travel_Assistant"
+        span.attributes = {
+            GEN_AI_INPUT_MESSAGES_KEY: '["Where should I go?"]',
+            GEN_AI_OUTPUT_MESSAGES_KEY: '["Try Barcelona!"]',
+        }
+
+        result = enrich_langchain_span(span)
+
+        assert result.attributes is not None
+        self.assertEqual(result.attributes[GEN_AI_INPUT_MESSAGES_KEY], '["Where should I go?"]')
+        self.assertEqual(result.attributes[GEN_AI_OUTPUT_MESSAGES_KEY], '["Try Barcelona!"]')
+
+    def test_invoke_agent_with_conversation_id(self):
+        """invoke_agent spans get both session_id mapping and message enrichment."""
+        span = Mock()
+        span.name = "invoke_agent MyAgent"
+        span.attributes = {
+            GEN_AI_CONVERSATION_ID_KEY: "conv-456",
+            GEN_AI_INPUT_MESSAGES_KEY: '["Hello"]',
+        }
+
+        result = enrich_langchain_span(span)
+
+        assert result.attributes is not None
+        self.assertEqual(result.attributes[SESSION_ID_KEY], "conv-456")
+        self.assertEqual(result.attributes[GEN_AI_INPUT_MESSAGES_KEY], '["Hello"]')
+
+    def test_execute_tool_span_enrichment(self):
+        """execute_tool spans map tool arguments and results."""
+        span = Mock()
+        span.name = "execute_tool get_weather"
+        span.attributes = {
+            GEN_AI_TOOL_ARGS_KEY: '{"city": "Barcelona"}',
+            GEN_AI_TOOL_CALL_RESULT_KEY: "Sunny, 25C",
+        }
+
+        result = enrich_langchain_span(span)
+
+        assert result.attributes is not None
+        self.assertEqual(result.attributes[GEN_AI_TOOL_ARGS_KEY], '{"city": "Barcelona"}')
+        self.assertEqual(result.attributes[GEN_AI_TOOL_CALL_RESULT_KEY], "Sunny, 25C")
+
+    def test_non_matching_span_returns_original(self):
+        """Non-matching spans without conversation_id return unchanged."""
+        span = Mock()
+        span.name = "other_operation"
+        span.attributes = {"key": "value"}
+
+        self.assertIs(enrich_langchain_span(span), span)
+
+    def test_none_attributes_returns_original(self):
+        """Spans with None attributes return unchanged."""
+        span = Mock()
+        span.name = "invoke_agent Test"
+        span.attributes = None
+
+        self.assertIs(enrich_langchain_span(span), span)
+
+    def test_empty_attributes_returns_original(self):
+        """Spans with empty attributes return unchanged."""
+        span = Mock()
+        span.name = "invoke_agent Test"
+        span.attributes = {}
+
+        self.assertIs(enrich_langchain_span(span), span)

tests/langchain/test_utils.py

@@ -35,7 +35,6 @@
     GEN_AI_TOOL_TYPE_KEY,
     GEN_AI_USAGE_INPUT_TOKENS_KEY,
     GEN_AI_USAGE_OUTPUT_TOKENS_KEY,
-    SESSION_ID_KEY,
     add_operation_type,
     as_utc_nano,
     build_llm_invocation,
@@ -236,17 +235,17 @@ class TestMetadata(TestCase):
     def test_extracts_session_id(self):
         run = _make_run(extra={"metadata": {"session_id": "sess-123"}})
         result = list(metadata(run))
-        self.assertEqual(result, [(SESSION_ID_KEY, "sess-123")])
+        self.assertEqual(result, [(GEN_AI_CONVERSATION_ID_KEY, "sess-123")])
 
     def test_extracts_conversation_id(self):
         run = _make_run(extra={"metadata": {"conversation_id": "conv-456"}})
         result = list(metadata(run))
-        self.assertEqual(result, [(SESSION_ID_KEY, "conv-456")])
+        self.assertEqual(result, [(GEN_AI_CONVERSATION_ID_KEY, "conv-456")])
 
     def test_extracts_thread_id(self):
         run = _make_run(extra={"metadata": {"thread_id": "thread-789"}})
         result = list(metadata(run))
-        self.assertEqual(result, [(SESSION_ID_KEY, "thread-789")])
+        self.assertEqual(result, [(GEN_AI_CONVERSATION_ID_KEY, "thread-789")])
 
     def test_returns_empty_on_no_extra(self):
         run = _make_run(extra=None)
@@ -565,7 +564,7 @@ class TestExtractSessionInfo(TestCase):
     def test_extracts_session_id(self):
         run = _make_run(extra={"metadata": {"session_id": "s-1"}})
         result = dict(extract_session_info(run))
-        self.assertEqual(result[SESSION_ID_KEY], "s-1")
+        self.assertEqual(result[GEN_AI_CONVERSATION_ID_KEY], "s-1")
 
     def test_extracts_conversation_id(self):
         run = _make_run(extra={"metadata": {"conversation_id": "c-1"}})