Enable sensitive data for langchain programmatically

samples/langchain/README.md

@@ -26,6 +26,25 @@ Demonstrates the internal langchain instrumentation.
 | `OTEL_SEMCONV_STABILITY_OPT_IN`                      | "gen_ai_latest_experimental" |
 | `AZURE_EXPERIMENTAL_ENABLE_GENAI_TRACING`            | "true"                       |
 
+> **Alternative** Instead of setting the `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` and `OTEL_SEMCONV_STABILITY_OPT_IN` environment variables, pass the config `enable_sensitive_data=True` to `use_microsoft_opentelemetry()`:
+
+```python
+use_microsoft_opentelemetry(
+    enable_sensitive_data=True,
+    ...
+)
+```
+
+When `enable_sensitive_data=True` is supplied:
+
+- Sensitive and experimental data attributes populate on the spans.
+- The content capture mode defaults to `SPAN_AND_EVENT`.
+- This setting takes **precedence over** the pre-existing values of the corresponding environment variables.
+
+> **Note:** `enable_sensitive_data` defaults to `False`. Only enable it in trusted, non-production environments where capturing message content is intentional. This configuration currently applies only to LangChain instrumentation and Microsoft Agent Framework.
+
+---
+
 **Placeholders to fill: If use azure endpoint and api key**
 
 | Placeholder | Value |

src/microsoft/opentelemetry/_constants.py

@@ -66,6 +66,8 @@
 # --- Microsoft OpenTelemetry Constants ---
 
 ENABLE_AZURE_MONITOR_ARG = "enable_azure_monitor"
+ENABLE_SENSITIVE_DATA_ARG = "enable_sensitive_data"
+
 
 # --- OTLP Environment Variable Constants ---
 
@@ -88,8 +90,6 @@
 
 # --- Spectra Sidecar Constants ---
 
-ENABLE_SENSITIVE_DATA_ARG = "enable_sensitive_data"
-
 ENABLE_SPECTRA_ARG = "enable_spectra"
 SPECTRA_ENDPOINT_ARG = "spectra_endpoint"
 SPECTRA_PROTOCOL_ARG = "spectra_protocol"

src/microsoft/opentelemetry/_distro.py

@@ -784,7 +784,7 @@ def _setup_instrumentations(otel_kwargs: Dict[str, Any], **kwargs: Any) -> None:
                 continue
             lib_kwargs = _get_instrumentation_kwargs(otel_kwargs, lib_name)
             merged_kwargs = {**kwargs, **lib_kwargs}
-            if lib_name == "agent_framework":
+            if lib_name in ["agent_framework", "langchain"]:
                 merged_kwargs[ENABLE_SENSITIVE_DATA_ARG] = enable_sensitive_data
             instrumentor: Any = entry_point.load()
             instrumentor().instrument(skip_dep_check=True, **merged_kwargs)

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

@@ -117,6 +117,7 @@ class LangChainTracer(BaseTracer):  # pylint: disable=too-many-ancestors, too-ma
         "_spans_by_run",
         "_event_logger",
         "_context_tokens",
+        "_enable_sensitive_data",
     )
 
     def __init__(
@@ -126,6 +127,7 @@ def __init__(
         *args: Any,
         agent_config: dict[str, Any] | None = None,
         event_logger: Any | None = None,
+        enable_sensitive_data: bool = False,
         **kwargs: Any,
     ) -> None:
         super().__init__(*args, **kwargs)
@@ -141,6 +143,7 @@ def __init__(
         self._event_logger = event_logger
         self._context_tokens: dict[UUID, list[Token]] = {}
         self._lock = RLock()  # type: ignore[misc]
+        self._enable_sensitive_data = enable_sensitive_data
 
     def get_span(self, run_id: UUID) -> Span | None:
         with self._lock:
@@ -298,9 +301,10 @@ def _end_trace(self, run: Run) -> None:
             invocation: LLMInvocation | None = None
             try:
                 if is_agent:
+                    # Single-span agent: finalize directly
                     self._finalize_agent_span(span, run)
                 else:
-                    invocation = _update_span(span, run)
+                    invocation = _update_span(span, run, self._enable_sensitive_data)
             except Exception:
                 logger.exception("Failed to update span with run data.")
             # Emit OTel GenAI event for LLM spans (respects env-var config)
@@ -622,7 +626,7 @@ def _finalize_agent_span(self, span: Span, run: Run) -> None:
             span.set_attribute(GEN_AI_USAGE_OUTPUT_TOKENS_KEY, output_tokens)
 
         # Set aggregated input/output messages only when content capture is enabled
-        if _should_capture_content_on_spans():
+        if _should_capture_content_on_spans(self._enable_sensitive_data):
             if tool_defs := content.get("tool_definitions"):
                 span.set_attribute(GEN_AI_TOOL_DEFINITIONS_KEY, tool_defs)
             if msgs := content.get("input_messages"):
@@ -661,7 +665,7 @@ def get_attributes_from_context() -> Iterator[tuple[str, AttributeValue]]:
             yield ctx_attr, cast(AttributeValue, val)
 
 
-def _update_span(span: Span, run: Run) -> LLMInvocation | None:
+def _update_span(span: Span, run: Run, enable_sensitive_data: bool = False) -> LLMInvocation | None:
     """Update a non-agent span with run data.
 
     Returns the ``LLMInvocation`` for LLM runs (used for event emission
@@ -691,7 +695,7 @@ def _update_span(span: Span, run: Run) -> LLMInvocation | None:
                     chain(
                         prompts(run.inputs),
                         invocation_parameters(run),
-                        function_calls(run.outputs),
+                        function_calls(run.outputs, enable_sensitive_data),
                         metadata(run),
                     )
                 )
@@ -705,9 +709,9 @@ def _update_span(span: Span, run: Run) -> LLMInvocation | None:
             flatten(
                 chain(
                     add_operation_type(run),
-                    chain_node_messages(run.inputs, GEN_AI_INPUT_MESSAGES_KEY),
-                    chain_node_messages(run.outputs, GEN_AI_OUTPUT_MESSAGES_KEY),
-                    tools(run),
+                    chain_node_messages(run.inputs, GEN_AI_INPUT_MESSAGES_KEY, enable_sensitive_data),
+                    chain_node_messages(run.outputs, GEN_AI_OUTPUT_MESSAGES_KEY, enable_sensitive_data),
+                    tools(run, enable_sensitive_data),
                     metadata(run),
                 )
             )

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

@@ -68,6 +68,8 @@ def _instrument(self, **kwargs: Any) -> None:
             tracer_provider=tracer_provider,
         )
 
+        enable_sensitive_data = kwargs.get("enable_sensitive_data", False)
+
         logger_provider = kwargs.get("logger_provider")
         event_logger = get_otel_logger(
             __name__,
@@ -88,6 +90,7 @@ def _instrument(self, **kwargs: Any) -> None:
             bool(kwargs.get("separate_trace_from_runtime_context")),
             agent_config=agent_config,
             event_logger=event_logger,
+            enable_sensitive_data=enable_sensitive_data,
         )
 
         self._original_cb_init = langchain_core.callbacks.BaseCallbackManager.__init__

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

@@ -103,8 +103,10 @@
 # ---- Core utilities ----------------------------------------------------------
 
 
-def _should_capture_content_on_spans() -> bool:
+def _should_capture_content_on_spans(enable_sensitive_data: bool = False) -> bool:
     """Check if content should be captured on span attributes."""
+    if enable_sensitive_data:
+        return True
     if not is_experimental_mode():
         return False
     mode = get_content_capturing_mode()
@@ -830,7 +832,7 @@ def _parse_token_usage(outputs: Mapping[str, Any] | None) -> Any:
 
 
 @stop_on_exception
-def function_calls(outputs: Mapping[str, Any] | None) -> Iterator[tuple[str, str]]:
+def function_calls(outputs: Mapping[str, Any] | None, enable_sensitive_data: bool = False) -> Iterator[tuple[str, str]]:
     if not outputs:
         return
     if not isinstance(outputs, Mapping):
@@ -851,7 +853,7 @@ def function_calls(outputs: Mapping[str, Any] | None) -> Iterator[tuple[str, str
     call_id = fc.get("id")
     if isinstance(call_id, str):
         yield GEN_AI_TOOL_CALL_ID_KEY, call_id
-    if _should_capture_content_on_spans():
+    if _should_capture_content_on_spans(enable_sensitive_data):
         args = fc.get("arguments")
         if args is not None:
             if isinstance(args, str):
@@ -868,7 +870,7 @@ def function_calls(outputs: Mapping[str, Any] | None) -> Iterator[tuple[str, str
 
 
 @stop_on_exception
-def tools(run: Run) -> Iterator[tuple[str, str]]:
+def tools(run: Run, enable_sensitive_data: bool = False) -> Iterator[tuple[str, str]]:
     if run.run_type.lower() != "tool":
         return
     if not (serialized := run.serialized):
@@ -883,7 +885,7 @@ def tools(run: Run) -> Iterator[tuple[str, str]]:
     if run.extra and hasattr(run.extra, "get"):
         if tool_call_id := run.extra.get("tool_call_id"):
             yield GEN_AI_TOOL_CALL_ID_KEY, tool_call_id
-    if _should_capture_content_on_spans():
+    if _should_capture_content_on_spans(enable_sensitive_data):
         if run.inputs and hasattr(run.inputs, "get"):
             _sentinel = object()
             input_val = run.inputs.get("input", _sentinel)
@@ -910,12 +912,13 @@ def tools(run: Run) -> Iterator[tuple[str, str]]:
 def chain_node_messages(
     data: Mapping[str, Any] | None,
     attr_key: str,
+    enable_sensitive_data: bool = False,
 ) -> Iterator[tuple[str, str]]:
     """Extract messages from a LangGraph chain node's inputs or outputs.
 
     Chain nodes typically store messages as ``{"messages": [BaseMessage, ...]}``.
     """
-    if not _should_capture_content_on_spans():
+    if not _should_capture_content_on_spans(enable_sensitive_data):
         return
     if not data or not isinstance(data, Mapping):
         return
@@ -1559,7 +1562,6 @@ def _output_message_to_input(out_msg: OutputMessage) -> InputMessage:
     return InputMessage(role=out_msg.role, parts=list(out_msg.parts))
 
 
-
 @stop_on_exception
 def invoke_agent_input_message(
     inputs: Mapping[str, Any] | None,

tests/langchain/test_tracer.py

@@ -616,7 +616,7 @@ class TestUpdateSpan(TestCase):
     def test_sets_ok_status_on_no_error(self):
         span = MagicMock()
         run = _make_run(run_type="chain", name="test", error=None)
-        _update_span(span, run)
+        _update_span(span, run, False)
         span.set_status.assert_called()
 
     def test_llm_run_returns_invocation(self):
@@ -628,13 +628,13 @@ def test_llm_run_returns_invocation(self):
             extra=None,
             inputs=None,
         )
-        result = _update_span(span, run)
+        result = _update_span(span, run, False)
         self.assertIsNotNone(result)
 
     def test_chain_run_returns_none(self):
         span = MagicMock()
         run = _make_run(run_type="chain", name="test")
-        result = _update_span(span, run)
+        result = _update_span(span, run, False)
         self.assertIsNone(result)
 
     def test_tool_run_sets_tool_attributes(self):
@@ -646,7 +646,7 @@ def test_tool_run_sets_tool_attributes(self):
             inputs={"input": "2+2"},
             outputs={"output": "4"},
         )
-        _update_span(span, run)
+        _update_span(span, run, False)
         span.set_attributes.assert_called()
 
     def test_chat_span_sets_provider_and_choice_count(self):
@@ -664,7 +664,7 @@ def test_chat_span_sets_provider_and_choice_count(self):
             inputs=None,
         )
 
-        _update_span(span, run)
+        _update_span(span, run, False)
 
         merged_attrs = {}
         for call in span.set_attributes.call_args_list:
@@ -1290,3 +1290,30 @@ def test_tool_role_message_becomes_tool_call_response(self):
         self.assertEqual(tool_part.type, "tool_call_response")
         self.assertEqual(tool_part.id, "tc1")
         self.assertEqual(tool_part.response, "rainy")
+
+
+# ---- LangChainTracer enable_sensitive_data -----------------------------------
+
+
+class TestLangChainTracerEnableSensitiveData(TestCase):
+    def test_default_enable_sensitive_data_is_false(self):
+        tracer, _, _ = _make_tracer()
+        self.assertFalse(tracer._enable_sensitive_data)
+
+    def test_enable_sensitive_data_stored_when_true(self):
+        otel_tracer = MagicMock()
+        tracer = LangChainTracer(
+            otel_tracer,
+            False,
+            enable_sensitive_data=True,
+        )
+        self.assertTrue(tracer._enable_sensitive_data)
+
+    def test_enable_sensitive_data_stored_when_false(self):
+        otel_tracer = MagicMock()
+        tracer = LangChainTracer(
+            otel_tracer,
+            False,
+            enable_sensitive_data=False,
+        )
+        self.assertFalse(tracer._enable_sensitive_data)

tests/langchain/test_tracer_instrumentor.py

@@ -140,3 +140,48 @@ def test_does_not_add_duplicate(self):
         mock_instance.inheritable_handlers = [mock_tracer]
         hook(mock_wrapped, mock_instance, (), {})
         mock_instance.add_handler.assert_not_called()
+
+
+class TestLangChainInstrumentorEnableSensitiveData(TestCase):
+    def setUp(self):
+        inst = LangChainInstrumentor()
+        if inst.is_instrumented_by_opentelemetry:
+            inst._uninstrument()
+
+    def tearDown(self):
+        inst = LangChainInstrumentor()
+        if inst.is_instrumented_by_opentelemetry:
+            inst._uninstrument()
+
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.get_otel_logger")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.trace_api.get_tracer")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.wrap_function_wrapper")
+    def test_enable_sensitive_data_true_passed_to_tracer(self, mock_wrap, mock_get_tracer, mock_get_logger):
+        """When enable_sensitive_data=True, the LangChainTracer receives the flag as True."""
+        mock_get_tracer.return_value = MagicMock()
+        mock_get_logger.return_value = MagicMock()
+        inst = LangChainInstrumentor()
+        inst._instrument(enable_sensitive_data=True)
+        self.assertTrue(inst._tracer._enable_sensitive_data)
+
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.get_otel_logger")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.trace_api.get_tracer")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.wrap_function_wrapper")
+    def test_enable_sensitive_data_defaults_to_false(self, mock_wrap, mock_get_tracer, mock_get_logger):
+        """When enable_sensitive_data is not passed, the LangChainTracer defaults to False."""
+        mock_get_tracer.return_value = MagicMock()
+        mock_get_logger.return_value = MagicMock()
+        inst = LangChainInstrumentor()
+        inst._instrument()
+        self.assertFalse(inst._tracer._enable_sensitive_data)
+
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.get_otel_logger")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.trace_api.get_tracer")
+    @patch("microsoft.opentelemetry._genai._langchain._tracer_instrumentor.wrap_function_wrapper")
+    def test_enable_sensitive_data_false_explicit(self, mock_wrap, mock_get_tracer, mock_get_logger):
+        """When enable_sensitive_data=False explicitly, the LangChainTracer stores False."""
+        mock_get_tracer.return_value = MagicMock()
+        mock_get_logger.return_value = MagicMock()
+        inst = LangChainInstrumentor()
+        inst._instrument(enable_sensitive_data=False)
+        self.assertFalse(inst._tracer._enable_sensitive_data)