test(langchain): Separate test to remove conditional

sentry_sdk/consts.py

@@ -486,6 +486,12 @@ class SPANDATA:
     Example: "Hello!"
     """
 
+    GEN_AI_FUNCTION_ID = "gen_ai.function_id"
+    """
+    Framework-specific tracing label for the execution of a function or other unit of execution in a generative AI system.
+    Example: "my-awesome-function"
+    """
+
     GEN_AI_OPERATION_NAME = "gen_ai.operation.name"
     """
     The name of the operation being performed.

sentry_sdk/integrations/langchain.py

@@ -1,4 +1,3 @@
-import contextvars
 import itertools
 import sys
 import json
@@ -162,44 +161,6 @@ def _transform_langchain_message_content(content: "Any") -> "Any":
     return content
 
 
-# Contextvar to track agent names in a stack for re-entrant agent support
-_agent_stack: "contextvars.ContextVar[Optional[List[Optional[str]]]]" = (
-    contextvars.ContextVar("langchain_agent_stack", default=None)
-)
-
-
-def _push_agent(agent_name: "Optional[str]") -> None:
-    """Push an agent name onto the stack."""
-    stack = _agent_stack.get()
-    if stack is None:
-        stack = []
-    else:
-        # Copy the list to maintain contextvar isolation across async contexts
-        stack = stack.copy()
-    stack.append(agent_name)
-    _agent_stack.set(stack)
-
-
-def _pop_agent() -> "Optional[str]":
-    """Pop an agent name from the stack and return it."""
-    stack = _agent_stack.get()
-    if stack:
-        # Copy the list to maintain contextvar isolation across async contexts
-        stack = stack.copy()
-        agent_name = stack.pop()
-        _agent_stack.set(stack)
-        return agent_name
-    return None
-
-
-def _get_current_agent() -> "Optional[str]":
-    """Get the current agent name (top of stack) without removing it."""
-    stack = _agent_stack.get()
-    if stack:
-        return stack[-1]
-    return None
-
-
 def _get_system_instructions(messages: "List[List[BaseMessage]]") -> "List[str]":
     system_instructions = []
 
@@ -465,9 +426,18 @@ def on_chat_model_start(
             if ai_system:
                 span.set_data(SPANDATA.GEN_AI_SYSTEM, ai_system)
 
-            agent_name = _get_current_agent()
-            if agent_name:
-                span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)
+            agent_metadata = kwargs.get("metadata")
+            if isinstance(agent_metadata, dict) and "lc_agent_name" in agent_metadata:
+                span.set_data(
+                    SPANDATA.GEN_AI_AGENT_NAME, agent_metadata["lc_agent_name"]
+                )
+
+            run_name = kwargs.get("name")
+            if run_name:
+                span.set_data(
+                    SPANDATA.GEN_AI_FUNCTION_ID,
+                    run_name,
+                )
 
             for key, attribute in DATA_FIELDS.items():
                 if key in all_params and all_params[key] is not None:
@@ -665,9 +635,18 @@ def on_tool_start(
             if tool_description is not None:
                 span.set_data(SPANDATA.GEN_AI_TOOL_DESCRIPTION, tool_description)
 
-            agent_name = _get_current_agent()
-            if agent_name:
-                span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)
+            agent_metadata = kwargs.get("metadata")
+            if isinstance(agent_metadata, dict) and "lc_agent_name" in agent_metadata:
+                span.set_data(
+                    SPANDATA.GEN_AI_AGENT_NAME, agent_metadata["lc_agent_name"]
+                )
+
+            run_name = kwargs.get("name")
+            if run_name:
+                span.set_data(
+                    SPANDATA.GEN_AI_FUNCTION_ID,
+                    run_name,
+                )
 
             if should_send_default_pii() and self.include_prompts:
                 set_data_normalized(
@@ -987,58 +966,53 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
         if integration is None:
             return f(self, *args, **kwargs)
 
-        agent_name, tools = _get_request_data(self, args, kwargs)
+        run_name, tools = _get_request_data(self, args, kwargs)
         start_span_function = get_start_span_function()
 
         with start_span_function(
             op=OP.GEN_AI_INVOKE_AGENT,
-            name=f"invoke_agent {agent_name}" if agent_name else "invoke_agent",
+            name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
             origin=LangchainIntegration.origin,
         ) as span:
-            _push_agent(agent_name)
-            try:
-                if agent_name:
-                    span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)
+            if run_name:
+                span.set_data(SPANDATA.GEN_AI_AGENT_NAME, run_name)
 
-                span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
-                span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)
+            span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
+            span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)
 
-                _set_tools_on_span(span, tools)
+            _set_tools_on_span(span, tools)
 
-                # Run the agent
-                result = f(self, *args, **kwargs)
+            # Run the agent
+            result = f(self, *args, **kwargs)
 
-                input = result.get("input")
-                if (
-                    input is not None
-                    and should_send_default_pii()
-                    and integration.include_prompts
-                ):
-                    normalized_messages = normalize_message_roles([input])
-                    scope = sentry_sdk.get_current_scope()
-                    messages_data = truncate_and_annotate_messages(
-                        normalized_messages, span, scope
+            input = result.get("input")
+            if (
+                input is not None
+                and should_send_default_pii()
+                and integration.include_prompts
+            ):
+                normalized_messages = normalize_message_roles([input])
+                scope = sentry_sdk.get_current_scope()
+                messages_data = truncate_and_annotate_messages(
+                    normalized_messages, span, scope
+                )
+                if messages_data is not None:
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        messages_data,
+                        unpack=False,
                     )
-                    if messages_data is not None:
-                        set_data_normalized(
-                            span,
-                            SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                            messages_data,
-                            unpack=False,
-                        )
 
-                output = result.get("output")
-                if (
-                    output is not None
-                    and should_send_default_pii()
-                    and integration.include_prompts
-                ):
-                    set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)
+            output = result.get("output")
+            if (
+                output is not None
+                and should_send_default_pii()
+                and integration.include_prompts
+            ):
+                set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)
 
-                return result
-            finally:
-                # Ensure agent is popped even if an exception occurs
-                _pop_agent()
+            return result
 
     return new_invoke
 
@@ -1050,20 +1024,18 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
         if integration is None:
             return f(self, *args, **kwargs)
 
-        agent_name, tools = _get_request_data(self, args, kwargs)
+        run_name, tools = _get_request_data(self, args, kwargs)
         start_span_function = get_start_span_function()
 
         span = start_span_function(
             op=OP.GEN_AI_INVOKE_AGENT,
-            name=f"invoke_agent {agent_name}" if agent_name else "invoke_agent",
+            name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
             origin=LangchainIntegration.origin,
         )
         span.__enter__()
 
-        _push_agent(agent_name)
-
-        if agent_name:
-            span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)
+        if run_name:
+            span.set_data(SPANDATA.GEN_AI_AGENT_NAME, run_name)
 
         span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
         span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, True)
@@ -1117,7 +1089,6 @@ def new_iterator() -> "Iterator[Any]":
                 raise
             finally:
                 # Ensure cleanup happens even if iterator is abandoned or fails
-                _pop_agent()
                 span.__exit__(*exc_info)
 
         async def new_iterator_async() -> "AsyncIterator[Any]":
@@ -1143,7 +1114,6 @@ async def new_iterator_async() -> "AsyncIterator[Any]":
                 raise
             finally:
                 # Ensure cleanup happens even if iterator is abandoned or fails
-                _pop_agent()
                 span.__exit__(*exc_info)
 
         if str(type(result)) == "<class 'async_generator'>":

tests/conftest.py

@@ -1265,26 +1265,31 @@ def streaming_chat_completions_model_response():
 
 @pytest.fixture
 def nonstreaming_chat_completions_model_response():
-    return openai.types.chat.ChatCompletion(
-        id="chatcmpl-test",
-        choices=[
-            openai.types.chat.chat_completion.Choice(
-                index=0,
-                finish_reason="stop",
-                message=openai.types.chat.ChatCompletionMessage(
-                    role="assistant", content="Test response"
-                ),
-            )
-        ],
-        created=1234567890,
-        model="gpt-3.5-turbo",
-        object="chat.completion",
-        usage=openai.types.CompletionUsage(
-            prompt_tokens=10,
-            completion_tokens=20,
-            total_tokens=30,
-        ),
-    )
+    def inner(
+        response_id: str,
+        response_model: str,
+        message_content: str,
+        created: int,
+        usage: openai.types.CompletionUsage,
+    ):
+        return openai.types.chat.ChatCompletion(
+            id=response_id,
+            choices=[
+                openai.types.chat.chat_completion.Choice(
+                    index=0,
+                    finish_reason="stop",
+                    message=openai.types.chat.ChatCompletionMessage(
+                        role="assistant", content=message_content
+                    ),
+                )
+            ],
+            created=created,
+            model=response_model,
+            object="chat.completion",
+            usage=usage,
+        )
+
+    return inner
 
 
 @pytest.fixture