feat(integration): add gen_ai.conversation.id if available

sentry_sdk/consts.py

@@ -464,6 +464,12 @@ class SPANDATA:
     Example: "ResearchAssistant"
     """
 
+    GEN_AI_CONVERSATION_ID = "gen_ai.conversation.id"
+    """
+    The unique identifier for the conversation/thread with the AI model.
+    Example: "conv_abc123"
+    """
+
     GEN_AI_CHOICE = "gen_ai.choice"
     """
     The model's response message.

sentry_sdk/integrations/openai_agents/__init__.py

@@ -4,6 +4,7 @@
     _create_get_model_wrapper,
     _create_get_all_tools_wrapper,
     _create_run_wrapper,
+    _create_run_streamed_wrapper,
     _patch_agent_run,
     _patch_error_tracing,
 )
@@ -25,12 +26,16 @@ def _patch_runner() -> None:
     # Create the root span for one full agent run (including eventual handoffs)
     # Note agents.run.DEFAULT_AGENT_RUNNER.run_sync is a wrapper around
     # agents.run.DEFAULT_AGENT_RUNNER.run. It does not need to be wrapped separately.
-    # TODO-anton: Also patch streaming runner: agents.Runner.run_streamed
     agents.run.DEFAULT_AGENT_RUNNER.run = _create_run_wrapper(
         agents.run.DEFAULT_AGENT_RUNNER.run
     )
 
-    # Creating the actual spans for each agent run.
+    # Patch streaming runner
+    agents.run.DEFAULT_AGENT_RUNNER.run_streamed = _create_run_streamed_wrapper(
+        agents.run.DEFAULT_AGENT_RUNNER.run_streamed
+    )
+
+    # Creating the actual spans for each agent run (works for both streaming and non-streaming).
     _patch_agent_run()
 
 

sentry_sdk/integrations/openai_agents/patches/__init__.py

@@ -1,5 +1,5 @@
 from .models import _create_get_model_wrapper  # noqa: F401
 from .tools import _create_get_all_tools_wrapper  # noqa: F401
-from .runner import _create_run_wrapper  # noqa: F401
+from .runner import _create_run_wrapper, _create_run_streamed_wrapper  # noqa: F401
 from .agent_run import _patch_agent_run  # noqa: F401
 from .error_tracing import _patch_error_tracing  # noqa: F401

sentry_sdk/integrations/openai_agents/patches/agent_run.py

@@ -1,6 +1,7 @@
 import sys
 from functools import wraps
 
+from sentry_sdk.consts import SPANDATA
 from sentry_sdk.integrations import DidNotEnable
 from sentry_sdk.utils import reraise
 from ..spans import (
@@ -31,22 +32,10 @@ def _patch_agent_run() -> None:
 
     # Store original methods
     original_run_single_turn = agents.run.AgentRunner._run_single_turn
+    original_run_single_turn_streamed = agents.run.AgentRunner._run_single_turn_streamed
     original_execute_handoffs = agents._run_impl.RunImpl.execute_handoffs
     original_execute_final_output = agents._run_impl.RunImpl.execute_final_output
 
-    def _start_invoke_agent_span(
-        context_wrapper: "agents.RunContextWrapper",
-        agent: "agents.Agent",
-        kwargs: "dict[str, Any]",
-    ) -> "Span":
-        """Start an agent invocation span"""
-        # Store the agent on the context wrapper so we can access it later
-        context_wrapper._sentry_current_agent = agent
-        span = invoke_agent_span(context_wrapper, agent, kwargs)
-        context_wrapper._sentry_agent_span = span
-
-        return span
-
     def _has_active_agent_span(context_wrapper: "agents.RunContextWrapper") -> bool:
         """Check if there's an active agent span for this context"""
         return getattr(context_wrapper, "_sentry_current_agent", None) is not None
@@ -57,6 +46,46 @@ def _get_current_agent(
         """Get the current agent from context wrapper"""
         return getattr(context_wrapper, "_sentry_current_agent", None)
 
+    def _close_streaming_workflow_span(agent: "Optional[agents.Agent]") -> None:
+        """Close the workflow span for streaming executions if it exists."""
+        if agent and hasattr(agent, "_sentry_workflow_span"):
+            workflow_span = agent._sentry_workflow_span
+            workflow_span.__exit__(*sys.exc_info())
+            delattr(agent, "_sentry_workflow_span")
+
+    def _maybe_start_agent_span(
+        context_wrapper: "agents.RunContextWrapper",
+        agent: "agents.Agent",
+        should_run_agent_start_hooks: bool,
+        span_kwargs: "dict[str, Any]",
+        is_streaming: bool = False,
+    ) -> "Optional[Span]":
+        """
+        Start an agent invocation span if conditions are met.
+        Handles ending any existing span for a different agent.
+
+        Returns the new span if started, or the existing span if conditions aren't met.
+        """
+        if not (should_run_agent_start_hooks and agent and context_wrapper):
+            return getattr(context_wrapper, "_sentry_agent_span", None)
+
+        # End any existing span for a different agent
+        if _has_active_agent_span(context_wrapper):
+            current_agent = _get_current_agent(context_wrapper)
+            if current_agent and current_agent != agent:
+                end_invoke_agent_span(context_wrapper, current_agent)
+
+        # Store the agent on the context wrapper so we can access it later
+        context_wrapper._sentry_current_agent = agent
+        span = invoke_agent_span(context_wrapper, agent, span_kwargs)
+        context_wrapper._sentry_agent_span = span
+        agent._sentry_agent_span = span
+
+        if is_streaming:
+            span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, True)
+
+        return span
+
     @wraps(
         original_run_single_turn.__func__
         if hasattr(original_run_single_turn, "__func__")
@@ -68,28 +97,18 @@ async def patched_run_single_turn(
         """Patched _run_single_turn that creates agent invocation spans"""
         agent = kwargs.get("agent")
         context_wrapper = kwargs.get("context_wrapper")
-        should_run_agent_start_hooks = kwargs.get("should_run_agent_start_hooks")
+        should_run_agent_start_hooks = kwargs.get("should_run_agent_start_hooks", False)
 
-        span = getattr(context_wrapper, "_sentry_agent_span", None)
-        # Start agent span when agent starts (but only once per agent)
-        if should_run_agent_start_hooks and agent and context_wrapper:
-            # End any existing span for a different agent
-            if _has_active_agent_span(context_wrapper):
-                current_agent = _get_current_agent(context_wrapper)
-                if current_agent and current_agent != agent:
-                    end_invoke_agent_span(context_wrapper, current_agent)
+        span = _maybe_start_agent_span(
+            context_wrapper, agent, should_run_agent_start_hooks, kwargs
+        )
 
-            span = _start_invoke_agent_span(context_wrapper, agent, kwargs)
-            agent._sentry_agent_span = span
-
-        # Call original method with all the correct parameters
         try:
             result = await original_run_single_turn(*args, **kwargs)
         except Exception as exc:
             if span is not None and span.timestamp is None:
                 _record_exception_on_span(span, exc)
                 end_invoke_agent_span(context_wrapper, agent)
-
             reraise(*sys.exc_info())
 
         return result
@@ -117,7 +136,9 @@ async def patched_execute_handoffs(
         # Call original method with all parameters
         try:
             result = await original_execute_handoffs(*args, **kwargs)
-
+        except Exception:
+            _close_streaming_workflow_span(agent)
+            raise
         finally:
             # End span for current agent after handoff processing is complete
             if agent and context_wrapper and _has_active_agent_span(context_wrapper):
@@ -139,18 +160,77 @@ async def patched_execute_final_output(
         context_wrapper = kwargs.get("context_wrapper")
         final_output = kwargs.get("final_output")
 
-        # Call original method with all parameters
         try:
             result = await original_execute_final_output(*args, **kwargs)
         finally:
-            # End span for current agent after final output processing is complete
             if agent and context_wrapper and _has_active_agent_span(context_wrapper):
                 end_invoke_agent_span(context_wrapper, agent, final_output)
+            # For streaming, close the workflow span (non-streaming uses context manager in _create_run_wrapper)
+            _close_streaming_workflow_span(agent)
+
+        return result
+
+    @wraps(
+        original_run_single_turn_streamed.__func__
+        if hasattr(original_run_single_turn_streamed, "__func__")
+        else original_run_single_turn_streamed
+    )
+    async def patched_run_single_turn_streamed(
+        cls: "agents.Runner", *args: "Any", **kwargs: "Any"
+    ) -> "Any":
+        """Patched _run_single_turn_streamed that creates agent invocation spans for streaming.
+
+        Note: Unlike _run_single_turn which uses keyword-only arguments (*,),
+        _run_single_turn_streamed uses positional arguments. The call signature is:
+        _run_single_turn_streamed(
+            streamed_result,              # args[0]
+            agent,                        # args[1]
+            hooks,                        # args[2]
+            context_wrapper,              # args[3]
+            run_config,                   # args[4]
+            should_run_agent_start_hooks, # args[5]
+            tool_use_tracker,             # args[6]
+            all_tools,                    # args[7]
+            server_conversation_tracker,  # args[8] (optional)
+        )
+        """
+        streamed_result = args[0] if len(args) > 0 else kwargs.get("streamed_result")
+        agent = args[1] if len(args) > 1 else kwargs.get("agent")
+        context_wrapper = args[3] if len(args) > 3 else kwargs.get("context_wrapper")
+        should_run_agent_start_hooks = bool(
+            args[5]
+            if len(args) > 5
+            else kwargs.get("should_run_agent_start_hooks", False)
+        )
+
+        span_kwargs: "dict[str, Any]" = {}
+        if streamed_result and hasattr(streamed_result, "input"):
+            span_kwargs["original_input"] = streamed_result.input
+
+        span = _maybe_start_agent_span(
+            context_wrapper,
+            agent,
+            should_run_agent_start_hooks,
+            span_kwargs,
+            is_streaming=True,
+        )
+
+        try:
+            result = await original_run_single_turn_streamed(*args, **kwargs)
+        except Exception as exc:
+            if span is not None and span.timestamp is None:
+                _record_exception_on_span(span, exc)
+                end_invoke_agent_span(context_wrapper, agent)
+            _close_streaming_workflow_span(agent)
+            reraise(*sys.exc_info())
 
         return result
 
     # Apply patches
     agents.run.AgentRunner._run_single_turn = classmethod(patched_run_single_turn)
+    agents.run.AgentRunner._run_single_turn_streamed = classmethod(
+        patched_run_single_turn_streamed
+    )
     agents._run_impl.RunImpl.execute_handoffs = classmethod(patched_execute_handoffs)
     agents._run_impl.RunImpl.execute_final_output = classmethod(
         patched_execute_final_output

sentry_sdk/integrations/openai_agents/patches/models.py

@@ -1,4 +1,5 @@
 import copy
+import sys
 from functools import wraps
 
 from sentry_sdk.integrations import DidNotEnable
@@ -9,15 +10,24 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from typing import Any, Callable
-
+    from typing import Any, Callable, Optional
 
 try:
     import agents
 except ImportError:
     raise DidNotEnable("OpenAI Agents not installed")
 
 
+def _set_response_model_on_agent_span(
+    agent: "agents.Agent", response_model: "Optional[str]"
+) -> None:
+    """Set the response model on the agent's invoke_agent span if available."""
+    if response_model:
+        agent_span = getattr(agent, "_sentry_agent_span", None)
+        if agent_span:
+            agent_span.set_data(SPANDATA.GEN_AI_RESPONSE_MODEL, response_model)
+
+
 def _create_get_model_wrapper(
     original_get_model: "Callable[..., Any]",
 ) -> "Callable[..., Any]":
@@ -37,15 +47,19 @@ def wrapped_get_model(
         # because we only patch its direct methods, all underlying data can remain unchanged.
         model = copy.copy(original_get_model(agent, run_config))
 
-        # Wrap _fetch_response if it exists (for OpenAI models) to capture raw response model
+        # Capture the request model name for spans (agent.model can be None when using defaults)
+        request_model_name = model.model if hasattr(model, "model") else str(model)
+        agent._sentry_request_model = request_model_name
+
+        # Wrap _fetch_response if it exists (for OpenAI models) to capture response model
         if hasattr(model, "_fetch_response"):
             original_fetch_response = model._fetch_response
 
             @wraps(original_fetch_response)
             async def wrapped_fetch_response(*args: "Any", **kwargs: "Any") -> "Any":
                 response = await original_fetch_response(*args, **kwargs)
-                if hasattr(response, "model"):
-                    agent._sentry_raw_response_model = str(response.model)
+                if hasattr(response, "model") and response.model:
+                    agent._sentry_response_model = str(response.model)
                 return response
 
             model._fetch_response = wrapped_fetch_response
@@ -57,22 +71,60 @@ async def wrapped_get_response(*args: "Any", **kwargs: "Any") -> "Any":
             with ai_client_span(agent, kwargs) as span:
                 result = await original_get_response(*args, **kwargs)
 
-                response_model = getattr(agent, "_sentry_raw_response_model", None)
+                # Get response model captured from _fetch_response and clean up
+                response_model = getattr(agent, "_sentry_response_model", None)
                 if response_model:
-                    agent_span = getattr(agent, "_sentry_agent_span", None)
-                    if agent_span:
-                        agent_span.set_data(
-                            SPANDATA.GEN_AI_RESPONSE_MODEL, response_model
-                        )
+                    delattr(agent, "_sentry_response_model")
 
-                    delattr(agent, "_sentry_raw_response_model")
-
-                update_ai_client_span(span, agent, kwargs, result, response_model)
+                _set_response_model_on_agent_span(agent, response_model)
+                update_ai_client_span(span, result, response_model, agent)
 
             return result
 
         model.get_response = wrapped_get_response
 
+        # Also wrap stream_response for streaming support
+        if hasattr(model, "stream_response"):
+            original_stream_response = model.stream_response
+
+            @wraps(original_stream_response)
+            async def wrapped_stream_response(*args: "Any", **kwargs: "Any") -> "Any":
+                # Uses explicit try/finally instead of context manager to ensure cleanup
+                # even if the consumer abandons the stream (GeneratorExit).
+                span_kwargs = dict(kwargs)
+                if len(args) > 0:
+                    span_kwargs["system_instructions"] = args[0]
+                if len(args) > 1:
+                    span_kwargs["input"] = args[1]
+
+                span = ai_client_span(agent, span_kwargs)
+                span.__enter__()
+                span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, True)
+
+                streaming_response = None
+                try:
+                    async for event in original_stream_response(*args, **kwargs):
+                        # Capture the full response from ResponseCompletedEvent
+                        if hasattr(event, "response"):
+                            streaming_response = event.response
+                        yield event
+
+                    # Update span with response data (usage, output, model)
+                    if streaming_response:
+                        response_model = (
+                            str(streaming_response.model)
+                            if hasattr(streaming_response, "model")
+                            and streaming_response.model
+                            else None
+                        )
+
+                        _set_response_model_on_agent_span(agent, response_model)
+                        update_ai_client_span(span, streaming_response, agent=agent)
+                finally:
+                    span.__exit__(*sys.exc_info())
+
+            model.stream_response = wrapped_stream_response
+
         return model
 
     return wrapped_get_model