fix: restore explicit span.end() to fix span end_time regression

PR #1293 wrapped event_loop_cycle() in use_span(end_on_exit=True) and removed
explicit span.end() calls. Because event_loop_cycle is an async generator,
yield keeps the context manager open across recursive cycles, causing all
execute_event_loop_cycle spans to share the same OTel end_time.

Switch to end_on_exit=False and explicitly call span.end() via _end_span()
in end_event_loop_cycle_span() and end_model_invoke_span(), restoring
end_span_with_error() in all exception paths.

Author: Di-Is

src/strands/event_loop/event_loop.py

@@ -139,25 +139,29 @@ async def event_loop_cycle(
     )
     invocation_state["event_loop_cycle_span"] = cycle_span
 
-    with trace_api.use_span(cycle_span, end_on_exit=True):
-        # Skipping model invocation if in interrupt state as interrupts are currently only supported for tool calls.
-        if agent._interrupt_state.activated:
-            stop_reason: StopReason = "tool_use"
-            message = agent._interrupt_state.context["tool_use_message"]
-        # Skip model invocation if the latest message contains ToolUse
-        elif _has_tool_use_in_latest_message(agent.messages):
-            stop_reason = "tool_use"
-            message = agent.messages[-1]
-        else:
-            model_events = _handle_model_execution(
-                agent, cycle_span, cycle_trace, invocation_state, tracer, structured_output_context
-            )
-            async for model_event in model_events:
-                if not isinstance(model_event, ModelStopReason):
-                    yield model_event
+    with trace_api.use_span(cycle_span, end_on_exit=False):
+        try:
+            # Skipping model invocation if in interrupt state as interrupts are currently only supported for tool calls.
+            if agent._interrupt_state.activated:
+                stop_reason: StopReason = "tool_use"
+                message = agent._interrupt_state.context["tool_use_message"]
+            # Skip model invocation if the latest message contains ToolUse
+            elif _has_tool_use_in_latest_message(agent.messages):
+                stop_reason = "tool_use"
+                message = agent.messages[-1]
+            else:
+                model_events = _handle_model_execution(
+                    agent, cycle_span, cycle_trace, invocation_state, tracer, structured_output_context
+                )
+                async for model_event in model_events:
+                    if not isinstance(model_event, ModelStopReason):
+                        yield model_event
 
-            stop_reason, message, *_ = model_event["stop"]
-            yield ModelMessageEvent(message=message)
+                stop_reason, message, *_ = model_event["stop"]
+                yield ModelMessageEvent(message=message)
+        except Exception as e:
+            tracer.end_span_with_error(cycle_span, str(e), e)
+            raise
 
         try:
             if stop_reason == "max_tokens":
@@ -196,42 +200,48 @@ async def event_loop_cycle(
 
             # End the cycle and return results
             agent.event_loop_metrics.end_cycle(cycle_start_time, cycle_trace, attributes)
-            # Set attributes before span auto-closes
+
+            # Force structured output tool call if LLM didn't use it automatically
+            if structured_output_context.is_enabled and stop_reason == "end_turn":
+                if structured_output_context.force_attempted:
+                    raise StructuredOutputException(
+                        "The model failed to invoke the structured output tool even after it was forced."
+                    )
+                structured_output_context.set_forced_mode()
+                logger.debug("Forcing structured output tool")
+                await agent._append_messages(
+                    {"role": "user", "content": [{"text": structured_output_context.structured_output_prompt}]}
+                )
+
+                tracer.end_event_loop_cycle_span(cycle_span, message)
+                events = recurse_event_loop(
+                    agent=agent, invocation_state=invocation_state, structured_output_context=structured_output_context
+                )
+                async for typed_event in events:
+                    yield typed_event
+                return
+
             tracer.end_event_loop_cycle_span(cycle_span, message)
-        except EventLoopException:
+            yield EventLoopStopEvent(stop_reason, message, agent.event_loop_metrics, invocation_state["request_state"])
+        except StructuredOutputException as e:
+            tracer.end_span_with_error(cycle_span, str(e), e)
+            raise
+        except EventLoopException as e:
+            tracer.end_span_with_error(cycle_span, str(e), e)
             # Don't yield or log the exception - we already did it when we
             # raised the exception and we don't need that duplication.
             raise
         except (ContextWindowOverflowException, MaxTokensReachedException) as e:
             # Special cased exceptions which we want to bubble up rather than get wrapped in an EventLoopException
+            tracer.end_span_with_error(cycle_span, str(e), e)
             raise e
         except Exception as e:
+            tracer.end_span_with_error(cycle_span, str(e), e)
             # Handle any other exceptions
             yield ForceStopEvent(reason=e)
             logger.exception("cycle failed")
             raise EventLoopException(e, invocation_state["request_state"]) from e
 
-        # Force structured output tool call if LLM didn't use it automatically
-        if structured_output_context.is_enabled and stop_reason == "end_turn":
-            if structured_output_context.force_attempted:
-                raise StructuredOutputException(
-                    "The model failed to invoke the structured output tool even after it was forced."
-                )
-            structured_output_context.set_forced_mode()
-            logger.debug("Forcing structured output tool")
-            await agent._append_messages(
-                {"role": "user", "content": [{"text": structured_output_context.structured_output_prompt}]}
-            )
-
-            events = recurse_event_loop(
-                agent=agent, invocation_state=invocation_state, structured_output_context=structured_output_context
-            )
-            async for typed_event in events:
-                yield typed_event
-            return
-
-        yield EventLoopStopEvent(stop_reason, message, agent.event_loop_metrics, invocation_state["request_state"])
-
 
 async def recurse_event_loop(
     agent: "Agent",
@@ -316,20 +326,21 @@ async def _handle_model_execution(
             system_prompt=agent.system_prompt,
             system_prompt_content=agent._system_prompt_content,
         )
-        with trace_api.use_span(model_invoke_span, end_on_exit=True):
-            await agent.hooks.invoke_callbacks_async(
-                BeforeModelCallEvent(
-                    agent=agent,
-                    invocation_state=invocation_state,
+        with trace_api.use_span(model_invoke_span, end_on_exit=False):
+            try:
+                await agent.hooks.invoke_callbacks_async(
+                    BeforeModelCallEvent(
+                        agent=agent,
+                        invocation_state=invocation_state,
+                    )
                 )
-            )
 
-            if structured_output_context.forced_mode:
-                tool_spec = structured_output_context.get_tool_spec()
-                tool_specs = [tool_spec] if tool_spec else []
-            else:
-                tool_specs = agent.tool_registry.get_all_tool_specs()
-            try:
+                if structured_output_context.forced_mode:
+                    tool_spec = structured_output_context.get_tool_spec()
+                    tool_specs = [tool_spec] if tool_spec else []
+                else:
+                    tool_specs = agent.tool_registry.get_all_tool_specs()
+
                 async for event in stream_messages(
                     agent.model,
                     agent.system_prompt,
@@ -363,17 +374,17 @@ async def _handle_model_execution(
                         "stop_reason=<%s>, retry_requested=<True> | hook requested model retry",
                         stop_reason,
                     )
+                    tracer.end_model_invoke_span(model_invoke_span, message, usage, metrics, stop_reason)
                     continue  # Retry the model call
 
                 if stop_reason == "max_tokens":
                     message = recover_message_on_max_tokens_reached(message)
 
-                # Set attributes before span auto-closes
                 tracer.end_model_invoke_span(model_invoke_span, message, usage, metrics, stop_reason)
                 break  # Success! Break out of retry loop
 
             except Exception as e:
-                # Exception is automatically recorded by use_span with end_on_exit=True
+                tracer.end_span_with_error(model_invoke_span, str(e), e)
                 after_model_call_event = AfterModelCallEvent(
                     agent=agent,
                     invocation_state=invocation_state,
@@ -541,7 +552,7 @@ async def _handle_tool_execution(
             interrupts,
             structured_output=structured_output_result,
         )
-        # Set attributes before span auto-closes (span is managed by use_span in event_loop_cycle)
+        # End the cycle span before yielding the recursive cycle.
         if cycle_span:
             tracer.end_event_loop_cycle_span(span=cycle_span, message=message)
 
@@ -559,7 +570,7 @@ async def _handle_tool_execution(
 
     yield ToolResultMessageEvent(message=tool_result_message)
 
-    # Set attributes before span auto-closes (span is managed by use_span in event_loop_cycle)
+    # End the cycle span before yielding the recursive cycle.
     if cycle_span:
         tracer.end_event_loop_cycle_span(span=cycle_span, message=message, tool_result_message=tool_result_message)
 

src/strands/telemetry/tracer.py

@@ -185,15 +185,17 @@ def _end_span(
         span: Span,
         attributes: dict[str, AttributeValue] | None = None,
         error: Exception | None = None,
+        error_message: str | None = None,
     ) -> None:
         """Generic helper method to end a span.
 
         Args:
             span: The span to end
             attributes: Optional attributes to set before ending the span
             error: Optional exception if an error occurred
+            error_message: Optional error message to set in the span status
         """
-        if not span:
+        if not span or not span.is_recording():
             return
 
         try:
@@ -206,7 +208,8 @@ def _end_span(
 
             # Handle error if present
             if error:
-                span.set_status(StatusCode.ERROR, str(error))
+                status_description = error_message or str(error) or type(error).__name__
+                span.set_status(StatusCode.ERROR, status_description)
                 span.record_exception(error)
             else:
                 span.set_status(StatusCode.OK)
@@ -229,11 +232,11 @@ def end_span_with_error(self, span: Span, error_message: str, exception: Excepti
             error_message: Error message to set in the span status.
             exception: Optional exception to record in the span.
         """
-        if not span:
+        if not span or not span.is_recording():
             return
 
         error = exception or Exception(error_message)
-        self._end_span(span, error=error)
+        self._end_span(span, error=error, error_message=error_message)
 
     def _add_event(
         self, span: Span | None, event_name: str, event_attributes: Attributes, to_span_attributes: bool = False
@@ -330,18 +333,15 @@ def end_model_invoke_span(
     ) -> None:
         """End a model invocation span with results and metrics.
 
-        Note: The span is automatically closed and exceptions recorded. This method just sets the necessary attributes.
-        Status in the span is automatically set to UNSET (OK) on success or ERROR on exception.
-
         Args:
-            span: The span to set attributes on.
+            span: The span to end.
             message: The message response from the model.
             usage: Token usage information from the model call.
             metrics: Metrics from the model call.
             stop_reason: The reason the model stopped generating.
         """
-        # Set end time attribute
-        span.set_attribute("gen_ai.event.end_time", datetime.now(timezone.utc).isoformat())
+        if not span or not span.is_recording():
+            return
 
         attributes: dict[str, AttributeValue] = {
             "gen_ai.usage.prompt_tokens": usage["inputTokens"],
@@ -378,7 +378,7 @@ def end_model_invoke_span(
                 event_attributes={"finish_reason": str(stop_reason), "message": serialize(message["content"])},
             )
 
-        span.set_attributes(attributes)
+        self._end_span(span, attributes)
 
     def start_tool_call_span(
         self,
@@ -553,20 +553,14 @@ def end_event_loop_cycle_span(
     ) -> None:
         """End an event loop cycle span with results.
 
-        Note: The span is automatically closed and exceptions recorded. This method just sets the necessary attributes.
-        Status in the span is automatically set to UNSET (OK) on success or ERROR on exception.
-
         Args:
-            span: The span to set attributes on.
+            span: The span to end.
             message: The message response from this cycle.
             tool_result_message: Optional tool result message if a tool was called.
         """
-        if not span:
+        if not span or not span.is_recording():
             return
 
-        # Set end time attribute
-        span.set_attribute("gen_ai.event.end_time", datetime.now(timezone.utc).isoformat())
-
         event_attributes: dict[str, AttributeValue] = {"message": serialize(message["content"])}
 
         if tool_result_message:
@@ -591,6 +585,8 @@ def end_event_loop_cycle_span(
             else:
                 self._add_event(span, "gen_ai.choice", event_attributes=event_attributes)
 
+        self._end_span(span)
+
     def start_agent_span(
         self,
         messages: Messages,

tests/strands/event_loop/test_event_loop.py

@@ -5,6 +5,9 @@
 from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
 
 import pytest
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
 
 import strands
 import strands.telemetry
@@ -19,6 +22,7 @@
 )
 from strands.interrupt import Interrupt, _InterruptState
 from strands.telemetry.metrics import EventLoopMetrics
+from strands.telemetry.tracer import Tracer
 from strands.tools.executors import SequentialToolExecutor
 from strands.tools.registry import ToolRegistry
 from strands.types._events import EventLoopStopEvent
@@ -583,6 +587,14 @@ async def test_event_loop_tracing_with_model_error(
         )
         await alist(stream)
 
+    assert mock_tracer.end_span_with_error.call_count == 2
+    mock_tracer.end_span_with_error.assert_has_calls(
+        [
+            call(model_span, "Input too long", model.stream.side_effect),
+            call(cycle_span, "Input too long", model.stream.side_effect),
+        ]
+    )
+
 
 @pytest.mark.asyncio
 async def test_event_loop_cycle_max_tokens_exception(
@@ -673,6 +685,53 @@ async def test_event_loop_tracing_with_tool_execution(
     assert mock_tracer.end_model_invoke_span.call_count == 2
 
 
+@pytest.mark.asyncio
+async def test_event_loop_cycle_closes_cycle_span_before_recursive_cycle(
+    agent,
+    model,
+    tool_stream,
+    agenerator,
+    alist,
+):
+    exporter = InMemorySpanExporter()
+    provider = TracerProvider()
+    provider.add_span_processor(SimpleSpanProcessor(exporter))
+
+    tracer = Tracer()
+    tracer.tracer_provider = provider
+    tracer.tracer = provider.get_tracer(tracer.service_name)
+
+    async def delayed_text_stream():
+        yield {"contentBlockDelta": {"delta": {"text": "test text"}}}
+        await asyncio.sleep(0.05)
+        yield {"contentBlockStop": {}}
+
+    agent.trace_span = None
+    agent._system_prompt_content = None
+    model.config = {"model_id": "test-model"}
+    model.stream.side_effect = [
+        agenerator(tool_stream),
+        delayed_text_stream(),
+    ]
+
+    with patch("strands.event_loop.event_loop.get_tracer", return_value=tracer):
+        stream = strands.event_loop.event_loop.event_loop_cycle(
+            agent=agent,
+            invocation_state={},
+        )
+        await alist(stream)
+
+    provider.force_flush()
+    cycle_spans = sorted(
+        [span for span in exporter.get_finished_spans() if span.name == "execute_event_loop_cycle"],
+        key=lambda span: span.start_time,
+    )
+
+    assert len(cycle_spans) == 2
+    assert cycle_spans[0].end_time <= cycle_spans[1].start_time
+    assert cycle_spans[0].end_time < cycle_spans[1].end_time
+
+
 @patch("strands.event_loop.event_loop.get_tracer")
 @pytest.mark.asyncio
 async def test_event_loop_tracing_with_throttling_exception(
@@ -709,6 +768,7 @@ async def test_event_loop_tracing_with_throttling_exception(
         )
         await alist(stream)
 
+    assert mock_tracer.end_span_with_error.call_count == 1
     # Verify span was created for the successful retry
     assert mock_tracer.start_model_invoke_span.call_count == 2
     assert mock_tracer.end_model_invoke_span.call_count == 1