When `old_execute_command` raises an exception, the `value` variable is never assigned. The `finally` block then attempts to use `value` in `_set_cache_data(cache_span, self, cache_properties, value)`, which will raise an `UnboundLocalError`. This causes the actual Redis exception to be suppressed and replaced with a confusing internal error.

The code calls `span.set_status(SpanStatus.ERROR)` on a `StreamedSpan` instance, but `StreamedSpan` does not have a `set_status()` method - it only has a `status` property setter. This will raise an `AttributeError` at runtime when a SQLAlchemy query errors in streaming mode. Other integrations (e.g., Celery at line 105) correctly use `span.status = SpanStatus.ERROR` instead.

When `propagation_context.baggage` exists but doesn't contain a valid `sample_rand` value, `propagation_context.baggage._sample_rand()` returns `None`. This `None` value is then used in the comparison `sampled = sample_rand < sample_rate` at line 1551, which will raise a `TypeError: '<' not supported between instances of 'NoneType' and 'float'`. This occurs when baggage is present but malformed or missing the sample_rand field.

The code calls `span.set_status(SpanStatus.ERROR)` on StreamedSpan instances at line 102, but StreamedSpan class in traces.py does not define a `set_status()` method. It only has a `status` property setter. When a SQL error occurs and the span is a StreamedSpan, this will raise an AttributeError, causing the error handler to fail.

The code calls `span.set_status(SpanStatus.ERROR)` on StreamedSpan instances at line 102, but StreamedSpan class in traces.py does not define a `set_status()` method. It only has a `status` property setter. When a SQL error occurs and the span is a StreamedSpan, this will raise an AttributeError, causing the error handler to fail.

The code calls `span.set_status(SpanStatus.ERROR)` on StreamedSpan instances at line 102, but StreamedSpan class in traces.py does not define a `set_status()` method. It only has a `status` property setter. When a SQL error occurs and the span is a StreamedSpan, this will raise an AttributeError, causing the error handler to fail.

In `make_sampling_decision()`, when `propagation_context.baggage` exists but `baggage._sample_rand()` returns `None` (which happens when the incoming trace has inconsistent sample_rate and parent_sampled values that cause `_fill_sample_rand()` to fail), the code proceeds to line 1551 where `sampled = sample_rand < sample_rate` raises `TypeError: '<' not supported between instances of 'NoneType' and 'float'`. This can crash the application when handling malformed incoming trace headers from upstream services.

❌ Patch coverage check failed: 17.19% < target 80%

❌ Patch coverage check failed: 18.66% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 17.19% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 17.19% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 17.19% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 17.19% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 14.83% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 14.83% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 18.60% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

❌ Patch coverage check failed: 14.83% < target 80%

Failed to upload coverage artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

Failed to upload test artifact: Failed to CreateArtifact: Received non-retryable error: Failed request: (409) Conflict: an artifact with this name already exists on the workflow run

When using streaming mode (`_experiments={"trace_lifecycle": "stream"}`), if an exception occurs during the Anthropic API call, the `StreamedSpan` will not have its `__exit__()` method called. The change from `span is not None` to `isinstance(span, Span)` excludes `StreamedSpan` instances from the error cleanup path. Since `_sentry_patched_create_common` uses `get_start_span_function()` which returns `StreamedSpan` in streaming mode, and `span.__enter__()` is called on line 413, the span's cleanup (`_end()`) will never run on error, leaving spans unclosed and scope state corrupted.

In span streaming mode, when the request type is 'http' but the method is not in `http_methods_to_capture`, the code still creates a span (lines 238-240). In contrast, the non-streaming mode correctly sets `transaction = None` and uses `nullcontext()`, creating no span. This causes streaming mode to trace HTTP methods (e.g., OPTIONS, HEAD) that should be filtered out according to the middleware configuration.

The `_set_status` function receives a `status` string parameter ('aborted' or 'internal_error'), but for `StreamedSpan` instances, it ignores this parameter and always sets `SpanStatus.ERROR`. This means that when Celery control flow exceptions (Retry, Ignore, Reject) trigger `_set_status('aborted')`, the span is incorrectly marked as ERROR instead of being handled appropriately. This could result in misleading error metrics in Sentry where task retries are counted as errors.

The StreamedSpan path only sets `span.status` and `reason` attribute but does not set `SPANDATA.HTTP_STATUS_CODE` (http.response.status_code). The old API via `set_http_status()` sets this attribute, which is used by downstream processing like `http_status_code_to_event_level()` in tracing_utils.py. This results in data parity loss between streaming and non-streaming modes.

The StreamedSpan path only sets `span.status` and `reason` attribute but does not set `SPANDATA.HTTP_STATUS_CODE` (http.response.status_code). The old API via `set_http_status()` sets this attribute, which is used by downstream processing like `http_status_code_to_event_level()` in tracing_utils.py. This results in data parity loss between streaming and non-streaming modes.

The StreamedSpan path only sets `span.status` and `reason` attribute but does not set `SPANDATA.HTTP_STATUS_CODE` (http.response.status_code). The old API via `set_http_status()` sets this attribute, which is used by downstream processing like `http_status_code_to_event_level()` in tracing_utils.py. This results in data parity loss between streaming and non-streaming modes.

When `client.is_active()` returns False at line 326, the method returns early without setting `self._finished = True`. However, the scope detachment (lines 317-321) and profiler stop (line 314) have already occurred. If `_end()` is called again later when the client becomes active, the method will: (1) try to access the deleted `_context_manager_state` attribute causing a silently-swallowed AttributeError, and (2) attempt to stop an already-stopped profiler. While `capture_internal_exceptions()` prevents crashes, this leads to inconsistent state and harder-to-debug issues.

NoOpStreamedSpan inherits from StreamedSpan but its `__init__` doesn't initialize many attributes that inherited methods/properties access. Properties like `active`, `timestamp`, `start_timestamp`, `status`, and methods like `get_baggage()`, `get_trace_context()`, `dynamic_sampling_context()` will raise `AttributeError` when called because attributes like `_active`, `_timestamp`, `_start_timestamp`, `_status`, `_baggage`, `_parent_span_id` are never set. Any code path using these NoOpStreamedSpan instances that accesses these inherited members will fail at runtime.

In span streaming mode, when `ty in ("http", "websocket")` but the method is NOT in `http_methods_to_capture`, the code still proceeds to call `sentry_sdk.traces.start_span()` at line 238. In contrast, the legacy path correctly sets `transaction = None` and uses `nullcontext()` to skip span creation. This causes spans to be created for HTTP methods that should be filtered out (e.g., OPTIONS, HEAD when only GET/POST are configured), leading to unwanted telemetry data.

When span streaming is enabled, the HTTP status code is not recorded as an attribute on the span. The legacy `Span.set_http_status()` method sets both the span status and `SPANDATA.HTTP_STATUS_CODE` (http.response.status_code), but the StreamedSpan path only sets the status and 'reason' attribute. This means spans created in streaming mode will be missing the http.response.status_code attribute, which may affect observability, querying, and dashboards in Sentry that rely on this attribute.

When span streaming is enabled, the HTTP status code is not recorded as an attribute on the span. The legacy `Span.set_http_status()` method sets both the span status and `SPANDATA.HTTP_STATUS_CODE` (http.response.status_code), but the StreamedSpan path only sets the status and 'reason' attribute. This means spans created in streaming mode will be missing the http.response.status_code attribute, which may affect observability, querying, and dashboards in Sentry that rely on this attribute.

In `_sentry_execute_command`, both `db_span` and `cache_span` are entered via `__enter__()` but if `await old_execute_command()` raises an exception, the `__exit__()` calls at lines 152-156 (in context after) are never executed. This causes span resource leaks and corrupts the span tree because active spans remain attached to the scope. The synchronous version in `_sync_common.py` correctly uses a `try/finally` block (lines 151-160) to ensure spans are always closed.

In `_sentry_execute_command`, both `db_span` and `cache_span` are entered via `__enter__()` but if `await old_execute_command()` raises an exception, the `__exit__()` calls at lines 152-156 (in context after) are never executed. This causes span resource leaks and corrupts the span tree because active spans remain attached to the scope. The synchronous version in `_sync_common.py` correctly uses a `try/finally` block (lines 151-160) to ensure spans are always closed.

In `_sentry_execute_command`, both `db_span` and `cache_span` are entered via `__enter__()` but if `await old_execute_command()` raises an exception, the `__exit__()` calls at lines 152-156 (in context after) are never executed. This causes span resource leaks and corrupts the span tree because active spans remain attached to the scope. The synchronous version in `_sync_common.py` correctly uses a `try/finally` block (lines 151-160) to ensure spans are always closed.

In `_sentry_execute_command`, both `db_span` and `cache_span` are entered via `__enter__()` but if `await old_execute_command()` raises an exception, the `__exit__()` calls at lines 152-156 (in context after) are never executed. This causes span resource leaks and corrupts the span tree because active spans remain attached to the scope. The synchronous version in `_sync_common.py` correctly uses a `try/finally` block (lines 151-160) to ensure spans are always closed.

At line 1240, `parent_span` is assigned from `self.span or self.get_current_scope().span`, which can be a legacy `Span` object (from `sentry_sdk.tracing.Span`). The code at line 1297 accesses `parent_span._segment`, but legacy `Span` does not have a `_segment` attribute. This will raise an `AttributeError` if a legacy span exists on the scope when `start_streamed_span` is called. While the PR states the old and new APIs should not be mixed, there's no runtime guard to provide a helpful error message or graceful degradation.