feat(anthropic): Set gen_ai.response.finish_reasons on span

Extract stop_reason from Anthropic responses and set it as the
gen_ai.response.finish_reasons span attribute. For non-streaming
responses this comes from result.stop_reason, and for streaming
responses from event.delta.stop_reason in message_delta events.

Refs PY-2136

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: ericapisani

sentry_sdk/integrations/anthropic.py

@@ -55,6 +55,7 @@ class _RecordedUsage:
     input_tokens: int = 0
     cache_write_input_tokens: "Optional[int]" = 0
     cache_read_input_tokens: "Optional[int]" = 0
+    finish_reason: "Optional[str]" = None
 
 
 class AnthropicIntegration(Integration):
@@ -186,6 +187,10 @@ def _collect_ai_data(
                     usage.cache_read_input_tokens = cache_read_input_tokens
                 # TODO: Record event.usage.server_tool_use
 
+                stop_reason = getattr(event.delta, "stop_reason", None)
+                if stop_reason is not None:
+                    usage.finish_reason = stop_reason
+
                 return (
                     model,
                     usage,
@@ -348,10 +353,13 @@ def _set_output_data(
     cache_write_input_tokens: "int | None",
     content_blocks: "list[Any]",
     finish_span: bool = False,
+    finish_reason: "str | None" = None,
 ) -> None:
     """
     Set output data for the span based on the AI response."""
     span.set_data(SPANDATA.GEN_AI_RESPONSE_MODEL, model)
+    if finish_reason is not None:
+        span.set_data(SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS, finish_reason)
     if should_send_default_pii() and integration.include_prompts:
         output_messages: "dict[str, list[Any]]" = {
             "response": [],
@@ -443,6 +451,7 @@ def _sentry_patched_create_common(f: "Any", *args: "Any", **kwargs: "Any") -> "A
                 cache_write_input_tokens=cache_write_input_tokens,
                 content_blocks=content_blocks,
                 finish_span=True,
+                finish_reason=getattr(result, "stop_reason", None),
             )
 
         # Streaming response
@@ -485,6 +494,7 @@ def new_iterator() -> "Iterator[MessageStreamEvent]":
                     cache_write_input_tokens=usage.cache_write_input_tokens,
                     content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
                     finish_span=True,
+                    finish_reason=usage.finish_reason,
                 )
 
             async def new_iterator_async() -> "AsyncIterator[MessageStreamEvent]":
@@ -523,6 +533,7 @@ async def new_iterator_async() -> "AsyncIterator[MessageStreamEvent]":
                     cache_write_input_tokens=usage.cache_write_input_tokens,
                     content_blocks=[{"text": "".join(content_blocks), "type": "text"}],
                     finish_span=True,
+                    finish_reason=usage.finish_reason,
                 )
 
             if str(type(result._iterator)) == "<class 'async_generator'>":

tests/integrations/anthropic/test_anthropic.py

@@ -64,6 +64,7 @@ async def __call__(self, *args, **kwargs):
     content=[TextBlock(type="text", text="Hi, I'm Claude.")],
     type="message",
     usage=Usage(input_tokens=10, output_tokens=20),
+    stop_reason="end_turn",
 )
 
 
@@ -134,6 +135,7 @@ def test_nonstreaming_create_message(
     assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20
     assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30
     assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is False
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "end_turn"
 
 
 @pytest.mark.asyncio
@@ -204,6 +206,122 @@ async def test_nonstreaming_create_message_async(
     assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20
     assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30
     assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is False
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "end_turn"
+
+
+def test_streaming_create_message_with_finish_reason(sentry_init, capture_events):
+    client = Anthropic(api_key="z")
+    returned_stream = Stream(cast_to=None, response=None, client=client)
+    returned_stream._iterator = [
+        MessageStartEvent(
+            message=EXAMPLE_MESSAGE,
+            type="message_start",
+        ),
+        ContentBlockStartEvent(
+            type="content_block_start",
+            index=0,
+            content_block=TextBlock(type="text", text=""),
+        ),
+        ContentBlockDeltaEvent(
+            delta=TextDelta(text="Hi!", type="text_delta"),
+            index=0,
+            type="content_block_delta",
+        ),
+        ContentBlockStopEvent(type="content_block_stop", index=0),
+        MessageDeltaEvent(
+            delta=Delta(stop_reason="end_turn"),
+            usage=MessageDeltaUsage(output_tokens=10),
+            type="message_delta",
+        ),
+    ]
+
+    sentry_init(
+        integrations=[AnthropicIntegration(include_prompts=True)],
+        traces_sample_rate=1.0,
+        send_default_pii=True,
+    )
+    events = capture_events()
+    client.messages._post = mock.Mock(return_value=returned_stream)
+
+    messages = [
+        {
+            "role": "user",
+            "content": "Hello, Claude",
+        }
+    ]
+
+    with start_transaction(name="anthropic"):
+        message = client.messages.create(
+            max_tokens=1024, messages=messages, model="model", stream=True
+        )
+        for _ in message:
+            pass
+
+    assert len(events) == 1
+    (event,) = events
+    (span,) = event["spans"]
+
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "end_turn"
+
+
+@pytest.mark.asyncio
+async def test_streaming_create_message_with_finish_reason_async(
+    sentry_init, capture_events, async_iterator
+):
+    client = AsyncAnthropic(api_key="z")
+    returned_stream = AsyncStream(cast_to=None, response=None, client=client)
+    returned_stream._iterator = async_iterator(
+        [
+            MessageStartEvent(
+                message=EXAMPLE_MESSAGE,
+                type="message_start",
+            ),
+            ContentBlockStartEvent(
+                type="content_block_start",
+                index=0,
+                content_block=TextBlock(type="text", text=""),
+            ),
+            ContentBlockDeltaEvent(
+                delta=TextDelta(text="Hi!", type="text_delta"),
+                index=0,
+                type="content_block_delta",
+            ),
+            ContentBlockStopEvent(type="content_block_stop", index=0),
+            MessageDeltaEvent(
+                delta=Delta(stop_reason="end_turn"),
+                usage=MessageDeltaUsage(output_tokens=10),
+                type="message_delta",
+            ),
+        ]
+    )
+
+    sentry_init(
+        integrations=[AnthropicIntegration(include_prompts=True)],
+        traces_sample_rate=1.0,
+        send_default_pii=True,
+    )
+    events = capture_events()
+    client.messages._post = AsyncMock(return_value=returned_stream)
+
+    messages = [
+        {
+            "role": "user",
+            "content": "Hello, Claude",
+        }
+    ]
+
+    with start_transaction(name="anthropic"):
+        message = await client.messages.create(
+            max_tokens=1024, messages=messages, model="model", stream=True
+        )
+        async for _ in message:
+            pass
+
+    assert len(events) == 1
+    (event,) = events
+    (span,) = event["spans"]
+
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "end_turn"
 
 
 @pytest.mark.parametrize(
@@ -545,6 +663,7 @@ def test_streaming_create_message_with_input_json_delta(
     assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 41
     assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 407
     assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "tool_use"
 
 
 @pytest.mark.asyncio
@@ -687,6 +806,7 @@ async def test_streaming_create_message_with_input_json_delta_async(
     assert span["data"][SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 41
     assert span["data"][SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 407
     assert span["data"][SPANDATA.GEN_AI_RESPONSE_STREAMING] is True
+    assert span["data"][SPANDATA.GEN_AI_RESPONSE_FINISH_REASONS] == "tool_use"
 
 
 def test_exception_message_create(sentry_init, capture_events):