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feat(anthropic): Set gen_ai.response.finish_reasons on span #5663

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feat(anthropic): Set gen_ai.response.finish_reasons on span #5663

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20febec
feat(anthropic): Set gen_ai.response.finish_reasons on span
ericapisani Mar 13, 2026
3f3ddda
fix(anthropic): Set finish_reasons as array per OTel conventions
ericapisani Mar 13, 2026
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2 changes: 1 addition & 1 deletion sentry_sdk/consts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -499,7 +499,7 @@ class SPANDATA:
GEN_AI_RESPONSE_FINISH_REASONS = "gen_ai.response.finish_reasons"
"""
The reason why the model stopped generating.
Example: "COMPLETE"
Example: ["COMPLETE"]
"""

GEN_AI_RESPONSE_ID = "gen_ai.response.id"
Expand Down
11 changes: 11 additions & 0 deletions sentry_sdk/integrations/anthropic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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": [],
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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]":
Expand Down Expand Up @@ -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'>":
Expand Down
120 changes: 120 additions & 0 deletions tests/integrations/anthropic/test_anthropic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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",
)


Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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(
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand Down
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