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fix(litellm): Set operation name from call type instead of chat fallback #6792

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fix(litellm): Set operation name from call type instead of chat fallback #6792

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8f02fad
fix(litellm): Set operation name from call type instead of chat fallback
VihaanAgarwal Jul 7, 2026
b57b7f9
Skip instrumentation for unknown call types and test through the lite…
VihaanAgarwal Jul 10, 2026
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43 changes: 25 additions & 18 deletions sentry_sdk/integrations/litellm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@

if TYPE_CHECKING:
from datetime import datetime
from typing import Any, Dict, List
from typing import Any, Dict, List, Tuple

try:
import litellm # type: ignore[import-not-found]
Expand All @@ -35,6 +35,19 @@
# to every callback, so it lives and dies with the request.
_SPAN_KEY = "_sentry_span"

# Call types whose gen_ai operation name we can determine accurately. Everything
# else is not instrumented, since guessing records wrong data.
_CALL_TYPE_OPERATIONS: "Dict[Any, Tuple[str, str]]" = {
"completion": ("chat", consts.OP.GEN_AI_CHAT),
"acompletion": ("chat", consts.OP.GEN_AI_CHAT),
"text_completion": ("text_completion", consts.OP.GEN_AI_TEXT_COMPLETION),
"atext_completion": ("text_completion", consts.OP.GEN_AI_TEXT_COMPLETION),
"embedding": ("embeddings", consts.OP.GEN_AI_EMBEDDINGS),
"aembedding": ("embeddings", consts.OP.GEN_AI_EMBEDDINGS),
"responses": ("responses", consts.OP.GEN_AI_RESPONSES),
"aresponses": ("responses", consts.OP.GEN_AI_RESPONSES),
}


def _store_span(kwargs: "Dict[str, Any]", span: "Any") -> None:
kwargs[_SPAN_KEY] = span
Expand Down Expand Up @@ -83,6 +96,12 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None:
if integration is None:
return

call_type = kwargs.get("call_type", None)
if call_type not in _CALL_TYPE_OPERATIONS:
return
Comment on lines +99 to +101

@sentry sentry Bot Jul 10, 2026

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Bug: The _input_callback returns early for unmapped call_types, preventing span creation and disabling exception capture for litellm calls like image_generation.
Severity: MEDIUM

Suggested Fix

To restore exception capturing for all litellm call types, ensure a span is created even for unmapped types. Consider creating a fallback span with a generic operation, similar to the previous behavior, instead of returning early from _input_callback when a call_type is not found.

Prompt for AI Agent 
Review the code at the location below. A potential bug has been identified by an AI
agent. Verify if this is a real issue. If it is, propose a fix; if not, explain why it's
not valid.

Location: sentry_sdk/integrations/litellm.py#L99-L101

Potential issue: The `_input_callback` returns early for any `litellm` `call_type` not
in `_CALL_TYPE_OPERATIONS`, such as `image_generation` or `speech`. This prevents a
Sentry span from being created for these calls. As a result, if an exception occurs
during one of these unmapped calls, the `_failure_callback` is unable to find a span to
process. It returns immediately, and the exception is not captured by Sentry. This is a
regression that silently disables error reporting for previously-instrumented call
types.

Did we get this right? 👍 / 👎 to inform future reviews.


operation, span_op = _CALL_TYPE_OPERATIONS[call_type]

# Get key parameters
full_model = kwargs.get("model", "")
try:
Expand All @@ -91,33 +110,21 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None:
model = full_model
provider = "unknown"

call_type = kwargs.get("call_type", None)
if call_type == "embedding" or call_type == "aembedding":
operation = "embeddings"
else:
operation = "chat"
span_name = f"{operation} {model}"

# Start a new span/transaction
if has_span_streaming_enabled(client.options):
span = sentry_sdk.traces.start_span(
name=f"{operation} {model}",
name=span_name,
attributes={
"sentry.op": (
consts.OP.GEN_AI_CHAT
if operation == "chat"
else consts.OP.GEN_AI_EMBEDDINGS
),
"sentry.op": span_op,
"sentry.origin": LiteLLMIntegration.origin,
},
)
else:
span = get_start_span_function()(
op=(
consts.OP.GEN_AI_CHAT
if operation == "chat"
else consts.OP.GEN_AI_EMBEDDINGS
),
name=f"{operation} {model}",
op=span_op,
name=span_name,

@cursor cursor Bot Jul 10, 2026

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New ops skip prompt capture

Medium Severity

_CALL_TYPE_OPERATIONS now yields text_completion and responses, but prompt recording still only special-cases embeddings and otherwise reads messages. Those call types take prompt and input, so with include_prompts enabled their inputs are never attached to the span.

Additional Locations (1)
Fix in Cursor Fix in Web

Reviewed by Cursor Bugbot for commit b57b7f9. Configure here.

origin=LiteLLMIntegration.origin,
)
span.__enter__()
Expand Down
150 changes: 149 additions & 1 deletion tests/integrations/litellm/test_litellm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,8 @@ async def __call__(self, *args, **kwargs):
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from openai import AsyncOpenAI, OpenAI
from openai.types import CompletionUsage
from openai.types import Completion, CompletionUsage, Image, ImagesResponse
from openai.types.completion_choice import CompletionChoice

from sentry_sdk import start_transaction
from sentry_sdk._types import BLOB_DATA_SUBSTITUTE
Expand Down Expand Up @@ -2477,6 +2478,7 @@ def test_response_without_usage(
kwargs = {
"model": "gpt-3.5-turbo",
"messages": messages,
"call_type": "completion",
}

_input_callback(kwargs)
Expand All @@ -2500,6 +2502,7 @@ def test_response_without_usage(
kwargs = {
"model": "gpt-3.5-turbo",
"messages": messages,
"call_type": "completion",
}

_input_callback(kwargs)
Expand Down Expand Up @@ -2559,6 +2562,7 @@ def test_litellm_message_truncation(sentry_init, capture_events):
kwargs = {
"model": "gpt-3.5-turbo",
"messages": messages,
"call_type": "completion",
}

_input_callback(kwargs)
Expand Down Expand Up @@ -3415,3 +3419,147 @@ def test_convert_message_parts_image_url_missing_url():
converted = _convert_message_parts(messages)
# Should return item unchanged
assert converted[0]["content"][0]["type"] == "image_url"


def test_text_completion_operation_name(
sentry_init,
capture_events,
get_model_response,
reset_litellm_executor,
):
"""text_completion calls get the text_completion op, not the chat fallback."""
sentry_init(
integrations=[LiteLLMIntegration()],
disabled_integrations=[StdlibIntegration],
traces_sample_rate=1.0,
stream_gen_ai_spans=False,
)
events = capture_events()

client = OpenAI(api_key="test-key")

model_response = get_model_response(
Completion(
id="cmpl-test",
choices=[
CompletionChoice(finish_reason="stop", index=0, text="Test response")
],
created=1234567890,
model="gpt-3.5-turbo-instruct",
object="text_completion",
usage=CompletionUsage(
prompt_tokens=10,
completion_tokens=20,
total_tokens=30,
),
),
serialize_pydantic=True,
request_headers={"X-Stainless-Raw-Response": "true"},
)

with mock.patch.object(
client.completions._client._client,
"send",
return_value=model_response,
), start_transaction(name="litellm test"):
litellm.text_completion(
model="gpt-3.5-turbo-instruct",
prompt="Hello!",
client=client,
)

litellm_utils.executor.shutdown(wait=True)

(event,) = events
(span,) = [s for s in event["spans"] if s["origin"] == "auto.ai.litellm"]

assert span["op"] == OP.GEN_AI_TEXT_COMPLETION
assert span["description"] == "text_completion gpt-3.5-turbo-instruct"
assert span["data"][SPANDATA.GEN_AI_OPERATION_NAME] == "text_completion"


def test_responses_operation_name(
sentry_init,
capture_events,
get_model_response,
nonstreaming_responses_model_response,
reset_litellm_executor,
):
"""Responses API calls get the responses op, not the chat fallback."""
sentry_init(
integrations=[LiteLLMIntegration()],
disabled_integrations=[StdlibIntegration],
traces_sample_rate=1.0,
stream_gen_ai_spans=False,
)
events = capture_events()

client = HTTPHandler()

model_response = get_model_response(
nonstreaming_responses_model_response,
serialize_pydantic=True,
)

with mock.patch.object(
client,
"post",
return_value=model_response,
), start_transaction(name="litellm test"):
litellm.responses(
model="gpt-4",
input="Hello!",
client=client,
api_key="test-key",
)

litellm_utils.executor.shutdown(wait=True)

(event,) = events
(span,) = [s for s in event["spans"] if s["origin"] == "auto.ai.litellm"]

assert span["op"] == OP.GEN_AI_RESPONSES
assert span["description"] == "responses gpt-4"
assert span["data"][SPANDATA.GEN_AI_OPERATION_NAME] == "responses"


def test_unknown_call_type_is_not_instrumented(
sentry_init,
capture_events,
get_model_response,
reset_litellm_executor,
):
"""Call types with no accurate operation name emit no span at all."""
sentry_init(
integrations=[LiteLLMIntegration()],
disabled_integrations=[StdlibIntegration],
traces_sample_rate=1.0,
stream_gen_ai_spans=False,
)
events = capture_events()

client = OpenAI(api_key="test-key")

model_response = get_model_response(
ImagesResponse(
created=1234567890,
data=[Image(url="https://example.com/image.png")],
),
serialize_pydantic=True,
)

with mock.patch.object(
client.images._client._client,
"send",
return_value=model_response,
), start_transaction(name="litellm test"):
litellm.image_generation(
model="dall-e-3",
prompt="A cat",
client=client,
)

litellm_utils.executor.shutdown(wait=True)

(event,) = events
assert [s for s in event["spans"] if s["origin"] == "auto.ai.litellm"] == []