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chore(langchain): Remove transaction-based tracing #7381

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a28d046
chore(langchain): Remove transaction-based tracing
alexander-alderman-webb Sep 4, 2026
e2d31aa
remove start_transaction
alexander-alderman-webb Sep 4, 2026
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297 changes: 94 additions & 203 deletions sentry_sdk/integrations/langchain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import sentry_sdk
from sentry_sdk.ai.utils import (
GEN_AI_ALLOWED_MESSAGE_ROLES,
get_start_span_function,
normalize_message_roles,
set_data_normalized,
transform_content_part,
Expand Down Expand Up @@ -263,7 +262,7 @@ class SentryLangchainCallback(BaseCallbackHandler):
"""Callback handler that creates Sentry spans."""

def __init__(self, include_prompts: bool) -> None:
self.span_map: "OrderedDict[UUID, Union[sentry_sdk.tracing.Span, StreamedSpan]]" = OrderedDict()
self.span_map: "OrderedDict[UUID, StreamedSpan]" = OrderedDict()
self.include_prompts = include_prompts

def _handle_error(self, run_id: "UUID", error: "Any") -> None:
Expand Down Expand Up @@ -299,12 +298,10 @@ def _create_span(
op: str,
name: str,
origin: str,
) -> "Union[sentry_sdk.tracing.Span, StreamedSpan]":
) -> "StreamedSpan":
span = None
if parent_id:
parent_span: "Optional[Union[sentry_sdk.tracing.Span, StreamedSpan]]" = (
self.span_map.get(parent_id)
)
parent_span: "Optional[StreamedSpan]" = self.span_map.get(parent_id)
if parent_span:
span = (
sentry_sdk.traces.start_span(
Expand All @@ -320,17 +317,12 @@ def _create_span(
)

if span is None:
span_streaming = has_span_streaming_enabled(sentry_sdk.get_client().options)
span = (
sentry_sdk.traces.start_span(
name=name,
attributes={
"sentry.op": op,
"sentry.origin": origin,
},
)
if span_streaming
else sentry_sdk.start_span(op=op, name=name, origin=origin)
span = sentry_sdk.traces.start_span(
name=name,
attributes={
"sentry.op": op,
"sentry.origin": origin,
},
)

span.__enter__()
Expand All @@ -339,7 +331,7 @@ def _create_span(

def _exit_span(
self: "SentryLangchainCallback",
span: "Union[sentry_sdk.tracing.Span, StreamedSpan]",
span: "StreamedSpan",
run_id: "UUID",
) -> None:
span.__exit__(None, None, None)
Expand Down Expand Up @@ -1157,89 +1149,46 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
record_inputs = True
record_outputs = True

if has_span_streaming_enabled(client.options):
with sentry_sdk.traces.start_span(
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
SPANDATA.GEN_AI_RESPONSE_STREAMING: False,
},
) as span:
if run_name:
span.set_attribute(SPANDATA.GEN_AI_FUNCTION_ID, run_name)

_set_tools_on_span(span, tools)

# Run the agent
result = f(self, *args, **kwargs)

input = result.get("input")
if input is not None and record_inputs:
normalized_messages = normalize_message_roles([input])

scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(normalized_messages, span, scope)
if not has_span_streaming_enabled(client.options)
else normalized_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)

output = result.get("output")
if output is not None and record_outputs:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)

return result
else:
start_span_function = get_start_span_function()

with start_span_function(
op=OP.GEN_AI_INVOKE_AGENT,
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
origin=LangchainIntegration.origin,
) as span:
if run_name:
span.set_data(SPANDATA.GEN_AI_FUNCTION_ID, run_name)

span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)
with sentry_sdk.traces.start_span(
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
SPANDATA.GEN_AI_RESPONSE_STREAMING: False,
},
) as span:
if run_name:
span.set_attribute(SPANDATA.GEN_AI_FUNCTION_ID, run_name)

_set_tools_on_span(span, tools)
_set_tools_on_span(span, tools)

# Run the agent
result = f(self, *args, **kwargs)
# Run the agent
result = f(self, *args, **kwargs)

input = result.get("input")
if input is not None and record_inputs:
normalized_messages = normalize_message_roles([input])
input = result.get("input")
if input is not None and record_inputs:
normalized_messages = normalize_message_roles([input])

scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(normalized_messages, span, scope)
if not has_span_streaming_enabled(client.options)
else normalized_messages
scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(normalized_messages, span, scope)
if not has_span_streaming_enabled(client.options)
else normalized_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)

output = result.get("output")
if output is not None and record_outputs:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)
output = result.get("output")
if output is not None and record_outputs:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)

return result
return result

return new_invoke

Expand All @@ -1264,34 +1213,18 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
record_inputs = True
record_outputs = True

if has_span_streaming_enabled(client.options):
span = sentry_sdk.traces.start_span(
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
SPANDATA.GEN_AI_RESPONSE_STREAMING: True,
},
)

if run_name:
span.set_attribute(SPANDATA.GEN_AI_FUNCTION_ID, run_name)
else:
start_span_function = get_start_span_function()

span = start_span_function(
op=OP.GEN_AI_INVOKE_AGENT,
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
origin=LangchainIntegration.origin,
)
span.__enter__()

span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, True)
span = sentry_sdk.traces.start_span(
name=f"invoke_agent {run_name}" if run_name else "invoke_agent",
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
SPANDATA.GEN_AI_RESPONSE_STREAMING: True,
},
)

if run_name:
span.set_data(SPANDATA.GEN_AI_FUNCTION_ID, run_name)
if run_name:
span.set_attribute(SPANDATA.GEN_AI_FUNCTION_ID, run_name)

_set_tools_on_span(span, tools)

Expand Down Expand Up @@ -1410,48 +1343,27 @@ def new_embedding_method(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
# TODO: Remove this branch once `send_default_pii` is deprecated
record_inputs = True

if has_span_streaming_enabled(client.options):
with sentry_sdk.traces.start_span(
name=f"embeddings {model_name}" if model_name else "embeddings",
attributes={
"sentry.op": OP.GEN_AI_EMBEDDINGS,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "embeddings",
},
) as span:
if model_name:
span.set_attribute(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)

result = f(self, *args, **kwargs)
return result
else:
with sentry_sdk.start_span(
op=OP.GEN_AI_EMBEDDINGS,
name=f"embeddings {model_name}" if model_name else "embeddings",
origin=LangchainIntegration.origin,
) as span:
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "embeddings")
if model_name:
span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)
with sentry_sdk.traces.start_span(
name=f"embeddings {model_name}" if model_name else "embeddings",
attributes={
"sentry.op": OP.GEN_AI_EMBEDDINGS,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "embeddings",
},
) as span:
if model_name:
span.set_attribute(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)

result = f(self, *args, **kwargs)
return result
result = f(self, *args, **kwargs)
return result

return new_embedding_method

Expand All @@ -1477,47 +1389,26 @@ async def new_async_embedding_method(
# TODO: Remove this branch once `send_default_pii` is deprecated
record_inputs = True

if has_span_streaming_enabled(client.options):
with sentry_sdk.traces.start_span(
name=f"embeddings {model_name}" if model_name else "embeddings",
attributes={
"sentry.op": OP.GEN_AI_EMBEDDINGS,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "embeddings",
},
) as span:
if model_name:
span.set_attribute(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)

result = await f(self, *args, **kwargs)
return result
else:
with sentry_sdk.start_span(
op=OP.GEN_AI_EMBEDDINGS,
name=f"embeddings {model_name}" if model_name else "embeddings",
origin=LangchainIntegration.origin,
) as span:
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "embeddings")
if model_name:
span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)
with sentry_sdk.traces.start_span(
name=f"embeddings {model_name}" if model_name else "embeddings",
attributes={
"sentry.op": OP.GEN_AI_EMBEDDINGS,
"sentry.origin": LangchainIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "embeddings",
},
) as span:
if model_name:
span.set_attribute(SPANDATA.GEN_AI_REQUEST_MODEL, model_name)

if record_inputs and len(args) > 0:
input_data = args[0]
# Normalize to list format
texts = input_data if isinstance(input_data, list) else [input_data]
set_data_normalized(
span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, texts, unpack=False
)

result = await f(self, *args, **kwargs)
return result
result = await f(self, *args, **kwargs)
return result

return new_async_embedding_method
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