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ref(langchain): Revert input truncation #6115

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9ce1ae5
ref(langchain): Revert input truncation
alexander-alderman-webb Apr 21, 2026
e9cd21a
Merge branch 'webb/remove-langgraph-truncation' into webb/remove-lang…
alexander-alderman-webb Apr 21, 2026
4b4e45d
Merge branch 'webb/remove-langgraph-truncation' into webb/remove-lang…
alexander-alderman-webb Apr 21, 2026
3217948
Merge branch 'webb/remove-langgraph-truncation' into webb/remove-lang…
alexander-alderman-webb Apr 21, 2026
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61 changes: 20 additions & 41 deletions sentry_sdk/integrations/langchain.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
get_start_span_function,
normalize_message_roles,
set_data_normalized,
truncate_and_annotate_messages,
)
from sentry_sdk.consts import OP, SPANDATA
from sentry_sdk.integrations import DidNotEnable, Integration
Expand Down Expand Up @@ -377,17 +376,12 @@ def on_llm_start(
}
for prompt in prompts
]
scope = sentry_sdk.get_current_scope()
messages_data = truncate_and_annotate_messages(
normalized_messages, span, scope
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
normalized_messages,
unpack=False,
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)

def on_chat_model_start(
self: "SentryLangchainCallback",
Expand Down Expand Up @@ -457,17 +451,12 @@ def on_chat_model_start(
self._normalize_langchain_message(message)
)
normalized_messages = normalize_message_roles(normalized_messages)
scope = sentry_sdk.get_current_scope()
messages_data = truncate_and_annotate_messages(
normalized_messages, span, scope
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
normalized_messages,
unpack=False,
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)

def on_chat_model_end(
self: "SentryLangchainCallback",
Expand Down Expand Up @@ -979,17 +968,12 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
and integration.include_prompts
):
normalized_messages = normalize_message_roles([input])
scope = sentry_sdk.get_current_scope()
messages_data = truncate_and_annotate_messages(
normalized_messages, span, scope
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
normalized_messages,
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 (
Expand Down Expand Up @@ -1041,17 +1025,12 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
and integration.include_prompts
):
normalized_messages = normalize_message_roles([input])
scope = sentry_sdk.get_current_scope()
messages_data = truncate_and_annotate_messages(
normalized_messages, span, scope
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
normalized_messages,
unpack=False,
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)

# Run the agent
result = f(self, *args, **kwargs)
Expand Down
78 changes: 0 additions & 78 deletions tests/integrations/langchain/test_langchain.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1290,84 +1290,6 @@ def test_langchain_message_role_normalization_units():
assert normalized[5] == "string message" # String message unchanged


def test_langchain_message_truncation(sentry_init, capture_items):
"""Test that large messages are truncated properly in Langchain integration."""
from langchain_core.outputs import LLMResult, Generation

sentry_init(
integrations=[LangchainIntegration(include_prompts=True)],
traces_sample_rate=1.0,
send_default_pii=True,
)
items = capture_items("transaction", "span")

callback = SentryLangchainCallback(max_span_map_size=100, include_prompts=True)

run_id = "12345678-1234-1234-1234-123456789012"
serialized = {"_type": "openai-chat", "model_name": "gpt-3.5-turbo"}

large_content = (
"This is a very long message that will exceed our size limits. " * 1000
)
prompts = [
"small message 1",
large_content,
large_content,
"small message 4",
"small message 5",
]

with start_transaction():
callback.on_llm_start(
serialized=serialized,
prompts=prompts,
run_id=run_id,
name="my_pipeline",
invocation_params={
"temperature": 0.7,
"max_tokens": 100,
"model": "gpt-3.5-turbo",
},
)

response = LLMResult(
generations=[[Generation(text="The response")]],
llm_output={
"token_usage": {
"total_tokens": 25,
"prompt_tokens": 10,
"completion_tokens": 15,
}
},
)
callback.on_llm_end(response=response, run_id=run_id)

tx = next(item.payload for item in items if item.type == "transaction")
assert tx["type"] == "transaction"

spans = [item.payload for item in items if item.type == "span"]
llm_spans = [
span
for span in spans
if span["attributes"].get("sentry.op") == "gen_ai.text_completion"
]
assert len(llm_spans) > 0

llm_span = llm_spans[0]
assert llm_span["attributes"]["gen_ai.operation.name"] == "text_completion"
assert llm_span["attributes"][SPANDATA.GEN_AI_PIPELINE_NAME] == "my_pipeline"

assert SPANDATA.GEN_AI_REQUEST_MESSAGES in llm_span["attributes"]
messages_data = llm_span["attributes"][SPANDATA.GEN_AI_REQUEST_MESSAGES]
assert isinstance(messages_data, str)

parsed_messages = json.loads(messages_data)
assert isinstance(parsed_messages, list)
assert len(parsed_messages) == 1
assert "small message 5" in str(parsed_messages[0])
assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5


@pytest.mark.parametrize(
"send_default_pii, include_prompts",
[
Expand Down
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