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test(langchain): Consolidate span origin assertion #5723

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test(langchain): Consolidate span origin assertion
alexander-alderman-webb Mar 20, 2026
d832e71
Merge branch 'webb/langchain/consolidate-tool-test' into webb/langcha…
alexander-alderman-webb Mar 20, 2026
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122 changes: 6 additions & 116 deletions tests/integrations/langchain/test_langchain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -214,11 +214,17 @@ def test_langchain_agent(

tx = events[0]
assert tx["type"] == "transaction"
assert tx["contexts"]["trace"]["origin"] == "manual"

chat_spans = list(x for x in tx["spans"] if x["op"] == "gen_ai.chat")
tool_exec_span = next(x for x in tx["spans"] if x["op"] == "gen_ai.execute_tool")

assert len(chat_spans) == 2

assert chat_spans[0]["origin"] == "auto.ai.langchain"
assert chat_spans[1]["origin"] == "auto.ai.langchain"
assert tool_exec_span["origin"] == "auto.ai.langchain"

# We can't guarantee anything about the "shape" of the langchain execution graph
assert len(list(x for x in tx["spans"] if x["op"] == "gen_ai.chat")) > 0

Expand Down Expand Up @@ -389,122 +395,6 @@ def test_span_status_error(sentry_init, capture_events):
assert transaction["contexts"]["trace"]["status"] == "internal_error"


def test_span_origin(sentry_init, capture_events):
sentry_init(
integrations=[LangchainIntegration()],
traces_sample_rate=1.0,
)
events = capture_events()

prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are very powerful assistant, but don't know current events",
),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
global stream_result_mock
stream_result_mock = Mock(
side_effect=[
[
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": "call_BbeyNhCKa6kYLYzrD40NGm3b",
"function": {
"arguments": "",
"name": "get_word_length",
},
"type": "function",
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": None,
"function": {
"arguments": '{"word": "eudca"}',
"name": None,
},
"type": None,
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="5",
usage_metadata={
"input_tokens": 142,
"output_tokens": 50,
"total_tokens": 192,
"input_token_details": {"audio": 0, "cache_read": 0},
"output_token_details": {"audio": 0, "reasoning": 0},
},
),
generation_info={"finish_reason": "function_call"},
),
],
[
ChatGenerationChunk(
text="The word eudca has 5 letters.",
type="ChatGenerationChunk",
message=AIMessageChunk(
content="The word eudca has 5 letters.",
usage_metadata={
"input_tokens": 89,
"output_tokens": 28,
"total_tokens": 117,
"input_token_details": {"audio": 0, "cache_read": 0},
"output_token_details": {"audio": 0, "reasoning": 0},
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
generation_info={"finish_reason": "stop"},
message=AIMessageChunk(content=""),
),
],
]
)
llm = MockOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
openai_api_key="badkey",
)
agent = create_openai_tools_agent(llm, [get_word_length], prompt)

agent_executor = AgentExecutor(agent=agent, tools=[get_word_length], verbose=True)

with start_transaction():
list(agent_executor.stream({"input": "How many letters in the word eudca"}))

(event,) = events

assert event["contexts"]["trace"]["origin"] == "manual"
for span in event["spans"]:
assert span["origin"] == "auto.ai.langchain"


def test_manual_callback_no_duplication(sentry_init):
"""
Test that when a user manually provides a SentryLangchainCallback,
Expand Down
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