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ref(langgraph): Stop setting gen_ai.response.model on Invoke Agent spans #7512

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Merged
alexander-alderman-webb merged 1 commit into from
Sep 15, 2026
Merged

ref(langgraph): Stop setting gen_ai.response.model on Invoke Agent spans #7512

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17 changes: 0 additions & 17 deletions sentry_sdk/integrations/langgraph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -299,22 +299,6 @@ def _set_usage_data(span: "Span", messages: "Any") -> None:
)


def _set_response_model_name(span: "Span", messages: "Any") -> None:
if len(messages) == 0:
return

last_message = messages[-1]
response_metadata = last_message.get("response_metadata")
if response_metadata is None:
return

model_name = response_metadata.get("model_name")
if model_name is None:
return

set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_MODEL, model_name)


def _set_response_attributes(
span: "Any",
input_messages: "Optional[List[Any]]",
Expand All @@ -328,7 +312,6 @@ def _set_response_attributes(
return

_set_usage_data(span, new_messages)
_set_response_model_name(span, new_messages)

if _should_record_outputs(integration):
llm_response_text = _extract_llm_response_text(new_messages)
Expand Down
336 changes: 0 additions & 336 deletions tests/integrations/langgraph/test_langgraph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -809,340 +809,6 @@ async def run_test():
assert invoke_agent_span["attributes"]["gen_ai.usage.total_tokens"] == 50


def test_pregel_invoke_span_includes_response_model(
sentry_init,
capture_items,
):
"""
Test that invoke_agent spans include the response model.
When an agent makes multiple LLM calls, it should report the last model used.
"""
sentry_init(
integrations=[LanggraphIntegration()],
traces_sample_rate=1.0,
)

test_state = {
"messages": [
MockMessage("Hello, can you help me?", name="user"),
MockMessage("Of course! How can I assist you?", name="assistant"),
]
}

pregel = MockPregelInstance("test_graph")

expected_assistant_response = "I'll help you with that task!"
expected_tool_calls = [
{
"id": "call_test_123",
"type": "function",
"function": {"name": "search_tool", "arguments": '{"query": "help"}'},
}
]

def original_invoke(self, *args, **kwargs):
input_messages = args[0].get("messages", [])
new_messages = input_messages + [
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 30,
"prompt_tokens": 10,
"completion_tokens": 20,
},
"model_name": "gpt-4.1-2025-04-14",
},
)
]
return {"messages": new_messages}

items = capture_items("span")

wrapped_invoke = _wrap_pregel_invoke(original_invoke)
result = wrapped_invoke(pregel, test_state)

assert result is not None

sentry_sdk.flush()
spans = [item.payload for item in items]
invoke_spans = [
span
for span in spans
if span["attributes"]["sentry.op"] == OP.GEN_AI_INVOKE_AGENT
]
assert len(invoke_spans) == 1

invoke_agent_span = invoke_spans[0]

# Verify invoke_agent span has response model
assert invoke_agent_span["name"] == "invoke_agent test_graph"
assert "gen_ai.response.model" in invoke_agent_span["attributes"]
assert (
invoke_agent_span["attributes"]["gen_ai.response.model"] == "gpt-4.1-2025-04-14"
)


def test_pregel_ainvoke_span_includes_response_model(
sentry_init,
capture_items,
):
"""
Test that invoke_agent spans include the response model.
When an agent makes multiple LLM calls, it should report the last model used.
"""
sentry_init(
integrations=[LanggraphIntegration()],
traces_sample_rate=1.0,
)

test_state = {
"messages": [
MockMessage("Hello, can you help me?", name="user"),
MockMessage("Of course! How can I assist you?", name="assistant"),
]
}

pregel = MockPregelInstance("test_graph")

expected_assistant_response = "I'll help you with that task!"
expected_tool_calls = [
{
"id": "call_test_123",
"type": "function",
"function": {"name": "search_tool", "arguments": '{"query": "help"}'},
}
]

async def original_ainvoke(self, *args, **kwargs):
input_messages = args[0].get("messages", [])
new_messages = input_messages + [
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 30,
"prompt_tokens": 10,
"completion_tokens": 20,
},
"model_name": "gpt-4.1-2025-04-14",
},
)
]
return {"messages": new_messages}

async def run_test():
wrapped_ainvoke = _wrap_pregel_ainvoke(original_ainvoke)
result = await wrapped_ainvoke(pregel, test_state)
return result

items = capture_items("span")

result = asyncio.run(run_test())
assert result is not None

sentry_sdk.flush()
spans = [item.payload for item in items]
invoke_spans = [
span
for span in spans
if span["attributes"]["sentry.op"] == OP.GEN_AI_INVOKE_AGENT
]
assert len(invoke_spans) == 1

invoke_agent_span = invoke_spans[0]

# Verify invoke_agent span has response model
assert invoke_agent_span["name"] == "invoke_agent test_graph"
assert "gen_ai.response.model" in invoke_agent_span["attributes"]
assert (
invoke_agent_span["attributes"]["gen_ai.response.model"] == "gpt-4.1-2025-04-14"
)


def test_pregel_invoke_span_uses_last_response_model(
sentry_init,
capture_items,
):
"""
Test that when an agent makes multiple LLM calls (e.g., with tools),
the invoke_agent span reports the last response model used.
"""
sentry_init(
integrations=[LanggraphIntegration()],
traces_sample_rate=1.0,
)

test_state = {
"messages": [
MockMessage("Hello, can you help me?", name="user"),
MockMessage("Of course! How can I assist you?", name="assistant"),
]
}

pregel = MockPregelInstance("test_graph")

expected_assistant_response = "I'll help you with that task!"
expected_tool_calls = [
{
"id": "call_test_123",
"type": "function",
"function": {"name": "search_tool", "arguments": '{"query": "help"}'},
}
]

def original_invoke(self, *args, **kwargs):
input_messages = args[0].get("messages", [])
new_messages = input_messages + [
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 15,
"prompt_tokens": 10,
"completion_tokens": 5,
},
"model_name": "gpt-4-0613",
},
),
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 35,
"prompt_tokens": 20,
"completion_tokens": 15,
},
"model_name": "gpt-4.1-2025-04-14",
},
),
]
return {"messages": new_messages}

items = capture_items("span")

wrapped_invoke = _wrap_pregel_invoke(original_invoke)
result = wrapped_invoke(pregel, test_state)

assert result is not None

sentry_sdk.flush()
spans = [item.payload for item in items]
invoke_spans = [
span
for span in spans
if span["attributes"]["sentry.op"] == OP.GEN_AI_INVOKE_AGENT
]
assert len(invoke_spans) == 1

invoke_agent_span = invoke_spans[0]

# Verify invoke_agent span uses the LAST response model
assert "gen_ai.response.model" in invoke_agent_span["attributes"]
assert (
invoke_agent_span["attributes"]["gen_ai.response.model"] == "gpt-4.1-2025-04-14"
)


def test_pregel_ainvoke_span_uses_last_response_model(
sentry_init,
capture_items,
):
"""
Test that when an agent makes multiple LLM calls (e.g., with tools),
the invoke_agent span reports the last response model used.
"""
sentry_init(
integrations=[LanggraphIntegration()],
traces_sample_rate=1.0,
)

test_state = {
"messages": [
MockMessage("Hello, can you help me?", name="user"),
MockMessage("Of course! How can I assist you?", name="assistant"),
]
}

pregel = MockPregelInstance("test_graph")

expected_assistant_response = "I'll help you with that task!"
expected_tool_calls = [
{
"id": "call_test_123",
"type": "function",
"function": {"name": "search_tool", "arguments": '{"query": "help"}'},
}
]

async def original_ainvoke(self, *args, **kwargs):
input_messages = args[0].get("messages", [])
new_messages = input_messages + [
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 15,
"prompt_tokens": 10,
"completion_tokens": 5,
},
"model_name": "gpt-4-0613",
},
),
MockMessage(
content=expected_assistant_response,
name="assistant",
tool_calls=expected_tool_calls,
response_metadata={
"token_usage": {
"total_tokens": 35,
"prompt_tokens": 20,
"completion_tokens": 15,
},
"model_name": "gpt-4.1-2025-04-14",
},
),
]
return {"messages": new_messages}

async def run_test():
wrapped_ainvoke = _wrap_pregel_ainvoke(original_ainvoke)
result = await wrapped_ainvoke(pregel, test_state)
return result

items = capture_items("span")

result = asyncio.run(run_test())
assert result is not None

sentry_sdk.flush()
spans = [item.payload for item in items]
invoke_spans = [
span
for span in spans
if span["attributes"]["sentry.op"] == OP.GEN_AI_INVOKE_AGENT
]
assert len(invoke_spans) == 1

invoke_agent_span = invoke_spans[0]

# Verify invoke_agent span uses the LAST response model
assert "gen_ai.response.model" in invoke_agent_span["attributes"]
assert (
invoke_agent_span["attributes"]["gen_ai.response.model"] == "gpt-4.1-2025-04-14"
)


def test_complex_message_parsing():
"""Test message parsing with complex message structures."""
messages = [
Expand Down Expand Up @@ -1713,7 +1379,6 @@ def original_invoke(self, *args, **kwargs):
assert data[SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10
assert data[SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20
assert data[SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30
assert data[SPANDATA.GEN_AI_RESPONSE_MODEL] == "gpt-4.1-2025-04-14"


def test_pregel_ainvoke_message_delta_ignores_gen_ai_inputs_setting(
Expand Down Expand Up @@ -1784,4 +1449,3 @@ async def run_test():
assert data[SPANDATA.GEN_AI_USAGE_INPUT_TOKENS] == 10
assert data[SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS] == 20
assert data[SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS] == 30
assert data[SPANDATA.GEN_AI_RESPONSE_MODEL] == "gpt-4.1-2025-04-14"
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