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fix(ai): remove setting of GEN_AI_AGENT_NAME from langchain #5728

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bd0b816
fix(ai): remove setting of GEN_AI_AGENT_NAME from langchain
harryautomazione Mar 20, 2026
80c772d
Fix Langchain GEN_AI_AGENT_NAME regression by adding contextvar propa…
harryautomazione Mar 20, 2026
ce10982
Fix test_langchain_agent_name_propagation placement
harryautomazione Mar 20, 2026
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161 changes: 65 additions & 96 deletions sentry_sdk/integrations/langchain.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,13 @@
from sentry_sdk.tracing_utils import _get_value, set_span_errored
from sentry_sdk.utils import capture_internal_exceptions, logger

CURRENT_LANGCHAIN_AGENT_NAME = contextvars.ContextVar("CURRENT_LANGCHAIN_AGENT_NAME", default=None)


def _get_current_langchain_agent_name() -> "Optional[str]":
return CURRENT_LANGCHAIN_AGENT_NAME.get(None)


if TYPE_CHECKING:
from typing import (
Any,
Expand Down Expand Up @@ -154,43 +161,6 @@ def _transform_langchain_message_content(content: "Any") -> "Any":


# Contextvar to track agent names in a stack for re-entrant agent support
_agent_stack: "contextvars.ContextVar[Optional[List[Optional[str]]]]" = (
contextvars.ContextVar("langchain_agent_stack", default=None)
)


def _push_agent(agent_name: "Optional[str]") -> None:
"""Push an agent name onto the stack."""
stack = _agent_stack.get()
if stack is None:
stack = []
else:
# Copy the list to maintain contextvar isolation across async contexts
stack = stack.copy()
stack.append(agent_name)
_agent_stack.set(stack)


def _pop_agent() -> "Optional[str]":
"""Pop an agent name from the stack and return it."""
stack = _agent_stack.get()
if stack:
# Copy the list to maintain contextvar isolation across async contexts
stack = stack.copy()
agent_name = stack.pop()
_agent_stack.set(stack)
return agent_name
return None


def _get_current_agent() -> "Optional[str]":
"""Get the current agent name (top of stack) without removing it."""
stack = _agent_stack.get()
if stack:
return stack[-1]
return None


def _get_system_instructions(messages: "List[List[BaseMessage]]") -> "List[str]":
system_instructions = []

Expand Down Expand Up @@ -327,6 +297,11 @@ def _create_span(
watched_span = WatchedSpan(sentry_sdk.start_span(**kwargs))

watched_span.span.__enter__()

agent_name = _get_current_langchain_agent_name()
if agent_name:
watched_span.span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)

self.span_map[run_id] = watched_span
self.gc_span_map()
return watched_span
Expand Down Expand Up @@ -455,10 +430,6 @@ def on_chat_model_start(
elif "openai" in ai_type:
span.set_data(SPANDATA.GEN_AI_SYSTEM, "openai")

agent_name = _get_current_agent()
if agent_name:
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)

for key, attribute in DATA_FIELDS.items():
if key in all_params and all_params[key] is not None:
set_data_normalized(span, attribute, all_params[key], unpack=False)
Expand Down Expand Up @@ -655,10 +626,6 @@ def on_tool_start(
if tool_description is not None:
span.set_data(SPANDATA.GEN_AI_TOOL_DESCRIPTION, tool_description)

agent_name = _get_current_agent()
if agent_name:
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)

if should_send_default_pii() and self.include_prompts:
set_data_normalized(
span,
Expand Down Expand Up @@ -978,57 +945,60 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
return f(self, *args, **kwargs)

agent_name, tools = _get_request_data(self, args, kwargs)
token = CURRENT_LANGCHAIN_AGENT_NAME.set(agent_name)
start_span_function = get_start_span_function()

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

span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)

_set_tools_on_span(span, tools)

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

input = result.get("input")
if (
input is not None
and should_send_default_pii()
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
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
try:
with start_span_function(
op=OP.GEN_AI_INVOKE_AGENT,
name=f"invoke_agent {agent_name}" if agent_name else "invoke_agent",
origin=LangchainIntegration.origin,
) as span:
try:
if agent_name:
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)

span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
span.set_data(SPANDATA.GEN_AI_RESPONSE_STREAMING, False)

_set_tools_on_span(span, tools)

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

input = result.get("input")
if (
input is not None
and should_send_default_pii()
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
)
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 should_send_default_pii()
and integration.include_prompts
):
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)

return result
finally:
# Ensure agent is popped even if an exception occurs
_pop_agent()
output = result.get("output")
if (
output is not None
and should_send_default_pii()
and integration.include_prompts
):
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, output)

return result
finally:
# Ensure agent is popped even if an exception occurs
pass
finally:
CURRENT_LANGCHAIN_AGENT_NAME.reset(token)

return new_invoke

Expand All @@ -1041,6 +1011,7 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
return f(self, *args, **kwargs)

agent_name, tools = _get_request_data(self, args, kwargs)
token = CURRENT_LANGCHAIN_AGENT_NAME.set(agent_name)
start_span_function = get_start_span_function()

span = start_span_function(
Expand All @@ -1050,8 +1021,6 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
)
span.__enter__()

_push_agent(agent_name)

if agent_name:
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, agent_name)

Expand Down Expand Up @@ -1107,8 +1076,8 @@ def new_iterator() -> "Iterator[Any]":
raise
finally:
# Ensure cleanup happens even if iterator is abandoned or fails
_pop_agent()
span.__exit__(*exc_info)
CURRENT_LANGCHAIN_AGENT_NAME.reset(token)

async def new_iterator_async() -> "AsyncIterator[Any]":
exc_info: "tuple[Any, Any, Any]" = (None, None, None)
Expand All @@ -1133,8 +1102,8 @@ async def new_iterator_async() -> "AsyncIterator[Any]":
raise
finally:
# Ensure cleanup happens even if iterator is abandoned or fails
_pop_agent()
span.__exit__(*exc_info)
CURRENT_LANGCHAIN_AGENT_NAME.reset(token)

if str(type(result)) == "<class 'async_generator'>":
result = new_iterator_async()
Expand Down
40 changes: 40 additions & 0 deletions tests/integrations/langchain/test_langchain.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -313,6 +313,46 @@ def test_langchain_agent(
)


def test_langchain_agent_name_propagation(sentry_init, capture_events, monkeypatch):
sentry_init(
integrations=[LangchainIntegration(include_prompts=True)],
traces_sample_rate=1.0,
send_default_pii=True,
)
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"),
]
)

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)

# Mock _get_request_data to return a test agent name
def mock_get_request_data(self, args, kwargs):
return "test_agent_name", [get_word_length]

monkeypatch.setattr(
"sentry_sdk.integrations.langchain._get_request_data", mock_get_request_data
)

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

tx = events[0]
assert tx["type"] == "transaction"
for span in tx["spans"]:
assert span["data"].get(SPANDATA.GEN_AI_AGENT_NAME) == "test_agent_name"


def test_langchain_error(sentry_init, capture_events):
sentry_init(
integrations=[LangchainIntegration(include_prompts=True)],
Expand Down
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