𝗖𝗨𝗥𝗥𝗘𝗡𝗧 𝗙𝗜𝗟𝗘 : /opt/hc_python/lib/python3.12/site-packages/sentry_sdk/integrations//langgraph.py
from functools import wraps
from typing import Any, Callable, List, Optional
import sentry_sdk
from sentry_sdk.ai.utils import (
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
from sentry_sdk.scope import should_send_default_pii
from sentry_sdk.traces import StreamedSpan
from sentry_sdk.tracing_utils import (
has_span_streaming_enabled,
should_truncate_gen_ai_input,
)
from sentry_sdk.utils import safe_serialize
try:
from langgraph.graph import StateGraph
from langgraph.pregel import Pregel
except ImportError:
raise DidNotEnable("langgraph not installed")
class LanggraphIntegration(Integration):
identifier = "langgraph"
origin = f"auto.ai.{identifier}"
def __init__(self: "LanggraphIntegration", include_prompts: bool = True) -> None:
self.include_prompts = include_prompts
@staticmethod
def setup_once() -> None:
# LangGraph lets users create agents using a StateGraph or the Functional API.
# StateGraphs are then compiled to a CompiledStateGraph. Both CompiledStateGraph and
# the functional API execute on a Pregel instance. Pregel is the runtime for the graph
# and the invocation happens on Pregel, so patching the invoke methods takes care of both.
# The streaming methods are not patched, because due to some internal reasons, LangGraph
# will automatically patch the streaming methods to run through invoke, and by doing this
# we prevent duplicate spans for invocations.
StateGraph.compile = _wrap_state_graph_compile(StateGraph.compile)
if hasattr(Pregel, "invoke"):
Pregel.invoke = _wrap_pregel_invoke(Pregel.invoke)
if hasattr(Pregel, "ainvoke"):
Pregel.ainvoke = _wrap_pregel_ainvoke(Pregel.ainvoke)
def _get_graph_name(graph_obj: "Any") -> "Optional[str]":
for attr in ["name", "graph_name", "__name__", "_name"]:
if hasattr(graph_obj, attr):
name = getattr(graph_obj, attr)
if name and isinstance(name, str):
return name
return None
def _normalize_langgraph_message(message: "Any") -> "Any":
if not hasattr(message, "content"):
return None
parsed = {"role": getattr(message, "type", None), "content": message.content}
for attr in [
"name",
"tool_calls",
"function_call",
"tool_call_id",
"response_metadata",
]:
if hasattr(message, attr):
value = getattr(message, attr)
if value is not None:
parsed[attr] = value
return parsed
def _parse_langgraph_messages(state: "Any") -> "Optional[List[Any]]":
if not state:
return None
messages = None
if isinstance(state, dict):
messages = state.get("messages")
elif hasattr(state, "messages"):
messages = state.messages
elif hasattr(state, "get") and callable(state.get):
try:
messages = state.get("messages")
except Exception:
pass
if not messages or not isinstance(messages, (list, tuple)):
return None
normalized_messages = []
for message in messages:
try:
normalized = _normalize_langgraph_message(message)
if normalized:
normalized_messages.append(normalized)
except Exception:
continue
return normalized_messages if normalized_messages else None
def _wrap_state_graph_compile(f: "Callable[..., Any]") -> "Callable[..., Any]":
@wraps(f)
def new_compile(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
client = sentry_sdk.get_client()
integration = client.get_integration(LanggraphIntegration)
if integration is None or has_span_streaming_enabled(client.options):
return f(self, *args, **kwargs)
with sentry_sdk.start_span(
op=OP.GEN_AI_CREATE_AGENT,
origin=LanggraphIntegration.origin,
) as span:
compiled_graph = f(self, *args, **kwargs)
compiled_graph_name = getattr(compiled_graph, "name", None)
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "create_agent")
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, compiled_graph_name)
if compiled_graph_name:
span.description = f"create_agent {compiled_graph_name}"
else:
span.description = "create_agent"
if kwargs.get("model", None) is not None:
span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, kwargs.get("model"))
tools = None
get_graph = getattr(compiled_graph, "get_graph", None)
if get_graph and callable(get_graph):
graph_obj = compiled_graph.get_graph()
nodes = getattr(graph_obj, "nodes", None)
if nodes and isinstance(nodes, dict):
tools_node = nodes.get("tools")
if tools_node:
data = getattr(tools_node, "data", None)
if data and hasattr(data, "tools_by_name"):
tools = list(data.tools_by_name.keys())
if tools is not None:
span.set_data(SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, tools)
return compiled_graph
return new_compile
def _wrap_pregel_invoke(f: "Callable[..., Any]") -> "Callable[..., Any]":
@wraps(f)
def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
client = sentry_sdk.get_client()
integration = client.get_integration(LanggraphIntegration)
if integration is None:
return f(self, *args, **kwargs)
graph_name = _get_graph_name(self)
span_name = (
f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
)
if has_span_streaming_enabled(client.options):
with sentry_sdk.traces.start_span(
name=span_name,
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LanggraphIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
},
) as span:
if graph_name:
span.set_attribute(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
span.set_attribute(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
# Store input messages to later compare with output
input_messages = None
if (
len(args) > 0
and should_send_default_pii()
and integration.include_prompts
):
input_messages = _parse_langgraph_messages(args[0])
if input_messages:
normalized_input_messages = normalize_message_roles(
input_messages
)
client = sentry_sdk.get_client()
scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(
normalized_input_messages, span, scope
)
if should_truncate_gen_ai_input(client.options)
else normalized_input_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)
result = f(self, *args, **kwargs)
_set_response_attributes(span, input_messages, result, integration)
return result
else:
with get_start_span_function()(
op=OP.GEN_AI_INVOKE_AGENT,
name=span_name,
origin=LanggraphIntegration.origin,
) as span:
if graph_name:
span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
# Store input messages to later compare with output
input_messages = None
if (
len(args) > 0
and should_send_default_pii()
and integration.include_prompts
):
input_messages = _parse_langgraph_messages(args[0])
if input_messages:
normalized_input_messages = normalize_message_roles(
input_messages
)
client = sentry_sdk.get_client()
scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(
normalized_input_messages, span, scope
)
if should_truncate_gen_ai_input(client.options)
else normalized_input_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)
result = f(self, *args, **kwargs)
_set_response_attributes(span, input_messages, result, integration)
return result
return new_invoke
def _wrap_pregel_ainvoke(f: "Callable[..., Any]") -> "Callable[..., Any]":
@wraps(f)
async def new_ainvoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
client = sentry_sdk.get_client()
integration = client.get_integration(LanggraphIntegration)
if integration is None:
return await f(self, *args, **kwargs)
graph_name = _get_graph_name(self)
span_name = (
f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
)
if has_span_streaming_enabled(client.options):
with sentry_sdk.traces.start_span(
name=span_name,
attributes={
"sentry.op": OP.GEN_AI_INVOKE_AGENT,
"sentry.origin": LanggraphIntegration.origin,
SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
},
) as span:
if graph_name:
span.set_attribute(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
span.set_attribute(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
input_messages = None
if (
len(args) > 0
and should_send_default_pii()
and integration.include_prompts
):
input_messages = _parse_langgraph_messages(args[0])
if input_messages:
normalized_input_messages = normalize_message_roles(
input_messages
)
client = sentry_sdk.get_client()
scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(
normalized_input_messages, span, scope
)
if should_truncate_gen_ai_input(client.options)
else normalized_input_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)
result = await f(self, *args, **kwargs)
_set_response_attributes(span, input_messages, result, integration)
return result
with get_start_span_function()(
op=OP.GEN_AI_INVOKE_AGENT,
name=span_name,
origin=LanggraphIntegration.origin,
) as span:
if graph_name:
span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
input_messages = None
if (
len(args) > 0
and should_send_default_pii()
and integration.include_prompts
):
input_messages = _parse_langgraph_messages(args[0])
if input_messages:
normalized_input_messages = normalize_message_roles(input_messages)
client = sentry_sdk.get_client()
scope = sentry_sdk.get_current_scope()
messages_data = (
truncate_and_annotate_messages(
normalized_input_messages, span, scope
)
if should_truncate_gen_ai_input(client.options)
else normalized_input_messages
)
if messages_data is not None:
set_data_normalized(
span,
SPANDATA.GEN_AI_REQUEST_MESSAGES,
messages_data,
unpack=False,
)
result = await f(self, *args, **kwargs)
_set_response_attributes(span, input_messages, result, integration)
return result
return new_ainvoke
def _get_new_messages(
input_messages: "Optional[List[Any]]", output_messages: "Optional[List[Any]]"
) -> "Optional[List[Any]]":
"""Extract only the new messages added during this invocation."""
if not output_messages:
return None
if not input_messages:
return output_messages
# only return the new messages, aka the output messages that are not in the input messages
input_count = len(input_messages)
new_messages = (
output_messages[input_count:] if len(output_messages) > input_count else []
)
return new_messages if new_messages else None
def _extract_llm_response_text(messages: "Optional[List[Any]]") -> "Optional[str]":
if not messages:
return None
for message in reversed(messages):
if isinstance(message, dict):
role = message.get("role")
if role in ["assistant", "ai"]:
content = message.get("content")
if content and isinstance(content, str):
return content
return None
def _extract_tool_calls(messages: "Optional[List[Any]]") -> "Optional[List[Any]]":
if not messages:
return None
tool_calls = []
for message in messages:
if isinstance(message, dict):
msg_tool_calls = message.get("tool_calls")
if msg_tool_calls and isinstance(msg_tool_calls, list):
tool_calls.extend(msg_tool_calls)
return tool_calls if tool_calls else None
def _set_usage_data(span: "sentry_sdk.tracing.Span", messages: "Any") -> None:
input_tokens = 0
output_tokens = 0
total_tokens = 0
for message in messages:
response_metadata = message.get("response_metadata")
if response_metadata is None:
continue
token_usage = response_metadata.get("token_usage")
if not token_usage:
continue
input_tokens += int(token_usage.get("prompt_tokens", 0))
output_tokens += int(token_usage.get("completion_tokens", 0))
total_tokens += int(token_usage.get("total_tokens", 0))
set_on_span = (
span.set_attribute if isinstance(span, StreamedSpan) else span.set_data
)
if input_tokens > 0:
set_on_span(SPANDATA.GEN_AI_USAGE_INPUT_TOKENS, input_tokens)
if output_tokens > 0:
set_on_span(SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens)
if total_tokens > 0:
set_on_span(
SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS,
total_tokens,
)
def _set_response_model_name(span: "sentry_sdk.tracing.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]]",
result: "Any",
integration: "LanggraphIntegration",
) -> None:
parsed_response_messages = _parse_langgraph_messages(result)
new_messages = _get_new_messages(input_messages, parsed_response_messages)
if new_messages is None:
return
_set_usage_data(span, new_messages)
_set_response_model_name(span, new_messages)
if not (should_send_default_pii() and integration.include_prompts):
return
llm_response_text = _extract_llm_response_text(new_messages)
if llm_response_text:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, llm_response_text)
elif new_messages:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, new_messages)
else:
set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, result)
tool_calls = _extract_tool_calls(new_messages)
if tool_calls:
set_data_normalized(
span,
SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
safe_serialize(tool_calls),
unpack=False,
)
