516 lines
18 KiB
Python
516 lines
18 KiB
Python
from functools import wraps
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from typing import Any, Callable, List, Optional
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import sentry_sdk
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from sentry_sdk.ai.utils import (
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get_start_span_function,
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normalize_message_roles,
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set_data_normalized,
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truncate_and_annotate_messages,
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)
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from sentry_sdk.consts import OP, SPANDATA
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from sentry_sdk.integrations import DidNotEnable, Integration
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# This is fine because langgraph depends on langchain-base, and LangchainIntegration only imports from langchain-base.
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from sentry_sdk.integrations.langchain import LangchainIntegration
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from sentry_sdk.scope import should_send_default_pii
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from sentry_sdk.traces import StreamedSpan
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from sentry_sdk.tracing_utils import (
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has_span_streaming_enabled,
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should_truncate_gen_ai_input,
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)
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from sentry_sdk.utils import safe_serialize
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try:
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from langgraph.errors import GraphBubbleUp
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from langgraph.graph import StateGraph
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from langgraph.pregel import Pregel
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except ImportError:
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raise DidNotEnable("langgraph not installed")
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class LanggraphIntegration(Integration):
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identifier = "langgraph"
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origin = f"auto.ai.{identifier}"
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def __init__(self: "LanggraphIntegration", include_prompts: bool = True) -> None:
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self.include_prompts = include_prompts
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@staticmethod
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def setup_once() -> None:
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LangchainIntegration._ignored_exceptions.add(GraphBubbleUp)
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# LangGraph lets users create agents using a StateGraph or the Functional API.
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# StateGraphs are then compiled to a CompiledStateGraph. Both CompiledStateGraph and
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# the functional API execute on a Pregel instance. Pregel is the runtime for the graph
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# and the invocation happens on Pregel, so patching the invoke methods takes care of both.
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# The streaming methods are not patched, because due to some internal reasons, LangGraph
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# will automatically patch the streaming methods to run through invoke, and by doing this
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# we prevent duplicate spans for invocations.
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StateGraph.compile = _wrap_state_graph_compile(StateGraph.compile)
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if hasattr(Pregel, "invoke"):
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Pregel.invoke = _wrap_pregel_invoke(Pregel.invoke)
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if hasattr(Pregel, "ainvoke"):
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Pregel.ainvoke = _wrap_pregel_ainvoke(Pregel.ainvoke)
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def _get_graph_name(graph_obj: "Any") -> "Optional[str]":
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for attr in ["name", "graph_name", "__name__", "_name"]:
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if hasattr(graph_obj, attr):
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name = getattr(graph_obj, attr)
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if name and isinstance(name, str):
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return name
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return None
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def _normalize_langgraph_message(message: "Any") -> "Any":
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if not hasattr(message, "content"):
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return None
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parsed = {"role": getattr(message, "type", None), "content": message.content}
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for attr in [
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"name",
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"tool_calls",
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"function_call",
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"tool_call_id",
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"response_metadata",
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]:
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if hasattr(message, attr):
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value = getattr(message, attr)
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if value is not None:
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parsed[attr] = value
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return parsed
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def _parse_langgraph_messages(state: "Any") -> "Optional[List[Any]]":
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if not state:
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return None
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messages = None
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if isinstance(state, dict):
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messages = state.get("messages")
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elif hasattr(state, "messages"):
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messages = state.messages
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elif hasattr(state, "get") and callable(state.get):
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try:
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messages = state.get("messages")
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except Exception:
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pass
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if not messages or not isinstance(messages, (list, tuple)):
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return None
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normalized_messages = []
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for message in messages:
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try:
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normalized = _normalize_langgraph_message(message)
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if normalized:
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normalized_messages.append(normalized)
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except Exception:
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continue
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return normalized_messages if normalized_messages else None
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def _wrap_state_graph_compile(f: "Callable[..., Any]") -> "Callable[..., Any]":
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@wraps(f)
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def new_compile(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
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client = sentry_sdk.get_client()
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integration = client.get_integration(LanggraphIntegration)
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if integration is None or has_span_streaming_enabled(client.options):
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return f(self, *args, **kwargs)
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with sentry_sdk.start_span(
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op=OP.GEN_AI_CREATE_AGENT,
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origin=LanggraphIntegration.origin,
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) as span:
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compiled_graph = f(self, *args, **kwargs)
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compiled_graph_name = getattr(compiled_graph, "name", None)
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span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "create_agent")
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span.set_data(SPANDATA.GEN_AI_AGENT_NAME, compiled_graph_name)
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if compiled_graph_name:
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span.description = f"create_agent {compiled_graph_name}"
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else:
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span.description = "create_agent"
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if kwargs.get("model", None) is not None:
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span.set_data(SPANDATA.GEN_AI_REQUEST_MODEL, kwargs.get("model"))
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tools = None
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get_graph = getattr(compiled_graph, "get_graph", None)
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if get_graph and callable(get_graph):
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graph_obj = compiled_graph.get_graph()
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nodes = getattr(graph_obj, "nodes", None)
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if nodes and isinstance(nodes, dict):
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tools_node = nodes.get("tools")
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if tools_node:
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data = getattr(tools_node, "data", None)
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if data and hasattr(data, "tools_by_name"):
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tools = list(data.tools_by_name.keys())
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if tools is not None:
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span.set_data(SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, tools)
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return compiled_graph
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return new_compile
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def _wrap_pregel_invoke(f: "Callable[..., Any]") -> "Callable[..., Any]":
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@wraps(f)
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def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
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client = sentry_sdk.get_client()
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integration = client.get_integration(LanggraphIntegration)
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if integration is None:
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return f(self, *args, **kwargs)
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graph_name = _get_graph_name(self)
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span_name = (
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f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
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)
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if has_span_streaming_enabled(client.options):
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with sentry_sdk.traces.start_span(
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name=span_name,
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attributes={
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"sentry.op": OP.GEN_AI_INVOKE_AGENT,
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"sentry.origin": LanggraphIntegration.origin,
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SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
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},
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) as span:
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if graph_name:
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span.set_attribute(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
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span.set_attribute(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
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# Store input messages to later compare with output
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input_messages = None
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if (
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len(args) > 0
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and should_send_default_pii()
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and integration.include_prompts
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):
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input_messages = _parse_langgraph_messages(args[0])
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if input_messages:
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normalized_input_messages = normalize_message_roles(
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input_messages
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)
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client = sentry_sdk.get_client()
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scope = sentry_sdk.get_current_scope()
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messages_data = (
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truncate_and_annotate_messages(
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normalized_input_messages, span, scope
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)
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if should_truncate_gen_ai_input(client.options)
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else normalized_input_messages
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_REQUEST_MESSAGES,
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messages_data,
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unpack=False,
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)
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result = f(self, *args, **kwargs)
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_set_response_attributes(span, input_messages, result, integration)
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return result
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else:
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with get_start_span_function()(
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op=OP.GEN_AI_INVOKE_AGENT,
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name=span_name,
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origin=LanggraphIntegration.origin,
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) as span:
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if graph_name:
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span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
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span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
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span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
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# Store input messages to later compare with output
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input_messages = None
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if (
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len(args) > 0
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and should_send_default_pii()
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and integration.include_prompts
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):
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input_messages = _parse_langgraph_messages(args[0])
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if input_messages:
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normalized_input_messages = normalize_message_roles(
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input_messages
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)
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client = sentry_sdk.get_client()
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scope = sentry_sdk.get_current_scope()
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messages_data = (
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truncate_and_annotate_messages(
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normalized_input_messages, span, scope
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)
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if should_truncate_gen_ai_input(client.options)
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else normalized_input_messages
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_REQUEST_MESSAGES,
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messages_data,
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unpack=False,
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)
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result = f(self, *args, **kwargs)
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_set_response_attributes(span, input_messages, result, integration)
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return result
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return new_invoke
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def _wrap_pregel_ainvoke(f: "Callable[..., Any]") -> "Callable[..., Any]":
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@wraps(f)
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async def new_ainvoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
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client = sentry_sdk.get_client()
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integration = client.get_integration(LanggraphIntegration)
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if integration is None:
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return await f(self, *args, **kwargs)
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graph_name = _get_graph_name(self)
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span_name = (
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f"invoke_agent {graph_name}".strip() if graph_name else "invoke_agent"
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)
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if has_span_streaming_enabled(client.options):
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with sentry_sdk.traces.start_span(
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name=span_name,
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attributes={
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"sentry.op": OP.GEN_AI_INVOKE_AGENT,
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"sentry.origin": LanggraphIntegration.origin,
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SPANDATA.GEN_AI_OPERATION_NAME: "invoke_agent",
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},
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) as span:
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if graph_name:
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span.set_attribute(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
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span.set_attribute(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
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input_messages = None
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if (
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len(args) > 0
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and should_send_default_pii()
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and integration.include_prompts
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):
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input_messages = _parse_langgraph_messages(args[0])
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if input_messages:
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normalized_input_messages = normalize_message_roles(
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input_messages
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)
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client = sentry_sdk.get_client()
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scope = sentry_sdk.get_current_scope()
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messages_data = (
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truncate_and_annotate_messages(
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normalized_input_messages, span, scope
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)
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if should_truncate_gen_ai_input(client.options)
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else normalized_input_messages
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_REQUEST_MESSAGES,
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messages_data,
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unpack=False,
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)
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result = await f(self, *args, **kwargs)
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_set_response_attributes(span, input_messages, result, integration)
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return result
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with get_start_span_function()(
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op=OP.GEN_AI_INVOKE_AGENT,
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name=span_name,
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origin=LanggraphIntegration.origin,
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) as span:
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if graph_name:
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span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, graph_name)
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span.set_data(SPANDATA.GEN_AI_AGENT_NAME, graph_name)
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span.set_data(SPANDATA.GEN_AI_OPERATION_NAME, "invoke_agent")
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input_messages = None
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if (
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len(args) > 0
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and should_send_default_pii()
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and integration.include_prompts
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):
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input_messages = _parse_langgraph_messages(args[0])
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if input_messages:
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normalized_input_messages = normalize_message_roles(input_messages)
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client = sentry_sdk.get_client()
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scope = sentry_sdk.get_current_scope()
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messages_data = (
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truncate_and_annotate_messages(
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normalized_input_messages, span, scope
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)
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if should_truncate_gen_ai_input(client.options)
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else normalized_input_messages
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_REQUEST_MESSAGES,
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messages_data,
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unpack=False,
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)
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result = await f(self, *args, **kwargs)
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_set_response_attributes(span, input_messages, result, integration)
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return result
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return new_ainvoke
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def _get_new_messages(
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input_messages: "Optional[List[Any]]", output_messages: "Optional[List[Any]]"
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) -> "Optional[List[Any]]":
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"""Extract only the new messages added during this invocation."""
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if not output_messages:
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return None
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if not input_messages:
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return output_messages
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# only return the new messages, aka the output messages that are not in the input messages
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input_count = len(input_messages)
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new_messages = (
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output_messages[input_count:] if len(output_messages) > input_count else []
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)
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return new_messages if new_messages else None
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def _extract_llm_response_text(messages: "Optional[List[Any]]") -> "Optional[str]":
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if not messages:
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return None
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for message in reversed(messages):
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if isinstance(message, dict):
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role = message.get("role")
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if role in ["assistant", "ai"]:
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content = message.get("content")
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if content and isinstance(content, str):
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return content
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return None
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def _extract_tool_calls(messages: "Optional[List[Any]]") -> "Optional[List[Any]]":
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if not messages:
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return None
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tool_calls = []
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for message in messages:
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if isinstance(message, dict):
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msg_tool_calls = message.get("tool_calls")
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if msg_tool_calls and isinstance(msg_tool_calls, list):
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tool_calls.extend(msg_tool_calls)
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return tool_calls if tool_calls else None
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def _set_usage_data(span: "sentry_sdk.tracing.Span", messages: "Any") -> None:
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input_tokens = 0
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output_tokens = 0
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total_tokens = 0
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for message in messages:
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response_metadata = message.get("response_metadata")
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if response_metadata is None:
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continue
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token_usage = response_metadata.get("token_usage")
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if not token_usage:
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continue
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input_tokens += int(token_usage.get("prompt_tokens", 0))
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output_tokens += int(token_usage.get("completion_tokens", 0))
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total_tokens += int(token_usage.get("total_tokens", 0))
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set_on_span = (
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span.set_attribute if isinstance(span, StreamedSpan) else span.set_data
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)
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if input_tokens > 0:
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set_on_span(SPANDATA.GEN_AI_USAGE_INPUT_TOKENS, input_tokens)
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if output_tokens > 0:
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set_on_span(SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens)
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if total_tokens > 0:
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set_on_span(
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SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS,
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total_tokens,
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)
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def _set_response_model_name(span: "sentry_sdk.tracing.Span", messages: "Any") -> None:
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if len(messages) == 0:
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return
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last_message = messages[-1]
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response_metadata = last_message.get("response_metadata")
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if response_metadata is None:
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return
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model_name = response_metadata.get("model_name")
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if model_name is None:
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return
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set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_MODEL, model_name)
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def _set_response_attributes(
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span: "Any",
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input_messages: "Optional[List[Any]]",
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result: "Any",
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integration: "LanggraphIntegration",
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) -> None:
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parsed_response_messages = _parse_langgraph_messages(result)
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new_messages = _get_new_messages(input_messages, parsed_response_messages)
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if new_messages is None:
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return
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_set_usage_data(span, new_messages)
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_set_response_model_name(span, new_messages)
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if not (should_send_default_pii() and integration.include_prompts):
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return
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llm_response_text = _extract_llm_response_text(new_messages)
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if llm_response_text:
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set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, llm_response_text)
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elif new_messages:
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set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, new_messages)
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else:
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set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, result)
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tool_calls = _extract_tool_calls(new_messages)
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if tool_calls:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
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safe_serialize(tool_calls),
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unpack=False,
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)
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