feat: enhance tencent trace integration with LLM core metrics (#27126)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -1,3 +1,4 @@
+import json
 import logging
 import re
 import time
@@ -60,6 +61,7 @@ from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTas
 from core.app.task_pipeline.message_cycle_manager import MessageCycleManager
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.enums import WorkflowExecutionStatus
 from core.workflow.nodes import NodeType
@@ -391,6 +393,14 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if should_direct_answer:
             return
 
+        current_time = time.perf_counter()
+        if self._task_state.first_token_time is None and delta_text.strip():
+            self._task_state.first_token_time = current_time
+            self._task_state.is_streaming_response = True
+
+        if delta_text.strip():
+            self._task_state.last_token_time = current_time
+
         # Only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
@@ -772,7 +782,33 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         message.answer = answer_text
         message.updated_at = naive_utc_now()
         message.provider_response_latency = time.perf_counter() - self._base_task_pipeline.start_at
-        message.message_metadata = self._task_state.metadata.model_dump_json()
+
+        # Set usage first before dumping metadata
+        if graph_runtime_state and graph_runtime_state.llm_usage:
+            usage = graph_runtime_state.llm_usage
+            message.message_tokens = usage.prompt_tokens
+            message.message_unit_price = usage.prompt_unit_price
+            message.message_price_unit = usage.prompt_price_unit
+            message.answer_tokens = usage.completion_tokens
+            message.answer_unit_price = usage.completion_unit_price
+            message.answer_price_unit = usage.completion_price_unit
+            message.total_price = usage.total_price
+            message.currency = usage.currency
+            self._task_state.metadata.usage = usage
+        else:
+            usage = LLMUsage.empty_usage()
+            self._task_state.metadata.usage = usage
+
+        # Add streaming metrics to usage if available
+        if self._task_state.is_streaming_response and self._task_state.first_token_time:
+            start_time = self._base_task_pipeline.start_at
+            first_token_time = self._task_state.first_token_time
+            last_token_time = self._task_state.last_token_time or first_token_time
+            usage.time_to_first_token = round(first_token_time - start_time, 3)
+            usage.time_to_generate = round(last_token_time - first_token_time, 3)
+
+        metadata = self._task_state.metadata.model_dump()
+        message.message_metadata = json.dumps(jsonable_encoder(metadata))
         message_files = [
             MessageFile(
                 message_id=message.id,
@@ -790,20 +826,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
-        if graph_runtime_state and graph_runtime_state.llm_usage:
-            usage = graph_runtime_state.llm_usage
-            message.message_tokens = usage.prompt_tokens
-            message.message_unit_price = usage.prompt_unit_price
-            message.message_price_unit = usage.prompt_price_unit
-            message.answer_tokens = usage.completion_tokens
-            message.answer_unit_price = usage.completion_unit_price
-            message.answer_price_unit = usage.completion_price_unit
-            message.total_price = usage.total_price
-            message.currency = usage.currency
-            self._task_state.metadata.usage = usage
-        else:
-            self._task_state.metadata.usage = LLMUsage.empty_usage()
-
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

api/core/app/entities/task_entities.py

@@ -48,6 +48,9 @@ class WorkflowTaskState(TaskState):
     """
 
     answer: str = ""
+    first_token_time: float | None = None
+    last_token_time: float | None = None
+    is_streaming_response: bool = False
 
 
 class StreamEvent(StrEnum):

api/core/model_runtime/entities/llm_entities.py

@@ -38,6 +38,8 @@ class LLMUsageMetadata(TypedDict, total=False):
     prompt_price: Union[float, str]
     completion_price: Union[float, str]
     latency: float
+    time_to_first_token: float
+    time_to_generate: float
 
 
 class LLMUsage(ModelUsage):
@@ -57,6 +59,8 @@ class LLMUsage(ModelUsage):
     total_price: Decimal
     currency: str
     latency: float
+    time_to_first_token: float | None = None
+    time_to_generate: float | None = None
 
     @classmethod
     def empty_usage(cls):
@@ -73,6 +77,8 @@ class LLMUsage(ModelUsage):
             total_price=Decimal("0.0"),
             currency="USD",
             latency=0.0,
+            time_to_first_token=None,
+            time_to_generate=None,
         )
 
     @classmethod
@@ -108,6 +114,8 @@ class LLMUsage(ModelUsage):
             prompt_price=Decimal(str(metadata.get("prompt_price", 0))),
             completion_price=Decimal(str(metadata.get("completion_price", 0))),
             latency=metadata.get("latency", 0.0),
+            time_to_first_token=metadata.get("time_to_first_token"),
+            time_to_generate=metadata.get("time_to_generate"),
         )
 
     def plus(self, other: LLMUsage) -> LLMUsage:
@@ -133,6 +141,8 @@ class LLMUsage(ModelUsage):
                 total_price=self.total_price + other.total_price,
                 currency=other.currency,
                 latency=self.latency + other.latency,
+                time_to_first_token=other.time_to_first_token,
+                time_to_generate=other.time_to_generate,
             )
 
     def __add__(self, other: LLMUsage) -> LLMUsage:

api/core/ops/entities/trace_entity.py

@@ -62,6 +62,9 @@ class MessageTraceInfo(BaseTraceInfo):
     file_list: Union[str, dict[str, Any], list] | None = None
     message_file_data: Any | None = None
     conversation_mode: str
+    gen_ai_server_time_to_first_token: float | None = None
+    llm_streaming_time_to_generate: float | None = None
+    is_streaming_request: bool = False
 
 
 class ModerationTraceInfo(BaseTraceInfo):

api/core/ops/ops_trace_manager.py

@@ -619,6 +619,8 @@ class TraceTask:
             file_url = f"{self.file_base_url}/{message_file_data.url}" if message_file_data else ""
             file_list.append(file_url)
 
+        streaming_metrics = self._extract_streaming_metrics(message_data)
+
         metadata = {
             "conversation_id": message_data.conversation_id,
             "ls_provider": message_data.model_provider,
@@ -651,6 +653,9 @@ class TraceTask:
             metadata=metadata,
             message_file_data=message_file_data,
             conversation_mode=conversation_mode,
+            gen_ai_server_time_to_first_token=streaming_metrics.get("gen_ai_server_time_to_first_token"),
+            llm_streaming_time_to_generate=streaming_metrics.get("llm_streaming_time_to_generate"),
+            is_streaming_request=streaming_metrics.get("is_streaming_request", False),
         )
 
         return message_trace_info
@@ -876,6 +881,24 @@ class TraceTask:
 
         return generate_name_trace_info
 
+    def _extract_streaming_metrics(self, message_data) -> dict:
+        if not message_data.message_metadata:
+            return {}
+
+        try:
+            metadata = json.loads(message_data.message_metadata)
+            usage = metadata.get("usage", {})
+            time_to_first_token = usage.get("time_to_first_token")
+            time_to_generate = usage.get("time_to_generate")
+
+            return {
+                "gen_ai_server_time_to_first_token": time_to_first_token,
+                "llm_streaming_time_to_generate": time_to_generate,
+                "is_streaming_request": time_to_first_token is not None,
+            }
+        except (json.JSONDecodeError, AttributeError):
+            return {}
+
 
 trace_manager_timer: threading.Timer | None = None
 trace_manager_queue: queue.Queue = queue.Queue()

api/core/ops/tencent_trace/client.py

@@ -11,6 +11,11 @@ import socket
 from typing import TYPE_CHECKING
 from urllib.parse import urlparse
 
+try:
+    from importlib.metadata import version
+except ImportError:
+    from importlib_metadata import version  # type: ignore[import-not-found]
+
 if TYPE_CHECKING:
     from opentelemetry.metrics import Meter
     from opentelemetry.metrics._internal.instrument import Histogram
@@ -27,12 +32,27 @@ from opentelemetry.util.types import AttributeValue
 
 from configs import dify_config
 
-from .entities.tencent_semconv import LLM_OPERATION_DURATION
+from .entities.semconv import (
+    GEN_AI_SERVER_TIME_TO_FIRST_TOKEN,
+    GEN_AI_STREAMING_TIME_TO_GENERATE,
+    GEN_AI_TOKEN_USAGE,
+    GEN_AI_TRACE_DURATION,
+    LLM_OPERATION_DURATION,
+)
 from .entities.tencent_trace_entity import SpanData
 
 logger = logging.getLogger(__name__)
 
 
+def _get_opentelemetry_sdk_version() -> str:
+    """Get OpenTelemetry SDK version dynamically."""
+    try:
+        return version("opentelemetry-sdk")
+    except Exception:
+        logger.debug("Failed to get opentelemetry-sdk version, using default")
+        return "1.27.0"  # fallback version
+
+
 class TencentTraceClient:
     """Tencent APM trace client using OpenTelemetry OTLP exporter"""
 
@@ -57,6 +77,9 @@ class TencentTraceClient:
                 ResourceAttributes.SERVICE_VERSION: f"dify-{dify_config.project.version}-{dify_config.COMMIT_SHA}",
                 ResourceAttributes.DEPLOYMENT_ENVIRONMENT: f"{dify_config.DEPLOY_ENV}-{dify_config.EDITION}",
                 ResourceAttributes.HOST_NAME: socket.gethostname(),
+                ResourceAttributes.TELEMETRY_SDK_LANGUAGE: "python",
+                ResourceAttributes.TELEMETRY_SDK_NAME: "opentelemetry",
+                ResourceAttributes.TELEMETRY_SDK_VERSION: _get_opentelemetry_sdk_version(),
             }
         )
         # Prepare gRPC endpoint/metadata
@@ -80,13 +103,18 @@ class TencentTraceClient:
         )
         self.tracer_provider.add_span_processor(self.span_processor)
 
-        self.tracer = self.tracer_provider.get_tracer("dify.tencent_apm")
+        # use dify api version as tracer version
+        self.tracer = self.tracer_provider.get_tracer("dify-sdk", dify_config.project.version)
 
         # Store span contexts for parent-child relationships
         self.span_contexts: dict[int, trace_api.SpanContext] = {}
 
         self.meter: Meter | None = None
         self.hist_llm_duration: Histogram | None = None
+        self.hist_token_usage: Histogram | None = None
+        self.hist_time_to_first_token: Histogram | None = None
+        self.hist_time_to_generate: Histogram | None = None
+        self.hist_trace_duration: Histogram | None = None
         self.metric_reader: MetricReader | None = None
 
         # Metrics exporter and instruments
@@ -99,7 +127,7 @@ class TencentTraceClient:
             use_http_protobuf = protocol in {"http/protobuf", "http-protobuf"}
             use_http_json = protocol in {"http/json", "http-json"}
 
-            # Set preferred temporality for histograms to DELTA
+            # Tencent APM works best with delta aggregation temporality
             preferred_temporality: dict[type, AggregationTemporality] = {Histogram: AggregationTemporality.DELTA}
 
             def _create_metric_exporter(exporter_cls, **kwargs):
@@ -177,20 +205,59 @@ class TencentTraceClient:
                 provider = MeterProvider(resource=self.resource, metric_readers=[metric_reader])
                 metrics.set_meter_provider(provider)
                 self.meter = metrics.get_meter("dify-sdk", dify_config.project.version)
+
+                # LLM operation duration histogram
                 self.hist_llm_duration = self.meter.create_histogram(
                     name=LLM_OPERATION_DURATION,
                     unit="s",
                     description="LLM operation duration (seconds)",
                 )
+
+                # Token usage histogram with exponential buckets
+                self.hist_token_usage = self.meter.create_histogram(
+                    name=GEN_AI_TOKEN_USAGE,
+                    unit="token",
+                    description="Number of tokens used in prompt and completions",
+                )
+
+                # Time to first token histogram
+                self.hist_time_to_first_token = self.meter.create_histogram(
+                    name=GEN_AI_SERVER_TIME_TO_FIRST_TOKEN,
+                    unit="s",
+                    description="Time to first token for streaming LLM responses (seconds)",
+                )
+
+                # Time to generate histogram
+                self.hist_time_to_generate = self.meter.create_histogram(
+                    name=GEN_AI_STREAMING_TIME_TO_GENERATE,
+                    unit="s",
+                    description="Total time to generate streaming LLM responses (seconds)",
+                )
+
+                # Trace duration histogram
+                self.hist_trace_duration = self.meter.create_histogram(
+                    name=GEN_AI_TRACE_DURATION,
+                    unit="s",
+                    description="End-to-end GenAI trace duration (seconds)",
+                )
+
                 self.metric_reader = metric_reader
             else:
                 self.meter = None
                 self.hist_llm_duration = None
+                self.hist_token_usage = None
+                self.hist_time_to_first_token = None
+                self.hist_time_to_generate = None
+                self.hist_trace_duration = None
                 self.metric_reader = None
         except Exception:
             logger.exception("[Tencent APM] Metrics initialization failed; metrics disabled")
             self.meter = None
             self.hist_llm_duration = None
+            self.hist_token_usage = None
+            self.hist_time_to_first_token = None
+            self.hist_time_to_generate = None
+            self.hist_trace_duration = None
             self.metric_reader = None
 
     def add_span(self, span_data: SpanData) -> None:
@@ -216,6 +283,117 @@ class TencentTraceClient:
         except Exception:
             logger.debug("[Tencent APM] Failed to record LLM duration", exc_info=True)
 
+    def record_token_usage(
+        self,
+        token_count: int,
+        token_type: str,
+        operation_name: str,
+        request_model: str,
+        response_model: str,
+        server_address: str,
+        provider: str,
+    ) -> None:
+        """Record token usage histogram.
+
+        Args:
+            token_count: Number of tokens used
+            token_type: "input" or "output"
+            operation_name: Operation name (e.g., "chat")
+            request_model: Model used in request
+            response_model: Model used in response
+            server_address: Server address
+            provider: Model provider name
+        """
+        try:
+            if not hasattr(self, "hist_token_usage") or self.hist_token_usage is None:
+                return
+
+            attributes = {
+                "gen_ai.operation.name": operation_name,
+                "gen_ai.request.model": request_model,
+                "gen_ai.response.model": response_model,
+                "gen_ai.system": provider,
+                "gen_ai.token.type": token_type,
+                "server.address": server_address,
+            }
+
+            self.hist_token_usage.record(token_count, attributes)  # type: ignore[attr-defined]
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record token usage", exc_info=True)
+
+    def record_time_to_first_token(
+        self, ttft_seconds: float, provider: str, model: str, operation_name: str = "chat"
+    ) -> None:
+        """Record time to first token histogram.
+
+        Args:
+            ttft_seconds: Time to first token in seconds
+            provider: Model provider name
+            model: Model name
+            operation_name: Operation name (default: "chat")
+        """
+        try:
+            if not hasattr(self, "hist_time_to_first_token") or self.hist_time_to_first_token is None:
+                return
+
+            attributes = {
+                "gen_ai.operation.name": operation_name,
+                "gen_ai.system": provider,
+                "gen_ai.request.model": model,
+                "gen_ai.response.model": model,
+                "stream": "true",
+            }
+
+            self.hist_time_to_first_token.record(ttft_seconds, attributes)  # type: ignore[attr-defined]
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record time to first token", exc_info=True)
+
+    def record_time_to_generate(
+        self, ttg_seconds: float, provider: str, model: str, operation_name: str = "chat"
+    ) -> None:
+        """Record time to generate histogram.
+
+        Args:
+            ttg_seconds: Time to generate in seconds
+            provider: Model provider name
+            model: Model name
+            operation_name: Operation name (default: "chat")
+        """
+        try:
+            if not hasattr(self, "hist_time_to_generate") or self.hist_time_to_generate is None:
+                return
+
+            attributes = {
+                "gen_ai.operation.name": operation_name,
+                "gen_ai.system": provider,
+                "gen_ai.request.model": model,
+                "gen_ai.response.model": model,
+                "stream": "true",
+            }
+
+            self.hist_time_to_generate.record(ttg_seconds, attributes)  # type: ignore[attr-defined]
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record time to generate", exc_info=True)
+
+    def record_trace_duration(self, duration_seconds: float, attributes: dict[str, str] | None = None) -> None:
+        """Record end-to-end trace duration histogram in seconds.
+
+        Args:
+            duration_seconds: Trace duration in seconds
+            attributes: Optional attributes (e.g., conversation_mode, app_id)
+        """
+        try:
+            if not hasattr(self, "hist_trace_duration") or self.hist_trace_duration is None:
+                return
+
+            attrs: dict[str, str] = {}
+            if attributes:
+                for k, v in attributes.items():
+                    attrs[k] = str(v) if not isinstance(v, (str, int, float, bool)) else v  # type: ignore[assignment]
+            self.hist_trace_duration.record(duration_seconds, attrs)  # type: ignore[attr-defined]
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record trace duration", exc_info=True)
+
     def _create_and_export_span(self, span_data: SpanData) -> None:
         """Create span using OpenTelemetry Tracer API"""
         try:

api/core/ops/tencent_trace/entities/semconv.py

@@ -47,6 +47,9 @@ GEN_AI_COMPLETION = "gen_ai.completion"
 
 GEN_AI_RESPONSE_FINISH_REASON = "gen_ai.response.finish_reason"
 
+# Streaming Span Attributes
+GEN_AI_IS_STREAMING_REQUEST = "llm.is_streaming"  # Same as OpenLLMetry semconv
+
 # Tool
 TOOL_NAME = "tool.name"
 
@@ -62,6 +65,19 @@ INSTRUMENTATION_LANGUAGE = "python"
 
 # Metrics
 LLM_OPERATION_DURATION = "gen_ai.client.operation.duration"
+GEN_AI_TOKEN_USAGE = "gen_ai.client.token.usage"
+GEN_AI_SERVER_TIME_TO_FIRST_TOKEN = "gen_ai.server.time_to_first_token"
+GEN_AI_STREAMING_TIME_TO_GENERATE = "gen_ai.streaming.time_to_generate"
+# The LLM trace duration which is exclusive to tencent apm
+GEN_AI_TRACE_DURATION = "gen_ai.trace.duration"
+
+# Token Usage Attributes
+GEN_AI_OPERATION_NAME = "gen_ai.operation.name"
+GEN_AI_REQUEST_MODEL = "gen_ai.request.model"
+GEN_AI_RESPONSE_MODEL = "gen_ai.response.model"
+GEN_AI_SYSTEM = "gen_ai.system"
+GEN_AI_TOKEN_TYPE = "gen_ai.token.type"
+SERVER_ADDRESS = "server.address"
 
 
 class GenAISpanKind(Enum):

api/core/ops/tencent_trace/span_builder.py

@@ -14,10 +14,11 @@ from core.ops.entities.trace_entity import (
     ToolTraceInfo,
     WorkflowTraceInfo,
 )
-from core.ops.tencent_trace.entities.tencent_semconv import (
+from core.ops.tencent_trace.entities.semconv import (
     GEN_AI_COMPLETION,
     GEN_AI_FRAMEWORK,
     GEN_AI_IS_ENTRY,
+    GEN_AI_IS_STREAMING_REQUEST,
     GEN_AI_MODEL_NAME,
     GEN_AI_PROMPT,
     GEN_AI_PROVIDER,
@@ -156,6 +157,25 @@ class TencentSpanBuilder:
         outputs = node_execution.outputs or {}
         usage_data = process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
 
+        attributes = {
+            GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
+            GEN_AI_SPAN_KIND: GenAISpanKind.GENERATION.value,
+            GEN_AI_FRAMEWORK: "dify",
+            GEN_AI_MODEL_NAME: process_data.get("model_name", ""),
+            GEN_AI_PROVIDER: process_data.get("model_provider", ""),
+            GEN_AI_USAGE_INPUT_TOKENS: str(usage_data.get("prompt_tokens", 0)),
+            GEN_AI_USAGE_OUTPUT_TOKENS: str(usage_data.get("completion_tokens", 0)),
+            GEN_AI_USAGE_TOTAL_TOKENS: str(usage_data.get("total_tokens", 0)),
+            GEN_AI_PROMPT: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
+            GEN_AI_COMPLETION: str(outputs.get("text", "")),
+            GEN_AI_RESPONSE_FINISH_REASON: outputs.get("finish_reason", ""),
+            INPUT_VALUE: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
+            OUTPUT_VALUE: str(outputs.get("text", "")),
+        }
+
+        if usage_data.get("time_to_first_token") is not None:
+            attributes[GEN_AI_IS_STREAMING_REQUEST] = "true"
+
         return SpanData(
             trace_id=trace_id,
             parent_span_id=workflow_span_id,
@@ -163,21 +183,7 @@ class TencentSpanBuilder:
             name="GENERATION",
             start_time=TencentSpanBuilder._get_time_nanoseconds(node_execution.created_at),
             end_time=TencentSpanBuilder._get_time_nanoseconds(node_execution.finished_at),
-            attributes={
+            attributes=attributes,
-                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
-                GEN_AI_SPAN_KIND: GenAISpanKind.GENERATION.value,
-                GEN_AI_FRAMEWORK: "dify",
-                GEN_AI_MODEL_NAME: process_data.get("model_name", ""),
-                GEN_AI_PROVIDER: process_data.get("model_provider", ""),
-                GEN_AI_USAGE_INPUT_TOKENS: str(usage_data.get("prompt_tokens", 0)),
-                GEN_AI_USAGE_OUTPUT_TOKENS: str(usage_data.get("completion_tokens", 0)),
-                GEN_AI_USAGE_TOTAL_TOKENS: str(usage_data.get("total_tokens", 0)),
-                GEN_AI_PROMPT: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
-                GEN_AI_COMPLETION: str(outputs.get("text", "")),
-                GEN_AI_RESPONSE_FINISH_REASON: outputs.get("finish_reason", ""),
-                INPUT_VALUE: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
-                OUTPUT_VALUE: str(outputs.get("text", "")),
-            },
             status=TencentSpanBuilder._get_workflow_node_status(node_execution),
         )
 
@@ -191,6 +197,19 @@ class TencentSpanBuilder:
         if trace_info.error:
             status = Status(StatusCode.ERROR, trace_info.error)
 
+        attributes = {
+            GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
+            GEN_AI_USER_ID: str(user_id),
+            GEN_AI_SPAN_KIND: GenAISpanKind.WORKFLOW.value,
+            GEN_AI_FRAMEWORK: "dify",
+            GEN_AI_IS_ENTRY: "true",
+            INPUT_VALUE: str(trace_info.inputs or ""),
+            OUTPUT_VALUE: str(trace_info.outputs or ""),
+        }
+
+        if trace_info.is_streaming_request:
+            attributes[GEN_AI_IS_STREAMING_REQUEST] = "true"
+
         return SpanData(
             trace_id=trace_id,
             parent_span_id=None,
@@ -198,15 +217,7 @@ class TencentSpanBuilder:
             name="message",
             start_time=TencentSpanBuilder._get_time_nanoseconds(trace_info.start_time),
             end_time=TencentSpanBuilder._get_time_nanoseconds(trace_info.end_time),
-            attributes={
+            attributes=attributes,
-                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
-                GEN_AI_USER_ID: str(user_id),
-                GEN_AI_SPAN_KIND: GenAISpanKind.WORKFLOW.value,
-                GEN_AI_FRAMEWORK: "dify",
-                GEN_AI_IS_ENTRY: "true",
-                INPUT_VALUE: str(trace_info.inputs or ""),
-                OUTPUT_VALUE: str(trace_info.outputs or ""),
-            },
             status=status,
             links=links,
         )

api/core/ops/tencent_trace/tencent_trace.py

@@ -90,6 +90,9 @@ class TencentDataTrace(BaseTraceInstance):
 
             self._process_workflow_nodes(trace_info, trace_id)
 
+            # Record trace duration for entry span
+            self._record_workflow_trace_duration(trace_info)
+
         except Exception:
             logger.exception("[Tencent APM] Failed to process workflow trace")
 
@@ -107,6 +110,11 @@ class TencentDataTrace(BaseTraceInstance):
 
             self.trace_client.add_span(message_span)
 
+            self._record_message_llm_metrics(trace_info)
+
+            # Record trace duration for entry span
+            self._record_message_trace_duration(trace_info)
+
         except Exception:
             logger.exception("[Tencent APM] Failed to process message trace")
 
@@ -290,24 +298,219 @@ class TencentDataTrace(BaseTraceInstance):
     def _record_llm_metrics(self, node_execution: WorkflowNodeExecution) -> None:
         """Record LLM performance metrics"""
         try:
-            if not hasattr(self.trace_client, "record_llm_duration"):
-                return
-
             process_data = node_execution.process_data or {}
-            usage = process_data.get("usage", {})
+            outputs = node_execution.outputs or {}
-            latency_s = float(usage.get("latency", 0.0))
+            usage = process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
 
-            if latency_s > 0:
+            model_provider = process_data.get("model_provider", "unknown")
-                attributes = {
+            model_name = process_data.get("model_name", "unknown")
-                    "provider": process_data.get("model_provider", ""),
+            model_mode = process_data.get("model_mode", "chat")
-                    "model": process_data.get("model_name", ""),
+
-                    "span_kind": "GENERATION",
+            # Record LLM duration
-                }
+            if hasattr(self.trace_client, "record_llm_duration"):
-                self.trace_client.record_llm_duration(latency_s, attributes)
+                latency_s = float(usage.get("latency", 0.0))
+
+                if latency_s > 0:
+                    # Determine if streaming from usage metrics
+                    is_streaming = usage.get("time_to_first_token") is not None
+
+                    attributes = {
+                        "gen_ai.system": model_provider,
+                        "gen_ai.response.model": model_name,
+                        "gen_ai.operation.name": model_mode,
+                        "stream": "true" if is_streaming else "false",
+                    }
+                    self.trace_client.record_llm_duration(latency_s, attributes)
+
+            # Record streaming metrics from usage
+            time_to_first_token = usage.get("time_to_first_token")
+            if time_to_first_token is not None and hasattr(self.trace_client, "record_time_to_first_token"):
+                ttft_seconds = float(time_to_first_token)
+                if ttft_seconds > 0:
+                    self.trace_client.record_time_to_first_token(
+                        ttft_seconds=ttft_seconds, provider=model_provider, model=model_name, operation_name=model_mode
+                    )
+
+            time_to_generate = usage.get("time_to_generate")
+            if time_to_generate is not None and hasattr(self.trace_client, "record_time_to_generate"):
+                ttg_seconds = float(time_to_generate)
+                if ttg_seconds > 0:
+                    self.trace_client.record_time_to_generate(
+                        ttg_seconds=ttg_seconds, provider=model_provider, model=model_name, operation_name=model_mode
+                    )
+
+            # Record token usage
+            if hasattr(self.trace_client, "record_token_usage"):
+                # Extract token counts
+                input_tokens = int(usage.get("prompt_tokens", 0))
+                output_tokens = int(usage.get("completion_tokens", 0))
+
+                if input_tokens > 0 or output_tokens > 0:
+                    server_address = f"{model_provider}"
+
+                    # Record input tokens
+                    if input_tokens > 0:
+                        self.trace_client.record_token_usage(
+                            token_count=input_tokens,
+                            token_type="input",
+                            operation_name=model_mode,
+                            request_model=model_name,
+                            response_model=model_name,
+                            server_address=server_address,
+                            provider=model_provider,
+                        )
+
+                    # Record output tokens
+                    if output_tokens > 0:
+                        self.trace_client.record_token_usage(
+                            token_count=output_tokens,
+                            token_type="output",
+                            operation_name=model_mode,
+                            request_model=model_name,
+                            response_model=model_name,
+                            server_address=server_address,
+                            provider=model_provider,
+                        )
 
         except Exception:
             logger.debug("[Tencent APM] Failed to record LLM metrics")
 
+    def _record_message_llm_metrics(self, trace_info: MessageTraceInfo) -> None:
+        """Record LLM metrics for message traces"""
+        try:
+            trace_metadata = trace_info.metadata or {}
+            message_data = trace_info.message_data or {}
+            provider_latency = 0.0
+            if isinstance(message_data, dict):
+                provider_latency = float(message_data.get("provider_response_latency", 0.0) or 0.0)
+            else:
+                provider_latency = float(getattr(message_data, "provider_response_latency", 0.0) or 0.0)
+
+            model_provider = trace_metadata.get("ls_provider") or (
+                message_data.get("model_provider", "") if isinstance(message_data, dict) else ""
+            )
+            model_name = trace_metadata.get("ls_model_name") or (
+                message_data.get("model_id", "") if isinstance(message_data, dict) else ""
+            )
+
+            # Record LLM duration
+            if provider_latency > 0 and hasattr(self.trace_client, "record_llm_duration"):
+                is_streaming = trace_info.is_streaming_request
+
+                duration_attributes = {
+                    "gen_ai.system": model_provider,
+                    "gen_ai.response.model": model_name,
+                    "gen_ai.operation.name": "chat",  # Message traces are always chat
+                    "stream": "true" if is_streaming else "false",
+                }
+                self.trace_client.record_llm_duration(provider_latency, duration_attributes)
+
+            # Record streaming metrics for message traces
+            if trace_info.is_streaming_request:
+                # Record time to first token
+                if trace_info.gen_ai_server_time_to_first_token is not None and hasattr(
+                    self.trace_client, "record_time_to_first_token"
+                ):
+                    ttft_seconds = float(trace_info.gen_ai_server_time_to_first_token)
+                    if ttft_seconds > 0:
+                        self.trace_client.record_time_to_first_token(
+                            ttft_seconds=ttft_seconds, provider=str(model_provider or ""), model=str(model_name or "")
+                        )
+
+                # Record time to generate
+                if trace_info.llm_streaming_time_to_generate is not None and hasattr(
+                    self.trace_client, "record_time_to_generate"
+                ):
+                    ttg_seconds = float(trace_info.llm_streaming_time_to_generate)
+                    if ttg_seconds > 0:
+                        self.trace_client.record_time_to_generate(
+                            ttg_seconds=ttg_seconds, provider=str(model_provider or ""), model=str(model_name or "")
+                        )
+
+            # Record token usage
+            if hasattr(self.trace_client, "record_token_usage"):
+                input_tokens = int(trace_info.message_tokens or 0)
+                output_tokens = int(trace_info.answer_tokens or 0)
+
+                if input_tokens > 0:
+                    self.trace_client.record_token_usage(
+                        token_count=input_tokens,
+                        token_type="input",
+                        operation_name="chat",
+                        request_model=str(model_name or ""),
+                        response_model=str(model_name or ""),
+                        server_address=str(model_provider or ""),
+                        provider=str(model_provider or ""),
+                    )
+
+                if output_tokens > 0:
+                    self.trace_client.record_token_usage(
+                        token_count=output_tokens,
+                        token_type="output",
+                        operation_name="chat",
+                        request_model=str(model_name or ""),
+                        response_model=str(model_name or ""),
+                        server_address=str(model_provider or ""),
+                        provider=str(model_provider or ""),
+                    )
+
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record message LLM metrics")
+
+    def _record_workflow_trace_duration(self, trace_info: WorkflowTraceInfo) -> None:
+        """Record end-to-end workflow trace duration."""
+        try:
+            if not hasattr(self.trace_client, "record_trace_duration"):
+                return
+
+            # Calculate duration from start_time and end_time to match span duration
+            if trace_info.start_time and trace_info.end_time:
+                duration_s = (trace_info.end_time - trace_info.start_time).total_seconds()
+            else:
+                # Fallback to workflow_run_elapsed_time if timestamps not available
+                duration_s = float(trace_info.workflow_run_elapsed_time)
+
+            if duration_s > 0:
+                attributes = {
+                    "conversation_mode": "workflow",
+                    "workflow_status": trace_info.workflow_run_status,
+                }
+
+                # Add conversation_id if available
+                if trace_info.conversation_id:
+                    attributes["has_conversation"] = "true"
+                else:
+                    attributes["has_conversation"] = "false"
+
+                self.trace_client.record_trace_duration(duration_s, attributes)
+
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record workflow trace duration")
+
+    def _record_message_trace_duration(self, trace_info: MessageTraceInfo) -> None:
+        """Record end-to-end message trace duration."""
+        try:
+            if not hasattr(self.trace_client, "record_trace_duration"):
+                return
+
+            # Calculate duration from start_time and end_time
+            if trace_info.start_time and trace_info.end_time:
+                duration = (trace_info.end_time - trace_info.start_time).total_seconds()
+
+                if duration > 0:
+                    attributes = {
+                        "conversation_mode": trace_info.conversation_mode,
+                    }
+
+                    # Add streaming flag if available
+                    if hasattr(trace_info, "is_streaming_request"):
+                        attributes["stream"] = "true" if trace_info.is_streaming_request else "false"
+
+                    self.trace_client.record_trace_duration(duration, attributes)
+
+        except Exception:
+            logger.debug("[Tencent APM] Failed to record message trace duration")
+
     def __del__(self):
         """Ensure proper cleanup on garbage collection."""
         try:

api/core/workflow/nodes/llm/node.py

@@ -3,6 +3,7 @@ import io
 import json
 import logging
 import re
+import time
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal
 
@@ -384,6 +385,8 @@ class LLMNode(Node):
             output_schema = LLMNode.fetch_structured_output_schema(
                 structured_output=structured_output or {},
             )
+            request_start_time = time.perf_counter()
+
             invoke_result = invoke_llm_with_structured_output(
                 provider=model_instance.provider,
                 model_schema=model_schema,
@@ -396,6 +399,8 @@ class LLMNode(Node):
                 user=user_id,
             )
         else:
+            request_start_time = time.perf_counter()
+
             invoke_result = model_instance.invoke_llm(
                 prompt_messages=list(prompt_messages),
                 model_parameters=node_data_model.completion_params,
@@ -411,6 +416,7 @@ class LLMNode(Node):
             node_id=node_id,
             node_type=node_type,
             reasoning_format=reasoning_format,
+            request_start_time=request_start_time,
         )
 
     @staticmethod
@@ -422,14 +428,20 @@ class LLMNode(Node):
         node_id: str,
         node_type: NodeType,
         reasoning_format: Literal["separated", "tagged"] = "tagged",
+        request_start_time: float | None = None,
     ) -> Generator[NodeEventBase | LLMStructuredOutput, None, None]:
         # For blocking mode
         if isinstance(invoke_result, LLMResult):
+            duration = None
+            if request_start_time is not None:
+                duration = time.perf_counter() - request_start_time
+                invoke_result.usage.latency = round(duration, 3)
             event = LLMNode.handle_blocking_result(
                 invoke_result=invoke_result,
                 saver=file_saver,
                 file_outputs=file_outputs,
                 reasoning_format=reasoning_format,
+                request_latency=duration,
             )
             yield event
             return
@@ -441,6 +453,12 @@ class LLMNode(Node):
         usage = LLMUsage.empty_usage()
         finish_reason = None
         full_text_buffer = io.StringIO()
+
+        # Initialize streaming metrics tracking
+        start_time = request_start_time if request_start_time is not None else time.perf_counter()
+        first_token_time = None
+        has_content = False
+
         collected_structured_output = None  # Collect structured_output from streaming chunks
         # Consume the invoke result and handle generator exception
         try:
@@ -457,6 +475,11 @@ class LLMNode(Node):
                         file_saver=file_saver,
                         file_outputs=file_outputs,
                     ):
+                        # Detect first token for TTFT calculation
+                        if text_part and not has_content:
+                            first_token_time = time.perf_counter()
+                            has_content = True
+
                         full_text_buffer.write(text_part)
                         yield StreamChunkEvent(
                             selector=[node_id, "text"],
@@ -489,6 +512,16 @@ class LLMNode(Node):
             # Extract clean text and reasoning from <think> tags
             clean_text, reasoning_content = LLMNode._split_reasoning(full_text, reasoning_format)
 
+        # Calculate streaming metrics
+        end_time = time.perf_counter()
+        total_duration = end_time - start_time
+        usage.latency = round(total_duration, 3)
+        if has_content and first_token_time:
+            gen_ai_server_time_to_first_token = first_token_time - start_time
+            llm_streaming_time_to_generate = end_time - first_token_time
+            usage.time_to_first_token = round(gen_ai_server_time_to_first_token, 3)
+            usage.time_to_generate = round(llm_streaming_time_to_generate, 3)
+
         yield ModelInvokeCompletedEvent(
             # Use clean_text for separated mode, full_text for tagged mode
             text=clean_text if reasoning_format == "separated" else full_text,
@@ -1068,6 +1101,7 @@ class LLMNode(Node):
         saver: LLMFileSaver,
         file_outputs: list["File"],
         reasoning_format: Literal["separated", "tagged"] = "tagged",
+        request_latency: float | None = None,
     ) -> ModelInvokeCompletedEvent:
         buffer = io.StringIO()
         for text_part in LLMNode._save_multimodal_output_and_convert_result_to_markdown(
@@ -1088,7 +1122,7 @@ class LLMNode(Node):
             # Extract clean text and reasoning from <think> tags
             clean_text, reasoning_content = LLMNode._split_reasoning(full_text, reasoning_format)
 
-        return ModelInvokeCompletedEvent(
+        event = ModelInvokeCompletedEvent(
             # Use clean_text for separated mode, full_text for tagged mode
             text=clean_text if reasoning_format == "separated" else full_text,
             usage=invoke_result.usage,
@@ -1098,6 +1132,9 @@ class LLMNode(Node):
             # Pass structured output if enabled
             structured_output=getattr(invoke_result, "structured_output", None),
         )
+        if request_latency is not None:
+            event.usage.latency = round(request_latency, 3)
+        return event
 
     @staticmethod
     def save_multimodal_image_output(

api/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.11, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",

web/app/components/base/icons/assets/public/tracing/tencent-icon-big.svg

@@ -0,0 +1,23 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="120px" height="27px" viewBox="0 0 80 18" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+    <title>logo</title>
+    <g id="页面-1" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
+        <g id="logo" fill-rule="nonzero">
+            <g id="XMLID_25_" transform="translate(30.592488, 1.100000)" fill="#253554">
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