fix(ops): add streaming metrics and LLM span for agent-chat traces

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

@@ -62,7 +62,8 @@
 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.ops.entities.trace_entity import TraceTaskName
+from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.workflow.enums import WorkflowExecutionStatus
 from core.workflow.nodes import NodeType
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
@@ -72,7 +73,7 @@
 from libs.datetime_utils import naive_utc_now
 from models import Account, Conversation, EndUser, Message, MessageFile
 from models.enums import CreatorUserRole
-from models.workflow import Workflow
+from models.workflow import Workflow, WorkflowNodeExecutionModel
 
 logger = logging.getLogger(__name__)
 
@@ -580,7 +581,7 @@ def _handle_stop_event(
 
             with self._database_session() as session:
                 # Save message
-                self._save_message(session=session, graph_runtime_state=resolved_state)
+                self._save_message(session=session, graph_runtime_state=resolved_state, trace_manager=trace_manager)
 
             yield workflow_finish_resp
         elif event.stopped_by in (
@@ -590,7 +591,7 @@ def _handle_stop_event(
             # When hitting input-moderation or annotation-reply, the workflow will not start
             with self._database_session() as session:
                 # Save message
-                self._save_message(session=session)
+                self._save_message(session=session, trace_manager=trace_manager)
 
         yield self._message_end_to_stream_response()
 
@@ -599,6 +600,7 @@ def _handle_advanced_chat_message_end_event(
         event: QueueAdvancedChatMessageEndEvent,
         *,
         graph_runtime_state: GraphRuntimeState | None = None,
+        trace_manager: TraceQueueManager | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
         """Handle advanced chat message end events."""
@@ -616,7 +618,7 @@ def _handle_advanced_chat_message_end_event(
 
         # Save message
         with self._database_session() as session:
-            self._save_message(session=session, graph_runtime_state=resolved_state)
+            self._save_message(session=session, graph_runtime_state=resolved_state, trace_manager=trace_manager)
 
         yield self._message_end_to_stream_response()
 
@@ -770,7 +772,13 @@ def _process_stream_response(
         if self._conversation_name_generate_thread:
             self._conversation_name_generate_thread.join()
 
-    def _save_message(self, *, session: Session, graph_runtime_state: GraphRuntimeState | None = None):
+    def _save_message(
+        self,
+        *,
+        session: Session,
+        graph_runtime_state: GraphRuntimeState | None = None,
+        trace_manager: TraceQueueManager | None = None,
+    ):
         message = self._get_message(session=session)
 
         # If there are assistant files, remove markdown image links from answer
@@ -809,6 +817,14 @@ def _save_message(self, *, session: Session, graph_runtime_state: GraphRuntimeSt
 
         metadata = self._task_state.metadata.model_dump()
         message.message_metadata = json.dumps(jsonable_encoder(metadata))
+
+        # Extract model provider and model_id from workflow node executions for tracing
+        if message.workflow_run_id:
+            model_info = self._extract_model_info_from_workflow(session, message.workflow_run_id)
+            if model_info:
+                message.model_provider = model_info.get("provider")
+                message.model_id = model_info.get("model")
+
         message_files = [
             MessageFile(
                 message_id=message.id,
@@ -826,6 +842,68 @@ def _save_message(self, *, session: Session, graph_runtime_state: GraphRuntimeSt
         ]
         session.add_all(message_files)
 
+        # Trigger MESSAGE_TRACE for tracing integrations
+        if trace_manager:
+            trace_manager.add_trace_task(
+                TraceTask(
+                    TraceTaskName.MESSAGE_TRACE, conversation_id=self._conversation_id, message_id=self._message_id
+                )
+            )
+
+    def _extract_model_info_from_workflow(self, session: Session, workflow_run_id: str) -> dict[str, str] | None:
+        """
+        Extract model provider and model_id from workflow node executions.
+        Returns dict with 'provider' and 'model' keys, or None if not found.
+        """
+        try:
+            # Query workflow node executions for LLM or Agent nodes
+            stmt = (
+                select(WorkflowNodeExecutionModel)
+                .where(WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id)
+                .where(WorkflowNodeExecutionModel.node_type.in_(["llm", "agent"]))
+                .order_by(WorkflowNodeExecutionModel.created_at.desc())
+                .limit(1)
+            )
+            node_execution = session.scalar(stmt)
+
+            if not node_execution:
+                return None
+
+            # Try to extract from execution_metadata for agent nodes
+            if node_execution.execution_metadata:
+                try:
+                    metadata = json.loads(node_execution.execution_metadata)
+                    agent_log = metadata.get("agent_log", [])
+                    # Look for the first agent thought with provider info
+                    for log_entry in agent_log:
+                        entry_metadata = log_entry.get("metadata", {})
+                        provider_str = entry_metadata.get("provider")
+                        if provider_str:
+                            # Parse format like "langgenius/deepseek/deepseek"
+                            parts = provider_str.split("/")
+                            if len(parts) >= 3:
+                                return {"provider": parts[1], "model": parts[2]}
+                            elif len(parts) == 2:
+                                return {"provider": parts[0], "model": parts[1]}
+                except (json.JSONDecodeError, KeyError, AttributeError) as e:
+                    logger.debug("Failed to parse execution_metadata: %s", e)
+
+            # Try to extract from process_data for llm nodes
+            if node_execution.process_data:
+                try:
+                    process_data = json.loads(node_execution.process_data)
+                    provider = process_data.get("model_provider")
+                    model = process_data.get("model_name")
+                    if provider and model:
+                        return {"provider": provider, "model": model}
+                except (json.JSONDecodeError, KeyError) as e:
+                    logger.debug("Failed to parse process_data: %s", e)
+
+            return None
+        except Exception as e:
+            logger.warning("Failed to extract model info from workflow: %s", e)
+            return None
+
     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

@@ -40,6 +40,9 @@ class EasyUITaskState(TaskState):
     """
 
     llm_result: LLMResult
+    first_token_time: float | None = None
+    last_token_time: float | None = None
+    is_streaming_response: bool = False
 
 
 class WorkflowTaskState(TaskState):

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -332,6 +332,12 @@ def _process_stream_response(
                 if not self._task_state.llm_result.prompt_messages:
                     self._task_state.llm_result.prompt_messages = chunk.prompt_messages
 
+                # Track streaming response times
+                if self._task_state.first_token_time is None:
+                    self._task_state.first_token_time = time.perf_counter()
+                    self._task_state.is_streaming_response = True
+                self._task_state.last_token_time = time.perf_counter()
+
                 # handle output moderation chunk
                 should_direct_answer = self._handle_output_moderation_chunk(cast(str, delta_text))
                 if should_direct_answer:
@@ -398,6 +404,18 @@ def _save_message(self, *, session: Session, trace_manager: TraceQueueManager |
         message.total_price = usage.total_price
         message.currency = usage.currency
         self._task_state.llm_result.usage.latency = message.provider_response_latency
+
+        # Add streaming metrics to usage if available
+        if self._task_state.is_streaming_response and self._task_state.first_token_time:
+            start_time = self.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)
+
+        # Update metadata with the complete usage info
+        self._task_state.metadata.usage = usage
+
         message.message_metadata = self._task_state.metadata.model_dump_json()
 
         if trace_manager:

api/core/ops/tencent_trace/span_builder.py

@@ -222,6 +222,59 @@ def build_message_span(
             links=links,
         )
 
+    @staticmethod
+    def build_message_llm_span(
+        trace_info: MessageTraceInfo, trace_id: int, parent_span_id: int, user_id: str
+    ) -> SpanData:
+        """Build LLM span for message traces with detailed LLM attributes."""
+        status = Status(StatusCode.OK)
+        if trace_info.error:
+            status = Status(StatusCode.ERROR, trace_info.error)
+
+        # Extract model information from `metadata`` or `message_data`
+        trace_metadata = trace_info.metadata or {}
+        message_data = trace_info.message_data or {}
+
+        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 ""
+        )
+
+        inputs_str = str(trace_info.inputs or "")
+        outputs_str = str(trace_info.outputs or "")
+
+        attributes = {
+            GEN_AI_SESSION_ID: trace_metadata.get("conversation_id", ""),
+            GEN_AI_USER_ID: str(user_id),
+            GEN_AI_SPAN_KIND: GenAISpanKind.GENERATION.value,
+            GEN_AI_FRAMEWORK: "dify",
+            GEN_AI_MODEL_NAME: str(model_name),
+            GEN_AI_PROVIDER: str(model_provider),
+            GEN_AI_USAGE_INPUT_TOKENS: str(trace_info.message_tokens or 0),
+            GEN_AI_USAGE_OUTPUT_TOKENS: str(trace_info.answer_tokens or 0),
+            GEN_AI_USAGE_TOTAL_TOKENS: str(trace_info.total_tokens or 0),
+            GEN_AI_PROMPT: inputs_str,
+            GEN_AI_COMPLETION: outputs_str,
+            INPUT_VALUE: inputs_str,
+            OUTPUT_VALUE: outputs_str,
+        }
+
+        if trace_info.is_streaming_request:
+            attributes[GEN_AI_IS_STREAMING_REQUEST] = "true"
+
+        return SpanData(
+            trace_id=trace_id,
+            parent_span_id=parent_span_id,
+            span_id=TencentTraceUtils.convert_to_span_id(trace_info.message_id, "llm"),
+            name="GENERATION",
+            start_time=TencentSpanBuilder._get_time_nanoseconds(trace_info.start_time),
+            end_time=TencentSpanBuilder._get_time_nanoseconds(trace_info.end_time),
+            attributes=attributes,
+            status=status,
+        )
+
     @staticmethod
     def build_tool_span(trace_info: ToolTraceInfo, trace_id: int, parent_span_id: int) -> SpanData:
         """Build tool span."""

api/core/ops/tencent_trace/tencent_trace.py

@@ -107,9 +107,13 @@ def message_trace(self, trace_info: MessageTraceInfo) -> None:
                 links.append(TencentTraceUtils.create_link(trace_info.trace_id))
 
             message_span = TencentSpanBuilder.build_message_span(trace_info, trace_id, str(user_id), links)
-
             self.trace_client.add_span(message_span)
 
+            # Add LLM child span with detailed attributes
+            parent_span_id = TencentTraceUtils.convert_to_span_id(trace_info.message_id, "message")
+            llm_span = TencentSpanBuilder.build_message_llm_span(trace_info, trace_id, parent_span_id, str(user_id))
+            self.trace_client.add_span(llm_span)
+
             self._record_message_llm_metrics(trace_info)
 
             # Record trace duration for entry span

api/models/model.py

@@ -1244,9 +1244,13 @@ def to_dict(self) -> dict[str, Any]:
             "id": self.id,
             "app_id": self.app_id,
             "conversation_id": self.conversation_id,
+            "model_provider": self.model_provider,
             "model_id": self.model_id,
             "inputs": self.inputs,
             "query": self.query,
+            "message_tokens": self.message_tokens,
+            "answer_tokens": self.answer_tokens,
+            "provider_response_latency": self.provider_response_latency,
             "total_price": self.total_price,
             "message": self.message,
             "answer": self.answer,
@@ -1268,8 +1272,12 @@ def from_dict(cls, data: dict[str, Any]) -> "Message":
             id=data["id"],
             app_id=data["app_id"],
             conversation_id=data["conversation_id"],
+            model_provider=data.get("model_provider"),
             model_id=data["model_id"],
             inputs=data["inputs"],
+            message_tokens=data.get("message_tokens", 0),
+            answer_tokens=data.get("answer_tokens", 0),
+            provider_response_latency=data.get("provider_response_latency", 0.0),
             total_price=data["total_price"],
             query=data["query"],
             message=data["message"],