feat(workflow): support token usage metrics and subgraph state output

- expose token consumption for workflow runs
- enable loop nodes to output subgraph states
- enhance executor logging

api/app/controllers/public_share_controller.py

@@ -567,6 +567,7 @@ async def chat(
             with get_db_read() as db:
                 source_config = WorkflowConfigRepository(db).get_by_app_id(release.app_id)
                 config.id = source_config.id
+        config.id = uuid.UUID(config.id)
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.workflow_chat_stream(

api/app/core/workflow/executor.py

@@ -8,6 +8,7 @@ import logging
 import uuid
 from typing import Any
 
+from langchain_core.runnables import RunnableConfig
 from langgraph.graph.state import CompiledStateGraph
 
 from app.core.workflow.graph_builder import GraphBuilder
@@ -53,11 +54,11 @@ class WorkflowExecutor:
         self.edges = workflow_config.get("edges", [])
         self.execution_config = workflow_config.get("execution_config", {})
 
-        self.checkpoint_config = {
-            "configurable": {
+        self.checkpoint_config = RunnableConfig(
+            configurable={
                 "thread_id": uuid.uuid4(),
             }
-        }
+        )
 
     def _prepare_initial_state(self, input_data: dict[str, Any]) -> WorkflowState:
         """准备初始状态（注入系统变量和会话变量）
@@ -214,13 +215,13 @@ class WorkflowExecutor:
         return {
             "status": "completed",
             "output": final_output,
+            "variables": result.get("variables", {}),
             "node_outputs": node_outputs,
             "messages": result.get("messages", []),
             "conversation_id": conversation_id,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
             "error": result.get("error"),
-            "variables": result.get("variables", {}),
         }
 
     def build_graph(self, stream=False) -> CompiledStateGraph:
@@ -326,11 +327,10 @@ class WorkflowExecutor:
         }
 
         # 1. 构建图
-        graph = self.build_graph(True)
+        graph = self.build_graph(stream=True)
 
         # 2. 初始化状态（自动注入系统变量）
         initial_state = self._prepare_initial_state(input_data)
-
         # 3. Execute workflow
         try:
             chunk_count = 0
@@ -346,14 +346,16 @@ class WorkflowExecutor:
                     mode, data = event
                 else:
                     # Unexpected format, log and skip
-                    logger.warning(f"[STREAM] Unexpected event format: {type(event)}, value: {event}")
+                    logger.warning(f"[STREAM] Unexpected event format: {type(event)}, value: {event}"
+                                   f"- execution_id: {self.execution_id}")
                     continue
 
                 if mode == "custom":
                     # Handle custom streaming events (chunks from nodes via stream writer)
                     chunk_count += 1
                     event_type = data.get("type", "node_chunk")  # "message" or "node_chunk"
-                    logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}")
+                    logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}"
+                                f"- execution_id: {self.execution_id}")
                     yield {
                         "event": event_type,  # "message" or "node_chunk"
                         "data": {
@@ -380,7 +382,8 @@ class WorkflowExecutor:
                         variables_sys = variables.get("sys", {})
                         conversation_id = input_data.get("conversation_id")
                         execution_id = variables_sys.get("execution_id")
-                        logger.info(f"[DEBUG] Node starts execution: {node_name}")
+                        logger.info(f"[NODE-START] Node starts execution: {node_name} "
+                                    f"- execution_id: {self.execution_id}")
 
                         yield {
                             "event": "node_start",
@@ -399,7 +402,8 @@ class WorkflowExecutor:
                         variables_sys = variables.get("sys", {})
                         conversation_id = input_data.get("conversation_id")
                         execution_id = variables_sys.get("execution_id")
-                        logger.info(f"[DEBUG] Node execution completed: {node_name}")
+                        logger.info(f"[NODE-END] Node execution completed: {node_name} "
+                                    f"- execution_id: {self.execution_id}")
 
                         yield {
                             "event": "node_end",
@@ -407,13 +411,15 @@ class WorkflowExecutor:
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
                                 "execution_id": execution_id,
-                                "timestamp": data.get("timestamp")
+                                "timestamp": data.get("timestamp"),
+                                "state": result.get("node_outputs", {}).get(node_name),
                             }
                         }
 
                 elif mode == "updates":
                     # Handle state updates - store final state
-                    logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())}")
+                    logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} "
+                                 f"- execution_id: {self.execution_id}")
 
             # 计算耗时
             end_time = datetime.datetime.now()
@@ -421,7 +427,7 @@ class WorkflowExecutor:
             result = graph.get_state(self.checkpoint_config).values
             logger.info(
                 f"Workflow execution completed (streaming), "
-                f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s"
+                f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s, execution_id: {self.execution_id}"
             )
 
             # 发送 workflow_end 事件
@@ -449,7 +455,8 @@ class WorkflowExecutor:
                 }
             }
 
-    def _extract_final_output(self, node_outputs: dict[str, Any]) -> str | None:
+    @staticmethod
+    def _extract_final_output(node_outputs: dict[str, Any]) -> str | None:
         """从节点输出中提取最终输出
 
         优先级：
@@ -473,7 +480,8 @@ class WorkflowExecutor:
 
         return None
 
-    def _aggregate_token_usage(self, node_outputs: dict[str, Any]) -> dict[str, int] | None:
+    @staticmethod
+    def _aggregate_token_usage(node_outputs: dict[str, Any]) -> dict[str, int] | None:
         """聚合所有节点的 token 使用情况
 
         Args:

api/app/core/workflow/nodes/cycle_graph/iteration.py

@@ -21,6 +21,7 @@ class IterationRuntime:
     optional parallel execution, flattening of output, and loop control via
     the workflow state.
     """
+
     def __init__(
             self,
             graph: CompiledStateGraph,
@@ -87,6 +88,7 @@ class IterationRuntime:
             self.result.append(output)
         if not result["looping"]:
             self.looping = False
+        return result
 
     def _create_iteration_tasks(self, array_obj, idx):
         """
@@ -124,7 +126,7 @@ class IterationRuntime:
         array_obj = VariablePool(self.state).get(input_expression)
         if not isinstance(array_obj, list):
             raise RuntimeError("Cannot iterate over a non-list variable")
-
+        child_state = []
         idx = 0
         if self.typed_config.parallel:
             # Execute iterations in parallel batches
@@ -132,15 +134,14 @@ class IterationRuntime:
                 tasks = self._create_iteration_tasks(array_obj, idx)
                 logger.info(f"Iteration node {self.node_id}: running, concurrency {len(tasks)}")
                 idx += self.typed_config.parallel_count
-                await asyncio.gather(*tasks)
-            logger.info(f"Iteration node {self.node_id}: execution completed")
-            return self.result
+                child_state.extend(await asyncio.gather(*tasks))
         else:
             # Execute iterations sequentially
             while idx < len(array_obj) and self.looping:
                 logger.info(f"Iteration node {self.node_id}: running")
                 item = array_obj[idx]
                 result = await self.graph.ainvoke(self._init_iteration_state(item, idx))
+                child_state.append(result)
                 output = VariablePool(result).get(self.output_value)
                 if isinstance(output, list) and self.typed_config.flatten:
                     self.result.extend(output)
@@ -150,5 +151,8 @@ class IterationRuntime:
                     self.looping = False
                 idx += 1
 
-            logger.info(f"Iteration node {self.node_id}: execution completed")
-            return self.result
+        logger.info(f"Iteration node {self.node_id}: execution completed")
+        return {
+            "output": self.result,
+            "__child_state": child_state
+        }

api/app/core/workflow/nodes/cycle_graph/loop.py

@@ -67,7 +67,9 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
+            )
+            if variable.input_type == ValueInputType.VARIABLE
+            else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         self.state["node_outputs"][self.node_id] = {
@@ -76,7 +78,9 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
+            )
+            if variable.input_type == ValueInputType.VARIABLE
+            else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         loopstate = WorkflowState(
@@ -171,10 +175,11 @@ class LoopRuntime:
         """
         loopstate = self._init_loop_state()
         loop_time = self.typed_config.max_loop
+        child_state = []
         while self.evaluate_conditional(loopstate) and loopstate["looping"] and loop_time > 0:
             logger.info(f"loop node {self.node_id}: running")
-            await self.graph.ainvoke(loopstate)
+            child_state.append(await self.graph.ainvoke(loopstate))
             loop_time -= 1
 
         logger.info(f"loop node {self.node_id}: execution completed")
-        return loopstate["runtime_vars"][self.node_id]
+        return loopstate["runtime_vars"][self.node_id] | {"__child_state": child_state}

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

@@ -269,12 +269,16 @@ class LLMNode(BaseNode):
         chunk_count = 0
 
         # 调用 LLM（流式，支持字符串或消息列表）
-        async for chunk in llm.astream(prompt_or_messages):
+        last_meta_data = {}
+        async for chunk in llm.astream(prompt_or_messages, stream_usage=True):
             # 提取内容
             if hasattr(chunk, 'content'):
                 content = chunk.content
             else:
                 content = str(chunk)
+            if hasattr(chunk, 'response_metadata'):
+                if chunk.response_metadata:
+                    last_meta_data = chunk.response_metadata
 
             # 只有当内容不为空时才处理
             if content:
@@ -288,13 +292,10 @@ class LLMNode(BaseNode):
         logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}, 总 chunks: {chunk_count}")
 
         # 构建完整的 AIMessage（包含元数据）
-        if isinstance(last_chunk, AIMessage):
-            final_message = AIMessage(
-                content=full_response,
-                response_metadata=last_chunk.response_metadata if hasattr(last_chunk, 'response_metadata') else {}
-            )
-        else:
-            final_message = AIMessage(content=full_response)
+        final_message = AIMessage(
+            content=full_response,
+            response_metadata=last_meta_data
+        )
 
         # yield 完成标记
         yield {"__final__": True, "result": final_message}

api/app/services/workflow_service.py

@@ -14,15 +14,14 @@ from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.core.workflow.validator import validate_workflow_config
 from app.db import get_db
-from app.models.conversation_model import Message
 from app.models.workflow_model import WorkflowConfig, WorkflowExecution
-from app.repositories.conversation_repository import MessageRepository
 from app.repositories.workflow_repository import (
     WorkflowConfigRepository,
     WorkflowExecutionRepository,
     WorkflowNodeExecutionRepository
 )
 from app.schemas import DraftRunRequest
+from app.services.conversation_service import ConversationService
 from app.services.multi_agent_service import convert_uuids_to_str
 
 logger = logging.getLogger(__name__)
@@ -36,7 +35,7 @@ class WorkflowService:
         self.config_repo = WorkflowConfigRepository(db)
         self.execution_repo = WorkflowExecutionRepository(db)
         self.node_execution_repo = WorkflowNodeExecutionRepository(db)
-        self.message_repo = MessageRepository(db)
+        self.conversation_service = ConversationService(db)
 
     # ==================== 配置管理 ====================
 
@@ -340,6 +339,7 @@ class WorkflowService:
             self,
             execution_id: str,
             status: str,
+            token_usage: int | None = None,
             output_data: dict[str, Any] | None = None,
             error_message: str | None = None,
             error_node_id: str | None = None
@@ -349,6 +349,7 @@ class WorkflowService:
         Args:
             execution_id: 执行 ID
             status: 状态
+            token_usage: token消耗
             output_data: 输出数据
             error_message: 错误信息
             error_node_id: 出错节点 ID
@@ -367,6 +368,8 @@ class WorkflowService:
             )
 
         execution.status = status
+        if token_usage is not None:
+            execution.token_usage = token_usage
         if output_data is not None:
             execution.output_data = convert_uuids_to_str(output_data)
         if error_message is not None:
@@ -513,20 +516,20 @@ class WorkflowService:
 
             # 更新执行结果
             if result.get("status") == "completed":
+                token_usage = result.get("token_usage", {}) or {}
                 self.update_execution_status(
                     execution.execution_id,
                     "completed",
-                    output_data=result
+                    output_data=result,
+                    token_usage=token_usage.get("total_tokens", None)
                 )
                 final_messages = result.get("messages", [])[init_message_length:]
                 for message in final_messages:
-                    message_obj = Message(
+                    self.conversation_service.add_message(
                         conversation_id=conversation_id_uuid,
                         role=message["role"],
-                        content=message["content"],
+                        content=message["content"]
                     )
-                    self.message_repo.add_message(message_obj)
-                self.db.commit()
                 logger.info(f"Workflow Run Success, "
                             f"execution_id: {execution.execution_id}, message count: {len(final_messages)}")
             else:
@@ -662,21 +665,21 @@ class WorkflowService:
                 if event.get("event") == "workflow_end":
 
                     status = event.get("data", {}).get("status")
+                    token_usage = event.get("data", {}).get("token_usage", {}) or {}
                     if status == "completed":
                         self.update_execution_status(
                             execution.execution_id,
                             "completed",
-                            output_data=event.get("data")
+                            output_data=event.get("data"),
+                            token_usage=token_usage.get("total_tokens", None)
                         )
                         final_messages = event.get("data", {}).get("messages", [])[init_message_length:]
                         for message in final_messages:
-                            message_obj = Message(
+                            self.conversation_service.add_message(
                                 conversation_id=conversation_id_uuid,
                                 role=message["role"],
-                                content=message["content"],
+                                content=message["content"]
                             )
-                            self.message_repo.add_message(message_obj)
-                        self.db.commit()
                         logger.info(f"Workflow Run Success, "
                                     f"execution_id: {execution.execution_id}, message count: {len(final_messages)}")
                     elif status == "failed":
@@ -793,10 +796,12 @@ class WorkflowService:
 
                 # 更新执行结果
                 if result.get("status") == "completed":
+                    token_usage = result.get("data").get("token_usage", {}) or {}
                     self.update_execution_status(
                         execution.execution_id,
                         "completed",
-                        output_data=result.get("node_outputs", {})
+                        output_data=result.get("node_outputs", {}),
+                        token_usage=token_usage.get("total_tokens", None)
                     )
                 else:
                     self.update_execution_status(
@@ -891,13 +896,14 @@ class WorkflowService:
             ):
                 # 直接转发事件（executor 已经返回正确格式）
                 if event.get("event") == "workflow_end":
-
+                    token_usage = event.get("data").get("token_usage", {}) or {}
                     status = event.get("data", {}).get("status")
                     if status == "completed":
                         self.update_execution_status(
                             execution_id,
                             "completed",
-                            output_data=event.get("data")
+                            output_data=event.get("data"),
+                            token_usage=token_usage.get("total_tokens", None)
                         )
                     elif status == "failed":
                         self.update_execution_status(