Merge branch 'refs/heads/develop' into feature/20260105_xjn

# Conflicts:
#	api/app/services/app_chat_service.py

api/app/controllers/app_controller.py

@@ -728,9 +728,23 @@ async def draft_run_compare(
             from app.core.exceptions import ResourceNotFoundException
             raise ResourceNotFoundException("模型配置", str(model_item.model_config_id))
 
+        # 获取 agent_cfg.model_parameters，如果是 ModelParameters 对象则转为字典
+        agent_model_params = agent_cfg.model_parameters
+        if hasattr(agent_model_params, 'model_dump'):
+            agent_model_params = agent_model_params.model_dump()
+        elif not isinstance(agent_model_params, dict):
+            agent_model_params = {}
+
+        # 获取 model_item.model_parameters，如果是 ModelParameters 对象则转为字典
+        item_model_params = model_item.model_parameters
+        if hasattr(item_model_params, 'model_dump'):
+            item_model_params = item_model_params.model_dump()
+        elif not isinstance(item_model_params, dict):
+            item_model_params = {}
+
         merged_parameters = {
-            **(agent_cfg.model_parameters or {}),
+            **(agent_model_params or {}),
-            **(model_item.model_parameters or {})
+            **(item_model_params or {})
         }
 
         model_configs.append({

api/app/controllers/public_share_controller.py

@@ -1,4 +1,5 @@
 import hashlib
+import json
 import uuid
 from typing import Annotated
 from fastapi import APIRouter, Depends, Query, Request
@@ -18,7 +19,7 @@ from app.services.conversation_service import ConversationService
 from app.services.release_share_service import ReleaseShareService
 from app.services.shared_chat_service import SharedChatService
 from app.services.app_chat_service import AppChatService, get_app_chat_service
-from app.utils.app_config_utils import dict_to_multi_agent_config, dict_to_workflow_config, agent_config_4_app_release, multi_agent_config_4_app_release
+from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, agent_config_4_app_release, multi_agent_config_4_app_release
 
 router = APIRouter(prefix="/public/share", tags=["Public Share"])
 logger = get_business_logger()
@@ -364,6 +365,9 @@ async def chat(
             config = release.config or {}
             if not config.get("sub_agents"):
                 raise BusinessException("多 Agent 应用未配置子 Agent", BizCode.AGENT_CONFIG_MISSING)
+        elif app_type == AppType.WORKFLOW:
+            # Multi-Agent 类型：验证多 Agent 配置
+            pass
         else:
             raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.APP_TYPE_NOT_SUPPORTED)
 
@@ -469,6 +473,7 @@ async def chat(
         )
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
+        # config = workflow_config_4_app_release(release)
         config = multi_agent_config_4_app_release(release)
         if payload.stream:
             async def event_generator():
@@ -553,8 +558,71 @@ async def chat(
         # )
 
         # return success(data=conversation_schema.ChatResponse(**result))
+    elif app_type == AppType.WORKFLOW:
+
+        config = workflow_config_4_app_release(release)
+        if payload.stream:
+            async def event_generator():
+                async for event in app_chat_service.workflow_chat_stream(
+
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id=new_end_user.id,  # 转换为字符串
+                    variables=payload.variables,
+                    config=config,
+                    web_search=payload.web_search,
+                    memory=payload.memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id,
+                    app_id=release.app_id,
+                    workspace_id=workspace_id
+                ):
+                    event_type = event.get("event", "message")
+                    event_data = event.get("data", {})
+
+                    # 转换为标准 SSE 格式（字符串）
+                    sse_message = f"event: {event_type}\ndata: {json.dumps(event_data)}\n\n"
+                    yield sse_message
+
+            return StreamingResponse(
+                event_generator(),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive",
+                    "X-Accel-Buffering": "no"
+                }
+            )
+
+        # 多 Agent 非流式返回
+        result = await app_chat_service.workflow_chat(
+
+            message=payload.message,
+            conversation_id=conversation.id,  # 使用已创建的会话 ID
+            user_id=new_end_user.id,  # 转换为字符串
+            variables=payload.variables,
+            config=config,
+            web_search=payload.web_search,
+            memory=payload.memory,
+            storage_type=storage_type,
+            user_rag_memory_id=user_rag_memory_id,
+            app_id=release.app_id,
+            workspace_id=workspace_id
+        )
+        logger.debug(
+            "工作流试运行返回结果",
+            extra={
+                "result_type": str(type(result)),
+                "has_response": "response" in result if isinstance(result, dict) else False
+            }
+        )
+        return success(
+            data=result,
+            msg="工作流任务执行成功"
+        )
+        # return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
+
     else:
         from app.core.exceptions import BusinessException
         from app.core.error_codes import BizCode
         raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.APP_TYPE_NOT_SUPPORTED)
-        pass

api/app/controllers/service/app_api_controller.py

@@ -1,4 +1,5 @@
 """App 服务接口 - 基于 API Key 认证"""
+import json
 from typing import Annotated
 
 from fastapi import APIRouter, Depends, Request, Body
@@ -21,7 +22,7 @@ from app.schemas.api_key_schema import ApiKeyAuth
 from app.services import workspace_service
 from app.services.app_chat_service import AppChatService, get_app_chat_service
 from app.services.conversation_service import ConversationService, get_conversation_service
-from app.utils.app_config_utils import dict_to_multi_agent_config, dict_to_workflow_config, agent_config_4_app_release, multi_agent_config_4_app_release
+from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, agent_config_4_app_release, multi_agent_config_4_app_release
 from app.services.app_service import get_app_service, AppService
 
 router = APIRouter(prefix="/app", tags=["V1 - App API"])
@@ -228,22 +229,29 @@ async def chat(
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.WORKFLOW:
         # 多 Agent 流式返回
-        config = dict_to_workflow_config(app.current_release.config,app.id)
+        config = workflow_config_4_app_release(app.current_release)
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.workflow_chat_stream(
 
                     message=payload.message,
                     conversation_id=conversation.id,  # 使用已创建的会话 ID
-                    user_id=end_user_id,  # 转换为字符串
+                    user_id=new_end_user.id,  # 转换为字符串
                     variables=payload.variables,
                     config=config,
-                    web_search=web_search,
+                    web_search=payload.web_search,
-                    memory=memory,
+                    memory=payload.memory,
-                        storage_type=storage_type,
+                    storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id
+                    user_rag_memory_id=user_rag_memory_id,
+                    app_id=app.app_id,
+                    workspace_id=workspace_id
                 ):
-                    yield event
+                    event_type = event.get("event", "message")
+                    event_data = event.get("data", {})
+
+                    # 转换为标准 SSE 格式（字符串）
+                    sse_message = f"event: {event_type}\ndata: {json.dumps(event_data)}\n\n"
+                    yield sse_message
 
             return StreamingResponse(
                 event_generator(),
@@ -255,21 +263,32 @@ async def chat(
                 }
             )
 
-        #  非流式返回
+        # 多 Agent 非流式返回
         result = await app_chat_service.workflow_chat(
 
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
-            user_id=end_user_id,  # 转换为字符串
+            user_id=new_end_user.id,  # 转换为字符串
             variables=payload.variables,
             config=config,
-            web_search=web_search,
+            web_search=payload.web_search,
-            memory=memory,
+            memory=payload.memory,
             storage_type=storage_type,
-            user_rag_memory_id=user_rag_memory_id
+            user_rag_memory_id=user_rag_memory_id,
+            app_id=app.app_id,
+            workspace_id=workspace_id
+        )
+        logger.debug(
+            "工作流试运行返回结果",
+            extra={
+                "result_type": str(type(result)),
+                "has_response": "response" in result if isinstance(result, dict) else False
+            }
+        )
+        return success(
+            data=result,
+            msg="工作流任务执行成功"
         )
-
-        return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     else:
         from app.core.exceptions import BusinessException
         from app.core.error_codes import BizCode

api/app/controllers/user_memory_controllers.py

@@ -11,6 +11,7 @@ from app.db import get_db
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success, fail
 from app.core.error_codes import BizCode
+from app.core.api_key_utils import timestamp_to_datetime
 from app.services.user_memory_service import (
     UserMemoryService,
     analytics_memory_types,
@@ -356,7 +357,7 @@ async def update_end_user_profile(
         if 'hire_date' in update_data:
             hire_date_timestamp = update_data['hire_date']
             if hire_date_timestamp is not None:
-                update_data['hire_date'] = UserMemoryService.timestamp_to_datetime(hire_date_timestamp)
+                update_data['hire_date'] = timestamp_to_datetime(hire_date_timestamp)
             # 如果是 None，保持 None（允许清空）
         
         for field, value in update_data.items():

api/app/core/memory/utils/prompt/prompts/user_summary.jinja2

@@ -85,33 +85,21 @@ Example Output:
 ===End of Example===
 
 
-===Reflection Process===
+===Internal Quality Checks (DO NOT OUTPUT)===
 
-After generating the profile, perform the following self-review steps:
+Before generating your final output, internally verify:
+1. All content is grounded in provided data (no fabrication)
+2. Format follows the specified structure with correct headers
+3. Tone is objective, third-person, and neutral
+4. All four sections are complete and within character limits
 
-**Step 1: Data Grounding Check**
+**IMPORTANT: These checks are for your internal use only. DO NOT include them in your output.**
-- Verify all statements are supported by the provided entities and statements
-- Ensure no fabricated or speculated information is included
-- Confirm all claims can be traced back to the input data
-
-**Step 2: Format Compliance**
-- Verify each section follows the specified format with section headers
-- Check character count limits for each section
-- Ensure proper use of section markers (【】)
-
-**Step 3: Tone and Style Review**
-- Confirm objective third-person perspective is maintained
-- Check for excessive adjectives or empty phrases
-- Verify neutral and restrained tone throughout
-
-**Step 4: Completeness Check**
-- Ensure all four sections are present and complete
-- Verify each section addresses its specific focus area
-- Confirm the one-sentence summary effectively captures the user's essence
 
 
 ===Output Requirements===
 
+**CRITICAL: Your response must ONLY contain the four sections below. Do not include any reflection, self-review, or meta-commentary.**
+
 **LANGUAGE REQUIREMENT:**
 - The output language should ALWAYS be Chinese (Simplified)
 - All section content must be in Chinese
@@ -122,3 +110,5 @@ After generating the profile, perform the following self-review steps:
 - Content follows immediately after the header
 - Sections are separated by blank lines
 - Strictly adhere to character limits for each section
+- **DO NOT include any text after the 【一句话总结】 section**
+- **DO NOT output reflection steps, self-review, or verification notes**

api/app/services/agent_config_converter.py

@@ -86,7 +86,12 @@ class AgentConfigConverter:
         # 1. 解析模型参数配置
         if model_parameters:
             from app.schemas.app_schema import ModelParameters
-            result["model_parameters"] = ModelParameters(**model_parameters)
+            if isinstance(model_parameters, ModelParameters):
+                result["model_parameters"] = model_parameters
+            elif isinstance(model_parameters, dict):
+                result["model_parameters"] = ModelParameters(**model_parameters)
+            else:
+                result["model_parameters"] = ModelParameters()
         
         # 2. 解析知识库检索配置
         if knowledge_retrieval:

api/app/services/app_chat_service.py

@@ -9,7 +9,11 @@ from fastapi import Depends
 from sqlalchemy.orm import Session
 
 from app.core.agent.langchain_agent import LangChainAgent
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
 from app.core.logging_config import get_business_logger
+from app.db import get_db, get_db_context
+from app.models import MultiAgentConfig, AgentConfig, WorkflowConfig
 from app.services.tool_service import ToolService
 from app.repositories.tool_repository import ToolRepository
 from app.db import get_db
@@ -20,6 +24,7 @@ from app.services.draft_run_service import create_knowledge_retrieval_tool, crea
 from app.services.draft_run_service import create_web_search_tool
 from app.services.model_service import ModelApiKeyService
 from app.services.multi_agent_orchestrator import MultiAgentOrchestrator
+from app.services.workflow_service import WorkflowService
 
 logger = get_business_logger()
 
@@ -100,6 +105,21 @@ class AppChatService:
                 memory_tool = create_long_term_memory_tool(memory_config, user_id)
                 tools.append(memory_tool)
 
+        # web_tools = config.tools
+        # web_search_choice = web_tools.get("web_search", {})
+        # web_search_enable = web_search_choice.get("enabled", False)
+        # if web_search == True:
+        #     if web_search_enable == True:
+        #         search_tool = create_web_search_tool({})
+        #         tools.append(search_tool)
+        #
+        #         logger.debug(
+        #             "已添加网络搜索工具",
+        #             extra={
+        #                 "tool_count": len(tools)
+        #             }
+        #         )
+
         # 获取模型参数
         model_parameters = config.model_parameters
 
@@ -482,7 +502,9 @@ class AppChatService:
             self,
             message: str,
             conversation_id: uuid.UUID,
-            config: AgentConfig,
+            config: WorkflowConfig,
+            app_id: uuid.UUID,
+            workspace_id: uuid.UUID,
             user_id: Optional[str] = None,
             variables: Optional[Dict[str, Any]] = None,
             web_search: bool = False,
@@ -491,280 +513,158 @@ class AppChatService:
             user_rag_memory_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """聊天（非流式）"""
+        workflow_service = WorkflowService(self.db)
 
-        start_time = time.time()
+        input_data = {"message":message, "variables": variables,
-        config_id = None
+                      "conversation_id": str(conversation_id)}
+        inconfig = workflow_service.get_workflow_config(app_id)
 
-        if variables is None:
+        # 2. 创建执行记录
-            variables = {}
+        execution = workflow_service.create_execution(
+            workflow_config_id=inconfig.id,
+            app_id=app_id,
+            trigger_type="manual",
+            triggered_by=None,
+            conversation_id=conversation_id,
+            input_data=input_data
+        )
 
-        # 获取模型配置ID
+        # 3. 构建工作流配置字典
-        model_config_id = config.default_model_config_id
+        workflow_config_dict = {
-        api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id)
+            "nodes": config.nodes,
-        # 处理系统提示词（支持变量替换）
+            "edges": config.edges,
-        system_prompt = config.get("system_prompt", "")
+            "variables": config.variables,
-        if variables:
+            "execution_config": config.execution_config
-            system_prompt_rendered = render_prompt_message(
+        }
-                system_prompt,
+
-                PromptMessageRole.USER,
+        # 4. 获取工作空间 ID（从 app 获取）
-                variables
+
+        # 5. 执行工作流
+        from app.core.workflow.executor import execute_workflow
+
+        try:
+            # 更新状态为运行中
+            workflow_service.update_execution_status(execution.execution_id, "running")
+
+            result = await execute_workflow(
+                workflow_config=workflow_config_dict,
+                input_data=input_data,
+                execution_id=execution.execution_id,
+                workspace_id=str(workspace_id),
+                user_id=user_id
             )
-            system_prompt = system_prompt_rendered.get_text_content() or system_prompt
 
-        # 准备工具列表
+            # 更新执行结果
-        tools = []
+            if result.get("status") == "completed":
-
+                workflow_service.update_execution_status(
-        # 添加知识库检索工具
+                    execution.execution_id,
-        knowledge_retrieval = config.get("knowledge_retrieval")
+                    "completed",
-        if knowledge_retrieval:
+                    output_data=result.get("node_outputs", {})
-            knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
+                )
-            kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
+            else:
-            if kb_ids:
+                workflow_service.update_execution_status(
-                kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
+                    execution.execution_id,
-                tools.append(kb_tool)
+                    "failed",
-
+                    error_message=result.get("error")
-        # 添加长期记忆工具
-        memory_flag = False
-        if memory == True:
-            memory_config = config.get("memory", {})
-            if memory_config.get("enabled") and user_id:
-                memory_flag = True
-                memory_tool = create_long_term_memory_tool(memory_config, user_id)
-                tools.append(memory_tool)
-
-        web_tools = config.get("tools")
-        web_search_choice = web_tools.get("web_search", {})
-        web_search_enable = web_search_choice.get("enabled", False)
-        if web_search == True:
-            if web_search_enable == True:
-                search_tool = create_web_search_tool({})
-                tools.append(search_tool)
-
-                logger.debug(
-                    "已添加网络搜索工具",
-                    extra={
-                        "tool_count": len(tools)
-                    }
                 )
 
-        # 获取模型参数
+            # 返回增强的响应结构
-        model_parameters = config.get("model_parameters", {})
+            return {
+                "execution_id": execution.execution_id,
+                "status": result.get("status"),
+                "output": result.get("output"),  # 最终输出（字符串）
+                "output_data": result.get("node_outputs", {}),  # 所有节点输出（详细数据）
+                "conversation_id": result.get("conversation_id"),  # 所有节点输出（详细数据）payload.,  # 会话 ID
+                "error_message": result.get("error"),
+                "elapsed_time": result.get("elapsed_time"),
+                "token_usage": result.get("token_usage")
+            }
 
-        # 创建 LangChain Agent
+        except Exception as e:
-        agent = LangChainAgent(
+            logger.error(f"工作流执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True)
-            model_name=api_key_obj.model_name,
+            workflow_service.update_execution_status(
-            api_key=api_key_obj.api_key,
+                execution.execution_id,
-            provider=api_key_obj.provider,
+                "failed",
-            api_base=api_key_obj.api_base,
+                error_message=str(e)
-            temperature=model_parameters.get("temperature", 0.7),
+            )
-            max_tokens=model_parameters.get("max_tokens", 2000),
+            raise BusinessException(
-            system_prompt=system_prompt,
+                code=BizCode.INTERNAL_ERROR,
-            tools=tools,
+                message=f"工作流执行失败: {str(e)}"
-
-        )
-
-        # 加载历史消息
-        history = []
-        memory_config = {"enabled": True, 'max_history': 10}
-        if memory_config.get("enabled"):
-            messages = self.conversation_service.get_messages(
-                conversation_id=conversation_id,
-                limit=memory_config.get("max_history", 10)
             )
-            history = [
-                {"role": msg.role, "content": msg.content}
-                for msg in messages
-            ]
-
-        # 调用 Agent
-        result = await agent.chat(
-            message=message,
-            history=history,
-            context=None,
-            end_user_id=user_id,
-            storage_type=storage_type,
-            user_rag_memory_id=user_rag_memory_id,
-            config_id=config_id,
-            memory_flag=memory_flag
-        )
-
-        # 保存消息
-        self.conversation_service.save_conversation_messages(
-            conversation_id=conversation_id,
-            user_message=message,
-            assistant_message=result["content"]
-        )
-
-        elapsed_time = time.time() - start_time
-
-        return {
-            "conversation_id": conversation_id,
-            "message": result["content"],
-            "usage": result.get("usage", {
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "total_tokens": 0
-            }),
-            "elapsed_time": elapsed_time
-        }
 
     async def workflow_chat_stream(
             self,
             message: str,
             conversation_id: uuid.UUID,
-            config: AgentConfig,
+            config: WorkflowConfig,
+            app_id: uuid.UUID,
+            workspace_id: uuid.UUID,
             user_id: Optional[str] = None,
             variables: Optional[Dict[str, Any]] = None,
             web_search: bool = False,
             memory: bool = True,
             storage_type: Optional[str] = None,
             user_rag_memory_id: Optional[str] = None,
+
     ) -> AsyncGenerator[str, None]:
         """聊天（流式）"""
+        workflow_service = WorkflowService(self.db)
+        input_data = {"message": message, "variables": variables,
+                      "conversation_id": str(conversation_id)}
+        inconfig = workflow_service.get_workflow_config(app_id)
+        # 2. 创建执行记录
+        execution = workflow_service.create_execution(
+            workflow_config_id=inconfig.id,
+            app_id=app_id,
+            trigger_type="manual",
+            triggered_by=None,
+            conversation_id=conversation_id,
+            input_data=input_data
+        )
+
+        # 3. 构建工作流配置字典
+        workflow_config_dict = {
+            "nodes": config.nodes,
+            "edges": config.edges,
+            "variables": config.variables,
+            "execution_config": config.execution_config
+        }
+
+        # 4. 获取工作空间 ID（从 app 获取）
+
+        # 5. 流式执行工作流
 
         try:
-            start_time = time.time()
+            # 更新状态为运行中
-            config_id = None
+            workflow_service.update_execution_status(execution.execution_id, "running")
 
-            if variables is None:
-                variables = {}
 
-            # 获取模型配置ID
+            # 调用流式执行（executor 会发送 workflow_start 和 workflow_end 事件）
-            model_config_id = config.default_model_config_id
+            async for event in workflow_service._run_workflow_stream(
-            api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id)
+                    workflow_config=workflow_config_dict,
-            # 处理系统提示词（支持变量替换）
+                    input_data=input_data,
-            system_prompt = config.get("system_prompt", "")
+                    execution_id=execution.execution_id,
-            if variables:
+                    workspace_id=str(workspace_id),
-                system_prompt_rendered = render_prompt_message(
+                    user_id=user_id
-                    system_prompt,
-                    PromptMessageRole.USER,
-                    variables
-                )
-                system_prompt = system_prompt_rendered.get_text_content() or system_prompt
-
-            # 准备工具列表
-            tools = []
-            
-            # 获取工具服务
-            tool_service = ToolService(self.db)
-            
-            # 从配置中获取启用的工具
-            if hasattr(config, 'tools') and config.tools:
-                for tool_id, tool_config in config.tools.items():
-                    if tool_config.get("enabled", False):
-                        # 根据工具名称查找工具实例
-                        tool_instance = tool_service._get_tool_instance(tool_id, ToolRepository.get_tenant_id_by_workspace_id(self.db, workspace_id))
-                        if tool_instance:
-                            # 转换为LangChain工具
-                            langchain_tool = tool_instance.to_langchain_tool(tool_config.get("config", {}).get("operation", None))
-                            tools.append(langchain_tool)
-
-            # 添加知识库检索工具
-            knowledge_retrieval = config.get("knowledge_retrieval")
-            if knowledge_retrieval:
-                knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
-                kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
-                if kb_ids:
-                    kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
-                    tools.append(kb_tool)
-
-            # 添加长期记忆工具
-            memory_flag = False
-            if memory:
-                memory_config = config.get("memory", {})
-                if memory_config.get("enabled") and user_id:
-                    memory_flag = True
-                    memory_tool = create_long_term_memory_tool(memory_config, user_id)
-                    tools.append(memory_tool)
-
-            # 获取模型参数
-            model_parameters = config.get("model_parameters", {})
-
-            # 创建 LangChain Agent
-            agent = LangChainAgent(
-                model_name=api_key_obj.model_name,
-                api_key=api_key_obj.api_key,
-                provider=api_key_obj.provider,
-                api_base=api_key_obj.api_base,
-                temperature=model_parameters.get("temperature", 0.7),
-                max_tokens=model_parameters.get("max_tokens", 2000),
-                system_prompt=system_prompt,
-                tools=tools,
-                streaming=True
-            )
-
-            # 加载历史消息
-            history = []
-            memory_config = {"enabled": True, 'max_history': 10}
-            if memory_config.get("enabled"):
-                messages = self.conversation_service.get_messages(
-                    conversation_id=conversation_id,
-                    limit=memory_config.get("max_history", 10)
-                )
-                history = [
-                    {"role": msg.role, "content": msg.content}
-                    for msg in messages
-                ]
-
-            # 发送开始事件
-            yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
-
-            # 流式调用 Agent
-            full_content = ""
-            async for chunk in agent.chat_stream(
-                    message=message,
-                    history=history,
-                    context=None,
-                    end_user_id=user_id,
-                    storage_type=storage_type,
-                    user_rag_memory_id=user_rag_memory_id,
-                    config_id=config_id,
-                    memory_flag=memory_flag
             ):
-                full_content += chunk
+                # 直接转发 executor 的事件（已经是正确的格式）
-                # 发送消息块事件
+                yield event
-                yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
 
-            elapsed_time = time.time() - start_time
-
-            # 保存消息
-            self.conversation_service.add_message(
-                conversation_id=conversation_id,
-                role="user",
-                content=message
-            )
-
-            self.conversation_service.add_message(
-                conversation_id=conversation_id,
-                role="assistant",
-                content=full_content,
-                meta_data={
-                    "model": api_key_obj.model_name,
-                    "usage": {}
-                }
-            )
-
-            # 发送结束事件
-            end_data = {"elapsed_time": elapsed_time, "message_length": len(full_content)}
-            yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
-
-            logger.info(
-                "流式聊天完成",
-                extra={
-                    "conversation_id": str(conversation_id),
-                    "elapsed_time": elapsed_time,
-                    "message_length": len(full_content)
-                }
-            )
-
-        except (GeneratorExit, asyncio.CancelledError):
-            # 生成器被关闭或任务被取消，正常退出
-            logger.debug("流式聊天被中断")
-            raise
         except Exception as e:
-            logger.error(f"流式聊天失败: {str(e)}", exc_info=True)
+            logger.error(f"工作流流式执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True)
+            workflow_service.update_execution_status(
+                execution.execution_id,
+                "failed",
+                error_message=str(e)
+            )
             # 发送错误事件
-            yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
+            yield {
+                "event": "error",
+                "data": {
+                    "execution_id": execution.execution_id,
+                    "error": str(e)
+                }
+            }
 
 # ==================== 依赖注入函数 ====================
 

api/app/services/app_service.py

@@ -21,6 +21,7 @@ from app.core.exceptions import (
     BusinessException,
 )
 from app.core.logging_config import get_business_logger
+from app.core.workflow.validator import WorkflowValidator
 from app.db import get_db
 from app.models import App, AgentConfig, AppRelease, MultiAgentConfig, WorkflowConfig
 from app.models.app_model import AppStatus, AppType
@@ -31,6 +32,7 @@ from app.schemas.workflow_schema import WorkflowConfigUpdate
 from app.services.agent_config_converter import AgentConfigConverter
 from app.models import AppShare, Workspace
 from app.services.model_service import ModelApiKeyService
+from app.services.workflow_service import WorkflowService
 
 # 获取业务日志器
 logger = get_business_logger()
@@ -1225,6 +1227,26 @@ class AppService:
                     "orchestration_mode": multi_agent_cfg.orchestration_mode
                 }
             )
+        elif app.type == AppType.WORKFLOW:
+            service = WorkflowService(self.db)
+            workflow_cfg = service.get_workflow_config(app_id)
+            if not workflow_cfg:
+                raise BusinessException("应用缺少有效配置，无法发布", BizCode.CONFIG_MISSING)
+
+            config = {
+                "nodes": workflow_cfg.nodes,
+                "edges": workflow_cfg.edges,
+                "variables": workflow_cfg.variables,
+                "execution_config": workflow_cfg.execution_config,
+                "triggers": workflow_cfg.triggers
+            }
+
+            is_valid, errors = WorkflowValidator.validate_for_publish(config)
+            if not is_valid:
+                raise BusinessException("应用缺少有效配置，无法发布", BizCode.CONFIG_MISSING)
+            logger.info(
+                "应用发布配置准备完成"
+            )
 
         now = datetime.datetime.now()
         version = self._get_next_version(app_id)

api/app/services/multi_agent_orchestrator.py

@@ -1293,6 +1293,7 @@ class MultiAgentOrchestrator:
             conversation_id: 会话 ID
             user_id: 用户 ID
 
+
         Returns:
             执行结果
         """

api/app/services/user_memory_service.py

@@ -1054,6 +1054,28 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
         core_values = core_values_match.group(1).strip() if core_values_match else ""
         one_sentence = one_sentence_match.group(1).strip() if one_sentence_match else ""
         
+        # 6) 清理可能包含的反思内容（防御性编程）
+        # 如果 LLM 仍然输出了反思内容，在这里过滤掉
+        def clean_reflection_content(text: str) -> str:
+            """移除可能包含的反思内容"""
+            if not text:
+                return text
+            # 移除 "---" 之后的所有内容（通常是反思部分的开始）
+            if '---' in text:
+                text = text.split('---')[0].strip()
+            # 移除 "**Step" 开头的内容
+            if '**Step' in text:
+                text = text.split('**Step')[0].strip()
+            # 移除 "Self-Review" 相关内容
+            if 'Self-Review' in text or 'self-review' in text:
+                text = re.sub(r'[\-\*]*\s*Self-Review.*$', '', text, flags=re.IGNORECASE | re.DOTALL).strip()
+            return text
+        
+        user_summary = clean_reflection_content(user_summary)
+        personality = clean_reflection_content(personality)
+        core_values = clean_reflection_content(core_values)
+        one_sentence = clean_reflection_content(one_sentence)
+        
         return {
             "user_summary": user_summary,
             "personality": personality,

api/app/services/workflow_service.py

@@ -17,6 +17,7 @@ from app.core.workflow.validator import validate_workflow_config
 from app.db import get_db, get_db_context
 from app.models.workflow_model import WorkflowConfig, WorkflowExecution
 from app.repositories.end_user_repository import EndUserRepository
+from app.services.multi_agent_service import convert_uuids_to_str
 from app.repositories.workflow_repository import (
     WorkflowConfigRepository,
     WorkflowExecutionRepository,
@@ -364,7 +365,7 @@ class WorkflowService:
 
         execution.status = status
         if output_data is not None:
-            execution.output_data = output_data
+            execution.output_data = convert_uuids_to_str(output_data)
         if error_message is not None:
             execution.error_message = error_message
         if error_node_id is not None:

api/app/utils/app_config_utils.py

@@ -8,7 +8,7 @@ import uuid
 from typing import Dict, Any, Optional
 from datetime import datetime
 
-from app.models import AppRelease
+from app.models import AppRelease, WorkflowConfig
 from app.models.agent_app_config_model import AgentConfig
 from app.models.multi_agent_model import MultiAgentConfig
 
@@ -63,6 +63,24 @@ def multi_agent_config_4_app_release(release: AppRelease ) -> MultiAgentConfig:
 
     return agent_config
 
+def workflow_config_4_app_release(release: AppRelease ) -> WorkflowConfig:
+
+    config_dict = release.config
+
+
+    config = WorkflowConfig(
+        id=release.id,
+        app_id=release.app_id,
+        nodes=config_dict.get("nodes", []),
+        edges=config_dict.get("edges", []),
+        variables=config_dict.get("variables", []),
+        execution_config=config_dict.get("execution_config", {}),
+        triggers=config_dict.get("triggers", [])
+
+    )
+
+    return config
+
 def dict_to_multi_agent_config(config_dict: Dict[str, Any], app_id: Optional[uuid.UUID] = None):
     """Convert dict to MultiAgentConfig model object
 

web/src/api/prompt.ts

@@ -1,5 +1,6 @@
 import { request } from '@/utils/request'
 import type { AiPromptForm } from '@/views/ApplicationConfig/types'
+import { handleSSE, type SSEMessage } from '@/utils/stream'
 
 export const createPromptSessions = () => {
   return request.post(`/prompt/sessions`)
@@ -7,6 +8,6 @@ export const createPromptSessions = () => {
 export const getPrompt = (session_id: string) => {
   return request.get(`/prompt/sessions/${session_id}`)
 }
-export const updatePromptMessages = (session_id: string, data: AiPromptForm) => {
+export const updatePromptMessages = (session_id: string, data: AiPromptForm, onMessage?: (data: SSEMessage[]) => void) => {
-  return request.post(`/prompt/sessions/${session_id}/messages`, data)
+  return handleSSE(`/prompt/sessions/${session_id}/messages`, data, onMessage)
 }

web/src/i18n/en.ts

@@ -1223,6 +1223,8 @@ export const en = {
       key_findings: 'Key Findings',
       behavior_pattern: 'Behavior Pattern',
       growth_trajectory: 'Growth Trajectory',
+      personality: 'Personality Traits',
+      core_values: 'Core Values',
     },
     space: {
       createSpace: 'Create Space',

web/src/i18n/zh.ts

@@ -1304,6 +1304,8 @@ export const zh = {
       key_findings: '关键发现',
       behavior_pattern: '行为模式',
       growth_trajectory: '成长轨迹',
+      personality: '性格特点',
+      core_values: '核心价值观',
     },
     space: {
       createSpace: '创建空间',

web/src/views/ApplicationConfig/components/AiPromptModal.tsx

@@ -16,6 +16,8 @@ import ConversationEmptyIcon from '@/assets/images/conversation/conversationEmpt
 import type { ChatItem } from '@/components/Chat/types' 
 import CustomSelect from '@/components/CustomSelect'
 import AiPromptVariableModal from './AiPromptVariableModal'
+import { type SSEMessage } from '@/utils/stream'
+import Editor from './Editor'
 
 interface AiPromptModalProps {
   refresh: (value: string) => void;
@@ -35,7 +37,8 @@ const AiPromptModal = forwardRef<AiPromptModalRef, AiPromptModalProps>(({
   const [variables, setVariables] = useState<string[]>([])
   const [promptSession, setPromptSession] = useState<string | null>(null)
   const aiPromptVariableModalRef = useRef<AiPromptVariableModalRef>(null)
-  const currentPromptRef = useRef<any>(null)
+  const editorRef = useRef<any>(null)
+  const currentPromptValueRef = useRef<string>('')
 
   const values = Form.useWatch([], form)
 
@@ -78,16 +81,45 @@ const AiPromptModal = forwardRef<AiPromptModalRef, AiPromptModalProps>(({
     setChatList(prev => {
       return [...prev, { role: 'user', content: messageContent}]
     })
-    form.setFieldsValue({ message: undefined })
+    form.setFieldsValue({ message: undefined, current_prompt: undefined })
-    updatePromptMessages(promptSession, values)
+
-      .then(res => {
+    const handleStreamMessage = (data: SSEMessage[]) => {
-        const response = res as { prompt: string; desc: string; variables: string[] }
+      data.map(item => {
-        form.setFieldsValue({ current_prompt: response.prompt })
+        const { content, desc, variables } = item.data as { content: string; desc: string; variables: string[] };
-        setChatList(prev => {
+
-          return [...prev, { role: 'assistant', content: response.desc }]
+        switch (item.event) {
-        })
+          case 'start':
-        setVariables(response.variables)
+            currentPromptValueRef.current = ''
+            break;
+          case 'message':
+            if (content) {
+              currentPromptValueRef.current += content;
+              form.setFieldsValue({ current_prompt: currentPromptValueRef.current })
+            }
+            if (desc) {
+              setChatList(prev => {
+                return [...prev, { role: 'assistant', content: desc }]
+              })
+            }
+            if (variables) {
+              setVariables(variables)
+            }
+            break;
+          case 'end':
+            setLoading(false)
+            break
+        }
       })
+    };
+    updatePromptMessages(promptSession, values, handleStreamMessage)
+      // .then(res => {
+      //   const response = res as { prompt: string; desc: string; variables: string[] }
+      //   form.setFieldsValue({ current_prompt: response.prompt })
+      //   setChatList(prev => {
+      //     return [...prev, { role: 'assistant', content: response.desc }]
+      //   })
+      //   setVariables(response.variables)
+      // })
       .finally(() => {
         setLoading(false)
       })
@@ -101,18 +133,8 @@ const AiPromptModal = forwardRef<AiPromptModalRef, AiPromptModalProps>(({
     aiPromptVariableModalRef.current?.handleOpen()
   }
   const handleVariableApply = (value: string) => {
-    const textArea = currentPromptRef.current?.resizableTextArea?.textArea
+    if (editorRef.current?.insertText) {
-    if (textArea) {
+      editorRef.current.insertText(value)
-      const cursorPosition = textArea.selectionStart
-      const currentValue = values.current_prompt || ''
-      const newValue = currentValue.slice(0, cursorPosition) + value + currentValue.slice(cursorPosition)
-      form.setFieldValue('current_prompt', newValue)
-      
-      // 设置新的光标位置
-      setTimeout(() => {
-        textArea.focus()
-        textArea.setSelectionRange(cursorPosition + value.length, cursorPosition + value.length)
-      }, 0)
     } else {
       form.setFieldValue('current_prompt', (values.current_prompt || '') + value)
     }
@@ -191,7 +213,11 @@ const AiPromptModal = forwardRef<AiPromptModalRef, AiPromptModalProps>(({
               </Col>
             </Row>
             <Form.Item name="current_prompt">
-              <Input.TextArea ref={currentPromptRef} className="rb:bg-[#FBFDFF]! rb:h-100.5!" />
+              <Editor 
+                ref={editorRef}
+                className="rb:h-100.5 " 
+                onChange={(value) => form.setFieldValue('current_prompt', value)}
+              />
             </Form.Item>
             <div className="rb:grid rb:grid-cols-2 rb:gap-4 rb:mt-6">
               <Button block disabled={!values?.current_prompt} onClick={handleCopy}>{t('common.copy')}</Button>

web/src/views/ApplicationConfig/components/Editor/index.tsx

@@ -0,0 +1,91 @@
+import {forwardRef, useImperativeHandle } from 'react';
+import clsx from 'clsx';
+import { LexicalComposer } from '@lexical/react/LexicalComposer';
+import { RichTextPlugin } from '@lexical/react/LexicalRichTextPlugin';
+import { ContentEditable } from '@lexical/react/LexicalContentEditable';
+import { LexicalErrorBoundary } from '@lexical/react/LexicalErrorBoundary';
+import { $getSelection } from 'lexical';
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+import InitialValuePlugin from './plugin/InitialValuePlugin'
+import LineBreakPlugin from './plugin/LineBreakPlugin';
+import InsertTextPlugin from './plugin/InsertTextPlugin';
+
+export interface EditorRef {
+  insertText: (text: string) => void;
+}
+
+interface LexicalEditorProps {
+  className?: string;
+  placeholder?: string;
+  value?: string;
+  onChange?: (value: string) => void;
+  height?: number;
+}
+
+const theme = {
+  paragraph: 'editor-paragraph',
+  text: {
+    bold: 'editor-text-bold',
+    italic: 'editor-text-italic',
+  },
+};
+
+const EditorContent = forwardRef<EditorRef, LexicalEditorProps>(({
+  className = '',
+  value,
+  placeholder = "请输入内容...",
+  onChange,
+}, ref) => {
+  const [editor] = useLexicalComposerContext();
+  
+  useImperativeHandle(ref, () => ({
+    insertText: (text: string) => {
+      editor.update(() => {
+        const selection = $getSelection();
+        if (selection) {
+          selection.insertText(text);
+        }
+      });
+    }
+  }), [editor]);
+
+  return (
+    <div style={{ position: 'relative' }}>
+      <RichTextPlugin
+        contentEditable={
+          <ContentEditable
+            className={clsx("rb:outline-none rb:resize-none rb:text-[14px] rb:leading-5 rb:px-4 rb:py-5 rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg rb:overflow-auto", className)}
+          />
+        }
+        placeholder={
+          <div className="rb:absolute rb:px-4 rb:py-5 rb:text-[14px] rb:text-[#5B6167] rb:leading-5 rb:pointer-none">
+            {placeholder}
+          </div>
+        }
+        ErrorBoundary={LexicalErrorBoundary}
+      />
+      <LineBreakPlugin onChange={onChange} />
+      <InitialValuePlugin value={value} />
+      <InsertTextPlugin />
+    </div>
+  );
+});
+
+const Editor = forwardRef<EditorRef, LexicalEditorProps>((props, ref) => {
+  const initialConfig = {
+    namespace: 'Editor',
+    theme,
+    nodes: [],
+    onError: (error: Error) => {
+      console.error(error);
+    },
+  };
+
+  return (
+    <LexicalComposer initialConfig={initialConfig}>
+      <EditorContent {...props} ref={ref} />
+    </LexicalComposer>
+  );
+});
+
+export default Editor;

web/src/views/ApplicationConfig/components/Editor/plugin/InitialValuePlugin.tsx

@@ -0,0 +1,25 @@
+import { type FC, useEffect } from 'react';
+import { $getRoot, $createParagraphNode, $createTextNode } from 'lexical';
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+
+// 设置初始值的插件
+const InitialValuePlugin: FC<{ value?: string }> = ({ value }) => {
+  const [editor] = useLexicalComposerContext();
+  
+  useEffect(() => {
+    if (value) {
+      editor.update(() => {
+        const root = $getRoot();
+        root.clear();
+        const paragraph = $createParagraphNode();
+        const textNode = $createTextNode(value);
+        paragraph.append(textNode);
+        root.append(paragraph);
+      });
+    }
+  }, [editor, value]);
+  
+  return null;
+};
+
+export default InitialValuePlugin

web/src/views/ApplicationConfig/components/Editor/plugin/InsertTextPlugin.tsx

@@ -0,0 +1,24 @@
+import { forwardRef, useImperativeHandle } from 'react';
+import { $getSelection } from 'lexical';
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+import type { EditorRef } from '../index'
+
+// 插入文本的插件
+const InsertTextPlugin = forwardRef<EditorRef>((_, ref) => {
+  const [editor] = useLexicalComposerContext();
+  
+  useImperativeHandle(ref, () => ({
+    insertText: (text: string) => {
+      editor.update(() => {
+        const selection = $getSelection();
+        if (selection) {
+          selection.insertText(text);
+        }
+      });
+    }
+  }), [editor]);
+  
+  return null;
+});
+
+export default InsertTextPlugin;

web/src/views/ApplicationConfig/components/Editor/plugin/LineBreakPlugin.tsx

@@ -0,0 +1,24 @@
+import { type FC, useEffect } from 'react';
+import { $getRoot } from 'lexical';
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+
+// 处理换行的插件
+const LineBreakPlugin: FC<{ onChange?: (value: string) => void }> = ({ onChange }) => {
+  const [editor] = useLexicalComposerContext();
+  
+  useEffect(() => {
+    return editor.registerUpdateListener(({ editorState }) => {
+      editorState.read(() => {
+        const root = $getRoot();
+        const textContent = root.getTextContent();
+        // 将\n转换为实际换行
+        const processedContent = textContent.replace(/\\n/g, '\n');
+        onChange?.(processedContent);
+      });
+    });
+  }, [editor, onChange]);
+  
+  return null;
+};
+
+export default LineBreakPlugin;

web/src/views/UserMemoryDetail/components/AboutMe.tsx

@@ -5,16 +5,25 @@ import { Skeleton } from 'antd';
 
 import RbCard from '@/components/RbCard/Card'
 import Empty from '@/components/Empty';
+import RbAlert from '@/components/RbAlert';
 import {
   getUserSummary,
 } from '@/api/memory'
 import type { AboutMeRef } from '../types'
 
+
+interface Data {
+  user_summary: string;
+  personality: string;
+  core_values: string;
+  one_sentence: string;
+  [key: string]: string;
+}
 const AboutMe = forwardRef<AboutMeRef>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
-  const [data, setData] = useState<string | null>(null)
+  const [data, setData] = useState<Data>({} as Data)
 
   useEffect(() => {
     if (!id) return
@@ -27,7 +36,7 @@ const AboutMe = forwardRef<AboutMeRef>((_props, ref) => {
     setLoading(true)
     getUserSummary(id)
       .then((res) => {
-        setData((res as { summary?: string }).summary || null)
+        setData((res as Data) || null)
       })
       .finally(() => {
         setLoading(false)
@@ -44,10 +53,29 @@ const AboutMe = forwardRef<AboutMeRef>((_props, ref) => {
     >
       {loading
         ? <Skeleton className="rb:mt-4" />
-        : data
+        : Object.keys(data).filter(key => data[key] !== null).length > 0
-          ? <div className="rb:font-regular rb:leading-5 rb:text-[#5B6167]">
+          ? <>
-            {data || '-'}
+            {data.user_summary && 
-          </div>
+              <div className="rb:font-regular rb:leading-5 rb:text-[#5B6167]">
+                {data.user_summary}
+              </div>
+            }
+            {data.personality && <>
+              <div className="rb:pt-4 rb:font-medium rb:leading-5 rb:mb-2">{t('userMemory.personality')}</div>
+              <div className="rb:font-regular rb:leading-5 rb:text-[#5B6167]">
+                {data.personality}
+              </div>
+            </>}
+            {data.core_values && <>
+              <div className="rb:pt-4 rb:font-medium rb:leading-5 rb:mb-2">{t('userMemory.core_values')}</div>
+              <div className="rb:font-regular rb:leading-5 rb:text-[#5B6167]">
+                {data.core_values}
+              </div>
+            </>}
+            {data.one_sentence && 
+              <RbAlert className="rb:mt-4">{data.one_sentence}</RbAlert>
+            }
+          </>
           : <Empty size={88} className="rb:mt-12 rb:mb-20.25" />
       }
     </RbCard>

web/src/views/Workflow/constant.ts

@@ -394,7 +394,8 @@ export const nodeLibrary: NodeLibrary[] = [
             defaultValue: {}
           },
           retry: {
-            type: 'define',
+            type: 'switch',
+            defaultValue: false
           },
           error_handle: {
             type: 'define',