feat: improve agent runtime observability

backend-fastapi/ai_platform/services/agent_service.py

@@ -43,6 +43,16 @@ class AgentService:
     def _attach_agent_collaboration(self, event: Dict[str, Any], agent: Agent) -> Dict[str, Any]:
         enriched = dict(event)
         collaboration = dict(enriched.get("collaboration") or {})
+        for key in (
+            "model_id",
+            "model",
+            "model_name",
+            "provider_id",
+            "provider_name",
+            "provider_type",
+        ):
+            if enriched.get(key):
+                collaboration.setdefault(key, enriched[key])
         for key, value in self._agent_collaboration_metadata(agent).items():
             collaboration.setdefault(key, value)
         enriched["collaboration"] = collaboration
@@ -55,6 +65,10 @@ class AgentService:
             "collaboration_role",
             "collaboration_mode",
             "model_id",
+            "model",
+            "model_name",
+            "provider_name",
+            "provider_type",
         ):
             if collaboration.get(key):
                 actor.setdefault(key, collaboration[key])
@@ -248,6 +262,34 @@ class AgentService:
         )
         return str(model.id)
 
+    async def _get_model_runtime_meta(self, model_id: Optional[str]) -> Dict[str, Any]:
+        if not self._db or not model_id:
+            return {}
+
+        result = await self._db.execute(
+            select(LLMModel, LLMProvider)
+            .join(LLMProvider, LLMProvider.id == LLMModel.provider_id)
+            .where(
+                LLMModel.id == model_id,
+                LLMModel.is_deleted == False,
+                LLMProvider.is_deleted == False,
+            )
+        )
+        row = result.first()
+        if not row:
+            return {"model_id": str(model_id)}
+
+        model, provider = row[0], row[1]
+        display_model = model.display_name or model.model_name or str(model.id)
+        return {
+            "model_id": str(model.id),
+            "model": display_model,
+            "model_name": model.model_name or display_model,
+            "provider_id": str(provider.id),
+            "provider_name": provider.name or provider.provider_type,
+            "provider_type": provider.provider_type,
+        }
+    
     async def _chat_autonomous_function_calling(
         self,
         agent: Agent,
@@ -264,6 +306,8 @@ class AgentService:
         from ai_platform.providers.base import LLMMessage
         
         start_time = time.time()
+        model_id: Optional[str] = None
+        model_meta: Dict[str, Any] = {}
         
         if not self._db:
             yield {'type': 'error', 'content': '数据库会话未初始化'}
@@ -315,11 +359,13 @@ class AgentService:
             model_id = await self._resolve_agent_model_id(agent)
             if not model_id:
                 raise ValueError('未找到可用的 chat 模型，请先在模型配置中启用一个模型')
+            model_meta = await self._get_model_runtime_meta(model_id)
 
             event = self._attach_agent_collaboration({
                 'type': 'thought',
                 'content': '智能体开始分析用户需求并调用默认 chat 模型',
                 'model_id': model_id,
+                **model_meta,
             }, agent)
             step = self._event_to_reasoning_step(event)
             if step:
@@ -390,6 +436,7 @@ class AgentService:
             model_id = await self._resolve_agent_model_id(agent)
             if not model_id:
                 raise ValueError('未找到可用的 chat 模型，请先在模型配置中启用一个模型')
+            model_meta = await self._get_model_runtime_meta(model_id)
             
             total_tokens = 0
             final_answer = ''
@@ -410,6 +457,7 @@ class AgentService:
                             'content': chunk.content,
                             'accumulated_content': accumulated_content,
                             'model_id': model_id,
+                            **model_meta,
                         }
                     
                     if chunk.is_finished:
@@ -431,6 +479,7 @@ class AgentService:
                     'type': 'answer',
                     'content': final_answer,
                     'model_id': model_id,
+                    **model_meta,
                 }
             
             # 更新助手消息
@@ -461,21 +510,20 @@ class AgentService:
                 'conversation_id': str(conversation.id),
                 'tokens_used': total_tokens,
                 'elapsed_time': elapsed_time,
+                **model_meta,
             }
             
         except Exception as e:
             logger.exception(f'Agent chat error: {e}')
             assistant_msg.status = 'failed'
             assistant_msg.error_message = str(e)
-            error_step = self._event_to_reasoning_step({'type': 'error', 'content': str(e)})
+            error_event = {'type': 'error', 'content': str(e), **model_meta}
+            error_step = self._event_to_reasoning_step(error_event)
             if error_step:
                 reasoning_steps.append(error_step)
                 assistant_msg.reasoning_steps = reasoning_steps.copy()
             await self._db.commit()
-            yield {
+            yield error_event
-                'type': 'error',
-                'content': str(e),
-            }
     
     async def _chat_autonomous_react(
         self,

web/apps/web-ele/src/components/ChatBox/MessageBubble.vue

@@ -134,48 +134,89 @@ const handleCopy = async () => {
 
 // 获取步骤类型标签
 const getStepLabel = (step: ReasoningStep) => {
+  if (step.status === 'failed') {
+    return '失败';
+  }
+
+  if (step.status === 'running') {
+    return '执行中';
+  }
+
   // 如果是节点类型，根据 status 显示不同的标签
   if (step.type === 'node_start' || step.type === 'node_complete') {
-    if (step.status === 'running') {
-      return '执行中';
-    }
     return '已完成';
   }
 
   const labels: Record<string, string> = {
-    thought: '思考',
     action: '执行',
+    annotation_reply: '标注回复',
+    error: '错误',
+    knowledge_retrieval: '知识库检索',
+    llm_chunk: '模型输出',
+    loop_complete: '循环完成',
+    loop_iteration_complete: '单次完成',
+    loop_iteration_error: '单次失败',
+    loop_iteration_start: '循环开始',
     observation: '观察',
+    parallel_complete: '并行完成',
+    parallel_start: '并行开始',
+    thought: '思考',
     tool_call: '调用工具',
     tool_result: '工具结果',
-    knowledge_retrieval: '知识库检索',
+    waiting_input: '等待输入',
-    annotation_reply: '标注回复',
   };
   return labels[step.type] || step.type;
 };
 
 // 获取步骤类型颜色
 const getStepColor = (step: ReasoningStep) => {
+  if (step.status === 'failed' || step.type === 'error') {
+    return 'text-red-500';
+  }
+
+  if (step.status === 'running') {
+    return 'text-blue-500';
+  }
+
   // 如果是节点类型，根据 status 显示不同的颜色
   if (step.type === 'node_start' || step.type === 'node_complete') {
-    if (step.status === 'running') {
-      return 'text-blue-500';
-    }
     return 'text-emerald-500';
   }
 
   const colors: Record<string, string> = {
-    thought: 'text-blue-500',
     action: 'text-orange-500',
+    annotation_reply: 'text-emerald-500',
+    knowledge_retrieval: 'text-cyan-500',
+    llm_chunk: 'text-blue-500',
+    loop_complete: 'text-emerald-500',
+    loop_iteration_complete: 'text-emerald-500',
+    loop_iteration_error: 'text-red-500',
+    loop_iteration_start: 'text-blue-500',
     observation: 'text-green-500',
+    parallel_complete: 'text-emerald-500',
+    parallel_start: 'text-blue-500',
+    thought: 'text-blue-500',
     tool_call: 'text-purple-500',
     tool_result: 'text-teal-500',
-    knowledge_retrieval: 'text-cyan-500',
+    waiting_input: 'text-amber-500',
-    annotation_reply: 'text-emerald-500',
   };
   return colors[step.type] || 'text-gray-500';
 };
 
+const getStepMeta = (step: ReasoningStep) => {
+  const items: string[] = [];
+  if (step.agent_name) items.push(step.agent_name);
+  if (step.branch_label) items.push(`分支 ${step.branch_label}`);
+  if (step.subflow_name) items.push(`子流程 ${step.subflow_name}`);
+
+  const modelText = [step.provider_name, step.model || step.model_id]
+    .filter(Boolean)
+    .join(' / ');
+  if (modelText) items.push(modelText);
+
+  return items.join(' · ');
+};
+
 // 格式化文件大小
 const formatFileSize = (bytes?: number) => {
   if (!bytes) return '';
@@ -302,6 +343,9 @@ const formatVoiceDuration = (seconds: number) => {
                 {{ getStepLabel(step) }}
               </span>
               <span class="step-text">{{ step.content }}</span>
+              <span v-if="getStepMeta(step)" class="step-meta">
+                {{ getStepMeta(step) }}
+              </span>
             </div>
           </div>
         </div>
@@ -495,6 +539,9 @@ const formatVoiceDuration = (seconds: number) => {
         class="chat-actions"
       >
         <div class="action-info">
+          <span v-if="message.provider_name || message.model_name">
+            {{ [message.provider_name, message.model_name].filter(Boolean).join(' / ') }}
+          </span>
           <span v-if="message.elapsed_time">{{ message.elapsed_time }}ms</span>
           <span v-if="message.tokens_used">{{ message.tokens_used }} tokens</span>
         </div>
@@ -1263,8 +1310,18 @@ const formatVoiceDuration = (seconds: number) => {
 
 .step-text {
   flex: 1;
+  min-width: 0;
   overflow: hidden;
   text-overflow: ellipsis;
   white-space: nowrap;
 }
+
+.step-meta {
+  max-width: 220px;
+  flex-shrink: 0;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  color: var(--el-text-color-placeholder);
+}
 </style>

web/apps/web-ele/src/components/ChatBox/types.ts

@@ -41,13 +41,22 @@ export interface ReasoningStep {
   type:
     | 'action'
     | 'annotation_reply'
+    | 'error'
     | 'knowledge_retrieval'
+    | 'llm_chunk'
+    | 'loop_complete'
+    | 'loop_iteration_complete'
+    | 'loop_iteration_error'
+    | 'loop_iteration_start'
     | 'node_complete'
     | 'node_start'
     | 'observation'
+    | 'parallel_complete'
+    | 'parallel_start'
     | 'thought'
     | 'tool_call'
-    | 'tool_result';
+    | 'tool_result'
+    | 'waiting_input';
   content: string;
   tool?: string;
   params?: Record<string, any>;
@@ -55,8 +64,21 @@ export interface ReasoningStep {
   node_id?: string;
   node_type?: string;
   output?: any;
-  /** 步骤状态：running 执行中，completed 已完成 */
+  branch_id?: string;
-  status?: 'completed' | 'running';
+  branch_label?: string;
+  agent_code?: string;
+  agent_name?: string;
+  model?: string;
+  model_id?: string;
+  provider_name?: string;
+  provider_type?: string;
+  subflow_name?: string;
+  from_subflow?: boolean;
+  collaboration_role?: string;
+  collaboration_mode?: string;
+  communication?: Record<string, any>;
+  /** 步骤状态：running 执行中，completed 已完成，failed 失败 */
+  status?: 'completed' | 'failed' | 'running';
   timestamp?: string;
 }
 
@@ -126,7 +148,12 @@ export interface ChatMessage {
   tokens_used?: number;
   error_message?: string;
   feedback?: 'dislike' | 'like' | null;
+  model_id?: string;
   model_name?: string;
+  provider_name?: string;
+  agent_code?: string;
+  agent_name?: string;
+  collaboration_mode?: string;
   /** 语音消息 */
   voice?: VoiceMessage;
   /** 对话流交互配置（等待用户输入时） */

web/apps/web-ele/src/components/ai-chat-panel/composables/useChatApi.ts

@@ -238,6 +238,19 @@ export function useChatApi(props: AiChatPanelProps): {
                     output: step.output,
                     status: step.status,
                     timestamp: step.timestamp,
+                    branch_id: step.branch_id,
+                    branch_label: step.branch_label,
+                    agent_code: step.agent_code,
+                    agent_name: step.agent_name,
+                    model: step.model,
+                    model_id: step.model_id,
+                    provider_name: step.provider_name,
+                    provider_type: step.provider_type,
+                    subflow_name: step.subflow_name,
+                    from_subflow: step.from_subflow,
+                    collaboration_role: step.collaboration_role,
+                    collaboration_mode: step.collaboration_mode,
+                    communication: step.communication,
                   })),
                   interaction: msg.interaction,
                   voice: msg.voice,

web/apps/web-ele/src/components/ai-chat-panel/composables/useEventHandler.ts

@@ -178,8 +178,14 @@ export function useEventHandler(config: EventHandlerConfig) {
       branch_label: event.branch_label,
       agent_code: collaboration.agent_code || event.agent_code,
       agent_name: collaboration.agent_name || event.agent_name,
-      model: collaboration.model || event.model,
+      model:
+        collaboration.model ||
+        collaboration.model_name ||
+        event.model ||
+        event.model_name,
       model_id: collaboration.model_id || event.model_id,
+      provider_name: collaboration.provider_name || event.provider_name,
+      provider_type: collaboration.provider_type || event.provider_type,
       subflow_name:
         event.subflow_name || collaboration.subflow_name || event.event?.subflow_name,
       from_subflow: Boolean(
@@ -198,10 +204,16 @@ export function useEventHandler(config: EventHandlerConfig) {
       event.model ||
       event.model_name ||
       collaboration.model ||
+      collaboration.model_name ||
       collaboration.model_id;
     return {
       ...(modelName ? { model_name: modelName } : {}),
-      ...(collaboration.model_id ? { model_id: collaboration.model_id } : {}),
+      ...(event.model_id || collaboration.model_id
+        ? { model_id: event.model_id || collaboration.model_id }
+        : {}),
+      ...(event.provider_name || collaboration.provider_name
+        ? { provider_name: event.provider_name || collaboration.provider_name }
+        : {}),
       ...(collaboration.agent_code
         ? { agent_code: collaboration.agent_code }
         : {}),
@@ -426,6 +438,7 @@ export function useEventHandler(config: EventHandlerConfig) {
           status: 'failed',
           error_message: errorMessage,
           reasoning_steps: [...currentSteps.value],
+          ...buildMessageMeta(event),
         });
         running.value = false;
         break;