new

src/langbot/pkg/api/http/service/workflow.py

@@ -395,6 +395,8 @@ class WorkflowService:
                     ),
                     raw_message=message_context_data.get('raw_message', {}),
                 )
+                # Set query from message_content for logging purposes
+                context.query = context.message_context.message_content
 
             # Note: Frontend panel logging has been removed.
             # A new solution will be implemented separately.
@@ -404,6 +406,17 @@ class WorkflowService:
             context.variables['_bot_id'] = bot_id or ''
             context.variables['_session_id'] = session_id or ''
             context.variables['_user_id'] = user_id
+            
+            # Store launcher info for monitoring (used when query is a string)
+            if message_context_data:
+                context.variables['_launcher_type'] = 'websocket'
+                context.variables['_launcher_id'] = message_context_data.get('sender_id', '')
+                context.variables['_sender_name'] = message_context_data.get('sender_name', 'User')
+
+        # Ensure query is always set for logging purposes
+        # For non-message triggers, use trigger_type as fallback
+        if not context.query:
+            context.query = f'[{trigger_type}]'
 
         max_execution_time = self.DEFAULT_MAX_EXECUTION_TIME
         workflow_settings = definition.get('settings', {}) if isinstance(definition, dict) else {}

src/langbot/pkg/platform/sources/websocket_adapter.py

@@ -449,20 +449,19 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
             try:
                 from ...api.http.service.workflow import WorkflowExecutionFailedError
 
+                # Log workflow execution start (matching pipeline logging)
+                session_id = f'{session_type}_{connection.connection_id}'
+                logger.info(f'Processing request from {session_id} (0): {message_content}')
+
                 execution_id = await self.ap.workflow_service.execute_workflow(
                     pipeline_uuid,
                     trigger_type='message',
                     trigger_data=trigger_data,
-                    session_id=f'{session_type}_{connection.connection_id}',
+                    session_id=session_id,
                     user_id=message_context['sender_id'],
                     bot_id=self.ap.platform_mgr.websocket_proxy_bot.bot_entity.uuid,
                 )
-                await connection.send_queue.put(
-                    {
-                        'type': 'broadcast',
-                        'message': f'Workflow execution started: {execution_id}',
-                    }
-                )
+                # Removed success broadcast - only show error on failure
             except WorkflowExecutionFailedError as e:
                 await connection.send_queue.put({'type': 'error', 'message': e.message})
             except Exception as e:

src/langbot/pkg/workflow/monitoring_helper.py

@@ -3,11 +3,13 @@ Monitoring helper for recording events during workflow execution.
 This module provides convenient methods to record monitoring data
 without cluttering the main workflow code.
 
-New logging scheme:
-- Trigger log: adapter → workflow_name → local-workflow (with original message)
-- LLM call log: adapter → workflow_name → local-workflow (with LLM info)
-- LLM response log: adapter → workflow_name → local-workflow (with response message)
-- Reply log: adapter → workflow_name → local-workflow (with reply content)
+Logging scheme (aligned with pipeline monitoring):
+- Trigger log: stores original user message content directly
+- LLM call log: uses record_llm_call only (no additional message record)
+- LLM response log: stores response message content directly
+- Reply log: stores reply content directly
+
+Fields are extracted from WorkflowQuery object when available, with fallback to context_vars.
 """
 
 from __future__ import annotations
@@ -25,65 +27,102 @@ class WorkflowMonitoringHelper:
     """Helper class for workflow monitoring operations"""
 
     @staticmethod
-    def _get_adapter_name(query: WorkflowQuery) -> str:
-        """Get adapter name from query"""
-        if query.adapter and hasattr(query.adapter, 'name'):
-            return query.adapter.name
-        if query.adapter and hasattr(query.adapter, 'adapter_name'):
-            return query.adapter.adapter_name
-        return 'WebChat'
+    def _get_session_id(query, context_vars: dict | None = None) -> str:
+        """Build session_id from query or context_vars"""
+        # Try to get from query first
+        if not isinstance(query, str) and query.launcher_type:
+            launcher_type = query.launcher_type.value if hasattr(query.launcher_type, 'value') else str(query.launcher_type)
+            launcher_id = query.launcher_id or 'unknown'
+            return f'{launcher_type}_{launcher_id}'
+        
+        # Fallback to context_vars
+        if context_vars and context_vars.get('_launcher_type') and context_vars.get('_launcher_id'):
+            return f"{context_vars['_launcher_type']}_{context_vars['_launcher_id']}"
+        
+        return 'workflow_session'
 
     @staticmethod
-    def _get_session_id(query: WorkflowQuery) -> str:
-        """Build session_id from launcher info"""
-        launcher_type = query.launcher_type.value if query.launcher_type else 'unknown'
-        launcher_id = query.launcher_id or 'unknown'
-        return f'{launcher_type}_{launcher_id}'
+    def _get_platform(query, context_vars: dict | None = None) -> str:
+        """Get platform name from query or context_vars"""
+        if not isinstance(query, str) and query.launcher_type:
+            if hasattr(query.launcher_type, 'value'):
+                return query.launcher_type.value
+            return str(query.launcher_type)
+        return 'workflow'
+
+    @staticmethod
+    def _get_sender_name(query, context_vars: dict | None = None) -> str | None:
+        """Get sender name from query or context_vars"""
+        # Try query first
+        if not isinstance(query, str):
+            if query.sender_name:
+                return query.sender_name
+            if query.message_event and hasattr(query.message_event, 'sender'):
+                sender = query.message_event.sender
+                if hasattr(sender, 'nickname'):
+                    return sender.nickname
+                if hasattr(sender, 'member_name'):
+                    return sender.member_name
+        
+        # Fallback to context_vars
+        if context_vars:
+            return context_vars.get('_sender_name')
+        
+        return None
 
     @staticmethod
     async def record_trigger_log(
         ap: app.Application,
-        query: WorkflowQuery,
+        query,
         workflow_id: str,
         workflow_name: str,
+        bot_name: str = 'Workflow',
+        context_vars: dict | None = None,
     ) -> str:
-        """Record trigger node log
+        """Record trigger node log (stores original user message content directly)
         
-        Format: adapter → workflow_name → local-workflow
-        Contains: original message content
+        Aligned with pipeline monitoring: record_query_start
         """
         try:
-            adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
-            session_id = WorkflowMonitoringHelper._get_session_id(query)
+            session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
+            platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
+            sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
             
-            # Get message content
+            # Get message content - store original content directly
             message_content = ''
-            if query.message_context and hasattr(query.message_context, 'message_content'):
+            if isinstance(query, str):
+                message_content = query
+            elif not isinstance(query, str) and query.message_context:
                 message_content = query.message_context.message_content
-            elif query.message_chain and hasattr(query.message_chain, 'model_dump'):
+            elif not isinstance(query, str) and query.message_chain and hasattr(query.message_chain, 'model_dump'):
                 message_content = json.dumps(query.message_chain.model_dump(), ensure_ascii=False)
+            elif not isinstance(query, str) and query.user_message:
+                message_content = str(query.user_message)
             
-            # Build pipeline_name: workflow_name/local-workflow
-            pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
-            
-            # Build log message: adapter → workflow_name → local-workflow
-            log_message = f'{adapter_name} → {workflow_name} → local-workflow'
-            if message_content:
-                log_message += f'\n{message_content}'
+            # Get bot_id and user_id
+            bot_id = ''
+            user_id = None
+            if not isinstance(query, str):
+                bot_id = query.bot_uuid or ''
+                user_id = query.sender_id
+            elif context_vars:
+                bot_id = context_vars.get('_bot_id', '') or ''
+                user_id = context_vars.get('_user_id')
             
             message_id = await ap.monitoring_service.record_message(
-                bot_id=query.bot_uuid or '',
-                bot_name=workflow_name or 'Workflow',
+                bot_id=bot_id,
+                bot_name=bot_name,
                 pipeline_id=workflow_id,
-                pipeline_name=pipeline_name,
-                message_content=log_message,
+                pipeline_name=workflow_name or 'Workflow',
+                message_content=message_content,
                 session_id=session_id,
                 status='success',
                 level='info',
-                platform='workflow',
-                user_id=query.sender_id,
-                user_name=query.sender_name,
+                platform=platform,
+                user_id=user_id,
+                user_name=sender_name,
                 role='user',
+                runner_name='local-workflow',
             )
             
             return message_id
@@ -94,7 +133,7 @@ class WorkflowMonitoringHelper:
     @staticmethod
     async def record_llm_call_log(
         ap: app.Application,
-        query: WorkflowQuery,
+        query,
         workflow_id: str,
         workflow_name: str,
         node_name: str,
@@ -104,37 +143,32 @@ class WorkflowMonitoringHelper:
         duration_ms: int,
         status: str = 'success',
         error_message: str | None = None,
+        bot_name: str = 'Workflow',
+        context_vars: dict | None = None,
+        input_message: str | None = None,
+        message_id: str | None = None,
     ):
-        """Record LLM call log (with LLM info)
+        """Record LLM call log with message_id association
         
-        Format: adapter → workflow_name → local-workflow
-        Contains: LLM call statistics
+        Aligned with pipeline monitoring: record_llm_call with message_id
+        LLM calls are aggregated under the trigger log via message_id.
         """
         try:
-            adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
-            session_id = WorkflowMonitoringHelper._get_session_id(query)
+            session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
             
-            # Build pipeline_name: workflow_name/local-workflow
-            pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
-            
-            # Build log message with LLM info
-            log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
-            log_message += f'LLM Call: {node_name}\n'
-            log_message += f'Model: {model_name}\n'
-            log_message += f'Status: {status}\n'
-            log_message += f'Duration: {duration_ms}ms\n'
-            log_message += f'Input Tokens: {input_tokens}\n'
-            log_message += f'Output Tokens: {output_tokens}\n'
-            log_message += f'Total Tokens: {input_tokens + output_tokens}'
-            
-            if error_message:
-                log_message += f'\nError: {error_message}'
+            # Get bot_id
+            bot_id = ''
+            if not isinstance(query, str):
+                bot_id = query.bot_uuid or ''
+            elif context_vars:
+                bot_id = context_vars.get('_bot_id', '') or ''
             
+            # Record LLM call with message_id for association
             await ap.monitoring_service.record_llm_call(
-                bot_id=query.bot_uuid or '',
-                bot_name=workflow_name or 'Workflow',
+                bot_id=bot_id,
+                bot_name=bot_name,
                 pipeline_id=workflow_id,
-                pipeline_name=pipeline_name,
+                pipeline_name=workflow_name or 'Workflow',
                 session_id=session_id,
                 model_name=model_name,
                 input_tokens=input_tokens,
@@ -142,22 +176,7 @@ class WorkflowMonitoringHelper:
                 duration=duration_ms,
                 status=status,
                 error_message=error_message,
-            )
-            
-            # Also record as message for display
-            await ap.monitoring_service.record_message(
-                bot_id=query.bot_uuid or '',
-                bot_name=workflow_name or 'Workflow',
-                pipeline_id=workflow_id,
-                pipeline_name=pipeline_name,
-                message_content=log_message,
-                session_id=session_id,
-                status=status,
-                level='info',
-                platform='workflow',
-                user_id=query.sender_id,
-                user_name=query.sender_name,
-                role='system',
+                message_id=message_id,
             )
         except Exception as e:
             ap.logger.error(f'Failed to record LLM call log: {e}')
@@ -165,42 +184,48 @@ class WorkflowMonitoringHelper:
     @staticmethod
     async def record_llm_response_log(
         ap: app.Application,
-        query: WorkflowQuery,
+        query,
         workflow_id: str,
         workflow_name: str,
         node_name: str,
         response_content: str,
+        bot_name: str = 'Workflow',
+        context_vars: dict | None = None,
     ):
-        """Record LLM response log (without LLM info, with response message)
+        """Record LLM response log (stores response content directly)
         
-        Format: adapter → workflow_name → local-workflow
-        Contains: response message content
+        Aligned with pipeline monitoring: record_query_response
         """
         try:
-            adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
-            session_id = WorkflowMonitoringHelper._get_session_id(query)
+            session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
+            platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
+            sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
             
-            # Build pipeline_name: workflow_name/local-workflow
-            pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
-            
-            # Build log message
-            log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
-            log_message += f'Node: {node_name}\n'
-            log_message += f'Response: {response_content[:500]}'  # Limit length
+            # Get bot_id and user_id
+            bot_id = ''
+            user_id = None
+            if not isinstance(query, str):
+                bot_id = query.bot_uuid or ''
+                user_id = query.sender_id
+            elif context_vars:
+                bot_id = context_vars.get('_bot_id', '') or ''
+                user_id = context_vars.get('_user_id')
             
+            # Store response content directly, no prefix
             await ap.monitoring_service.record_message(
-                bot_id=query.bot_uuid or '',
-                bot_name=workflow_name or 'Workflow',
+                bot_id=bot_id,
+                bot_name=bot_name,
                 pipeline_id=workflow_id,
-                pipeline_name=pipeline_name,
-                message_content=log_message,
+                pipeline_name=workflow_name or 'Workflow',
+                message_content=response_content[:2000],  # Limit length
                 session_id=session_id,
                 status='success',
                 level='info',
-                platform='workflow',
-                user_id=query.sender_id,
-                user_name=query.sender_name,
+                platform=platform,
+                user_id=user_id,
+                user_name=sender_name,
                 role='assistant',
+                runner_name='local-workflow',
             )
         except Exception as e:
             ap.logger.error(f'Failed to record LLM response log: {e}')
@@ -208,42 +233,48 @@ class WorkflowMonitoringHelper:
     @staticmethod
     async def record_reply_log(
         ap: app.Application,
-        query: WorkflowQuery,
+        query,
         workflow_id: str,
         workflow_name: str,
         node_name: str,
         reply_content: str,
+        bot_name: str = 'Workflow',
+        context_vars: dict | None = None,
     ):
-        """Record reply message log
+        """Record reply message log (stores reply content directly)
         
-        Format: adapter → workflow_name → local-workflow
-        Contains: reply message content
+        Aligned with pipeline monitoring: record_query_response
         """
         try:
-            adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
-            session_id = WorkflowMonitoringHelper._get_session_id(query)
+            session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
+            platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
+            sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
             
-            # Build pipeline_name: workflow_name/local-workflow
-            pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
-            
-            # Build log message
-            log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
-            log_message += f'Node: {node_name}\n'
-            log_message += f'Reply: {reply_content[:500]}'  # Limit length
+            # Get bot_id and user_id
+            bot_id = ''
+            user_id = None
+            if not isinstance(query, str):
+                bot_id = query.bot_uuid or ''
+                user_id = query.sender_id
+            elif context_vars:
+                bot_id = context_vars.get('_bot_id', '') or ''
+                user_id = context_vars.get('_user_id')
             
+            # Store reply content directly, no prefix
             await ap.monitoring_service.record_message(
-                bot_id=query.bot_uuid or '',
-                bot_name=workflow_name or 'Workflow',
+                bot_id=bot_id,
+                bot_name=bot_name,
                 pipeline_id=workflow_id,
-                pipeline_name=pipeline_name,
-                message_content=log_message,
+                pipeline_name=workflow_name or 'Workflow',
+                message_content=reply_content[:2000],  # Limit length
                 session_id=session_id,
                 status='success',
                 level='info',
-                platform='workflow',
-                user_id=query.sender_id,
-                user_name=query.sender_name,
+                platform=platform,
+                user_id=user_id,
+                user_name=sender_name,
                 role='assistant',
+                runner_name='local-workflow',
             )
         except Exception as e:
             ap.logger.error(f'Failed to record reply log: {e}')
@@ -255,13 +286,14 @@ class LLMCallMonitor:
     def __init__(
         self,
         ap: app.Application,
-        query: WorkflowQuery,
+        query,
         bot_id: str,
         bot_name: str,
         workflow_id: str,
         workflow_name: str,
         node_name: str,
         model_name: str,
+        context_vars: dict | None = None,
     ):
         self.ap = ap
         self.query = query
@@ -271,6 +303,7 @@ class LLMCallMonitor:
         self.workflow_name = workflow_name
         self.node_name = node_name
         self.model_name = model_name
+        self.context_vars = context_vars
         self.start_time = None
         self.input_tokens = 0
         self.output_tokens = 0
@@ -295,6 +328,8 @@ class LLMCallMonitor:
                 duration_ms=duration_ms,
                 status='error',
                 error_message=str(exc_val) if exc_val else None,
+                bot_name=self.bot_name,
+                context_vars=self.context_vars,
             )
         else:
             await WorkflowMonitoringHelper.record_llm_call_log(
@@ -308,6 +343,8 @@ class LLMCallMonitor:
                 output_tokens=self.output_tokens,
                 duration_ms=duration_ms,
                 status='success',
+                bot_name=self.bot_name,
+                context_vars=self.context_vars,
             )
 
         return False

src/langbot/pkg/workflow/nodes/llm_call.py

@@ -615,11 +615,12 @@ Respond in the same language as the user's input.
             return s0
         logger.info(f'[LLM:{self.node_id}] Response: {_cut_str(response_text)}')
 
-        # Record LLM call log and response log
+        # Record LLM call log only (response log is redundant)
         try:
             if self.ap and context.query:
                 workflow_id = context.workflow_id or ''
                 workflow_name = context.variables.get('_workflow_name', 'Workflow')
+                bot_name = context.variables.get('_bot_name', 'Workflow')
                 node_name = self.get_config('name', self.node_id)
                 model_name = used_model.model_entity.name if used_model else 'unknown'
                 
@@ -628,7 +629,10 @@ Respond in the same language as the user's input.
                 if hasattr(self, '_llm_start_time'):
                     duration_ms = int((time.time() - self._llm_start_time) * 1000)
                 
-                # Record LLM call log (with LLM info)
+                # Get message_id for LLM call association
+                message_id = context.variables.get('_monitoring_message_id')
+                
+                # Record LLM call log with message_id association
                 await monitoring_helper.WorkflowMonitoringHelper.record_llm_call_log(
                     ap=self.ap,
                     query=context.query,
@@ -640,16 +644,9 @@ Respond in the same language as the user's input.
                     output_tokens=usage.get('completion_tokens', 0),
                     duration_ms=duration_ms,
                     status='success',
-                )
-                
-                # Record LLM response log (with response message)
-                await monitoring_helper.WorkflowMonitoringHelper.record_llm_response_log(
-                    ap=self.ap,
-                    query=context.query,
-                    workflow_id=workflow_id,
-                    workflow_name=workflow_name,
-                    node_name=node_name,
-                    response_content=response_text,
+                    bot_name=bot_name,
+                    context_vars=context.variables,
+                    message_id=message_id,
                 )
         except Exception as e:
             logger.warning(f'[LLM:{self.node_id}] Failed to record LLM logs: {e}')

src/langbot/pkg/workflow/nodes/message_trigger.py

@@ -26,17 +26,23 @@ class MessageTriggerNode(WorkflowNode):
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         msg_ctx = context.message_context
 
-        # Record trigger log
+        # Record trigger log and store message_id for LLM call association
         try:
             if self.ap and context.query:
                 workflow_id = context.workflow_id or ''
                 workflow_name = context.variables.get('_workflow_name', 'Workflow')
-                await monitoring_helper.WorkflowMonitoringHelper.record_trigger_log(
+                bot_name = context.variables.get('_bot_name', 'Workflow')
+                message_id = await monitoring_helper.WorkflowMonitoringHelper.record_trigger_log(
                     ap=self.ap,
                     query=context.query,
                     workflow_id=workflow_id,
                     workflow_name=workflow_name,
+                    bot_name=bot_name,
+                    context_vars=context.variables,
                 )
+                # Store message_id for LLM call monitoring association
+                if message_id:
+                    context.variables['_monitoring_message_id'] = message_id
         except Exception as e:
             logger.warning(f'[MessageTrigger:{self.node_id}] Failed to record trigger log: {e}')
 

src/langbot/pkg/workflow/nodes/reply_message.py

@@ -112,6 +112,7 @@ class ReplyMessageNode(WorkflowNode):
             if self.ap and context.query and send_success:
                 workflow_id = context.workflow_id or ''
                 workflow_name = context.variables.get('_workflow_name', 'Workflow')
+                bot_name = context.variables.get('_bot_name', 'Workflow')
                 node_name = self.get_config('name', self.node_id)
                 await monitoring_helper.WorkflowMonitoringHelper.record_reply_log(
                     ap=self.ap,
@@ -120,6 +121,8 @@ class ReplyMessageNode(WorkflowNode):
                     workflow_name=workflow_name,
                     node_name=node_name,
                     reply_content=message_str,
+                    bot_name=bot_name,
+                    context_vars=context.variables,
                 )
         except Exception as e:
             logger.warning(f'[ReplyMessage:{self.node_id}] Failed to record reply log: {e}')

web/src/app/home/workflows/components/workflow-executions/WorkflowExecutionsTab.tsx

@@ -247,7 +247,7 @@ export default function WorkflowExecutionsTab({
       await backendClient.executeWorkflow(workflowId, {
         trigger_type: 'manual',
       });
-      toast.success(t('workflows.manualTrigger') + ' ✓');
+      // Removed success toast - only show error toast on failure
       loadExecutions();
       loadStats();
     } catch (err: unknown) {
@@ -560,17 +560,13 @@ export default function WorkflowExecutionsTab({
               onValueChange={setSelectedTab}
               className="flex-1 flex flex-col overflow-hidden"
             >
-              <TabsList className="grid w-full grid-cols-3">
+              <TabsList className="grid w-full grid-cols-2">
                 <TabsTrigger value="details">
                   {t('workflows.details')}
                 </TabsTrigger>
                 <TabsTrigger value="nodes">
                   {t('workflows.nodeExecutions')}
                 </TabsTrigger>
-                <TabsTrigger value="logs">
-                  <FileText className="size-3 mr-1" />
-                  {t('workflows.logs')}
-                </TabsTrigger>
               </TabsList>
 
               <TabsContent
@@ -763,54 +759,6 @@ export default function WorkflowExecutionsTab({
                 )}
               </TabsContent>
 
-              <TabsContent value="logs" className="flex-1 overflow-hidden mt-4">
-                {logsLoading ? (
-                  <div className="flex items-center justify-center py-8">
-                    <Loader2 className="size-6 animate-spin text-muted-foreground" />
-                  </div>
-                ) : executionLogs.length > 0 ? (
-                  <ScrollArea className="h-[400px] border rounded">
-                    <div className="p-3 space-y-3 text-xs">
-                      {executionLogs.map((log) => (
-                        <div
-                          key={log.id}
-                          className="rounded-md border border-border/60 bg-muted/20 p-3"
-                        >
-                          <div className="flex flex-wrap items-center gap-2 font-mono">
-                            <span className="text-muted-foreground shrink-0">
-                              {log.timestamp
-                                ? new Date(log.timestamp).toLocaleTimeString()
-                                : '-'}
-                            </span>
-                            <span
-                              className={`uppercase font-semibold ${logLevelColors[log.level]}`}
-                            >
-                              [{log.level}]
-                            </span>
-                            {log.node_id && (
-                              <span className="text-muted-foreground break-all">
-                                [{log.node_id}]
-                              </span>
-                            )}
-                          </div>
-                          <div className="mt-2 whitespace-pre-wrap break-words text-foreground font-mono">
-                            {log.message}
-                          </div>
-                          {log.data && Object.keys(log.data).length > 0 && (
-                            <pre className="mt-3 overflow-x-auto rounded bg-background/80 p-2 text-[11px] text-muted-foreground whitespace-pre-wrap break-words font-mono">
-                              {JSON.stringify(log.data, null, 2)}
-                            </pre>
-                          )}
-                        </div>
-                      ))}
-                    </div>
-                  </ScrollArea>
-                ) : (
-                  <div className="text-center py-8 text-muted-foreground">
-                    {t('workflows.noLogs')}
-                  </div>
-                )}
-              </TabsContent>
             </Tabs>
           )}
         </DialogContent>