shit

src/langbot/pkg/api/http/controller/groups/workflows/workflows.py

@@ -312,6 +312,164 @@ class WorkflowsRouterGroup(group.RouterGroup):
             except ValueError as e:
                 return self.http_status(404, -1, str(e))
 
+        # LLM Node Performance Test Endpoint
+        # Tests each step of LLM node execution with detailed timing
+        @self.route('/_/test/llm-node', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            """Test LLM node performance with detailed step-by-step timing.
+            
+            Request body:
+            {
+                "model_uuid": "uuid-of-model",
+                "system_prompt": "optional system prompt",
+                "user_prompt": "test message",
+                "enable_tools": false,
+                "tools": [],
+                "temperature": 0.7,
+                "max_tokens": 100
+            }
+            
+            Response includes timing for each step:
+            - model_fetch: Time to get model from model_mgr
+            - tool_fetch: Time to load tools (if enabled)
+            - prompt_build: Time to build messages
+            - llm_call: Time for actual LLM invocation
+            - total: Total time
+            """
+            import time
+            
+            json_data = await quart.request.json
+            if not json_data:
+                return self.http_status(400, -1, 'Request body is required')
+            
+            model_uuid = json_data.get('model_uuid', '')
+            if not model_uuid:
+                return self.http_status(400, -1, 'model_uuid is required')
+            
+            user_prompt = json_data.get('user_prompt', 'test')
+            system_prompt = json_data.get('system_prompt', '')
+            enable_tools = json_data.get('enable_tools', False)
+            tools_config = json_data.get('tools', [])
+            temperature = json_data.get('temperature')
+            max_tokens = json_data.get('max_tokens', 0)
+            
+            timings = {}
+            errors = []
+            
+            # Step 1: Model fetch
+            t_start = time.perf_counter()
+            try:
+                runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
+                timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['model_found'] = True
+                timings['model_name'] = runtime_model.model_entity.name if runtime_model else None
+            except Exception as e:
+                timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['model_found'] = False
+                errors.append(f'Model fetch failed: {str(e)}')
+                return self.http_status(400, -1, {
+                    'error': errors[0],
+                    'timings': timings,
+                })
+            
+            # Step 2: Tool fetch (if enabled)
+            timings['tool_fetch_ms'] = 0
+            timings['tools_loaded'] = 0
+            if enable_tools and tools_config:
+                t_start = time.perf_counter()
+                try:
+                    import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
+                    all_tools = await self.ap.tool_mgr.get_tools()
+                    tool_names = tools_config if isinstance(tools_config, list) else []
+                    funcs = [t for t in all_tools if t.name in tool_names]
+                    timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                    timings['tools_loaded'] = len(funcs)
+                    timings['tool_names'] = [t.name for t in funcs]
+                except Exception as e:
+                    timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                    errors.append(f'Tool fetch failed: {str(e)}')
+            
+            # Step 3: Build messages
+            t_start = time.perf_counter()
+            import langbot_plugin.api.entities.builtin.provider.message as provider_message
+            messages = []
+            if system_prompt:
+                messages.append(provider_message.Message(role='system', content=system_prompt))
+            messages.append(provider_message.Message(role='user', content=user_prompt))
+            timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+            
+            # Step 4: Build extra args
+            extra_args = {}
+            if temperature is not None:
+                extra_args['temperature'] = float(temperature)
+            if max_tokens and int(max_tokens) > 0:
+                extra_args['max_tokens'] = int(max_tokens)
+            
+            # Step 5: LLM call
+            t_start = time.perf_counter()
+            try:
+                result_message = await runtime_model.provider.invoke_llm(
+                    query=None,
+                    model=runtime_model,
+                    messages=messages,
+                    funcs=funcs if enable_tools else None,
+                    extra_args=extra_args,
+                )
+                timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['llm_call_success'] = True
+                
+                # Extract response text
+                response_text = ''
+                if isinstance(result_message.content, str):
+                    response_text = result_message.content
+                elif isinstance(result_message.content, list):
+                    for elem in result_message.content:
+                        if hasattr(elem, 'text') and elem.text:
+                            response_text += elem.text
+                        elif isinstance(elem, str):
+                            response_text += elem
+                
+                timings['response_length'] = len(response_text)
+                timings['response_preview'] = response_text[:200]
+                
+                # Extract usage
+                usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
+                if hasattr(result_message, 'usage') and result_message.usage:
+                    u = result_message.usage
+                    usage = {
+                        'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
+                        'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
+                        'total_tokens': getattr(u, 'total_tokens', 0) or 0,
+                    }
+                timings['usage'] = usage
+                
+            except Exception as e:
+                timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
+                timings['llm_call_success'] = False
+                errors.append(f'LLM call failed: {str(e)}')
+            
+            # Calculate total
+            timings['total_ms'] = round(sum([
+                timings.get('model_fetch_ms', 0),
+                timings.get('tool_fetch_ms', 0),
+                timings.get('prompt_build_ms', 0),
+                timings.get('llm_call_ms', 0),
+            ]), 2)
+            
+            # Add breakdown percentage
+            if timings['total_ms'] > 0:
+                timings['breakdown'] = {
+                    'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
+                    'tool_fetch_pct': round(timings.get('tool_fetch_ms', 0) / timings['total_ms'] * 100, 1),
+                    'prompt_build_pct': round(timings.get('prompt_build_ms', 0) / timings['total_ms'] * 100, 1),
+                    'llm_call_pct': round(timings.get('llm_call_ms', 0) / timings['total_ms'] * 100, 1),
+                }
+            
+            if errors:
+                timings['errors'] = errors
+            
+            return self.success(data={'test_result': timings})
+
 
 @group.group_class('executions', '/api/v1/executions')
 class ExecutionsRouterGroup(group.RouterGroup):

src/langbot/pkg/workflow/adapters.py

@@ -0,0 +1,204 @@
+"""Workflow-Pipeline通信适配器
+
+这个模块提供了Workflow和Pipeline之间的通信适配，使用SDK标准的MessageEnvelope格式。
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class _WorkflowPipelineCaptureAdapter:
+    """Workflow-Pipeline通信适配器
+    
+    用于在Workflow节点和Pipeline之间进行标准化的消息传递。
+    支持MessageEnvelope格式的双向转换。
+    """
+    
+    def __init__(self, context: Any):
+        """初始化适配器
+        
+        Args:
+            context: ExecutionContext - Workflow执行上下文
+        """
+        self.context = context
+        self.responses: list[dict[str, Any]] = []
+        self.bot_account_id: Optional[str] = None
+        self._logger = logging.getLogger(__name__)
+    
+    async def call_pipeline_with_envelope(
+        self,
+        envelope: Any,
+        pipeline_executor: Any
+    ) -> Any:
+        """使用MessageEnvelope调用Pipeline
+        
+        Args:
+            envelope: MessageEnvelope - 标准消息信封
+            pipeline_executor: Pipeline执行器实例
+            
+        Returns:
+            MessageEnvelope - 执行结果信封
+        """
+        try:
+            # 动态导入以避免循环依赖
+            from langbot_plugin_sdk.workflow import envelope_to_query, query_to_envelope
+            
+            # 1. 转换为Query
+            query = envelope_to_query(envelope)
+            
+            # 2. 调用Pipeline
+            result_query = await pipeline_executor.execute(query)
+            
+            # 3. 转换回Envelope
+            result_envelope = query_to_envelope(result_query, envelope)
+            
+            self._logger.debug(
+                f'Pipeline execution completed for workflow {envelope.workflow_id}',
+                extra={
+                    'workflow_id': envelope.workflow_id,
+                    'execution_id': envelope.execution_id,
+                    'node_id': envelope.node_id,
+                }
+            )
+            
+            return result_envelope
+            
+        except Exception as e:
+            self._logger.error(
+                f'Pipeline execution failed: {e}',
+                exc_info=True,
+                extra={
+                    'workflow_id': envelope.workflow_id,
+                    'execution_id': envelope.execution_id,
+                    'node_id': envelope.node_id,
+                }
+            )
+            raise
+    
+    def validate_envelope(self, envelope: Any) -> bool:
+        """验证MessageEnvelope的有效性
+        
+        Args:
+            envelope: MessageEnvelope - 要验证的消息信封
+            
+        Returns:
+            bool - 验证是否通过
+        """
+        required_fields = [
+            'message_id',
+            'workflow_id',
+            'node_id',
+            'execution_id',
+            'payload',
+            'launcher_type',
+        ]
+        
+        for field in required_fields:
+            if not hasattr(envelope, field):
+                self._logger.warning(
+                    f'MessageEnvelope missing required field: {field}'
+                )
+                return False
+        
+        return True
+    
+    def get_responses(self) -> list[dict[str, Any]]:
+        """获取所有响应
+        
+        Returns:
+            list - 响应列表
+        """
+        return self.responses.copy()
+    
+    def add_response(self, response: dict[str, Any]) -> None:
+        """添加响应
+        
+        Args:
+            response: dict - 响应数据
+        """
+        self.responses.append(response)
+    
+    def get_last_text_response(self) -> str:
+        """获取最后一个文本响应
+        
+        Returns:
+            str - 最后一个响应的文本内容
+        """
+        if not self.responses:
+            return ''
+        
+        last_response = self.responses[-1]
+        return str(last_response.get('content', '') or '')
+    
+    def clear_responses(self) -> None:
+        """清空所有响应"""
+        self.responses.clear()
+
+
+class WorkflowPipelineCompatibilityLayer:
+    """Workflow-Pipeline兼容性层
+    
+    提供向后兼容性，支持旧的Pipeline Query格式和新的MessageEnvelope格式。
+    """
+    
+    def __init__(self):
+        """初始化兼容性层"""
+        self._logger = logging.getLogger(__name__)
+    
+    def is_workflow_context(self, query: Any) -> bool:
+        """检查Query是否包含Workflow上下文
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            bool - 是否来自Workflow
+        """
+        if hasattr(query, 'is_from_workflow'):
+            return query.is_from_workflow()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return bool(context and context.get('workflow_id'))
+        
+        return False
+    
+    def get_workflow_id(self, query: Any) -> Optional[str]:
+        """从Query获取Workflow ID
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            str - Workflow ID，如果不存在则返回None
+        """
+        if hasattr(query, 'get_workflow_id'):
+            return query.get_workflow_id()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return context.get('workflow_id') if context else None
+        
+        return None
+    
+    def get_execution_id(self, query: Any) -> Optional[str]:
+        """从Query获取执行ID
+        
+        Args:
+            query: Query - Pipeline Query对象
+            
+        Returns:
+            str - 执行ID，如果不存在则返回None
+        """
+        if hasattr(query, 'get_execution_id'):
+            return query.get_execution_id()
+        
+        if hasattr(query, 'get_workflow_context'):
+            context = query.get_workflow_context()
+            return context.get('execution_id') if context else None
+        
+        return None

src/langbot/pkg/workflow/entities.py

@@ -1,12 +1,27 @@
-"""Workflow entities and data models"""
+"""Workflow entities and data models
+
+This module defines workflow entities using SDK standard entities where available,
+and local-specific entities for LangBot_copy-specific functionality.
+"""
 
 from __future__ import annotations
 
-import enum
 from datetime import datetime
 from typing import Any, Optional
 import pydantic
 
+# Import SDK entities for standard workflow protocol types
+from langbot_plugin.api.entities.builtin.workflow import (
+    ExecutionContext,
+    ExecutionStep,
+    ExecutionStatus,
+    MessageContext,
+    NodeDefinition,
+    NodeState,
+    NodeStatus,
+    PortDefinition,
+)
+
 
 class Position(pydantic.BaseModel):
     """Node position on canvas"""
@@ -15,31 +30,6 @@ class Position(pydantic.BaseModel):
     y: float = 0
 
 
-class PortDefinition(pydantic.BaseModel):
-    """Node port definition"""
-
-    name: str
-    type: str = 'any'  # any, string, number, boolean, object, array
-    description: str = ''
-    required: bool = True
-
-
-class NodeDefinition(pydantic.BaseModel):
-    """Workflow node definition"""
-
-    id: str
-    type: str
-    name: str = ''
-    position: Position = Position()
-    config: dict[str, Any] = {}
-    inputs: list[PortDefinition] = []
-    outputs: list[PortDefinition] = []
-
-    # UI metadata
-    description: str = ''
-    comment: str = ''  # User comment/annotation
-
-
 class EdgeDefinition(pydantic.BaseModel):
     """Workflow edge definition (connection between nodes)"""
 
@@ -161,125 +151,3 @@ class WorkflowDefinition(pydantic.BaseModel):
     # Source tracking (for imported workflows)
     source: Optional[str] = None  # dify, n8n, langflow, etc.
     source_id: Optional[str] = None
-
-
-class ExecutionStatus(enum.Enum):
-    """Workflow execution status"""
-
-    PENDING = 'pending'
-    RUNNING = 'running'
-    WAITING = 'waiting'
-    COMPLETED = 'completed'
-    FAILED = 'failed'
-    CANCELLED = 'cancelled'
-
-
-class NodeStatus(enum.Enum):
-    """Node execution status"""
-
-    PENDING = 'pending'
-    RUNNING = 'running'
-    COMPLETED = 'completed'
-    FAILED = 'failed'
-    SKIPPED = 'skipped'
-
-
-class NodeState(pydantic.BaseModel):
-    """Runtime state of a node during execution"""
-
-    node_id: str
-    status: NodeStatus = NodeStatus.PENDING
-    inputs: dict[str, Any] = {}
-    outputs: dict[str, Any] = {}
-    start_time: Optional[datetime] = None
-    end_time: Optional[datetime] = None
-    error: Optional[str] = None
-    retry_count: int = 0
-
-
-class MessageContext(pydantic.BaseModel):
-    """Message context for message-triggered workflows"""
-
-    message_id: str
-    message_content: str
-    sender_id: str
-    sender_name: str = ''
-    platform: str = ''
-    conversation_id: str = ''
-    is_group: bool = False
-    group_id: Optional[str] = None
-    mentions: list[str] = []
-    reply_to: Optional[str] = None
-    raw_message: dict[str, Any] = {}
-
-
-class ExecutionStep(pydantic.BaseModel):
-    """Execution history step"""
-
-    timestamp: datetime
-    node_id: str
-    node_type: str
-    status: str
-    inputs: dict[str, Any] = {}
-    outputs: dict[str, Any] = {}
-    duration_ms: int = 0
-    error: Optional[str] = None
-
-
-class ExecutionContext(pydantic.BaseModel):
-    """Workflow execution context"""
-
-    execution_id: str
-    workflow_id: str
-    workflow_version: int = 1
-    status: ExecutionStatus = ExecutionStatus.PENDING
-
-    # Runtime data
-    variables: dict[str, Any] = {}
-    conversation_variables: dict[str, Any] = {}  # Session-level persistent variables
-    node_states: dict[str, NodeState] = {}
-    memory: dict[str, Any] = {}  # Workflow memory for storing/retrieving data
-
-    # Timing
-    start_time: Optional[datetime] = None
-    end_time: Optional[datetime] = None
-
-    # Error
-    error: Optional[str] = None
-
-    # Message context (if triggered by message)
-    message_context: Optional[MessageContext] = None
-
-    # Trigger info
-    trigger_type: Optional[str] = None
-    trigger_data: dict[str, Any] = {}
-
-    # Execution history
-    history: list[ExecutionStep] = []
-
-    # Session info
-    session_id: Optional[str] = None
-    user_id: Optional[str] = None
-    bot_id: Optional[str] = None
-
-    def get_node_output(self, node_id: str, output_name: str = 'output') -> Any:
-        """Get output from a specific node"""
-        if node_id in self.node_states:
-            return self.node_states[node_id].outputs.get(output_name)
-        return None
-
-    def set_variable(self, name: str, value: Any):
-        """Set a workflow variable"""
-        self.variables[name] = value
-
-    def get_variable(self, name: str, default: Any = None) -> Any:
-        """Get a workflow variable"""
-        return self.variables.get(name, default)
-
-    def set_conversation_variable(self, name: str, value: Any):
-        """Set a conversation-level variable (persisted across executions)"""
-        self.conversation_variables[name] = value
-
-    def get_conversation_variable(self, name: str, default: Any = None) -> Any:
-        """Get a conversation-level variable"""
-        return self.conversation_variables.get(name, default)

src/langbot/pkg/workflow/executor.py

@@ -178,7 +178,7 @@ class WorkflowExecutor:
             # Initialize node states
             for node in workflow.nodes:
                 if node.id not in context.node_states:
-                    context.node_states[node.id] = NodeState(node_id=node.id)
+                    context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
 
             # Find start node(s)
             if start_node_id:
@@ -662,14 +662,15 @@ class WorkflowExecutor:
             duration_ms = int((node_state.end_time - node_state.start_time).total_seconds() * 1000)
 
         step = ExecutionStep(
+            step_id=f"step_{uuid.uuid4().hex[:8]}",
             timestamp=datetime.now(),
             node_id=node.id,
             node_type=node.type,
-            status=node_state.status.value,
+            status=node_state.status,
-            inputs=node_state.inputs,
-            outputs=node_state.outputs,
             duration_ms=duration_ms,
             error=node_state.error,
+            inputs=node_state.inputs,
+            outputs=node_state.outputs,
         )
         context.history.append(step)
 
@@ -801,7 +802,7 @@ class LoopExecutor:
 
             for node in loop_body:
                 # Reset node state for this iteration
-                context.node_states[node.id] = NodeState(node_id=node.id)
+                context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
 
                 await self.executor._execute_node(node, context, max_retries=3)
 
@@ -823,5 +824,3 @@ class LoopExecutor:
         context.variables.pop('loop_is_last', None)
 
         return results
-
-

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

@@ -5,18 +5,61 @@ Node metadata is loaded from: ../../templates/metadata/nodes/call_pipeline.yaml
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Optional
+
+import pydantic
 
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
+import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_event_logger
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
+
+class _NoOpEventLogger(abstract_event_logger.AbstractEventLogger):
+    """No-op event logger for workflow pipeline adapter."""
+
+    async def info(
+        self,
+        text: str,
+        images: Optional[list[platform_message.Image]] = None,
+        message_session_id: Optional[str] = None,
+        no_throw: bool = True,
+    ):
+        pass
+
+    async def debug(
+        self,
+        text: str,
+        images: Optional[list[platform_message.Image]] = None,
+        message_session_id: Optional[str] = None,
+        no_throw: bool = True,
+    ):
+        pass
+
+    async def warning(
+        self,
+        text: str,
+        images: Optional[list[platform_message.Image]] = None,
+        message_session_id: Optional[str] = None,
+        no_throw: bool = True,
+    ):
+        pass
+
+    async def error(
+        self,
+        text: str,
+        images: Optional[list[platform_message.Image]] = None,
+        message_session_id: Optional[str] = None,
+        no_throw: bool = True,
+    ):
+        pass
+
 @workflow_node('call_pipeline')
 class CallPipelineNode(WorkflowNode):
     """Call pipeline node - invoke an existing pipeline"""
@@ -139,10 +182,16 @@ class CallPipelineNode(WorkflowNode):
         )
 
 class _WorkflowPipelineCaptureAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
+    """Adapter to capture pipeline responses for workflow execution."""
+
+    class Config:
+        arbitrary_types_allowed = True
+
     responses: list[dict[str, Any]] = []
+    context: ExecutionContext = pydantic.Field(exclude=True)
 
     def __init__(self, context: ExecutionContext):
-        super().__init__(config={}, logger=None)
+        super().__init__(config={}, logger=_NoOpEventLogger())
         self.context = context
         self.responses = []
 

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

@@ -9,7 +9,7 @@ import json
 import re
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('code_executor')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 from ..safe_eval import safe_eval_with_vars
 

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('coze_bot')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('cron_trigger')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 from ..safe_eval import safe_eval_with_vars
 

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('database_query')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('dify_knowledge_query')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('dify_workflow')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('end')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('event_trigger')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('http_request')

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

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('iterator')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('knowledge_retrieval')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('langflow_flow')

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

@@ -10,7 +10,7 @@ from typing import Any, AsyncGenerator
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 logger = logging.getLogger(__name__)
@@ -181,7 +181,6 @@ class LLMCallNode(WorkflowNode):
         # Get error handling config
         exception_handling = self.get_config('exception_handling', 'show-error')
         failure_hint = self.get_config('failure_hint', 'Request failed.')
-        remove_think = self.get_config('remove_think', False)
         track_function_calls = self.get_config('track_function_calls', False)
 
         # Get output format and json_schema config
@@ -192,11 +191,10 @@ class LLMCallNode(WorkflowNode):
         enable_tools = self.get_config('enable_tools', False)
         tools_config = self.get_config('tools', [])
 
-        # Resolve prompts - handle null user_prompt_template
+        # Resolve prompts
         system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
         user_prompt_template = self.get_config('user_prompt_template')
         if user_prompt_template is None:
-            # Default to input if not set
             user_prompt_template = '{{input}}'
         user_prompt = self._resolve_template(user_prompt_template, inputs, context)
 
@@ -229,14 +227,11 @@ class LLMCallNode(WorkflowNode):
                 if tool_names:
                     all_tools = await self.ap.tool_mgr.get_tools()
                     funcs = [t for t in all_tools if t.name in tool_names]
-                    if funcs:
-                        logger.info(f'LLM call node {self.node_id}: using tools: {[t.name for t in funcs]}')
             except Exception as e:
-                logger.warning(f'LLM call node {self.node_id}: failed to load tools - {e}')
+                logger.warning(f'[LLM:{self.node_id}] Failed to load tools: {e}')
                 funcs = None
 
-        # Invoke LLM with error handling
+        # Invoke LLM
-        logger.info(f'LLM call node {self.node_id}: invoking model {model_uuid}')
         try:
             result_message = await runtime_model.provider.invoke_llm(
                 query=None,
@@ -246,7 +241,7 @@ class LLMCallNode(WorkflowNode):
                 extra_args=extra_args,
             )
         except Exception as e:
-            logger.warning(f'LLM call node {self.node_id}: request failed - {e}')
+            logger.warning(f'[LLM:{self.node_id}] LLM call failed: {e}')
 
             # Handle based on exception handling strategy
             if exception_handling == 'show-error':
@@ -284,21 +279,20 @@ class LLMCallNode(WorkflowNode):
                 elif isinstance(elem, str):
                     response_text += elem
 
-        # Remove CoT (Chain of Thought) content if configured
+        # Remove CoT content (always remove to avoid leaking internal reasoning)
-        if remove_think:
+        response_text = self._remove_think_content(response_text)
-            response_text = self._remove_think_content(response_text)
 
         # Apply content safety filter
         response_text, is_blocked, filter_notice = self._apply_content_filter(response_text)
         if is_blocked:
-            logger.warning(f'LLM call node {self.node_id}: response blocked by content filter - {filter_notice}')
+            logger.warning(f'[LLM:{self.node_id}] Response blocked by content filter: {filter_notice}')
             return {
                 'response': filter_notice,
                 'usage': usage,
                 'blocked_by_filter': True,
             }
 
-        # Extract usage info if available
+        # Extract usage info
         usage = {
             'prompt_tokens': 0,
             'completion_tokens': 0,
@@ -319,6 +313,7 @@ class LLMCallNode(WorkflowNode):
                 'total_tokens': getattr(u, 'total_tokens', 0) or 0,
             }
 
+        # Build result
         result: dict[str, Any] = {
             'response': response_text,
             'usage': usage,
@@ -329,7 +324,7 @@ class LLMCallNode(WorkflowNode):
             try:
                 result['parsed'] = json.loads(response_text)
             except json.JSONDecodeError as e:
-                logger.warning(f'LLM call node {self.node_id}: failed to parse JSON response - {e}')
+                logger.warning(f'[LLM:{self.node_id}] Failed to parse JSON: {e}')
                 result['parsed'] = None
                 result['parse_error'] = str(e)
 
@@ -354,7 +349,6 @@ class LLMCallNode(WorkflowNode):
         if not self.ap:
             raise RuntimeError('Application instance not available - cannot call LLM')
 
-        remove_think = self.get_config('remove_think', False)
         exception_handling = self.get_config('exception_handling', 'show-error')
         failure_hint = self.get_config('failure_hint', 'Request failed.')
 
@@ -383,7 +377,7 @@ class LLMCallNode(WorkflowNode):
         if max_tokens and int(max_tokens) > 0:
             extra_args['max_tokens'] = int(max_tokens)
 
-        logger.info(f'LLM call node {self.node_id}: streaming model {model_uuid}')
+        logger.info(f'[LLM:{self.node_id}] Streaming model {model_uuid}')
 
         try:
             # Try streaming first
@@ -396,6 +390,7 @@ class LLMCallNode(WorkflowNode):
             )
 
             full_response = ''
+            in_think_block = False
             async for chunk in stream:
                 chunk_text = ''
                 if hasattr(chunk, 'content'):
@@ -409,16 +404,25 @@ class LLMCallNode(WorkflowNode):
                                 chunk_text += elem
 
                 if chunk_text:
-                    if remove_think:
+                    # Filter <think> blocks in streaming mode
-                        chunk_text = self._remove_think_content(chunk_text)
+                    if '<think>' in chunk_text or '<thought>' in chunk_text:
-                    full_response += chunk_text
+                        in_think_block = True
-                    yield chunk_text
+                    if in_think_block:
+                        if '</think>' in chunk_text or '</thought>' in chunk_text:
+                            in_think_block = False
+                            chunk_text = chunk_text.split('</think>')[-1].split('</thought>')[-1]
+                        else:
+                            chunk_text = ''
+                    
+                    if chunk_text:
+                        full_response += chunk_text
+                        yield chunk_text
 
             # Store in context for downstream nodes
             context.variables['_last_llm_response'] = full_response
 
         except Exception as e:
-            logger.warning(f'LLM call node {self.node_id}: streaming failed, falling back - {e}')
+            logger.warning(f'[LLM:{self.node_id}] Streaming failed, falling back - {e}')
             # Fallback to non-streaming
             try:
                 result_message = await runtime_model.provider.invoke_llm(
@@ -429,12 +433,12 @@ class LLMCallNode(WorkflowNode):
                     extra_args=extra_args,
                 )
                 response_text = self._extract_response_text(result_message)
-                if remove_think:
+                # Always remove <think> content in fallback
-                    response_text = self._remove_think_content(response_text)
+                response_text = self._remove_think_content(response_text)
                 yield response_text
                 context.variables['_last_llm_response'] = response_text
             except Exception as e2:
-                logger.error(f'LLM call node {self.node_id}: fallback also failed - {e2}')
+                logger.error(f'[LLM:{self.node_id}] Fallback also failed - {e2}')
                 if exception_handling == 'show-hint':
                     yield failure_hint
                 elif exception_handling != 'hide':

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

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('loop')

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

@@ -11,7 +11,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('mcp_tool')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 class MemoryHelper:

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('merge')

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

@@ -9,7 +9,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('message_trigger')

src/langbot/pkg/workflow/nodes/n8n_workflow.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('n8n_workflow')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('opening_statement')

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

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('parallel')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('parameter_extractor')

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

@@ -7,7 +7,7 @@
 
 # from typing import Any
 
-# from ..entities import ExecutionContext
+# from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 # from ..node import WorkflowNode, workflow_node
 
 # @workflow_node('plugin_call')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('question_classifier')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('redis_operation')

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

@@ -8,7 +8,7 @@ from __future__ import annotations
 import logging
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 logger = logging.getLogger(__name__)
@@ -20,13 +20,14 @@ class ReplyMessageNode(WorkflowNode):
     category = 'action'
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
-        message = inputs.get('message')
+        # Priority: content/response (from upstream nodes) > message (original input) > context
-        if message in (None, ''):
+        message = inputs.get('content')
-            message = inputs.get('input')
         if message in (None, ''):
             message = inputs.get('response')
         if message in (None, ''):
-            message = inputs.get('content')
+            message = inputs.get('input')
+        if message in (None, ''):
+            message = inputs.get('message')
         if message in (None, '') and context.message_context:
             message = context.message_context.message_content
         if message is None:

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('send_message')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('set_variable')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('store_data')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('switch')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('variable_aggregator')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('wait')

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

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ..entities import ExecutionContext
+from langbot_plugin.api.entities.builtin.workflow import ExecutionContext
 from ..node import WorkflowNode, workflow_node
 
 @workflow_node('webhook_trigger')

src/langbot/pkg/workflow/variable_manager.py

@@ -0,0 +1,185 @@
+"""Variable management for workflow execution.
+
+This module provides utilities for managing workflow variables, including:
+- Reserved variable protection
+- Variable namespace validation
+- Variable inheritance between nodes
+"""
+
+from typing import Set, Dict, Any
+
+# 保留变量列表 - 这些变量由系统管理，不能被用户覆盖
+RESERVED_VARIABLES = {
+    'workflow_id',
+    'execution_id',
+    'node_id',
+    'timestamp',
+    'launcher_type',
+    'trigger_type',
+    'message_id',
+    'execution_status',
+}
+
+
+def get_reserved_variables() -> Set[str]:
+    """获取保留变量列表
+    
+    Returns:
+        保留变量名称的集合
+    """
+    return RESERVED_VARIABLES.copy()
+
+
+def validate_variable_namespace(
+    variables: Dict[str, Any],
+    namespace: str = 'workflow'
+) -> bool:
+    """验证变量命名空间
+    
+    检查变量是否使用了保留的名称。允许不带前缀的变量，但建议使用命名空间前缀。
+    
+    Args:
+        variables: 要验证的变量字典
+        namespace: 命名空间前缀（可选）
+        
+    Returns:
+        True 如果验证通过
+        
+    Raises:
+        ValueError: 如果变量名称与保留变量冲突
+    """
+    reserved = get_reserved_variables()
+    
+    for var_name in variables.keys():
+        if var_name in reserved:
+            raise ValueError(
+                f"Variable '{var_name}' is reserved and cannot be used. "
+                f"Reserved variables: {', '.join(sorted(reserved))}"
+            )
+        
+        # 检查命名空间前缀（可选建议）
+        if namespace and not var_name.startswith(f"{namespace}_"):
+            # 允许不带前缀的变量，但建议使用前缀
+            pass
+    
+    return True
+
+
+def inherit_variables(
+    parent_variables: Dict[str, Any],
+    child_namespace: str
+) -> Dict[str, Any]:
+    """继承父节点变量到子节点
+    
+    将父节点的变量继承到子节点，跳过保留变量，并添加命名空间前缀。
+    
+    Args:
+        parent_variables: 父节点的变量字典
+        child_namespace: 子节点的命名空间前缀
+        
+    Returns:
+        带有命名空间前缀的继承变量字典
+    """
+    inherited = {}
+    
+    for key, value in parent_variables.items():
+        # 跳过保留变量
+        if key in RESERVED_VARIABLES:
+            continue
+        
+        # 添加命名空间前缀
+        namespaced_key = f"{child_namespace}_{key}"
+        inherited[namespaced_key] = value
+    
+    return inherited
+
+
+def merge_variables(
+    base_variables: Dict[str, Any],
+    override_variables: Dict[str, Any],
+    allow_reserved: bool = False
+) -> Dict[str, Any]:
+    """合并变量字典
+    
+    将override_variables合并到base_variables中。
+    
+    Args:
+        base_variables: 基础变量字典
+        override_variables: 要合并的变量字典
+        allow_reserved: 是否允许覆盖保留变量（默认False）
+        
+    Returns:
+        合并后的变量字典
+        
+    Raises:
+        ValueError: 如果尝试覆盖保留变量且allow_reserved=False
+    """
+    result = base_variables.copy()
+    
+    for key, value in override_variables.items():
+        if key in RESERVED_VARIABLES and not allow_reserved:
+            raise ValueError(
+                f"Cannot override reserved variable '{key}'. "
+                f"Set allow_reserved=True to override."
+            )
+        result[key] = value
+    
+    return result
+
+
+def extract_namespace_variables(
+    variables: Dict[str, Any],
+    namespace: str
+) -> Dict[str, Any]:
+    """提取特定命名空间的变量
+    
+    从变量字典中提取具有特定命名空间前缀的变量。
+    
+    Args:
+        variables: 变量字典
+        namespace: 命名空间前缀
+        
+    Returns:
+        提取的变量字典（不包含命名空间前缀）
+    """
+    prefix = f"{namespace}_"
+    result = {}
+    
+    for key, value in variables.items():
+        if key.startswith(prefix):
+            # 移除命名空间前缀
+            clean_key = key[len(prefix):]
+            result[clean_key] = value
+    
+    return result
+
+
+def sanitize_variables(
+    variables: Dict[str, Any],
+    allowed_keys: Set[str] | None = None
+) -> Dict[str, Any]:
+    """清理变量字典
+    
+    移除保留变量和不在允许列表中的变量。
+    
+    Args:
+        variables: 要清理的变量字典
+        allowed_keys: 允许的键集合（如果为None，则允许所有非保留键）
+        
+    Returns:
+        清理后的变量字典
+    """
+    result = {}
+    
+    for key, value in variables.items():
+        # 跳过保留变量
+        if key in RESERVED_VARIABLES:
+            continue
+        
+        # 检查允许列表
+        if allowed_keys is not None and key not in allowed_keys:
+            continue
+        
+        result[key] = value
+    
+    return result

uv.lock

@@ -1973,7 +1973,7 @@ requires-dist = [
     { name = "ebooklib", specifier = ">=0.18" },
     { name = "gewechat-client", specifier = ">=0.1.5" },
     { name = "html2text", specifier = ">=2024.2.26" },
-    { name = "langbot-plugin", specifier = "==0.3.11" },
+    { name = "langbot-plugin", directory = "/home/typer/Desktop/langbot-plugin-sdk" },
     { name = "langchain", specifier = ">=0.2.0" },
     { name = "langchain-core", specifier = ">=1.2.28" },
     { name = "langchain-text-splitters", specifier = ">=1.1.2" },
@@ -2037,7 +2037,7 @@ dev = [
 [[package]]
 name = "langbot-plugin"
 version = "0.3.11"
-source = { registry = "https://pypi.org/simple" }
+source = { directory = "/home/typer/Desktop/langbot-plugin-sdk" }
 dependencies = [
     { name = "aiofiles" },
     { name = "dotenv" },
@@ -2054,9 +2054,31 @@ dependencies = [
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