backend

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

@@ -5,7 +5,7 @@ Node metadata is loaded from: ../../templates/metadata/nodes/call_pipeline.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -15,26 +15,13 @@ 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 ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('call_pipeline')
 class CallPipelineNode(WorkflowNode):
     """Call pipeline node - invoke an existing pipeline"""
 
-    type_name = 'call_pipeline'
     category = 'action'
-    icon = 'Workflow'
-    name = 'call_pipeline'
-    description = 'call_pipeline'
-    name_zh = '调用 Pipeline'
-    name_en = 'Call Pipeline'
-    description_zh = '调用现有的 Pipeline 进行处理'
-    description_en = 'Invoke an existing Pipeline for processing'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         if not self.ap:
@@ -124,7 +111,6 @@ class CallPipelineNode(WorkflowNode):
         if context.message_context and context.message_context.is_group:
             group = platform_entities.Group(
                 id=context.message_context.group_id or context.session_id or 'workflow_group',
-                name='Workflow Group',
                 permission=platform_entities.Permission.Member,
             )
             sender = platform_entities.GroupMember(
@@ -152,7 +138,6 @@ class CallPipelineNode(WorkflowNode):
             else None,
         )
 
-
 class _WorkflowPipelineCaptureAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
     responses: list[dict[str, Any]] = []
 

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

@@ -7,29 +7,16 @@ from __future__ import annotations
 
 import json
 import re
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('code_executor')
 class CodeExecutorNode(WorkflowNode):
     """Code executor node - run Python or JavaScript code"""
 
-    type_name = 'code_executor'
     category = 'process'
-    icon = 'Code'
-    name = 'code_executor'
-    description = 'code_executor'
-    name_zh = '代码执行'
-    name_en = 'Code Executor'
-    description_zh = '执行自定义代码处理数据'
-    description_en = 'Execute custom code to process data'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         code = self.get_config('code', '')

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

@@ -5,30 +5,17 @@ Node metadata is loaded from: ../../templates/metadata/nodes/condition.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
+from ..node import WorkflowNode, workflow_node
 from ..safe_eval import safe_eval_with_vars
 
-
 @workflow_node('condition')
 class ConditionNode(WorkflowNode):
     """Condition node - branch based on condition"""
 
-    type_name = 'condition'
     category = 'control'
-    icon = 'GitBranch'
-    name = 'condition'
-    description = 'condition'
-    name_zh = '条件分支'
-    name_en = 'Condition'
-    description_zh = '根据条件分支工作流'
-    description_en = 'Branch workflow based on a condition'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         condition_type = self.get_config('condition_type', 'expression')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/coze_bot.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('coze_bot')
 class CozeBotNode(WorkflowNode):
     """Coze bot node - call Coze API bot"""
 
-    type_name = 'coze_bot'
     category = 'integration'
-    icon = 'MessageSquare'
-    name = 'coze_bot'
-    description = 'coze_bot'
-    name_zh = 'Coze Bot'
-    name_en = 'Coze Bot'
-    description_zh = '调用扣子 Bot'
-    description_en = 'Call a Coze Bot'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         api_key = self.get_config('api_key', '')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/cron_trigger.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('cron_trigger')
 class CronTriggerNode(WorkflowNode):
     """Cron trigger node - triggers workflow on schedule"""
 
-    type_name = 'cron_trigger'
     category = 'trigger'
-    icon = 'Timer'
-    name = 'cron_trigger'
-    description = 'cron_trigger'
-    name_zh = '定时触发'
-    name_en = 'Scheduled Trigger'
-    description_zh = '按定时计划触发工作流'
-    description_en = 'Trigger workflow on a scheduled time'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         from datetime import datetime

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

@@ -5,30 +5,17 @@ Node metadata is loaded from: ../../templates/metadata/nodes/data_transform.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
+from ..node import WorkflowNode, workflow_node
 from ..safe_eval import safe_eval_with_vars
 
-
 @workflow_node('data_transform')
 class DataTransformNode(WorkflowNode):
     """Data transform node - transform data using templates or JSONPath"""
 
-    type_name = 'data_transform'
     category = 'process'
-    icon = 'ArrowRightLeft'
-    name = 'data_transform'
-    description = 'data_transform'
-    name_zh = '数据转换'
-    name_en = 'Data Transform'
-    description_zh = '使用模板或 JSONPath 转换数据'
-    description_en = 'Transform data using templates or JSONPath'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         data = inputs.get('data')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/database_query.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('database_query')
 class DatabaseQueryNode(WorkflowNode):
     """Database query node - execute database queries"""
 
-    type_name = 'database_query'
     category = 'integration'
-    icon = 'Database'
-    name = 'database_query'
-    description = 'database_query'
-    name_zh = '数据库查询'
-    name_en = 'Database Query'
-    description_zh = '执行数据库查询'
-    description_en = 'Execute database queries'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         connection_type = self.get_config('connection_type', 'postgresql')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/dify_knowledge_quer
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('dify_knowledge_query')
 class DifyKnowledgeQueryNode(WorkflowNode):
     """Dify knowledge base query node - query Dify knowledge base"""
 
-    type_name = 'dify_knowledge_query'
     category = 'integration'
-    icon = 'BookOpen'
-    name = 'dify_knowledge_query'
-    description = 'dify_knowledge_query'
-    name_zh = 'Dify 知识库查询'
-    name_en = 'Dify Knowledge Query'
-    description_zh = '查询 Dify 知识库'
-    description_en = 'Query Dify knowledge base'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         base_url = self.get_config('base_url', 'https://api.dify.ai/v1')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/dify_workflow.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('dify_workflow')
 class DifyWorkflowNode(WorkflowNode):
     """Dify workflow node - call Dify service API"""
 
-    type_name = 'dify_workflow'
     category = 'integration'
-    icon = 'Bot'
-    name = 'dify_workflow'
-    description = 'dify_workflow'
-    name_zh = 'Dify 工作流'
-    name_en = 'Dify Workflow'
-    description_zh = '调用 Dify 平台工作流'
-    description_en = 'Call a Dify platform workflow'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         base_url = self.get_config('base_url', 'https://api.dify.ai/v1')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/end.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('end')
 class EndNode(WorkflowNode):
     """End node - marks the end of workflow execution"""
 
-    type_name = 'end'
-    category = 'action'
-    icon = 'PauseCircle'
-    name = 'end'
-    description = 'end'
-    name_zh = '结束'
-    name_en = 'End'
-    description_zh = '结束工作流执行'
-    description_en = 'End the workflow execution'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
+    category = 'control'
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         result = inputs.get('result')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/event_trigger.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('event_trigger')
 class EventTriggerNode(WorkflowNode):
     """Event trigger node - triggers workflow on system events"""
 
-    type_name = 'event_trigger'
     category = 'trigger'
-    icon = 'Bell'
-    name = 'event_trigger'
-    description = 'event_trigger'
-    name_zh = '事件触发'
-    name_en = 'Event Trigger'
-    description_zh = '当系统事件发生时触发工作流'
-    description_en = 'Trigger workflow when a system event occurs'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         from datetime import datetime

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/http_request.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('http_request')
 class HTTPRequestNode(WorkflowNode):
     """HTTP request node - make HTTP API calls"""
 
-    type_name = 'http_request'
-    category = 'process'
-    icon = 'Globe'
-    name = 'http_request'
-    description = 'http_request'
-    name_zh = 'HTTP 请求'
-    name_en = 'HTTP Request'
-    description_zh = '向外部 API 发送 HTTP 请求'
-    description_en = 'Make HTTP requests to external APIs'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
+    category = 'action'
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         import aiohttp

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

@@ -2,47 +2,16 @@
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('iterator')
 class IteratorNode(WorkflowNode):
     """Iterator node - iterate over array items one by one"""
 
-    type_name = 'iterator'
     category = 'control'
-    icon = 'Repeat'
-    name = 'iterator'
-    name_zh = '迭代器'
-    name_en = 'Iterator'
-    description = 'iterator'
-    description_zh = '逐个遍历数组元素'
-    description_en = 'Iterate over array elements one by one'
-
-    inputs: ClassVar[list[NodePort]] = [
-        NodePort(name='items', type='array', description='Array to iterate over', required=True),
-    ]
-    outputs: ClassVar[list[NodePort]] = [
-        NodePort(name='item', type='any', description='Current item'),
-        NodePort(name='index', type='number', description='Current index'),
-        NodePort(name='is_first', type='boolean', description='Whether this is the first item'),
-        NodePort(name='is_last', type='boolean', description='Whether this is the last item'),
-        NodePort(name='results', type='array', description='All iteration results'),
-        NodePort(name='completed', type='boolean', description='Whether iteration completed'),
-    ]
-    config_schema: ClassVar[list[NodeConfig]] = [
-        NodeConfig(
-            name='max_iterations',
-            type='integer',
-            required=False,
-            default=1000,
-            description='Maximum iterations (safety limit)',
-            label={'en_US': 'Max Iterations', 'zh_Hans': '最大迭代次数'},
-        ),
-    ]
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         items = inputs.get('items', [])

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/knowledge_retrieval
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('knowledge_retrieval')
 class KnowledgeRetrievalNode(WorkflowNode):
     """Knowledge retrieval node - search in knowledge base"""
 
-    type_name = 'knowledge_retrieval'
     category = 'process'
-    icon = 'Search'
-    name = 'knowledge_retrieval'
-    description = 'knowledge_retrieval'
-    name_zh = '知识库检索'
-    name_en = 'Knowledge Retrieval'
-    description_zh = '从知识库中检索相关信息'
-    description_en = 'Retrieve relevant information from knowledge bases'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         query = inputs.get('query', '')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/langflow_flow.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('langflow_flow')
 class LangflowFlowNode(WorkflowNode):
     """Langflow flow node - call Langflow API"""
 
-    type_name = 'langflow_flow'
     category = 'integration'
-    icon = 'GitBranch'
-    name = 'langflow_flow'
-    description = 'langflow_flow'
-    name_zh = 'Langflow 流程'
-    name_en = 'Langflow Flow'
-    description_zh = '调用 Langflow 流程'
-    description_en = 'Call a Langflow flow'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         base_url = self.get_config('base_url', 'http://localhost:7860')

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

@@ -4,81 +4,20 @@ from __future__ import annotations
 
 import logging
 import re
-from typing import Any, ClassVar
+from typing import Any
 
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
+from ..node import WorkflowNode, workflow_node
 
 logger = logging.getLogger(__name__)
 
-
 @workflow_node('llm_call')
 class LLMCallNode(WorkflowNode):
     """LLM call node - invoke large language model"""
 
-    type_name = 'llm_call'
     category = 'process'
-    icon = 'Brain'
-    name = 'llm_call'
-    name_zh = 'LLM 调用'
-    name_en = 'LLM Call'
-    description = 'llm_call'
-    description_zh = '调用大语言模型生成响应'
-    description_en = 'Call a large language model to generate responses'
-
-    inputs: ClassVar[list[NodePort]] = [
-        NodePort(name='input', type='string', description='Input text to send to the model', required=False),
-        NodePort(name='context', type='object', description='Additional context data', required=False),
-    ]
-    outputs: ClassVar[list[NodePort]] = [
-        NodePort(name='response', type='string', description='Model response text'),
-        NodePort(name='usage', type='object', description='Token usage information'),
-    ]
-    config_schema: ClassVar[list[NodeConfig]] = [
-        NodeConfig(
-            name='model',
-            type='llm-model-selector',
-            required=True,
-            description='Select the LLM model to use',
-            label={'en_US': 'Model', 'zh_Hans': '模型'},
-        ),
-        NodeConfig(
-            name='system_prompt',
-            type='textarea',
-            required=False,
-            default='',
-            description='System prompt to set model behavior',
-            label={'en_US': 'System Prompt', 'zh_Hans': '系统提示词'},
-        ),
-        NodeConfig(
-            name='user_prompt_template',
-            type='textarea',
-            required=True,
-            default='{{input}}',
-            description='User prompt template with variable placeholders',
-            label={'en_US': 'User Prompt Template', 'zh_Hans': '用户提示词模板'},
-        ),
-        NodeConfig(
-            name='temperature',
-            type='number',
-            required=False,
-            default=0.7,
-            description='Controls randomness (0.0-2.0)',
-            label={'en_US': 'Temperature', 'zh_Hans': '温度'},
-            min_value=0.0,
-            max_value=2.0,
-        ),
-        NodeConfig(
-            name='max_tokens',
-            type='integer',
-            required=False,
-            default=0,
-            description='Max tokens to generate (0 = model default)',
-            label={'en_US': 'Max Tokens', 'zh_Hans': '最大令牌数'},
-        ),
-    ]
 
     def _resolve_template(self, template: str, inputs: dict[str, Any], context: ExecutionContext) -> str:
         """Resolve {{variable}} placeholders in a template string."""

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

@@ -2,54 +2,16 @@
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('loop')
 class LoopNode(WorkflowNode):
     """Loop node - iterate over items"""
 
-    type_name = 'loop'
     category = 'control'
-    icon = 'Repeat'
-    name = 'loop'
-    name_zh = '循环'
-    name_en = 'Loop'
-    description = 'loop'
-    description_zh = '遍历项目或重复直到满足条件'
-    description_en = 'Iterate over items or repeat until condition'
-
-    inputs: ClassVar[list[NodePort]] = [
-        NodePort(name='items', type='array', description='Items to iterate over', required=False),
-    ]
-    outputs: ClassVar[list[NodePort]] = [
-        NodePort(name='item', type='any', description='Current item in iteration'),
-        NodePort(name='index', type='number', description='Current iteration index'),
-        NodePort(name='results', type='array', description='All iteration results'),
-        NodePort(name='completed', type='boolean', description='Whether loop completed'),
-    ]
-    config_schema: ClassVar[list[NodeConfig]] = [
-        NodeConfig(
-            name='loop_type',
-            type='select',
-            required=True,
-            default='foreach',
-            description='Type of loop',
-            label={'en_US': 'Loop Type', 'zh_Hans': '循环类型'},
-            options=['foreach', 'while', 'count'],
-        ),
-        NodeConfig(
-            name='max_iterations',
-            type='integer',
-            required=False,
-            default=100,
-            description='Maximum iterations (safety limit)',
-            label={'en_US': 'Max Iterations', 'zh_Hans': '最大迭代次数'},
-        ),
-    ]
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         items = inputs.get('items', [])

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

@@ -9,36 +9,24 @@ The i18n for label and description is handled on the frontend side.
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('mcp_tool')
 class MCPToolNode(WorkflowNode):
     """MCP tool node - invoke MCP (Model Context Protocol) tools"""
 
     # Node type for registration
-    type_name = 'mcp_tool'
 
     # Category and icon - these are not i18n
     category = 'integration'
-    icon = 'Wrench'
 
     # Name and description - i18n handled on frontend side
     # Frontend will use node type key to look up translation
-    name = 'mcp_tool'
-    description = 'mcp_tool'
-    name_zh = 'MCP 工具'
-    name_en = 'MCP Tool'
-    description_zh = '调用 MCP 工具'
-    description_en = 'Invoke an MCP (Model Context Protocol) tool'
 
     # Inputs/outputs/config - loaded from YAML at runtime
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         """Execute the MCP tool node

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

@@ -5,11 +5,10 @@ Node metadata is loaded from: ../../templates/metadata/nodes/memory_store.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 class MemoryHelper:
     """Helper class wrapping context.memory dict with get/set/delete/list_all/append operations"""
@@ -47,24 +46,11 @@ class MemoryHelper:
         self.set(key, current, scope=scope, ttl=ttl)
         return current
 
-
 @workflow_node('memory_store')
 class MemoryStoreNode(WorkflowNode):
     """Memory store node - store and retrieve from workflow memory"""
 
-    type_name = 'memory_store'
     category = 'integration'
-    icon = 'HardDrive'
-    name = 'memory_store'
-    description = 'memory_store'
-    name_zh = '记忆存储'
-    name_en = 'Memory Store'
-    description_zh = '从工作流记忆中存储和检索数据'
-    description_en = 'Store and retrieve data from workflow memory'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         operation = self.get_config('operation', 'get')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/merge.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('merge')
 class MergeNode(WorkflowNode):
     """Merge node - combine multiple inputs"""
 
-    type_name = 'merge'
     category = 'control'
-    icon = 'GitMerge'
-    name = 'merge'
-    description = 'merge'
-    name_zh = '合并'
-    name_en = 'Merge'
-    description_zh = '将多个分支合并在一起'
-    description_en = 'Merge multiple branches back together'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         strategy = self.get_config('merge_strategy', 'object')

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

@@ -7,29 +7,16 @@ Node metadata (label, description, inputs, outputs, config) is loaded from:
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('message_trigger')
 class MessageTriggerNode(WorkflowNode):
     """Message trigger node - triggers workflow on message arrival"""
 
-    type_name = 'message_trigger'
     category = 'trigger'
-    icon = 'MessageSquare'
-    name = 'message_trigger'
-    description = 'message_trigger'
-    name_zh = '消息触发'
-    name_en = 'Message Trigger'
-    description_zh = '当收到消息时触发工作流'
-    description_en = 'Trigger workflow when a message is received'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         msg_ctx = context.message_context

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/n8n_workflow.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('n8n_workflow')
 class N8nWorkflowNode(WorkflowNode):
     """n8n workflow node - call n8n workflow API"""
 
-    type_name = 'n8n_workflow'
     category = 'integration'
-    icon = 'Workflow'
-    name = 'n8n_workflow'
-    description = 'n8n_workflow'
-    name_zh = 'n8n 工作流'
-    name_en = 'N8n Workflow'
-    description_zh = '通过 webhook 调用 n8n 工作流'
-    description_en = 'Call an n8n workflow via webhook'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         webhook_url = self.get_config('webhook_url', '')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/opening_statement.y
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('opening_statement')
 class OpeningStatementNode(WorkflowNode):
     """Opening statement node - provide conversation opener and suggested questions"""
 
-    type_name = 'opening_statement'
     category = 'action'
-    icon = 'MessageSquare'
-    name = 'opening_statement'
-    description = 'opening_statement'
-    name_zh = '对话开场白'
-    name_en = 'Opening Statement'
-    description_zh = '提供对话开场白和建议问题'
-    description_en = 'Provide conversation opener and suggested questions'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         statement = self.get_config('statement', '')

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

@@ -2,51 +2,16 @@
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('parallel')
 class ParallelNode(WorkflowNode):
     """Parallel node - execute multiple branches simultaneously"""
 
-    type_name = 'parallel'
     category = 'control'
-    icon = 'Layers'
-    name = 'parallel'
-    name_zh = '并行执行'
-    name_en = 'Parallel'
-    description = 'parallel'
-    description_zh = '并行执行多个分支'
-    description_en = 'Execute multiple branches in parallel'
-
-    inputs: ClassVar[list[NodePort]] = [
-        NodePort(name='input', type='any', description='Input data for all branches', required=False),
-    ]
-    outputs: ClassVar[list[NodePort]] = [
-        NodePort(name='results', type='object', description='Combined results from all branches'),
-        NodePort(name='errors', type='array', description='Errors from branches (if any)'),
-    ]
-    config_schema: ClassVar[list[NodeConfig]] = [
-        NodeConfig(
-            name='wait_all',
-            type='boolean',
-            required=False,
-            default=True,
-            description='Wait for all branches to complete',
-            label={'en_US': 'Wait for All', 'zh_Hans': '等待全部完成'},
-        ),
-        NodeConfig(
-            name='fail_fast',
-            type='boolean',
-            required=False,
-            default=False,
-            description='Stop all branches if any fails',
-            label={'en_US': 'Fail Fast', 'zh_Hans': '快速失败'},
-        ),
-    ]
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         return {

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

@@ -5,29 +5,17 @@ Node metadata is loaded from: ../../templates/metadata/nodes/parameter_extractor
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('parameter_extractor')
 class ParameterExtractorNode(WorkflowNode):
     """Parameter extractor node - extract structured parameters from text"""
 
-    type_name = 'parameter_extractor'
     category = 'process'
     icon: str = 'Variable'
-    name = 'parameter_extractor'
-    description = 'parameter_extractor'
-    name_zh = '参数提取器'
-    name_en = 'Parameter Extractor'
-    description_zh = '使用 AI 从文本中提取结构化参数'
-    description_en = 'Extract structured parameters from text using AI'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         param_defs = self.get_config('parameters', [])

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

@@ -5,11 +5,10 @@
 
 # from __future__ import annotations
 
-# from typing import Any, ClassVar
+# from typing import Any
 
 # from ..entities import ExecutionContext
-# from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+# from ..node import WorkflowNode, workflow_node
 
 # @workflow_node('plugin_call')
 # class PluginCallNode(WorkflowNode):

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/question_classifier
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('question_classifier')
 class QuestionClassifierNode(WorkflowNode):
     """Question classifier node - classify user questions into categories"""
 
-    type_name = 'question_classifier'
     category = 'process'
-    icon = 'ListFilter'
-    name = 'question_classifier'
-    description = 'question_classifier'
-    name_zh = '问题分类器'
-    name_en = 'Question Classifier'
-    description_zh = '使用 AI 将问题分类到预定义类别'
-    description_en = 'Classify questions into predefined categories using AI'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         categories = self.get_config('categories', [])

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/redis_operation.yam
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('redis_operation')
 class RedisOperationNode(WorkflowNode):
     """Redis operation node - perform Redis cache operations"""
 
-    type_name = 'redis_operation'
     category = 'integration'
-    icon = 'Server'
-    name = 'redis_operation'
-    description = 'redis_operation'
-    name_zh = 'Redis 操作'
-    name_en = 'Redis Operation'
-    description_zh = '执行 Redis 缓存操作'
-    description_en = 'Perform Redis cache operations'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         connection_url = self.get_config('connection_url', 'redis://localhost:6379')

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

@@ -6,31 +6,18 @@ Node metadata is loaded from: ../../templates/metadata/nodes/reply_message.yaml
 from __future__ import annotations
 
 import logging
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
+from ..node import WorkflowNode, workflow_node
 
 logger = logging.getLogger(__name__)
 
-
 @workflow_node('reply_message')
 class ReplyMessageNode(WorkflowNode):
     """Reply message node - reply to the triggering message"""
 
-    type_name = 'reply_message'
     category = 'action'
-    icon = 'Send'
-    name = 'reply_message'
-    description = 'reply_message'
-    name_zh = '回复消息'
-    name_en = 'Reply Message'
-    description_zh = '回复触发工作流的消息'
-    description_en = 'Reply to the message that triggered the workflow'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         message = inputs.get('message')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/send_message.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('send_message')
 class SendMessageNode(WorkflowNode):
     """Send message node - send message to a target"""
 
-    type_name = 'send_message'
     category = 'action'
-    icon = 'MessageCircle'
-    name = 'send_message'
-    description = 'send_message'
-    name_zh = '发送消息'
-    name_en = 'Send Message'
-    description_zh = '向聊天或用户发送消息'
-    description_en = 'Send a message to a chat or user'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         return {'status': 'sent', 'message_id': f'msg_{context.execution_id}'}

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/set_variable.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('set_variable')
 class SetVariableNode(WorkflowNode):
     """Set variable node - set workflow or conversation variable"""
 
-    type_name = 'set_variable'
     category = 'action'
-    icon = 'Variable'
-    name = 'set_variable'
-    description = 'set_variable'
-    name_zh = '设置变量'
-    name_en = 'Set Variable'
-    description_zh = '设置上下文变量值'
-    description_en = 'Set a context variable value'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         value = inputs.get('value')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/store_data.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('store_data')
 class StoreDataNode(WorkflowNode):
     """Store data node - save data to storage"""
 
-    type_name = 'store_data'
     category = 'action'
-    icon = 'Database'
-    name = 'store_data'
-    description = 'store_data'
-    name_zh = '存储数据'
-    name_en = 'Store Data'
-    description_zh = '将数据存储到持久化存储'
-    description_en = 'Store data to persistent storage'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         key = inputs.get('key', '')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/switch.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('switch')
 class SwitchNode(WorkflowNode):
     """Switch node - multi-way branch based on value"""
 
-    type_name = 'switch'
     category = 'control'
-    icon = 'Split'
-    name = 'switch'
-    description = 'switch'
-    name_zh = '多路分支'
-    name_en = 'Switch'
-    description_zh = '根据多个条件分支工作流'
-    description_en = 'Branch workflow based on multiple cases'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         expression = self.get_config('expression', '')

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/variable_aggregator
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('variable_aggregator')
 class VariableAggregatorNode(WorkflowNode):
     """Variable aggregator node - aggregate variables from multiple branches"""
 
-    type_name = 'variable_aggregator'
     category = 'control'
-    icon = 'GitMerge'
-    name = 'variable_aggregator'
-    description = 'variable_aggregator'
-    name_zh = '变量聚合器'
-    name_en = 'Variable Aggregator'
-    description_zh = '聚合多个分支的变量输出'
-    description_en = 'Aggregate variable outputs from multiple branches'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         variables = inputs.get('variables', {})

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/wait.yaml
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('wait')
 class WaitNode(WorkflowNode):
     """Wait node - pause execution for a duration"""
 
-    type_name = 'wait'
     category = 'control'
-    icon = 'Clock'
-    name = 'wait'
-    description = 'wait'
-    name_zh = '等待'
-    name_en = 'Wait'
-    description_zh = '暂停工作流执行指定时间'
-    description_en = 'Pause workflow execution for a specified duration'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         import asyncio

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

@@ -5,29 +5,16 @@ Node metadata is loaded from: ../../templates/metadata/nodes/webhook_trigger.yam
 
 from __future__ import annotations
 
-from typing import Any, ClassVar
+from typing import Any
 
 from ..entities import ExecutionContext
-from ..node import WorkflowNode, workflow_node, NodePort, NodeConfig
-
+from ..node import WorkflowNode, workflow_node
 
 @workflow_node('webhook_trigger')
 class WebhookTriggerNode(WorkflowNode):
     """Webhook trigger node - triggers workflow via HTTP request"""
 
-    type_name = 'webhook_trigger'
     category = 'trigger'
-    icon = 'Webhook'
-    name = 'webhook_trigger'
-    description = 'webhook_trigger'
-    name_zh = 'Webhook 触发'
-    name_en = 'Webhook Trigger'
-    description_zh = '通过 HTTP 请求触发工作流'
-    description_en = 'Trigger workflow via HTTP webhook'
-
-    inputs: ClassVar[list[NodePort]] = []
-    outputs: ClassVar[list[NodePort]] = []
-    config_schema: ClassVar[list[NodeConfig]] = []
 
     async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
         trigger_data = context.trigger_data