fix: iteration and loop node single step run

Signed-off-by: -LAN- <laipz8200@outlook.com>

api/.env.example

@@ -460,6 +460,16 @@ WORKFLOW_CALL_MAX_DEPTH=5
 WORKFLOW_PARALLEL_DEPTH_LIMIT=3
 MAX_VARIABLE_SIZE=204800
 
+# GraphEngine Worker Pool Configuration
+# Minimum number of workers per GraphEngine instance (default: 1)
+GRAPH_ENGINE_MIN_WORKERS=1
+# Maximum number of workers per GraphEngine instance (default: 10)
+GRAPH_ENGINE_MAX_WORKERS=10
+# Queue depth threshold that triggers worker scale up (default: 3)
+GRAPH_ENGINE_SCALE_UP_THRESHOLD=3
+# Seconds of idle time before scaling down workers (default: 5.0)
+GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME=5.0
+
 # Workflow storage configuration
 # Options: rdbms, hybrid
 # rdbms: Use only the relational database (default)

api/.importlinter

@@ -0,0 +1,112 @@
+[importlinter]
+root_packages =
+    core
+    configs
+    controllers
+    models
+    tasks
+    services
+
+[importlinter:contract:workflow]
+name = Workflow
+type=layers
+layers =
+    graph_engine
+    graph_events
+    graph
+    nodes
+    node_events
+    entities
+containers =
+    core.workflow
+ignore_imports =
+    core.workflow.nodes.base.node -> core.workflow.graph_events
+    core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_events
+    core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_engine
+    core.workflow.nodes.iteration.iteration_node -> core.workflow.graph
+    core.workflow.nodes.iteration.iteration_node -> core.workflow.graph_engine.command_channels
+    core.workflow.nodes.loop.loop_node -> core.workflow.graph_events
+    core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine
+    core.workflow.nodes.loop.loop_node -> core.workflow.graph
+    core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine.command_channels
+    core.workflow.nodes.node_factory -> core.workflow.graph
+
+[importlinter:contract:rsc]
+name = RSC
+type = layers
+layers =
+    graph_engine
+    response_coordinator
+containers =
+    core.workflow.graph_engine
+
+[importlinter:contract:worker]
+name = Worker
+type = layers
+layers =
+    graph_engine
+    worker
+containers =
+    core.workflow.graph_engine
+
+[importlinter:contract:graph-engine-architecture]
+name = Graph Engine Architecture
+type = layers
+layers =
+    graph_engine
+    orchestration
+    command_processing
+    event_management
+    error_handling
+    graph_traversal
+    state_management
+    worker_management
+    domain
+containers =
+    core.workflow.graph_engine
+
+[importlinter:contract:domain-isolation]
+name = Domain Model Isolation
+type = forbidden
+source_modules =
+    core.workflow.graph_engine.domain
+forbidden_modules =
+    core.workflow.graph_engine.worker_management
+    core.workflow.graph_engine.command_channels
+    core.workflow.graph_engine.layers
+    core.workflow.graph_engine.protocols
+
+[importlinter:contract:worker-management]
+name = Worker Management
+type = forbidden
+source_modules =
+    core.workflow.graph_engine.worker_management
+forbidden_modules =
+    core.workflow.graph_engine.orchestration
+    core.workflow.graph_engine.command_processing
+    core.workflow.graph_engine.event_management
+
+[importlinter:contract:error-handling-strategies]
+name = Error Handling Strategies
+type = independence
+modules =
+    core.workflow.graph_engine.error_handling.abort_strategy
+    core.workflow.graph_engine.error_handling.retry_strategy
+    core.workflow.graph_engine.error_handling.fail_branch_strategy
+    core.workflow.graph_engine.error_handling.default_value_strategy
+
+[importlinter:contract:graph-traversal-components]
+name = Graph Traversal Components
+type = layers
+layers =
+    edge_processor
+    skip_propagator
+containers =
+    core.workflow.graph_engine.graph_traversal
+
+[importlinter:contract:command-channels]
+name = Command Channels Independence
+type = independence
+modules =
+    core.workflow.graph_engine.command_channels.in_memory_channel
+    core.workflow.graph_engine.command_channels.redis_channel

api/commands.py

@@ -13,7 +13,6 @@ from sqlalchemy.exc import SQLAlchemyError
 
 from configs import dify_config
 from constants.languages import languages
-from core.plugin.entities.plugin import ToolProviderID
 from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.index_processor.constant.built_in_field import BuiltInField
@@ -31,6 +30,7 @@ from models.dataset import Dataset, DatasetCollectionBinding, DatasetMetadata, D
 from models.dataset import Document as DatasetDocument
 from models.model import Account, App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation
 from models.provider import Provider, ProviderModel
+from models.provider_ids import ToolProviderID
 from models.tools import ToolOAuthSystemClient
 from services.account_service import AccountService, RegisterService, TenantService
 from services.clear_free_plan_tenant_expired_logs import ClearFreePlanTenantExpiredLogs

api/configs/feature/__init__.py

@@ -529,6 +529,28 @@ class WorkflowConfig(BaseSettings):
         default=200 * 1024,
     )
 
+    # GraphEngine Worker Pool Configuration
+    GRAPH_ENGINE_MIN_WORKERS: PositiveInt = Field(
+        description="Minimum number of workers per GraphEngine instance",
+        default=1,
+    )
+
+    GRAPH_ENGINE_MAX_WORKERS: PositiveInt = Field(
+        description="Maximum number of workers per GraphEngine instance",
+        default=10,
+    )
+
+    GRAPH_ENGINE_SCALE_UP_THRESHOLD: PositiveInt = Field(
+        description="Queue depth threshold that triggers worker scale up",
+        default=3,
+    )
+
+    GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME: float = Field(
+        description="Seconds of idle time before scaling down workers",
+        default=5.0,
+        ge=0.1,
+    )
+
 
 class WorkflowNodeExecutionConfig(BaseSettings):
     """

api/controllers/console/app/generator.py

@@ -16,7 +16,10 @@ from core.helper.code_executor.javascript.javascript_code_provider import Javasc
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
+from extensions.ext_database import db
 from libs.login import login_required
+from models import App
+from services.workflow_service import WorkflowService
 
 
 class RuleGenerateApi(Resource):
@@ -135,9 +138,6 @@ class InstructionGenerateApi(Resource):
         try:
             # Generate from nothing for a workflow node
             if (args["current"] == code_template or args["current"] == "") and args["node_id"] != "":
-                from models import App, db
-                from services.workflow_service import WorkflowService
-
                 app = db.session.query(App).where(App.id == args["flow_id"]).first()
                 if not app:
                     return {"error": f"app {args['flow_id']} not found"}, 400

api/controllers/console/app/workflow.py

@@ -24,6 +24,7 @@ from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.file.models import File
 from core.helper.trace_id_helper import get_external_trace_id
+from core.workflow.graph_engine.manager import GraphEngineManager
 from extensions.ext_database import db
 from factories import file_factory, variable_factory
 from fields.workflow_fields import workflow_fields, workflow_pagination_fields
@@ -413,7 +414,12 @@ class WorkflowTaskStopApi(Resource):
         if not current_user.is_editor:
             raise Forbidden()
 
-        AppQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, current_user.id)
+        # Stop using both mechanisms for backward compatibility
+        # Legacy stop flag mechanism (without user check)
+        AppQueueManager.set_stop_flag_no_user_check(task_id)
+
+        # New graph engine command channel mechanism
+        GraphEngineManager.send_stop_command(task_id)
 
         return {"result": "success"}
 

api/controllers/console/app/workflow_app_log.py

@@ -6,7 +6,7 @@ from sqlalchemy.orm import Session
 from controllers.console import api
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
-from core.workflow.entities.workflow_execution import WorkflowExecutionStatus
+from core.workflow.enums import WorkflowExecutionStatus
 from extensions.ext_database import db
 from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs.login import login_required

api/controllers/console/app/workflow_draft_variable.py

@@ -17,10 +17,11 @@ from core.variables.segment_group import SegmentGroup
 from core.variables.segments import ArrayFileSegment, FileSegment, Segment
 from core.variables.types import SegmentType
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
+from extensions.ext_database import db
 from factories.file_factory import build_from_mapping, build_from_mappings
 from factories.variable_factory import build_segment_with_type
 from libs.login import current_user, login_required
-from models import App, AppMode, db
+from models import App, AppMode
 from models.account import Account
 from models.workflow import WorkflowDraftVariable
 from services.workflow_draft_variable_service import WorkflowDraftVariableList, WorkflowDraftVariableService

api/controllers/console/datasets/datasets.py

@@ -19,7 +19,6 @@ from controllers.console.wraps import (
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
-from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.extractor.entity.extract_setting import ExtractSetting
@@ -31,6 +30,7 @@ from fields.document_fields import document_status_fields
 from libs.login import login_required
 from models import ApiToken, Dataset, Document, DocumentSegment, UploadFile
 from models.dataset import DatasetPermissionEnum
+from models.provider_ids import ModelProviderID
 from services.dataset_service import DatasetPermissionService, DatasetService, DocumentService
 
 

api/controllers/console/explore/workflow.py

@@ -20,6 +20,7 @@ from core.errors.error import (
     QuotaExceededError,
 )
 from core.model_runtime.errors.invoke import InvokeError
+from core.workflow.graph_engine.manager import GraphEngineManager
 from libs import helper
 from libs.login import current_user
 from models.model import AppMode, InstalledApp
@@ -78,6 +79,11 @@ class InstalledAppWorkflowTaskStopApi(InstalledAppResource):
             raise NotWorkflowAppError()
         assert current_user is not None
 
-        AppQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
+        # Stop using both mechanisms for backward compatibility
+        # Legacy stop flag mechanism (without user check)
+        AppQueueManager.set_stop_flag_no_user_check(task_id)
+
+        # New graph engine command channel mechanism
+        GraphEngineManager.send_stop_command(task_id)
 
         return {"result": "success"}

api/controllers/console/workspace/tool_providers.py

@@ -21,11 +21,11 @@ from core.mcp.auth.auth_provider import OAuthClientProvider
 from core.mcp.error import MCPAuthError, MCPError
 from core.mcp.mcp_client import MCPClient
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.plugin.entities.plugin import ToolProviderID
 from core.plugin.impl.oauth import OAuthHandler
 from core.tools.entities.tool_entities import CredentialType
 from libs.helper import StrLen, alphanumeric, uuid_value
 from libs.login import login_required
+from models.provider_ids import ToolProviderID
 from services.plugin.oauth_service import OAuthProxyService
 from services.tools.api_tools_manage_service import ApiToolManageService
 from services.tools.builtin_tools_manage_service import BuiltinToolManageService

api/controllers/files/tool_files.py

@@ -8,7 +8,7 @@ from controllers.common.errors import UnsupportedFileTypeError
 from controllers.files import files_ns
 from core.tools.signature import verify_tool_file_signature
 from core.tools.tool_file_manager import ToolFileManager
-from models import db as global_db
+from extensions.ext_database import db as global_db
 
 
 @files_ns.route("/tools/<uuid:file_id>.<string:extension>")

api/controllers/service_api/app/workflow.py

@@ -26,7 +26,8 @@ from core.errors.error import (
 )
 from core.helper.trace_id_helper import get_external_trace_id
 from core.model_runtime.errors.invoke import InvokeError
-from core.workflow.entities.workflow_execution import WorkflowExecutionStatus
+from core.workflow.enums import WorkflowExecutionStatus
+from core.workflow.graph_engine.manager import GraphEngineManager
 from extensions.ext_database import db
 from fields.workflow_app_log_fields import build_workflow_app_log_pagination_model
 from libs import helper
@@ -262,7 +263,12 @@ class WorkflowTaskStopApi(Resource):
         if app_mode != AppMode.WORKFLOW:
             raise NotWorkflowAppError()
 
-        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+        # Stop using both mechanisms for backward compatibility
+        # Legacy stop flag mechanism (without user check)
+        AppQueueManager.set_stop_flag_no_user_check(task_id)
+
+        # New graph engine command channel mechanism
+        GraphEngineManager.send_stop_command(task_id)
 
         return {"result": "success"}
 

api/controllers/service_api/dataset/dataset.py

@@ -13,13 +13,13 @@ from controllers.service_api.wraps import (
     validate_dataset_token,
 )
 from core.model_runtime.entities.model_entities import ModelType
-from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
 from fields.dataset_fields import dataset_detail_fields
 from fields.tag_fields import build_dataset_tag_fields
 from libs.login import current_user
 from models.account import Account
 from models.dataset import Dataset, DatasetPermissionEnum
+from models.provider_ids import ModelProviderID
 from services.dataset_service import DatasetPermissionService, DatasetService, DocumentService
 from services.entities.knowledge_entities.knowledge_entities import RetrievalModel
 from services.tag_service import TagService

api/controllers/web/workflow.py

@@ -21,6 +21,7 @@ from core.errors.error import (
     QuotaExceededError,
 )
 from core.model_runtime.errors.invoke import InvokeError
+from core.workflow.graph_engine.manager import GraphEngineManager
 from libs import helper
 from models.model import App, AppMode, EndUser
 from services.app_generate_service import AppGenerateService
@@ -110,7 +111,12 @@ class WorkflowTaskStopApi(WebApiResource):
         if app_mode != AppMode.WORKFLOW:
             raise NotWorkflowAppError()
 
-        AppQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
+        # Stop using both mechanisms for backward compatibility
+        # Legacy stop flag mechanism (without user check)
+        AppQueueManager.set_stop_flag_no_user_check(task_id)
+
+        # New graph engine command channel mechanism
+        GraphEngineManager.send_stop_command(task_id)
 
         return {"result": "success"}
 

api/core/app/app_config/easy_ui_based_app/model_config/manager.py

@@ -4,8 +4,8 @@ from typing import Any
 from core.app.app_config.entities import ModelConfigEntity
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
-from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
+from models.provider_ids import ModelProviderID
 
 
 class ModelConfigManager:

api/core/app/apps/advanced_chat/app_runner.py

@@ -1,11 +1,11 @@
 import logging
+import time
 from collections.abc import Mapping
 from typing import Any, Optional, cast
 
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from configs import dify_config
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
@@ -23,16 +23,17 @@ from core.app.features.annotation_reply.annotation_reply import AnnotationReplyF
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
 from core.variables.variables import VariableUnion
-from core.workflow.callbacks import WorkflowCallback, WorkflowLoggingCallback
-from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities import GraphRuntimeState, VariablePool
+from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import VariableLoader
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
+from extensions.ext_redis import redis_client
 from models import Workflow
 from models.enums import UserFrom
 from models.model import App, Conversation, Message, MessageAnnotation
-from models.workflow import ConversationVariable, WorkflowType
+from models.workflow import ConversationVariable
 
 logger = logging.getLogger(__name__)
 
@@ -76,24 +77,32 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         if not app_record:
             raise ValueError("App not found")
 
-        workflow_callbacks: list[WorkflowCallback] = []
-        if dify_config.DEBUG:
-            workflow_callbacks.append(WorkflowLoggingCallback())
-
         if self.application_generate_entity.single_iteration_run:
             # if only single iteration run is requested
+            graph_runtime_state = GraphRuntimeState(
+                variable_pool=VariablePool.empty(),
+                start_at=time.time(),
+            )
             graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
                 workflow=self._workflow,
                 node_id=self.application_generate_entity.single_iteration_run.node_id,
                 user_inputs=dict(self.application_generate_entity.single_iteration_run.inputs),
+                graph_runtime_state=graph_runtime_state,
             )
+            graph_runtime_state.variable_pool = variable_pool
         elif self.application_generate_entity.single_loop_run:
             # if only single loop run is requested
+            graph_runtime_state = GraphRuntimeState(
+                variable_pool=VariablePool.empty(),
+                start_at=time.time(),
+            )
             graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
                 workflow=self._workflow,
                 node_id=self.application_generate_entity.single_loop_run.node_id,
                 user_inputs=dict(self.application_generate_entity.single_loop_run.inputs),
+                graph_runtime_state=graph_runtime_state,
             )
+            graph_runtime_state.variable_pool = variable_pool
         else:
             inputs = self.application_generate_entity.inputs
             query = self.application_generate_entity.query
@@ -144,16 +153,27 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
             )
 
             # init graph
-            graph = self._init_graph(graph_config=self._workflow.graph_dict)
+            graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.time())
+            graph = self._init_graph(
+                graph_config=self._workflow.graph_dict,
+                graph_runtime_state=graph_runtime_state,
+                workflow_id=self._workflow.id,
+                tenant_id=self._workflow.tenant_id,
+                user_id=self.application_generate_entity.user_id,
+            )
 
         db.session.close()
 
         # RUN WORKFLOW
+        # Create Redis command channel for this workflow execution
+        task_id = self.application_generate_entity.task_id
+        channel_key = f"workflow:{task_id}:commands"
+        command_channel = RedisChannel(redis_client, channel_key)
+
         workflow_entry = WorkflowEntry(
             tenant_id=self._workflow.tenant_id,
             app_id=self._workflow.app_id,
             workflow_id=self._workflow.id,
-            workflow_type=WorkflowType.value_of(self._workflow.type),
             graph=graph,
             graph_config=self._workflow.graph_dict,
             user_id=self.application_generate_entity.user_id,
@@ -164,12 +184,11 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
             ),
             invoke_from=self.application_generate_entity.invoke_from,
             call_depth=self.application_generate_entity.call_depth,
-            variable_pool=variable_pool,
+            graph_runtime_state=graph_runtime_state,
+            command_channel=command_channel,
         )
 
-        generator = workflow_entry.run(
-            callbacks=workflow_callbacks,
-        )
+        generator = workflow_entry.run()
 
         for event in generator:
             self._handle_event(workflow_entry, event)

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -31,14 +31,9 @@ from core.app.entities.queue_entities import (
     QueueMessageReplaceEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
-    QueueNodeInIterationFailedEvent,
-    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueParallelBranchRunFailedEvent,
-    QueueParallelBranchRunStartedEvent,
-    QueueParallelBranchRunSucceededEvent,
     QueuePingEvent,
     QueueRetrieverResourcesEvent,
     QueueStopEvent,
@@ -65,8 +60,8 @@ from core.app.task_pipeline.message_cycle_manager import MessageCycleManager
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.workflow_execution import WorkflowExecutionStatus, WorkflowType
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.entities import GraphRuntimeState
+from core.workflow.enums import WorkflowExecutionStatus, WorkflowType
 from core.workflow.nodes import NodeType
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
@@ -395,9 +390,7 @@ class AdvancedChatAppGenerateTaskPipeline:
 
     def _handle_node_failed_events(
         self,
-        event: Union[
-            QueueNodeFailedEvent, QueueNodeInIterationFailedEvent, QueueNodeInLoopFailedEvent, QueueNodeExceptionEvent
-        ],
+        event: Union[QueueNodeFailedEvent, QueueNodeExceptionEvent],
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
         """Handle various node failure events."""
@@ -442,32 +435,6 @@ class AdvancedChatAppGenerateTaskPipeline:
             answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
         )
 
-    def _handle_parallel_branch_started_event(
-        self, event: QueueParallelBranchRunStartedEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle parallel branch started events."""
-        self._ensure_workflow_initialized()
-
-        parallel_start_resp = self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
-            task_id=self._application_generate_entity.task_id,
-            workflow_execution_id=self._workflow_run_id,
-            event=event,
-        )
-        yield parallel_start_resp
-
-    def _handle_parallel_branch_finished_events(
-        self, event: Union[QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent], **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle parallel branch finished events."""
-        self._ensure_workflow_initialized()
-
-        parallel_finish_resp = self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
-            task_id=self._application_generate_entity.task_id,
-            workflow_execution_id=self._workflow_run_id,
-            event=event,
-        )
-        yield parallel_finish_resp
-
     def _handle_iteration_start_event(
         self, event: QueueIterationStartEvent, **kwargs
     ) -> Generator[StreamResponse, None, None]:
@@ -759,8 +726,6 @@ class AdvancedChatAppGenerateTaskPipeline:
             QueueNodeRetryEvent: self._handle_node_retry_event,
             QueueNodeStartedEvent: self._handle_node_started_event,
             QueueNodeSucceededEvent: self._handle_node_succeeded_event,
-            # Parallel branch events
-            QueueParallelBranchRunStartedEvent: self._handle_parallel_branch_started_event,
             # Iteration events
             QueueIterationStartEvent: self._handle_iteration_start_event,
             QueueIterationNextEvent: self._handle_iteration_next_event,
@@ -808,8 +773,6 @@ class AdvancedChatAppGenerateTaskPipeline:
             event,
             (
                 QueueNodeFailedEvent,
-                QueueNodeInIterationFailedEvent,
-                QueueNodeInLoopFailedEvent,
                 QueueNodeExceptionEvent,
             ),
         ):
@@ -822,17 +785,6 @@ class AdvancedChatAppGenerateTaskPipeline:
             )
             return
 
-        # Handle parallel branch finished events with isinstance check
-        if isinstance(event, (QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent)):
-            yield from self._handle_parallel_branch_finished_events(
-                event,
-                graph_runtime_state=graph_runtime_state,
-                tts_publisher=tts_publisher,
-                trace_manager=trace_manager,
-                queue_message=queue_message,
-            )
-            return
-
         # For unhandled events, we continue (original behavior)
         return
 
@@ -856,11 +808,6 @@ class AdvancedChatAppGenerateTaskPipeline:
                     graph_runtime_state = event.graph_runtime_state
                     yield from self._handle_workflow_started_event(event)
 
-                case QueueTextChunkEvent():
-                    yield from self._handle_text_chunk_event(
-                        event, tts_publisher=tts_publisher, queue_message=queue_message
-                    )
-
                 case QueueErrorEvent():
                     yield from self._handle_error_event(event)
                     break

api/core/app/apps/base_app_generator.py

@@ -6,7 +6,7 @@ from sqlalchemy.orm import Session
 from core.app.app_config.entities import VariableEntityType
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.file import File, FileUploadConfig
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaver,
     DraftVariableSaverFactory,

api/core/app/apps/base_app_queue_manager.py

@@ -126,6 +126,21 @@ class AppQueueManager:
         stopped_cache_key = cls._generate_stopped_cache_key(task_id)
         redis_client.setex(stopped_cache_key, 600, 1)
 
+    @classmethod
+    def set_stop_flag_no_user_check(cls, task_id: str) -> None:
+        """
+        Set task stop flag without user permission check.
+        This method allows stopping workflows without user context.
+
+        :param task_id: The task ID to stop
+        :return:
+        """
+        if not task_id:
+            return
+
+        stopped_cache_key = cls._generate_stopped_cache_key(task_id)
+        redis_client.setex(stopped_cache_key, 600, 1)
+
     def _is_stopped(self) -> bool:
         """
         Check if task is stopped

api/core/app/apps/common/workflow_response_converter.py

@@ -1,7 +1,7 @@
 import time
 from collections.abc import Mapping, Sequence
 from datetime import UTC, datetime
-from typing import Any, Optional, Union, cast
+from typing import Any, Optional, Union
 
 from sqlalchemy.orm import Session
 
@@ -16,14 +16,9 @@ from core.app.entities.queue_entities import (
     QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
-    QueueNodeInIterationFailedEvent,
-    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueParallelBranchRunFailedEvent,
-    QueueParallelBranchRunStartedEvent,
-    QueueParallelBranchRunSucceededEvent,
 )
 from core.app.entities.task_entities import (
     AgentLogStreamResponse,
@@ -36,18 +31,16 @@ from core.app.entities.task_entities import (
     NodeFinishStreamResponse,
     NodeRetryStreamResponse,
     NodeStartStreamResponse,
-    ParallelBranchFinishedStreamResponse,
-    ParallelBranchStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
 )
 from core.file import FILE_MODEL_IDENTITY, File
+from core.tools.entities.tool_entities import ToolProviderType
 from core.tools.tool_manager import ToolManager
 from core.variables.segments import ArrayFileSegment, FileSegment, Segment
-from core.workflow.entities.workflow_execution import WorkflowExecution
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecution, WorkflowNodeExecutionStatus
+from core.workflow.entities import WorkflowExecution, WorkflowNodeExecution
+from core.workflow.enums import WorkflowNodeExecutionStatus
 from core.workflow.nodes import NodeType
-from core.workflow.nodes.tool.entities import ToolNodeData
 from core.workflow.workflow_type_encoder import WorkflowRuntimeTypeConverter
 from libs.datetime_utils import naive_utc_now
 from models import (
@@ -171,11 +164,10 @@ class WorkflowResponseConverter:
 
         # extras logic
         if event.node_type == NodeType.TOOL:
-            node_data = cast(ToolNodeData, event.node_data)
             response.data.extras["icon"] = ToolManager.get_tool_icon(
                 tenant_id=self._application_generate_entity.app_config.tenant_id,
-                provider_type=node_data.provider_type,
-                provider_id=node_data.provider_id,
+                provider_type=ToolProviderType(event.provider_type),
+                provider_id=event.provider_id,
             )
 
         return response
@@ -183,11 +175,7 @@ class WorkflowResponseConverter:
     def workflow_node_finish_to_stream_response(
         self,
         *,
-        event: QueueNodeSucceededEvent
-        | QueueNodeFailedEvent
-        | QueueNodeInIterationFailedEvent
-        | QueueNodeInLoopFailedEvent
-        | QueueNodeExceptionEvent,
+        event: QueueNodeSucceededEvent | QueueNodeFailedEvent | QueueNodeExceptionEvent,
         task_id: str,
         workflow_node_execution: WorkflowNodeExecution,
     ) -> Optional[NodeFinishStreamResponse]:
@@ -221,9 +209,6 @@ class WorkflowResponseConverter:
                 finished_at=int(workflow_node_execution.finished_at.timestamp()),
                 files=self.fetch_files_from_node_outputs(workflow_node_execution.outputs or {}),
                 parallel_id=event.parallel_id,
-                parallel_start_node_id=event.parallel_start_node_id,
-                parent_parallel_id=event.parent_parallel_id,
-                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
             ),
@@ -275,50 +260,6 @@ class WorkflowResponseConverter:
             ),
         )
 
-    def workflow_parallel_branch_start_to_stream_response(
-        self,
-        *,
-        task_id: str,
-        workflow_execution_id: str,
-        event: QueueParallelBranchRunStartedEvent,
-    ) -> ParallelBranchStartStreamResponse:
-        return ParallelBranchStartStreamResponse(
-            task_id=task_id,
-            workflow_run_id=workflow_execution_id,
-            data=ParallelBranchStartStreamResponse.Data(
-                parallel_id=event.parallel_id,
-                parallel_branch_id=event.parallel_start_node_id,
-                parent_parallel_id=event.parent_parallel_id,
-                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                iteration_id=event.in_iteration_id,
-                loop_id=event.in_loop_id,
-                created_at=int(time.time()),
-            ),
-        )
-
-    def workflow_parallel_branch_finished_to_stream_response(
-        self,
-        *,
-        task_id: str,
-        workflow_execution_id: str,
-        event: QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent,
-    ) -> ParallelBranchFinishedStreamResponse:
-        return ParallelBranchFinishedStreamResponse(
-            task_id=task_id,
-            workflow_run_id=workflow_execution_id,
-            data=ParallelBranchFinishedStreamResponse.Data(
-                parallel_id=event.parallel_id,
-                parallel_branch_id=event.parallel_start_node_id,
-                parent_parallel_id=event.parent_parallel_id,
-                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                iteration_id=event.in_iteration_id,
-                loop_id=event.in_loop_id,
-                status="succeeded" if isinstance(event, QueueParallelBranchRunSucceededEvent) else "failed",
-                error=event.error if isinstance(event, QueueParallelBranchRunFailedEvent) else None,
-                created_at=int(time.time()),
-            ),
-        )
-
     def workflow_iteration_start_to_stream_response(
         self,
         *,
@@ -333,13 +274,11 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
                 inputs=event.inputs or {},
                 metadata=event.metadata or {},
-                parallel_id=event.parallel_id,
-                parallel_start_node_id=event.parallel_start_node_id,
             ),
         )
 
@@ -357,15 +296,10 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 index=event.index,
-                pre_iteration_output=event.output,
                 created_at=int(time.time()),
                 extras={},
-                parallel_id=event.parallel_id,
-                parallel_start_node_id=event.parallel_start_node_id,
-                parallel_mode_run_id=event.parallel_mode_run_id,
-                duration=event.duration,
             ),
         )
 
@@ -384,7 +318,7 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 outputs=json_converter.to_json_encodable(event.outputs),
                 created_at=int(time.time()),
                 extras={},
@@ -398,8 +332,6 @@ class WorkflowResponseConverter:
                 execution_metadata=event.metadata,
                 finished_at=int(time.time()),
                 steps=event.steps,
-                parallel_id=event.parallel_id,
-                parallel_start_node_id=event.parallel_start_node_id,
             ),
         )
 
@@ -413,7 +345,7 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
                 inputs=event.inputs or {},
@@ -437,7 +369,7 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 index=event.index,
                 pre_loop_output=event.output,
                 created_at=int(time.time()),
@@ -445,7 +377,6 @@ class WorkflowResponseConverter:
                 parallel_id=event.parallel_id,
                 parallel_start_node_id=event.parallel_start_node_id,
                 parallel_mode_run_id=event.parallel_mode_run_id,
-                duration=event.duration,
             ),
         )
 
@@ -463,7 +394,7 @@ class WorkflowResponseConverter:
                 id=event.node_id,
                 node_id=event.node_id,
                 node_type=event.node_type.value,
-                title=event.node_data.title,
+                title=event.node_title,
                 outputs=WorkflowRuntimeTypeConverter().to_json_encodable(event.outputs),
                 created_at=int(time.time()),
                 extras={},

api/core/app/apps/workflow/app_generator.py

@@ -3,7 +3,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Literal, Optional, Union, overload
+from typing import Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -53,7 +53,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: Literal[True],
         call_depth: int,
-        workflow_thread_pool_id: Optional[str],
     ) -> Generator[Mapping | str, None, None]: ...
 
     @overload
@@ -67,7 +66,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: Literal[False],
         call_depth: int,
-        workflow_thread_pool_id: Optional[str],
     ) -> Mapping[str, Any]: ...
 
     @overload
@@ -81,7 +79,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: bool,
         call_depth: int,
-        workflow_thread_pool_id: Optional[str],
     ) -> Union[Mapping[str, Any], Generator[Mapping | str, None, None]]: ...
 
     def generate(
@@ -94,7 +91,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: bool = True,
         call_depth: int = 0,
-        workflow_thread_pool_id: Optional[str] = None,
     ) -> Union[Mapping[str, Any], Generator[Mapping | str, None, None]]:
         files: Sequence[Mapping[str, Any]] = args.get("files") or []
 
@@ -186,7 +182,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             workflow_execution_repository=workflow_execution_repository,
             workflow_node_execution_repository=workflow_node_execution_repository,
             streaming=streaming,
-            workflow_thread_pool_id=workflow_thread_pool_id,
         )
 
     def _generate(
@@ -200,7 +195,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         streaming: bool = True,
-        workflow_thread_pool_id: Optional[str] = None,
         variable_loader: VariableLoader = DUMMY_VARIABLE_LOADER,
     ) -> Union[Mapping[str, Any], Generator[str | Mapping[str, Any], None, None]]:
         """
@@ -214,7 +208,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param workflow_execution_repository: repository for workflow execution
         :param workflow_node_execution_repository: repository for workflow node execution
         :param streaming: is stream
-        :param workflow_thread_pool_id: workflow thread pool id
         """
         # init queue manager
         queue_manager = WorkflowAppQueueManager(
@@ -237,7 +230,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 "application_generate_entity": application_generate_entity,
                 "queue_manager": queue_manager,
                 "context": context,
-                "workflow_thread_pool_id": workflow_thread_pool_id,
                 "variable_loader": variable_loader,
             },
         )
@@ -434,17 +426,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         queue_manager: AppQueueManager,
         context: contextvars.Context,
         variable_loader: VariableLoader,
-        workflow_thread_pool_id: Optional[str] = None,
     ) -> None:
-        """
-        Generate worker in a new thread.
-        :param flask_app: Flask app
-        :param application_generate_entity: application generate entity
-        :param queue_manager: queue manager
-        :param workflow_thread_pool_id: workflow thread pool id
-        :return:
-        """
-
         with preserve_flask_contexts(flask_app, context_vars=context):
             with Session(db.engine, expire_on_commit=False) as session:
                 workflow = session.scalar(
@@ -474,7 +456,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             runner = WorkflowAppRunner(
                 application_generate_entity=application_generate_entity,
                 queue_manager=queue_manager,
-                workflow_thread_pool_id=workflow_thread_pool_id,
                 variable_loader=variable_loader,
                 workflow=workflow,
                 system_user_id=system_user_id,

api/core/app/apps/workflow/app_runner.py

@@ -1,7 +1,7 @@
 import logging
-from typing import Optional, cast
+import time
+from typing import cast
 
-from configs import dify_config
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
 from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
@@ -9,13 +9,14 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
     WorkflowAppGenerateEntity,
 )
-from core.workflow.callbacks import WorkflowCallback, WorkflowLoggingCallback
-from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities import GraphRuntimeState, VariablePool
+from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import VariableLoader
 from core.workflow.workflow_entry import WorkflowEntry
+from extensions.ext_redis import redis_client
 from models.enums import UserFrom
-from models.workflow import Workflow, WorkflowType
+from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -31,7 +32,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         application_generate_entity: WorkflowAppGenerateEntity,
         queue_manager: AppQueueManager,
         variable_loader: VariableLoader,
-        workflow_thread_pool_id: Optional[str] = None,
         workflow: Workflow,
         system_user_id: str,
     ) -> None:
@@ -41,7 +41,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
             app_id=application_generate_entity.app_config.app_id,
         )
         self.application_generate_entity = application_generate_entity
-        self.workflow_thread_pool_id = workflow_thread_pool_id
         self._workflow = workflow
         self._sys_user_id = system_user_id
 
@@ -52,24 +51,30 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         app_config = self.application_generate_entity.app_config
         app_config = cast(WorkflowAppConfig, app_config)
 
-        workflow_callbacks: list[WorkflowCallback] = []
-        if dify_config.DEBUG:
-            workflow_callbacks.append(WorkflowLoggingCallback())
-
         # if only single iteration run is requested
         if self.application_generate_entity.single_iteration_run:
             # if only single iteration run is requested
+            graph_runtime_state = GraphRuntimeState(
+                variable_pool=VariablePool.empty(),
+                start_at=time.time(),
+            )
             graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
                 workflow=self._workflow,
                 node_id=self.application_generate_entity.single_iteration_run.node_id,
                 user_inputs=self.application_generate_entity.single_iteration_run.inputs,
+                graph_runtime_state=graph_runtime_state,
             )
         elif self.application_generate_entity.single_loop_run:
             # if only single loop run is requested
+            graph_runtime_state = GraphRuntimeState(
+                variable_pool=VariablePool.empty(),
+                start_at=time.time(),
+            )
             graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
                 workflow=self._workflow,
                 node_id=self.application_generate_entity.single_loop_run.node_id,
                 user_inputs=self.application_generate_entity.single_loop_run.inputs,
+                graph_runtime_state=graph_runtime_state,
             )
         else:
             inputs = self.application_generate_entity.inputs
@@ -92,15 +97,26 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
                 conversation_variables=[],
             )
 
+            graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
+
             # init graph
-            graph = self._init_graph(graph_config=self._workflow.graph_dict)
+            graph = self._init_graph(
+                graph_config=self._workflow.graph_dict,
+                graph_runtime_state=graph_runtime_state,
+                workflow_id=self._workflow.id,
+                tenant_id=self._workflow.tenant_id,
+            )
 
         # RUN WORKFLOW
+        # Create Redis command channel for this workflow execution
+        task_id = self.application_generate_entity.task_id
+        channel_key = f"workflow:{task_id}:commands"
+        command_channel = RedisChannel(redis_client, channel_key)
+
         workflow_entry = WorkflowEntry(
             tenant_id=self._workflow.tenant_id,
             app_id=self._workflow.app_id,
             workflow_id=self._workflow.id,
-            workflow_type=WorkflowType.value_of(self._workflow.type),
             graph=graph,
             graph_config=self._workflow.graph_dict,
             user_id=self.application_generate_entity.user_id,
@@ -111,11 +127,11 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
             ),
             invoke_from=self.application_generate_entity.invoke_from,
             call_depth=self.application_generate_entity.call_depth,
-            variable_pool=variable_pool,
-            thread_pool_id=self.workflow_thread_pool_id,
+            graph_runtime_state=graph_runtime_state,
+            command_channel=command_channel,
         )
 
-        generator = workflow_entry.run(callbacks=workflow_callbacks)
+        generator = workflow_entry.run()
 
         for event in generator:
             self._handle_event(workflow_entry, event)

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -2,7 +2,7 @@ import logging
 import time
 from collections.abc import Callable, Generator
 from contextlib import contextmanager
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 from sqlalchemy.orm import Session
 
@@ -14,6 +14,7 @@ from core.app.entities.app_invoke_entities import (
     WorkflowAppGenerateEntity,
 )
 from core.app.entities.queue_entities import (
+    AppQueueEvent,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -25,14 +26,9 @@ from core.app.entities.queue_entities import (
     QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
-    QueueNodeInIterationFailedEvent,
-    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueParallelBranchRunFailedEvent,
-    QueueParallelBranchRunStartedEvent,
-    QueueParallelBranchRunSucceededEvent,
     QueuePingEvent,
     QueueStopEvent,
     QueueTextChunkEvent,
@@ -57,8 +53,8 @@ from core.app.entities.task_entities import (
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.workflow_execution import WorkflowExecution, WorkflowExecutionStatus, WorkflowType
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.entities import GraphRuntimeState, WorkflowExecution
+from core.workflow.enums import WorkflowExecutionStatus, WorkflowType
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
@@ -350,9 +346,7 @@ class WorkflowAppGenerateTaskPipeline:
 
     def _handle_node_failed_events(
         self,
-        event: Union[
-            QueueNodeFailedEvent, QueueNodeInIterationFailedEvent, QueueNodeInLoopFailedEvent, QueueNodeExceptionEvent
-        ],
+        event: Union[QueueNodeFailedEvent, QueueNodeExceptionEvent],
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
         """Handle various node failure events."""
@@ -371,32 +365,6 @@ class WorkflowAppGenerateTaskPipeline:
         if node_failed_response:
             yield node_failed_response
 
-    def _handle_parallel_branch_started_event(
-        self, event: QueueParallelBranchRunStartedEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle parallel branch started events."""
-        self._ensure_workflow_initialized()
-
-        parallel_start_resp = self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
-            task_id=self._application_generate_entity.task_id,
-            workflow_execution_id=self._workflow_run_id,
-            event=event,
-        )
-        yield parallel_start_resp
-
-    def _handle_parallel_branch_finished_events(
-        self, event: Union[QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent], **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle parallel branch finished events."""
-        self._ensure_workflow_initialized()
-
-        parallel_finish_resp = self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
-            task_id=self._application_generate_entity.task_id,
-            workflow_execution_id=self._workflow_run_id,
-            event=event,
-        )
-        yield parallel_finish_resp
-
     def _handle_iteration_start_event(
         self, event: QueueIterationStartEvent, **kwargs
     ) -> Generator[StreamResponse, None, None]:
@@ -618,8 +586,6 @@ class WorkflowAppGenerateTaskPipeline:
             QueueNodeRetryEvent: self._handle_node_retry_event,
             QueueNodeStartedEvent: self._handle_node_started_event,
             QueueNodeSucceededEvent: self._handle_node_succeeded_event,
-            # Parallel branch events
-            QueueParallelBranchRunStartedEvent: self._handle_parallel_branch_started_event,
             # Iteration events
             QueueIterationStartEvent: self._handle_iteration_start_event,
             QueueIterationNextEvent: self._handle_iteration_next_event,
@@ -634,7 +600,7 @@ class WorkflowAppGenerateTaskPipeline:
 
     def _dispatch_event(
         self,
-        event: Any,
+        event: AppQueueEvent,
         *,
         graph_runtime_state: Optional[GraphRuntimeState] = None,
         tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
@@ -661,8 +627,6 @@ class WorkflowAppGenerateTaskPipeline:
             event,
             (
                 QueueNodeFailedEvent,
-                QueueNodeInIterationFailedEvent,
-                QueueNodeInLoopFailedEvent,
                 QueueNodeExceptionEvent,
             ),
         ):
@@ -675,17 +639,6 @@ class WorkflowAppGenerateTaskPipeline:
             )
             return
 
-        # Handle parallel branch finished events with isinstance check
-        if isinstance(event, (QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent)):
-            yield from self._handle_parallel_branch_finished_events(
-                event,
-                graph_runtime_state=graph_runtime_state,
-                tts_publisher=tts_publisher,
-                trace_manager=trace_manager,
-                queue_message=queue_message,
-            )
-            return
-
         # Handle workflow failed and stop events with isinstance check
         if isinstance(event, (QueueWorkflowFailedEvent, QueueStopEvent)):
             yield from self._handle_workflow_failed_and_stop_events(

api/core/app/apps/workflow_app_runner.py

@@ -2,6 +2,7 @@ from collections.abc import Mapping
 from typing import Any, cast
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
     QueueAgentLogEvent,
@@ -13,14 +14,9 @@ from core.app.entities.queue_entities import (
     QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
-    QueueNodeInIterationFailedEvent,
-    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueParallelBranchRunFailedEvent,
-    QueueParallelBranchRunStartedEvent,
-    QueueParallelBranchRunSucceededEvent,
     QueueRetrieverResourcesEvent,
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
@@ -28,42 +24,39 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.graph_engine.entities.event import (
-    AgentLogEvent,
+from core.workflow.entities import GraphInitParams, GraphRuntimeState, VariablePool
+from core.workflow.graph import Graph
+from core.workflow.graph_events import (
     GraphEngineEvent,
     GraphRunFailedEvent,
     GraphRunPartialSucceededEvent,
     GraphRunStartedEvent,
     GraphRunSucceededEvent,
-    IterationRunFailedEvent,
-    IterationRunNextEvent,
-    IterationRunStartedEvent,
-    IterationRunSucceededEvent,
-    LoopRunFailedEvent,
-    LoopRunNextEvent,
-    LoopRunStartedEvent,
-    LoopRunSucceededEvent,
-    NodeInIterationFailedEvent,
-    NodeInLoopFailedEvent,
+    NodeRunAgentLogEvent,
     NodeRunExceptionEvent,
     NodeRunFailedEvent,
+    NodeRunIterationFailedEvent,
+    NodeRunIterationNextEvent,
+    NodeRunIterationStartedEvent,
+    NodeRunIterationSucceededEvent,
+    NodeRunLoopFailedEvent,
+    NodeRunLoopNextEvent,
+    NodeRunLoopStartedEvent,
+    NodeRunLoopSucceededEvent,
     NodeRunRetrieverResourceEvent,
     NodeRunRetryEvent,
     NodeRunStartedEvent,
     NodeRunStreamChunkEvent,
     NodeRunSucceededEvent,
-    ParallelBranchRunFailedEvent,
-    ParallelBranchRunStartedEvent,
-    ParallelBranchRunSucceededEvent,
 )
-from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_events.graph import GraphRunAbortedEvent
 from core.workflow.nodes import NodeType
+from core.workflow.nodes.node_factory import DifyNodeFactory
 from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader, load_into_variable_pool
 from core.workflow.workflow_entry import WorkflowEntry
+from models.enums import UserFrom
 from models.workflow import Workflow
 
 
@@ -79,7 +72,14 @@ class WorkflowBasedAppRunner:
         self._variable_loader = variable_loader
         self._app_id = app_id
 
-    def _init_graph(self, graph_config: Mapping[str, Any]) -> Graph:
+    def _init_graph(
+        self,
+        graph_config: Mapping[str, Any],
+        graph_runtime_state: GraphRuntimeState,
+        workflow_id: str = "",
+        tenant_id: str = "",
+        user_id: str = "",
+    ) -> Graph:
         """
         Init graph
         """
@@ -91,8 +91,28 @@ class WorkflowBasedAppRunner:
 
         if not isinstance(graph_config.get("edges"), list):
             raise ValueError("edges in workflow graph must be a list")
+
+        # Create required parameters for Graph.init
+        graph_init_params = GraphInitParams(
+            tenant_id=tenant_id or "",
+            app_id=self._app_id,
+            workflow_id=workflow_id,
+            graph_config=graph_config,
+            user_id=user_id,
+            user_from=UserFrom.ACCOUNT.value,
+            invoke_from=InvokeFrom.SERVICE_API.value,
+            call_depth=0,
+        )
+
+        # Use the provided graph_runtime_state for consistent state management
+
+        node_factory = DifyNodeFactory(
+            graph_init_params=graph_init_params,
+            graph_runtime_state=graph_runtime_state,
+        )
+
         # init graph
-        graph = Graph.init(graph_config=graph_config)
+        graph = Graph.init(graph_config=graph_config, node_factory=node_factory)
 
         if not graph:
             raise ValueError("graph not found in workflow")
@@ -104,6 +124,7 @@ class WorkflowBasedAppRunner:
         workflow: Workflow,
         node_id: str,
         user_inputs: dict,
+        graph_runtime_state: GraphRuntimeState,
     ) -> tuple[Graph, VariablePool]:
         """
         Get variable pool of single iteration
@@ -128,7 +149,9 @@ class WorkflowBasedAppRunner:
         node_configs = [
             node
             for node in graph_config.get("nodes", [])
-            if node.get("id") == node_id or node.get("data", {}).get("iteration_id", "") == node_id
+            if node.get("id") == node_id
+            or node.get("data", {}).get("iteration_id", "") == node_id
+            or node.get("id") == f"{node_id}start"
         ]
 
         graph_config["nodes"] = node_configs
@@ -145,8 +168,25 @@ class WorkflowBasedAppRunner:
 
         graph_config["edges"] = edge_configs
 
+        # Create required parameters for Graph.init
+        graph_init_params = GraphInitParams(
+            tenant_id=workflow.tenant_id,
+            app_id=self._app_id,
+            workflow_id=workflow.id,
+            graph_config=graph_config,
+            user_id="",
+            user_from=UserFrom.ACCOUNT.value,
+            invoke_from=InvokeFrom.SERVICE_API.value,
+            call_depth=0,
+        )
+
+        node_factory = DifyNodeFactory(
+            graph_init_params=graph_init_params,
+            graph_runtime_state=graph_runtime_state,
+        )
+
         # init graph
-        graph = Graph.init(graph_config=graph_config, root_node_id=node_id)
+        graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=node_id)
 
         if not graph:
             raise ValueError("graph not found in workflow")
@@ -201,6 +241,7 @@ class WorkflowBasedAppRunner:
         workflow: Workflow,
         node_id: str,
         user_inputs: dict,
+        graph_runtime_state: GraphRuntimeState,
     ) -> tuple[Graph, VariablePool]:
         """
         Get variable pool of single loop
@@ -225,7 +266,9 @@ class WorkflowBasedAppRunner:
         node_configs = [
             node
             for node in graph_config.get("nodes", [])
-            if node.get("id") == node_id or node.get("data", {}).get("loop_id", "") == node_id
+            if node.get("id") == node_id
+            or node.get("data", {}).get("loop_id", "") == node_id
+            or node.get("id") == f"{node_id}start"
         ]
 
         graph_config["nodes"] = node_configs
@@ -242,8 +285,25 @@ class WorkflowBasedAppRunner:
 
         graph_config["edges"] = edge_configs
 
+        # Create required parameters for Graph.init
+        graph_init_params = GraphInitParams(
+            tenant_id=workflow.tenant_id,
+            app_id=self._app_id,
+            workflow_id=workflow.id,
+            graph_config=graph_config,
+            user_id="",
+            user_from=UserFrom.ACCOUNT.value,
+            invoke_from=InvokeFrom.SERVICE_API.value,
+            call_depth=0,
+        )
+
+        node_factory = DifyNodeFactory(
+            graph_init_params=graph_init_params,
+            graph_runtime_state=graph_runtime_state,
+        )
+
         # init graph
-        graph = Graph.init(graph_config=graph_config, root_node_id=node_id)
+        graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=node_id)
 
         if not graph:
             raise ValueError("graph not found in workflow")
@@ -310,29 +370,21 @@ class WorkflowBasedAppRunner:
             )
         elif isinstance(event, GraphRunFailedEvent):
             self._publish_event(QueueWorkflowFailedEvent(error=event.error, exceptions_count=event.exceptions_count))
+        elif isinstance(event, GraphRunAbortedEvent):
+            self._publish_event(QueueWorkflowFailedEvent(error=event.reason or "Unknown error", exceptions_count=0))
         elif isinstance(event, NodeRunRetryEvent):
-            node_run_result = event.route_node_state.node_run_result
-            inputs: Mapping[str, Any] | None = {}
-            process_data: Mapping[str, Any] | None = {}
-            outputs: Mapping[str, Any] | None = {}
-            execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] | None = {}
-            if node_run_result:
-                inputs = node_run_result.inputs
-                process_data = node_run_result.process_data
-                outputs = node_run_result.outputs
-                execution_metadata = node_run_result.metadata
+            node_run_result = event.node_run_result
+            inputs = node_run_result.inputs
+            process_data = node_run_result.process_data
+            outputs = node_run_result.outputs
+            execution_metadata = node_run_result.metadata
             self._publish_event(
                 QueueNodeRetryEvent(
                     node_execution_id=event.id,
                     node_id=event.node_id,
+                    node_title=event.node_title,
                     node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                     start_at=event.start_at,
-                    node_run_index=event.route_node_state.index,
                     predecessor_node_id=event.predecessor_node_id,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
@@ -343,6 +395,8 @@ class WorkflowBasedAppRunner:
                     error=event.error,
                     execution_metadata=execution_metadata,
                     retry_index=event.retry_index,
+                    provider_type=event.provider_type,
+                    provider_id=event.provider_id,
                 )
             )
         elif isinstance(event, NodeRunStartedEvent):
@@ -350,44 +404,30 @@ class WorkflowBasedAppRunner:
                 QueueNodeStartedEvent(
                     node_execution_id=event.id,
                     node_id=event.node_id,
+                    node_title=event.node_title,
                     node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
-                    node_run_index=event.route_node_state.index,
+                    start_at=event.start_at,
                     predecessor_node_id=event.predecessor_node_id,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                     parallel_mode_run_id=event.parallel_mode_run_id,
                     agent_strategy=event.agent_strategy,
+                    provider_type=event.provider_type,
+                    provider_id=event.provider_id,
                 )
             )
         elif isinstance(event, NodeRunSucceededEvent):
-            node_run_result = event.route_node_state.node_run_result
-            if node_run_result:
-                inputs = node_run_result.inputs
-                process_data = node_run_result.process_data
-                outputs = node_run_result.outputs
-                execution_metadata = node_run_result.metadata
-            else:
-                inputs = {}
-                process_data = {}
-                outputs = {}
-                execution_metadata = {}
+            node_run_result = event.node_run_result
+            inputs = node_run_result.inputs
+            process_data = node_run_result.process_data
+            outputs = node_run_result.outputs
+            execution_metadata = node_run_result.metadata
             self._publish_event(
                 QueueNodeSucceededEvent(
                     node_execution_id=event.id,
                     node_id=event.node_id,
                     node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
+                    start_at=event.start_at,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -396,34 +436,18 @@ class WorkflowBasedAppRunner:
                     in_loop_id=event.in_loop_id,
                 )
             )
-
         elif isinstance(event, NodeRunFailedEvent):
             self._publish_event(
                 QueueNodeFailedEvent(
                     node_execution_id=event.id,
                     node_id=event.node_id,
                     node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
-                    inputs=event.route_node_state.node_run_result.inputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    process_data=event.route_node_state.node_run_result.process_data
-                    if event.route_node_state.node_run_result
-                    else {},
-                    outputs=event.route_node_state.node_run_result.outputs or {}
-                    if event.route_node_state.node_run_result
-                    else {},
-                    error=event.route_node_state.node_run_result.error
-                    if event.route_node_state.node_run_result and event.route_node_state.node_run_result.error
-                    else "Unknown error",
-                    execution_metadata=event.route_node_state.node_run_result.metadata
-                    if event.route_node_state.node_run_result
-                    else {},
+                    start_at=event.start_at,
+                    inputs=event.node_run_result.inputs,
+                    process_data=event.node_run_result.process_data,
+                    outputs=event.node_run_result.outputs,
+                    error=event.node_run_result.error or "Unknown error",
+                    execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                 )
@@ -434,93 +458,21 @@ class WorkflowBasedAppRunner:
                     node_execution_id=event.id,
                     node_id=event.node_id,
                     node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
-                    inputs=event.route_node_state.node_run_result.inputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    process_data=event.route_node_state.node_run_result.process_data
-                    if event.route_node_state.node_run_result
-                    else {},
-                    outputs=event.route_node_state.node_run_result.outputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    error=event.route_node_state.node_run_result.error
-                    if event.route_node_state.node_run_result and event.route_node_state.node_run_result.error
-                    else "Unknown error",
-                    execution_metadata=event.route_node_state.node_run_result.metadata
-                    if event.route_node_state.node_run_result
-                    else {},
+                    start_at=event.start_at,
+                    inputs=event.node_run_result.inputs,
+                    process_data=event.node_run_result.process_data,
+                    outputs=event.node_run_result.outputs,
+                    error=event.node_run_result.error or "Unknown error",
+                    execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                 )
             )
-
-        elif isinstance(event, NodeInIterationFailedEvent):
-            self._publish_event(
-                QueueNodeInIterationFailedEvent(
-                    node_execution_id=event.id,
-                    node_id=event.node_id,
-                    node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
-                    inputs=event.route_node_state.node_run_result.inputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    process_data=event.route_node_state.node_run_result.process_data
-                    if event.route_node_state.node_run_result
-                    else {},
-                    outputs=event.route_node_state.node_run_result.outputs or {}
-                    if event.route_node_state.node_run_result
-                    else {},
-                    execution_metadata=event.route_node_state.node_run_result.metadata
-                    if event.route_node_state.node_run_result
-                    else {},
-                    in_iteration_id=event.in_iteration_id,
-                    error=event.error,
-                )
-            )
-        elif isinstance(event, NodeInLoopFailedEvent):
-            self._publish_event(
-                QueueNodeInLoopFailedEvent(
-                    node_execution_id=event.id,
-                    node_id=event.node_id,
-                    node_type=event.node_type,
-                    node_data=event.node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    start_at=event.route_node_state.start_at,
-                    inputs=event.route_node_state.node_run_result.inputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    process_data=event.route_node_state.node_run_result.process_data
-                    if event.route_node_state.node_run_result
-                    else {},
-                    outputs=event.route_node_state.node_run_result.outputs or {}
-                    if event.route_node_state.node_run_result
-                    else {},
-                    execution_metadata=event.route_node_state.node_run_result.metadata
-                    if event.route_node_state.node_run_result
-                    else {},
-                    in_loop_id=event.in_loop_id,
-                    error=event.error,
-                )
-            )
         elif isinstance(event, NodeRunStreamChunkEvent):
             self._publish_event(
                 QueueTextChunkEvent(
-                    text=event.chunk_content,
-                    from_variable_selector=event.from_variable_selector,
+                    text=event.chunk,
+                    from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                 )
@@ -533,10 +485,10 @@ class WorkflowBasedAppRunner:
                     in_loop_id=event.in_loop_id,
                 )
             )
-        elif isinstance(event, AgentLogEvent):
+        elif isinstance(event, NodeRunAgentLogEvent):
             self._publish_event(
                 QueueAgentLogEvent(
-                    id=event.id,
+                    id=event.message_id,
                     label=event.label,
                     node_execution_id=event.node_execution_id,
                     parent_id=event.parent_id,
@@ -547,51 +499,13 @@ class WorkflowBasedAppRunner:
                     node_id=event.node_id,
                 )
             )
-        elif isinstance(event, ParallelBranchRunStartedEvent):
-            self._publish_event(
-                QueueParallelBranchRunStartedEvent(
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    in_iteration_id=event.in_iteration_id,
-                    in_loop_id=event.in_loop_id,
-                )
-            )
-        elif isinstance(event, ParallelBranchRunSucceededEvent):
-            self._publish_event(
-                QueueParallelBranchRunSucceededEvent(
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    in_iteration_id=event.in_iteration_id,
-                    in_loop_id=event.in_loop_id,
-                )
-            )
-        elif isinstance(event, ParallelBranchRunFailedEvent):
-            self._publish_event(
-                QueueParallelBranchRunFailedEvent(
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
-                    in_iteration_id=event.in_iteration_id,
-                    in_loop_id=event.in_loop_id,
-                    error=event.error,
-                )
-            )
-        elif isinstance(event, IterationRunStartedEvent):
+        elif isinstance(event, NodeRunIterationStartedEvent):
             self._publish_event(
                 QueueIterationStartEvent(
-                    node_execution_id=event.iteration_id,
-                    node_id=event.iteration_node_id,
-                    node_type=event.iteration_node_type,
-                    node_data=event.iteration_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     start_at=event.start_at,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     inputs=event.inputs,
@@ -599,55 +513,41 @@ class WorkflowBasedAppRunner:
                     metadata=event.metadata,
                 )
             )
-        elif isinstance(event, IterationRunNextEvent):
+        elif isinstance(event, NodeRunIterationNextEvent):
             self._publish_event(
                 QueueIterationNextEvent(
-                    node_execution_id=event.iteration_id,
-                    node_id=event.iteration_node_id,
-                    node_type=event.iteration_node_type,
-                    node_data=event.iteration_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     index=event.index,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     output=event.pre_iteration_output,
-                    parallel_mode_run_id=event.parallel_mode_run_id,
-                    duration=event.duration,
                 )
             )
-        elif isinstance(event, (IterationRunSucceededEvent | IterationRunFailedEvent)):
+        elif isinstance(event, (NodeRunIterationSucceededEvent | NodeRunIterationFailedEvent)):
             self._publish_event(
                 QueueIterationCompletedEvent(
-                    node_execution_id=event.iteration_id,
-                    node_id=event.iteration_node_id,
-                    node_type=event.iteration_node_type,
-                    node_data=event.iteration_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     start_at=event.start_at,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     inputs=event.inputs,
                     outputs=event.outputs,
                     metadata=event.metadata,
                     steps=event.steps,
-                    error=event.error if isinstance(event, IterationRunFailedEvent) else None,
+                    error=event.error if isinstance(event, NodeRunIterationFailedEvent) else None,
                 )
             )
-        elif isinstance(event, LoopRunStartedEvent):
+        elif isinstance(event, NodeRunLoopStartedEvent):
             self._publish_event(
                 QueueLoopStartEvent(
-                    node_execution_id=event.loop_id,
-                    node_id=event.loop_node_id,
-                    node_type=event.loop_node_type,
-                    node_data=event.loop_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     start_at=event.start_at,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     inputs=event.inputs,
@@ -655,42 +555,32 @@ class WorkflowBasedAppRunner:
                     metadata=event.metadata,
                 )
             )
-        elif isinstance(event, LoopRunNextEvent):
+        elif isinstance(event, NodeRunLoopNextEvent):
             self._publish_event(
                 QueueLoopNextEvent(
-                    node_execution_id=event.loop_id,
-                    node_id=event.loop_node_id,
-                    node_type=event.loop_node_type,
-                    node_data=event.loop_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     index=event.index,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     output=event.pre_loop_output,
-                    parallel_mode_run_id=event.parallel_mode_run_id,
-                    duration=event.duration,
                 )
             )
-        elif isinstance(event, (LoopRunSucceededEvent | LoopRunFailedEvent)):
+        elif isinstance(event, (NodeRunLoopSucceededEvent | NodeRunLoopFailedEvent)):
             self._publish_event(
                 QueueLoopCompletedEvent(
-                    node_execution_id=event.loop_id,
-                    node_id=event.loop_node_id,
-                    node_type=event.loop_node_type,
-                    node_data=event.loop_node_data,
-                    parallel_id=event.parallel_id,
-                    parallel_start_node_id=event.parallel_start_node_id,
-                    parent_parallel_id=event.parent_parallel_id,
-                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_title=event.node_title,
                     start_at=event.start_at,
                     node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
                     inputs=event.inputs,
                     outputs=event.outputs,
                     metadata=event.metadata,
                     steps=event.steps,
-                    error=event.error if isinstance(event, LoopRunFailedEvent) else None,
+                    error=event.error if isinstance(event, NodeRunLoopFailedEvent) else None,
                 )
             )
 

api/core/app/entities/app_invoke_entities.py

@@ -1,5 +1,5 @@
 from collections.abc import Mapping, Sequence
-from enum import Enum
+from enum import StrEnum
 from typing import Any, Optional
 
 from pydantic import BaseModel, ConfigDict, Field, ValidationInfo, field_validator
@@ -11,7 +11,7 @@ from core.file import File, FileUploadConfig
 from core.model_runtime.entities.model_entities import AIModelEntity
 
 
-class InvokeFrom(Enum):
+class InvokeFrom(StrEnum):
     """
     Invoke From.
     """

api/core/app/entities/queue_entities.py

@@ -7,11 +7,9 @@ from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities.node_entities import AgentNodeStrategyInit
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.entities import AgentNodeStrategyInit, GraphRuntimeState
+from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
-from core.workflow.nodes.base import BaseNodeData
 
 
 class QueueEvent(StrEnum):
@@ -43,9 +41,6 @@ class QueueEvent(StrEnum):
     ANNOTATION_REPLY = "annotation_reply"
     AGENT_THOUGHT = "agent_thought"
     MESSAGE_FILE = "message_file"
-    PARALLEL_BRANCH_RUN_STARTED = "parallel_branch_run_started"
-    PARALLEL_BRANCH_RUN_SUCCEEDED = "parallel_branch_run_succeeded"
-    PARALLEL_BRANCH_RUN_FAILED = "parallel_branch_run_failed"
     AGENT_LOG = "agent_log"
     ERROR = "error"
     PING = "ping"
@@ -80,15 +75,7 @@ class QueueIterationStartEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
-    parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
+    node_title: str
     start_at: datetime
 
     node_run_index: int
@@ -108,20 +95,9 @@ class QueueIterationNextEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
-    parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    parallel_mode_run_id: Optional[str] = None
-    """iteration run in parallel mode run id"""
+    node_title: str
     node_run_index: int
     output: Optional[Any] = None  # output for the current iteration
-    duration: Optional[float] = None
 
 
 class QueueIterationCompletedEvent(AppQueueEvent):
@@ -134,15 +110,7 @@ class QueueIterationCompletedEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
-    parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
+    node_title: str
     start_at: datetime
 
     node_run_index: int
@@ -163,7 +131,7 @@ class QueueLoopStartEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
+    node_title: str
     parallel_id: Optional[str] = None
     """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
@@ -191,7 +159,7 @@ class QueueLoopNextEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
+    node_title: str
     parallel_id: Optional[str] = None
     """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
@@ -204,7 +172,6 @@ class QueueLoopNextEvent(AppQueueEvent):
     """iteration run in parallel mode run id"""
     node_run_index: int
     output: Optional[Any] = None  # output for the current loop
-    duration: Optional[float] = None
 
 
 class QueueLoopCompletedEvent(AppQueueEvent):
@@ -217,7 +184,7 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
+    node_title: str
     parallel_id: Optional[str] = None
     """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
@@ -364,27 +331,24 @@ class QueueNodeStartedEvent(AppQueueEvent):
 
     node_execution_id: str
     node_id: str
+    node_title: str
     node_type: NodeType
-    node_data: BaseNodeData
-    node_run_index: int = 1
+    node_run_index: int = 1  # FIXME(-LAN-): may not used
     predecessor_node_id: Optional[str] = None
     parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
     parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
     parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
     in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
     start_at: datetime
     parallel_mode_run_id: Optional[str] = None
-    """iteration run in parallel mode run id"""
     agent_strategy: Optional[AgentNodeStrategyInit] = None
 
+    # FIXME(-LAN-): only for ToolNode, need to refactor
+    provider_type: str  # should be a core.tools.entities.tool_entities.ToolProviderType
+    provider_id: str
+
 
 class QueueNodeSucceededEvent(AppQueueEvent):
     """
@@ -396,7 +360,6 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
     parallel_id: Optional[str] = None
     """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
@@ -417,10 +380,6 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: Optional[str] = None
-    """single iteration duration map"""
-    iteration_duration_map: Optional[dict[str, float]] = None
-    """single loop duration map"""
-    loop_duration_map: Optional[dict[str, float]] = None
 
 
 class QueueAgentLogEvent(AppQueueEvent):
@@ -454,72 +413,6 @@ class QueueNodeRetryEvent(QueueNodeStartedEvent):
     retry_index: int  # retry index
 
 
-class QueueNodeInIterationFailedEvent(AppQueueEvent):
-    """
-    QueueNodeInIterationFailedEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.NODE_FAILED
-
-    node_execution_id: str
-    node_id: str
-    node_type: NodeType
-    node_data: BaseNodeData
-    parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
-    in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
-    start_at: datetime
-
-    inputs: Optional[Mapping[str, Any]] = None
-    process_data: Optional[Mapping[str, Any]] = None
-    outputs: Optional[Mapping[str, Any]] = None
-    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
-
-    error: str
-
-
-class QueueNodeInLoopFailedEvent(AppQueueEvent):
-    """
-    QueueNodeInLoopFailedEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.NODE_FAILED
-
-    node_execution_id: str
-    node_id: str
-    node_type: NodeType
-    node_data: BaseNodeData
-    parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
-    in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
-    start_at: datetime
-
-    inputs: Optional[Mapping[str, Any]] = None
-    process_data: Optional[Mapping[str, Any]] = None
-    outputs: Optional[Mapping[str, Any]] = None
-    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
-
-    error: str
-
-
 class QueueNodeExceptionEvent(AppQueueEvent):
     """
     QueueNodeExceptionEvent entity
@@ -530,7 +423,6 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
     parallel_id: Optional[str] = None
     """parallel id if node is in parallel"""
     parallel_start_node_id: Optional[str] = None
@@ -563,15 +455,7 @@ class QueueNodeFailedEvent(AppQueueEvent):
     node_execution_id: str
     node_id: str
     node_type: NodeType
-    node_data: BaseNodeData
     parallel_id: Optional[str] = None
-    """parallel id if node is in parallel"""
-    parallel_start_node_id: Optional[str] = None
-    """parallel start node id if node is in parallel"""
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
     in_loop_id: Optional[str] = None
@@ -678,61 +562,3 @@ class WorkflowQueueMessage(QueueMessage):
     """
 
     pass
-
-
-class QueueParallelBranchRunStartedEvent(AppQueueEvent):
-    """
-    QueueParallelBranchRunStartedEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_STARTED
-
-    parallel_id: str
-    parallel_start_node_id: str
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
-    in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
-
-
-class QueueParallelBranchRunSucceededEvent(AppQueueEvent):
-    """
-    QueueParallelBranchRunSucceededEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_SUCCEEDED
-
-    parallel_id: str
-    parallel_start_node_id: str
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
-    in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
-
-
-class QueueParallelBranchRunFailedEvent(AppQueueEvent):
-    """
-    QueueParallelBranchRunFailedEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_FAILED
-
-    parallel_id: str
-    parallel_start_node_id: str
-    parent_parallel_id: Optional[str] = None
-    """parent parallel id if node is in parallel"""
-    parent_parallel_start_node_id: Optional[str] = None
-    """parent parallel start node id if node is in parallel"""
-    in_iteration_id: Optional[str] = None
-    """iteration id if node is in iteration"""
-    in_loop_id: Optional[str] = None
-    """loop id if node is in loop"""
-    error: str

api/core/app/entities/task_entities.py

@@ -7,8 +7,8 @@ from pydantic import BaseModel, ConfigDict, Field
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities.node_entities import AgentNodeStrategyInit
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.enums import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 
 
 class AnnotationReplyAccount(BaseModel):
@@ -71,8 +71,6 @@ class StreamEvent(Enum):
     NODE_STARTED = "node_started"
     NODE_FINISHED = "node_finished"
     NODE_RETRY = "node_retry"
-    PARALLEL_BRANCH_STARTED = "parallel_branch_started"
-    PARALLEL_BRANCH_FINISHED = "parallel_branch_finished"
     ITERATION_STARTED = "iteration_started"
     ITERATION_NEXT = "iteration_next"
     ITERATION_COMPLETED = "iteration_completed"
@@ -440,54 +438,6 @@ class NodeRetryStreamResponse(StreamResponse):
         }
 
 
-class ParallelBranchStartStreamResponse(StreamResponse):
-    """
-    ParallelBranchStartStreamResponse entity
-    """
-
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        parallel_id: str
-        parallel_branch_id: str
-        parent_parallel_id: Optional[str] = None
-        parent_parallel_start_node_id: Optional[str] = None
-        iteration_id: Optional[str] = None
-        loop_id: Optional[str] = None
-        created_at: int
-
-    event: StreamEvent = StreamEvent.PARALLEL_BRANCH_STARTED
-    workflow_run_id: str
-    data: Data
-
-
-class ParallelBranchFinishedStreamResponse(StreamResponse):
-    """
-    ParallelBranchFinishedStreamResponse entity
-    """
-
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        parallel_id: str
-        parallel_branch_id: str
-        parent_parallel_id: Optional[str] = None
-        parent_parallel_start_node_id: Optional[str] = None
-        iteration_id: Optional[str] = None
-        loop_id: Optional[str] = None
-        status: str
-        error: Optional[str] = None
-        created_at: int
-
-    event: StreamEvent = StreamEvent.PARALLEL_BRANCH_FINISHED
-    workflow_run_id: str
-    data: Data
-
-
 class IterationNodeStartStreamResponse(StreamResponse):
     """
     NodeStartStreamResponse entity
@@ -506,8 +456,6 @@ class IterationNodeStartStreamResponse(StreamResponse):
         extras: dict = Field(default_factory=dict)
         metadata: Mapping = {}
         inputs: Mapping = {}
-        parallel_id: Optional[str] = None
-        parallel_start_node_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.ITERATION_STARTED
     workflow_run_id: str
@@ -530,12 +478,7 @@ class IterationNodeNextStreamResponse(StreamResponse):
         title: str
         index: int
         created_at: int
-        pre_iteration_output: Optional[Any] = None
         extras: dict = Field(default_factory=dict)
-        parallel_id: Optional[str] = None
-        parallel_start_node_id: Optional[str] = None
-        parallel_mode_run_id: Optional[str] = None
-        duration: Optional[float] = None
 
     event: StreamEvent = StreamEvent.ITERATION_NEXT
     workflow_run_id: str
@@ -567,8 +510,6 @@ class IterationNodeCompletedStreamResponse(StreamResponse):
         execution_metadata: Optional[Mapping] = None
         finished_at: int
         steps: int
-        parallel_id: Optional[str] = None
-        parallel_start_node_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.ITERATION_COMPLETED
     workflow_run_id: str
@@ -622,7 +563,6 @@ class LoopNodeNextStreamResponse(StreamResponse):
         parallel_id: Optional[str] = None
         parallel_start_node_id: Optional[str] = None
         parallel_mode_run_id: Optional[str] = None
-        duration: Optional[float] = None
 
     event: StreamEvent = StreamEvent.LOOP_NEXT
     workflow_run_id: str

api/core/entities/provider_configuration.py

@@ -28,7 +28,6 @@ from core.model_runtime.entities.provider_entities import (
 )
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
-from core.plugin.entities.plugin import ModelProviderID
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
 from models.provider import (
@@ -41,6 +40,7 @@ from models.provider import (
     ProviderType,
     TenantPreferredModelProvider,
 )
+from models.provider_ids import ModelProviderID
 
 logger = logging.getLogger(__name__)
 
@@ -627,6 +627,7 @@ class ProviderConfiguration(BaseModel):
         Get custom model credentials.
         """
         # get provider model
+
         model_provider_id = ModelProviderID(self.provider.provider)
         provider_names = [self.provider.provider]
         if model_provider_id.is_langgenius():
@@ -1124,6 +1125,7 @@ class ProviderConfiguration(BaseModel):
         """
         Get provider model setting.
         """
+
         model_provider_id = ModelProviderID(self.provider.provider)
         provider_names = [self.provider.provider]
         if model_provider_id.is_langgenius():
@@ -1207,6 +1209,7 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
+
         model_provider_id = ModelProviderID(self.provider.provider)
         provider_names = [self.provider.provider]
         if model_provider_id.is_langgenius():

api/core/helper/encrypter.py

@@ -12,8 +12,8 @@ def obfuscated_token(token: str):
 
 
 def encrypt_token(tenant_id: str, token: str):
+    from extensions.ext_database import db
     from models.account import Tenant
-    from models.engine import db
 
     if not (tenant := db.session.query(Tenant).where(Tenant.id == tenant_id).first()):
         raise ValueError(f"Tenant with id {tenant_id} not found")

api/core/llm_generator/llm_generator.py

@@ -28,8 +28,9 @@ from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.ops.utils import measure_time
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.graph_engine.entities.event import AgentLogEvent
-from models import App, Message, WorkflowNodeExecutionModel, db
+from core.workflow.node_events import AgentLogEvent
+from extensions.ext_database import db
+from models import App, Message, WorkflowNodeExecutionModel
 
 logger = logging.getLogger(__name__)
 

api/core/memory/token_buffer_memory.py

@@ -167,11 +167,11 @@ class TokenBufferMemory:
             else:
                 prompt_messages.append(AssistantPromptMessage(content=message.answer))
 
-        if not prompt_messages:
-            return []
+            if not prompt_messages:
+                return []
 
-        # prune the chat message if it exceeds the max token limit
-        curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
+            # prune the chat message if it exceeds the max token limit
+            curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
 
         if curr_message_tokens > max_token_limit:
             while curr_message_tokens > max_token_limit and len(prompt_messages) > 1:

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -24,8 +24,7 @@ from core.model_runtime.errors.invoke import (
     InvokeRateLimitError,
     InvokeServerUnavailableError,
 )
-from core.plugin.entities.plugin_daemon import PluginDaemonInnerError, PluginModelProviderEntity
-from core.plugin.impl.model import PluginModelClient
+from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
 
 
 class AIModel(BaseModel):
@@ -53,6 +52,8 @@ class AIModel(BaseModel):
 
         :return: Invoke error mapping
         """
+        from core.plugin.entities.plugin_daemon import PluginDaemonInnerError
+
         return {
             InvokeConnectionError: [InvokeConnectionError],
             InvokeServerUnavailableError: [InvokeServerUnavailableError],
@@ -140,6 +141,8 @@ class AIModel(BaseModel):
         :param credentials: model credentials
         :return: model schema
         """
+        from core.plugin.impl.model import PluginModelClient
+
         plugin_model_manager = PluginModelClient()
         cache_key = f"{self.tenant_id}:{self.plugin_id}:{self.provider_name}:{self.model_type.value}:{model}"
         # sort credentials

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -22,7 +22,6 @@ from core.model_runtime.entities.model_entities import (
     PriceType,
 )
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 logger = logging.getLogger(__name__)
 
@@ -142,6 +141,8 @@ class LargeLanguageModel(AIModel):
         result: Union[LLMResult, Generator[LLMResultChunk, None, None]]
 
         try:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             result = plugin_model_manager.invoke_llm(
                 tenant_id=self.tenant_id,
@@ -340,6 +341,8 @@ class LargeLanguageModel(AIModel):
         :return:
         """
         if dify_config.PLUGIN_BASED_TOKEN_COUNTING_ENABLED:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.get_llm_num_tokens(
                 tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/__base/moderation_model.py

@@ -5,7 +5,6 @@ from pydantic import ConfigDict
 
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 
 class ModerationModel(AIModel):
@@ -31,6 +30,8 @@ class ModerationModel(AIModel):
         self.started_at = time.perf_counter()
 
         try:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.invoke_moderation(
                 tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/__base/rerank_model.py

@@ -3,7 +3,6 @@ from typing import Optional
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.entities.rerank_entities import RerankResult
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 
 class RerankModel(AIModel):
@@ -36,6 +35,8 @@ class RerankModel(AIModel):
         :return: rerank result
         """
         try:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.invoke_rerank(
                 tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/__base/speech2text_model.py

@@ -4,7 +4,6 @@ from pydantic import ConfigDict
 
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 
 class Speech2TextModel(AIModel):
@@ -28,6 +27,8 @@ class Speech2TextModel(AIModel):
         :return: text for given audio file
         """
         try:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.invoke_speech_to_text(
                 tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/__base/text_embedding_model.py

@@ -6,7 +6,6 @@ from core.entities.embedding_type import EmbeddingInputType
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 
 class TextEmbeddingModel(AIModel):
@@ -37,6 +36,8 @@ class TextEmbeddingModel(AIModel):
         :param input_type: input type
         :return: embeddings result
         """
+        from core.plugin.impl.model import PluginModelClient
+
         try:
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.invoke_text_embedding(
@@ -61,6 +62,8 @@ class TextEmbeddingModel(AIModel):
         :param texts: texts to embed
         :return:
         """
+        from core.plugin.impl.model import PluginModelClient
+
         plugin_model_manager = PluginModelClient()
         return plugin_model_manager.get_text_embedding_num_tokens(
             tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/__base/tts_model.py

@@ -6,7 +6,6 @@ from pydantic import ConfigDict
 
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.plugin.impl.model import PluginModelClient
 
 logger = logging.getLogger(__name__)
 
@@ -42,6 +41,8 @@ class TTSModel(AIModel):
         :return: translated audio file
         """
         try:
+            from core.plugin.impl.model import PluginModelClient
+
             plugin_model_manager = PluginModelClient()
             return plugin_model_manager.invoke_tts(
                 tenant_id=self.tenant_id,
@@ -65,6 +66,8 @@ class TTSModel(AIModel):
         :param credentials: The credentials required to access the TTS model.
         :return: A list of voices supported by the TTS model.
         """
+        from core.plugin.impl.model import PluginModelClient
+
         plugin_model_manager = PluginModelClient()
         return plugin_model_manager.get_tts_model_voices(
             tenant_id=self.tenant_id,

api/core/model_runtime/model_providers/model_provider_factory.py

@@ -20,10 +20,8 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.model_runtime.schema_validators.model_credential_schema_validator import ModelCredentialSchemaValidator
 from core.model_runtime.schema_validators.provider_credential_schema_validator import ProviderCredentialSchemaValidator
-from core.plugin.entities.plugin import ModelProviderID
 from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
-from core.plugin.impl.asset import PluginAssetManager
-from core.plugin.impl.model import PluginModelClient
+from models.provider_ids import ModelProviderID
 
 logger = logging.getLogger(__name__)
 
@@ -37,6 +35,8 @@ class ModelProviderFactory:
     provider_position_map: dict[str, int]
 
     def __init__(self, tenant_id: str) -> None:
+        from core.plugin.impl.model import PluginModelClient
+
         self.provider_position_map = {}
 
         self.tenant_id = tenant_id
@@ -71,7 +71,7 @@ class ModelProviderFactory:
 
         return [extension.plugin_model_provider_entity.declaration for extension in sorted_extensions.values()]
 
-    def get_plugin_model_providers(self) -> Sequence[PluginModelProviderEntity]:
+    def get_plugin_model_providers(self) -> Sequence["PluginModelProviderEntity"]:
         """
         Get all plugin model providers
         :return: list of plugin model providers
@@ -109,7 +109,7 @@ class ModelProviderFactory:
         plugin_model_provider_entity = self.get_plugin_model_provider(provider=provider)
         return plugin_model_provider_entity.declaration
 
-    def get_plugin_model_provider(self, provider: str) -> PluginModelProviderEntity:
+    def get_plugin_model_provider(self, provider: str) -> "PluginModelProviderEntity":
         """
         Get plugin model provider
         :param provider: provider name
@@ -366,6 +366,8 @@ class ModelProviderFactory:
         mime_type = image_mime_types.get(extension, "image/png")
 
         # get icon bytes from plugin asset manager
+        from core.plugin.impl.asset import PluginAssetManager
+
         plugin_asset_manager = PluginAssetManager()
         return plugin_asset_manager.fetch_asset(tenant_id=self.tenant_id, id=file_name), mime_type
 
@@ -375,5 +377,6 @@ class ModelProviderFactory:
         :param provider: provider name
         :return: plugin id and provider name
         """
+
         provider_id = ModelProviderID(provider)
         return provider_id.plugin_id, provider_id.provider_name

api/core/ops/aliyun_trace/aliyun_trace.py

@@ -54,13 +54,10 @@ from core.ops.entities.trace_entity import (
 )
 from core.rag.models.document import Document
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.workflow.entities.workflow_node_execution import (
-    WorkflowNodeExecution,
-    WorkflowNodeExecutionMetadataKey,
-    WorkflowNodeExecutionStatus,
-)
-from core.workflow.nodes import NodeType
-from models import Account, App, EndUser, TenantAccountJoin, WorkflowNodeExecutionTriggeredFrom, db
+from core.workflow.entities import WorkflowNodeExecution
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from extensions.ext_database import db
+from models import Account, App, EndUser, TenantAccountJoin, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -29,7 +29,7 @@ from core.ops.langfuse_trace.entities.langfuse_trace_entity import (
 )
 from core.ops.utils import filter_none_values
 from core.repositories import DifyCoreRepositoryFactory
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType
 from extensions.ext_database import db
 from models import EndUser, WorkflowNodeExecutionTriggeredFrom
 from models.enums import MessageStatus

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -28,8 +28,7 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
 )
 from core.ops.utils import filter_none_values, generate_dotted_order
 from core.repositories import DifyCoreRepositoryFactory
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 

api/core/ops/opik_trace/opik_trace.py

@@ -22,8 +22,7 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.repositories import DifyCoreRepositoryFactory
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 

api/core/ops/ops_trace_manager.py

@@ -5,7 +5,7 @@ import queue
 import threading
 import time
 from datetime import timedelta
-from typing import Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 from uuid import UUID, uuid4
 
 from cachetools import LRUCache
@@ -30,13 +30,15 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.ops.utils import get_message_data
-from core.workflow.entities.workflow_execution import WorkflowExecution
 from extensions.ext_database import db
 from extensions.ext_storage import storage
 from models.model import App, AppModelConfig, Conversation, Message, MessageFile, TraceAppConfig
 from models.workflow import WorkflowAppLog, WorkflowRun
 from tasks.ops_trace_task import process_trace_tasks
 
+if TYPE_CHECKING:
+    from core.workflow.entities import WorkflowExecution
+
 logger = logging.getLogger(__name__)
 
 
@@ -410,7 +412,7 @@ class TraceTask:
         self,
         trace_type: Any,
         message_id: Optional[str] = None,
-        workflow_execution: Optional[WorkflowExecution] = None,
+        workflow_execution: Optional["WorkflowExecution"] = None,
         conversation_id: Optional[str] = None,
         user_id: Optional[str] = None,
         timer: Optional[Any] = None,

api/core/ops/weave_trace/weave_trace.py

@@ -23,8 +23,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
 from core.repositories import DifyCoreRepositoryFactory
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 

api/core/plugin/backwards_invocation/app.py

@@ -164,7 +164,6 @@ class PluginAppBackwardsInvocation(BaseBackwardsInvocation):
             invoke_from=InvokeFrom.SERVICE_API,
             streaming=stream,
             call_depth=1,
-            workflow_thread_pool_id=None,
         )
 
     @classmethod

api/core/plugin/backwards_invocation/node.py

@@ -1,5 +1,5 @@
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
-from core.workflow.nodes.enums import NodeType
+from core.workflow.enums import NodeType
 from core.workflow.nodes.parameter_extractor.entities import (
     ModelConfig as ParameterExtractorModelConfig,
 )

api/core/plugin/entities/parameters.py

@@ -1,11 +1,11 @@
 import enum
+import json
 from typing import Any, Optional, Union
 
 from pydantic import BaseModel, Field, field_validator
 
 from core.entities.parameter_entities import CommonParameterType
 from core.tools.entities.common_entities import I18nObject
-from core.workflow.nodes.base.entities import NumberType
 
 
 class PluginParameterOption(BaseModel):
@@ -154,7 +154,7 @@ def cast_parameter_value(typ: enum.StrEnum, value: Any, /):
                     raise ValueError("The tools selector must be a list.")
                 return value
             case PluginParameterType.ANY:
-                if value and not isinstance(value, str | dict | list | NumberType):
+                if value and not isinstance(value, str | dict | list | int | float):
                     raise ValueError("The var selector must be a string, dictionary, list or number.")
                 return value
             case PluginParameterType.ARRAY:
@@ -162,8 +162,6 @@ def cast_parameter_value(typ: enum.StrEnum, value: Any, /):
                     # Try to parse JSON string for arrays
                     if isinstance(value, str):
                         try:
-                            import json
-
                             parsed_value = json.loads(value)
                             if isinstance(parsed_value, list):
                                 return parsed_value
@@ -176,8 +174,6 @@ def cast_parameter_value(typ: enum.StrEnum, value: Any, /):
                     # Try to parse JSON string for objects
                     if isinstance(value, str):
                         try:
-                            import json
-
                             parsed_value = json.loads(value)
                             if isinstance(parsed_value, dict):
                                 return parsed_value

api/core/plugin/entities/plugin.py

@@ -1,11 +1,9 @@
 import datetime
 import enum
-import re
 from collections.abc import Mapping
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field, model_validator
-from werkzeug.exceptions import NotFound
 
 from core.agent.plugin_entities import AgentStrategyProviderEntity
 from core.model_runtime.entities.provider_entities import ProviderEntity
@@ -135,55 +133,6 @@ class PluginEntity(PluginInstallation):
         return self
 
 
-class GenericProviderID:
-    organization: str
-    plugin_name: str
-    provider_name: str
-    is_hardcoded: bool
-
-    def to_string(self) -> str:
-        return str(self)
-
-    def __str__(self) -> str:
-        return f"{self.organization}/{self.plugin_name}/{self.provider_name}"
-
-    def __init__(self, value: str, is_hardcoded: bool = False) -> None:
-        if not value:
-            raise NotFound("plugin not found, please add plugin")
-        # check if the value is a valid plugin id with format: $organization/$plugin_name/$provider_name
-        if not re.match(r"^[a-z0-9_-]+\/[a-z0-9_-]+\/[a-z0-9_-]+$", value):
-            # check if matches [a-z0-9_-]+, if yes, append with langgenius/$value/$value
-            if re.match(r"^[a-z0-9_-]+$", value):
-                value = f"langgenius/{value}/{value}"
-            else:
-                raise ValueError(f"Invalid plugin id {value}")
-
-        self.organization, self.plugin_name, self.provider_name = value.split("/")
-        self.is_hardcoded = is_hardcoded
-
-    def is_langgenius(self) -> bool:
-        return self.organization == "langgenius"
-
-    @property
-    def plugin_id(self) -> str:
-        return f"{self.organization}/{self.plugin_name}"
-
-
-class ModelProviderID(GenericProviderID):
-    def __init__(self, value: str, is_hardcoded: bool = False) -> None:
-        super().__init__(value, is_hardcoded)
-        if self.organization == "langgenius" and self.provider_name == "google":
-            self.plugin_name = "gemini"
-
-
-class ToolProviderID(GenericProviderID):
-    def __init__(self, value: str, is_hardcoded: bool = False) -> None:
-        super().__init__(value, is_hardcoded)
-        if self.organization == "langgenius":
-            if self.provider_name in ["jina", "siliconflow", "stepfun", "gitee_ai"]:
-                self.plugin_name = f"{self.provider_name}_tool"
-
-
 class PluginDependency(BaseModel):
     class Type(enum.StrEnum):
         Github = PluginInstallationSource.Github.value

api/core/plugin/impl/agent.py

@@ -2,13 +2,13 @@ from collections.abc import Generator
 from typing import Any, Optional
 
 from core.agent.entities import AgentInvokeMessage
-from core.plugin.entities.plugin import GenericProviderID
 from core.plugin.entities.plugin_daemon import (
     PluginAgentProviderEntity,
 )
 from core.plugin.entities.request import PluginInvokeContext
 from core.plugin.impl.base import BasePluginClient
 from core.plugin.utils.chunk_merger import merge_blob_chunks
+from models.provider_ids import GenericProviderID
 
 
 class PluginAgentClient(BasePluginClient):

api/core/plugin/impl/dynamic_select.py

@@ -1,9 +1,9 @@
 from collections.abc import Mapping
 from typing import Any
 
-from core.plugin.entities.plugin import GenericProviderID
 from core.plugin.entities.plugin_daemon import PluginDynamicSelectOptionsResponse
 from core.plugin.impl.base import BasePluginClient
+from models.provider_ids import GenericProviderID
 
 
 class DynamicSelectClient(BasePluginClient):

api/core/plugin/impl/plugin.py

@@ -2,7 +2,6 @@ from collections.abc import Sequence
 
 from core.plugin.entities.bundle import PluginBundleDependency
 from core.plugin.entities.plugin import (
-    GenericProviderID,
     MissingPluginDependency,
     PluginDeclaration,
     PluginEntity,
@@ -16,6 +15,7 @@ from core.plugin.entities.plugin_daemon import (
     PluginListResponse,
 )
 from core.plugin.impl.base import BasePluginClient
+from models.provider_ids import GenericProviderID
 
 
 class PluginInstaller(BasePluginClient):

api/core/plugin/impl/tool.py

@@ -3,11 +3,11 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from core.plugin.entities.plugin import GenericProviderID, ToolProviderID
 from core.plugin.entities.plugin_daemon import PluginBasicBooleanResponse, PluginToolProviderEntity
 from core.plugin.impl.base import BasePluginClient
 from core.plugin.utils.chunk_merger import merge_blob_chunks
 from core.tools.entities.tool_entities import CredentialType, ToolInvokeMessage, ToolParameter
+from models.provider_ids import GenericProviderID, ToolProviderID
 
 
 class PluginToolManager(BasePluginClient):

api/core/provider_manager.py

@@ -34,7 +34,6 @@ from core.model_runtime.entities.provider_entities import (
     ProviderEntity,
 )
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
-from core.plugin.entities.plugin import ModelProviderID
 from extensions import ext_hosting_provider
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -49,6 +48,7 @@ from models.provider import (
     TenantDefaultModel,
     TenantPreferredModelProvider,
 )
+from models.provider_ids import ModelProviderID
 from services.feature_service import FeatureService
 
 

api/core/rag/index_processor/index_processor_base.py

@@ -1,10 +1,9 @@
 """Abstract interface for document loader implementations."""
 
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import TYPE_CHECKING, Optional
 
 from configs import dify_config
-from core.model_manager import ModelInstance
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.models.document import Document
 from core.rag.splitter.fixed_text_splitter import (
@@ -14,6 +13,9 @@ from core.rag.splitter.fixed_text_splitter import (
 from core.rag.splitter.text_splitter import TextSplitter
 from models.dataset import Dataset, DatasetProcessRule
 
+if TYPE_CHECKING:
+    from core.model_manager import ModelInstance
+
 
 class BaseIndexProcessor(ABC):
     """Interface for extract files."""
@@ -51,7 +53,7 @@ class BaseIndexProcessor(ABC):
         max_tokens: int,
         chunk_overlap: int,
         separator: str,
-        embedding_model_instance: Optional[ModelInstance],
+        embedding_model_instance: Optional["ModelInstance"],
     ) -> TextSplitter:
         """
         Get the NodeParser object according to the processing rule.

api/core/repositories/sqlalchemy_workflow_execution_repository.py

@@ -9,11 +9,8 @@ from typing import Optional, Union
 from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 
-from core.workflow.entities.workflow_execution import (
-    WorkflowExecution,
-    WorkflowExecutionStatus,
-    WorkflowType,
-)
+from core.workflow.entities import WorkflowExecution
+from core.workflow.enums import WorkflowExecutionStatus, WorkflowType
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.workflow_type_encoder import WorkflowRuntimeTypeConverter
 from libs.helper import extract_tenant_id
@@ -203,5 +200,4 @@ class SQLAlchemyWorkflowExecutionRepository(WorkflowExecutionRepository):
             session.commit()
 
             # Update the in-memory cache for faster subsequent lookups
-            logger.debug("Updating cache for execution_id: %s", db_model.id)
             self._execution_cache[db_model.id] = db_model

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -12,12 +12,8 @@ from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.workflow_node_execution import (
-    WorkflowNodeExecution,
-    WorkflowNodeExecutionMetadataKey,
-    WorkflowNodeExecutionStatus,
-)
-from core.workflow.nodes.enums import NodeType
+from core.workflow.entities import WorkflowNodeExecution
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.repositories.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
 from core.workflow.workflow_type_encoder import WorkflowRuntimeTypeConverter
 from libs.helper import extract_tenant_id
@@ -215,7 +211,6 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             # Update the in-memory cache for faster subsequent lookups
             # Only cache if we have a node_execution_id to use as the cache key
             if db_model.node_execution_id:
-                logger.debug("Updating cache for node_execution_id: %s", db_model.node_execution_id)
                 self._node_execution_cache[db_model.node_execution_id] = db_model
 
     def get_db_models_by_workflow_run(

api/core/tools/tool_engine.py

@@ -152,7 +152,6 @@ class ToolEngine:
         user_id: str,
         workflow_tool_callback: DifyWorkflowCallbackHandler,
         workflow_call_depth: int,
-        thread_pool_id: Optional[str] = None,
         conversation_id: Optional[str] = None,
         app_id: Optional[str] = None,
         message_id: Optional[str] = None,
@@ -166,7 +165,6 @@ class ToolEngine:
 
             if isinstance(tool, WorkflowTool):
                 tool.workflow_call_depth = workflow_call_depth + 1
-                tool.thread_pool_id = thread_pool_id
 
             if tool.runtime and tool.runtime.runtime_parameters:
                 tool_parameters = {**tool.runtime.runtime_parameters, **tool_parameters}

api/core/tools/tool_manager.py

@@ -13,31 +13,16 @@ from sqlalchemy.orm import Session
 from yarl import URL
 
 import contexts
-from core.helper.provider_cache import ToolProviderCredentialsCache
-from core.plugin.entities.plugin import ToolProviderID
-from core.plugin.impl.oauth import OAuthHandler
-from core.plugin.impl.tool import PluginToolManager
-from core.tools.__base.tool_provider import ToolProviderController
-from core.tools.__base.tool_runtime import ToolRuntime
-from core.tools.mcp_tool.provider import MCPToolProviderController
-from core.tools.mcp_tool.tool import MCPTool
-from core.tools.plugin_tool.provider import PluginToolProviderController
-from core.tools.plugin_tool.tool import PluginTool
-from core.tools.utils.uuid_utils import is_valid_uuid
-from core.tools.workflow_as_tool.provider import WorkflowToolProviderController
-from core.workflow.entities.variable_pool import VariablePool
-from services.tools.mcp_tools_manage_service import MCPToolManageService
-
-if TYPE_CHECKING:
-    from core.workflow.nodes.tool.entities import ToolEntity
-
 from configs import dify_config
 from core.agent.entities import AgentToolEntity
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.helper.module_import_helper import load_single_subclass_from_source
 from core.helper.position_helper import is_filtered
+from core.helper.provider_cache import ToolProviderCredentialsCache
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.tools.__base.tool import Tool
+from core.tools.__base.tool_provider import ToolProviderController
+from core.tools.__base.tool_runtime import ToolRuntime
 from core.tools.builtin_tool.provider import BuiltinToolProviderController
 from core.tools.builtin_tool.providers._positions import BuiltinToolProviderSort
 from core.tools.builtin_tool.tool import BuiltinTool
@@ -53,16 +38,28 @@ from core.tools.entities.tool_entities import (
     ToolProviderType,
 )
 from core.tools.errors import ToolProviderNotFoundError
+from core.tools.mcp_tool.provider import MCPToolProviderController
+from core.tools.mcp_tool.tool import MCPTool
+from core.tools.plugin_tool.provider import PluginToolProviderController
+from core.tools.plugin_tool.tool import PluginTool
 from core.tools.tool_label_manager import ToolLabelManager
 from core.tools.utils.configuration import (
     ToolParameterConfigurationManager,
 )
 from core.tools.utils.encryption import create_provider_encrypter, create_tool_provider_encrypter
+from core.tools.utils.uuid_utils import is_valid_uuid
+from core.tools.workflow_as_tool.provider import WorkflowToolProviderController
 from core.tools.workflow_as_tool.tool import WorkflowTool
 from extensions.ext_database import db
+from models.provider_ids import ToolProviderID
 from models.tools import ApiToolProvider, BuiltinToolProvider, MCPToolProvider, WorkflowToolProvider
+from services.tools.mcp_tools_manage_service import MCPToolManageService
 from services.tools.tools_transform_service import ToolTransformService
 
+if TYPE_CHECKING:
+    from core.workflow.entities import VariablePool
+    from core.workflow.nodes.tool.entities import ToolEntity
+
 logger = logging.getLogger(__name__)
 
 
@@ -117,6 +114,8 @@ class ToolManager:
         get the plugin provider
         """
         # check if context is set
+        from core.plugin.impl.tool import PluginToolManager
+
         try:
             contexts.plugin_tool_providers.get()
         except LookupError:
@@ -172,6 +171,7 @@ class ToolManager:
 
         :return: the tool
         """
+
         if provider_type == ToolProviderType.BUILT_IN:
             # check if the builtin tool need credentials
             provider_controller = cls.get_builtin_provider(provider_id, tenant_id)
@@ -216,16 +216,16 @@ class ToolManager:
                 # fallback to the default provider
                 if builtin_provider is None:
                     # use the default provider
-                    builtin_provider = (
-                        db.session.query(BuiltinToolProvider)
-                        .where(
-                            BuiltinToolProvider.tenant_id == tenant_id,
-                            (BuiltinToolProvider.provider == str(provider_id_entity))
-                            | (BuiltinToolProvider.provider == provider_id_entity.provider_name),
+                    with Session(db.engine) as session:
+                        builtin_provider = session.scalar(
+                            sa.select(BuiltinToolProvider)
+                            .where(
+                                BuiltinToolProvider.tenant_id == tenant_id,
+                                (BuiltinToolProvider.provider == str(provider_id_entity))
+                                | (BuiltinToolProvider.provider == provider_id_entity.provider_name),
+                            )
+                            .order_by(BuiltinToolProvider.is_default.desc(), BuiltinToolProvider.created_at.asc())
                         )
-                        .order_by(BuiltinToolProvider.is_default.desc(), BuiltinToolProvider.created_at.asc())
-                        .first()
-                    )
                     if builtin_provider is None:
                         raise ToolProviderNotFoundError(f"no default provider for {provider_id}")
             else:
@@ -256,6 +256,7 @@ class ToolManager:
             # check if the credentials is expired
             if builtin_provider.expires_at != -1 and (builtin_provider.expires_at - 60) < int(time.time()):
                 # TODO: circular import
+                from core.plugin.impl.oauth import OAuthHandler
                 from services.tools.builtin_tools_manage_service import BuiltinToolManageService
 
                 # refresh the credentials
@@ -263,6 +264,7 @@ class ToolManager:
                 provider_name = tool_provider.provider_name
                 redirect_uri = f"{dify_config.CONSOLE_API_URL}/console/api/oauth/plugin/{provider_id}/tool/callback"
                 system_credentials = BuiltinToolManageService.get_oauth_client(tenant_id, provider_id)
+
                 oauth_handler = OAuthHandler()
                 # refresh the credentials
                 refreshed_credentials = oauth_handler.refresh_credentials(
@@ -358,7 +360,7 @@ class ToolManager:
         app_id: str,
         agent_tool: AgentToolEntity,
         invoke_from: InvokeFrom = InvokeFrom.DEBUGGER,
-        variable_pool: Optional[VariablePool] = None,
+        variable_pool: Optional["VariablePool"] = None,
     ) -> Tool:
         """
         get the agent tool runtime
@@ -400,7 +402,7 @@ class ToolManager:
         node_id: str,
         workflow_tool: "ToolEntity",
         invoke_from: InvokeFrom = InvokeFrom.DEBUGGER,
-        variable_pool: Optional[VariablePool] = None,
+        variable_pool: Optional["VariablePool"] = None,
     ) -> Tool:
         """
         get the workflow tool runtime
@@ -516,6 +518,8 @@ class ToolManager:
         """
         list all the plugin providers
         """
+        from core.plugin.impl.tool import PluginToolManager
+
         manager = PluginToolManager()
         provider_entities = manager.fetch_tool_providers(tenant_id)
         return [
@@ -977,7 +981,7 @@ class ToolManager:
     def _convert_tool_parameters_type(
         cls,
         parameters: list[ToolParameter],
-        variable_pool: Optional[VariablePool],
+        variable_pool: Optional["VariablePool"],
         tool_configurations: dict[str, Any],
         typ: Literal["agent", "workflow", "tool"] = "workflow",
     ) -> dict[str, Any]:

api/core/tools/workflow_as_tool/tool.py

@@ -37,14 +37,12 @@ class WorkflowTool(Tool):
         entity: ToolEntity,
         runtime: ToolRuntime,
         label: str = "Workflow",
-        thread_pool_id: Optional[str] = None,
     ):
         self.workflow_app_id = workflow_app_id
         self.workflow_as_tool_id = workflow_as_tool_id
         self.version = version
         self.workflow_entities = workflow_entities
         self.workflow_call_depth = workflow_call_depth
-        self.thread_pool_id = thread_pool_id
         self.label = label
 
         super().__init__(entity=entity, runtime=runtime)
@@ -88,7 +86,6 @@ class WorkflowTool(Tool):
             invoke_from=self.runtime.invoke_from,
             streaming=False,
             call_depth=self.workflow_call_depth + 1,
-            workflow_thread_pool_id=self.thread_pool_id,
         )
         assert isinstance(result, dict)
         data = result.get("data", {})

api/core/variables/segments.py

@@ -130,7 +130,7 @@ class ArraySegment(Segment):
     def markdown(self) -> str:
         items = []
         for item in self.value:
-            items.append(str(item))
+            items.append(f"- {item}")
         return "\n".join(items)
 
 

api/core/workflow/callbacks/__init__.py

@@ -1,7 +0,0 @@
-from .base_workflow_callback import WorkflowCallback
-from .workflow_logging_callback import WorkflowLoggingCallback
-
-__all__ = [
-    "WorkflowCallback",
-    "WorkflowLoggingCallback",
-]

api/core/workflow/callbacks/base_workflow_callback.py

@@ -1,12 +0,0 @@
-from abc import ABC, abstractmethod
-
-from core.workflow.graph_engine.entities.event import GraphEngineEvent
-
-
-class WorkflowCallback(ABC):
-    @abstractmethod
-    def on_event(self, event: GraphEngineEvent) -> None:
-        """
-        Published event
-        """
-        raise NotImplementedError

api/core/workflow/callbacks/workflow_logging_callback.py

@@ -1,263 +0,0 @@
-from typing import Optional
-
-from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.graph_engine.entities.event import (
-    GraphEngineEvent,
-    GraphRunFailedEvent,
-    GraphRunPartialSucceededEvent,
-    GraphRunStartedEvent,
-    GraphRunSucceededEvent,
-    IterationRunFailedEvent,
-    IterationRunNextEvent,
-    IterationRunStartedEvent,
-    IterationRunSucceededEvent,
-    LoopRunFailedEvent,
-    LoopRunNextEvent,
-    LoopRunStartedEvent,
-    LoopRunSucceededEvent,
-    NodeRunFailedEvent,
-    NodeRunStartedEvent,
-    NodeRunStreamChunkEvent,
-    NodeRunSucceededEvent,
-    ParallelBranchRunFailedEvent,
-    ParallelBranchRunStartedEvent,
-    ParallelBranchRunSucceededEvent,
-)
-
-from .base_workflow_callback import WorkflowCallback
-
-_TEXT_COLOR_MAPPING = {
-    "blue": "36;1",
-    "yellow": "33;1",
-    "pink": "38;5;200",
-    "green": "32;1",
-    "red": "31;1",
-}
-
-
-class WorkflowLoggingCallback(WorkflowCallback):
-    def __init__(self) -> None:
-        self.current_node_id: Optional[str] = None
-
-    def on_event(self, event: GraphEngineEvent) -> None:
-        if isinstance(event, GraphRunStartedEvent):
-            self.print_text("\n[GraphRunStartedEvent]", color="pink")
-        elif isinstance(event, GraphRunSucceededEvent):
-            self.print_text("\n[GraphRunSucceededEvent]", color="green")
-        elif isinstance(event, GraphRunPartialSucceededEvent):
-            self.print_text("\n[GraphRunPartialSucceededEvent]", color="pink")
-        elif isinstance(event, GraphRunFailedEvent):
-            self.print_text(f"\n[GraphRunFailedEvent] reason: {event.error}", color="red")
-        elif isinstance(event, NodeRunStartedEvent):
-            self.on_workflow_node_execute_started(event=event)
-        elif isinstance(event, NodeRunSucceededEvent):
-            self.on_workflow_node_execute_succeeded(event=event)
-        elif isinstance(event, NodeRunFailedEvent):
-            self.on_workflow_node_execute_failed(event=event)
-        elif isinstance(event, NodeRunStreamChunkEvent):
-            self.on_node_text_chunk(event=event)
-        elif isinstance(event, ParallelBranchRunStartedEvent):
-            self.on_workflow_parallel_started(event=event)
-        elif isinstance(event, ParallelBranchRunSucceededEvent | ParallelBranchRunFailedEvent):
-            self.on_workflow_parallel_completed(event=event)
-        elif isinstance(event, IterationRunStartedEvent):
-            self.on_workflow_iteration_started(event=event)
-        elif isinstance(event, IterationRunNextEvent):
-            self.on_workflow_iteration_next(event=event)
-        elif isinstance(event, IterationRunSucceededEvent | IterationRunFailedEvent):
-            self.on_workflow_iteration_completed(event=event)
-        elif isinstance(event, LoopRunStartedEvent):
-            self.on_workflow_loop_started(event=event)
-        elif isinstance(event, LoopRunNextEvent):
-            self.on_workflow_loop_next(event=event)
-        elif isinstance(event, LoopRunSucceededEvent | LoopRunFailedEvent):
-            self.on_workflow_loop_completed(event=event)
-        else:
-            self.print_text(f"\n[{event.__class__.__name__}]", color="blue")
-
-    def on_workflow_node_execute_started(self, event: NodeRunStartedEvent) -> None:
-        """
-        Workflow node execute started
-        """
-        self.print_text("\n[NodeRunStartedEvent]", color="yellow")
-        self.print_text(f"Node ID: {event.node_id}", color="yellow")
-        self.print_text(f"Node Title: {event.node_data.title}", color="yellow")
-        self.print_text(f"Type: {event.node_type.value}", color="yellow")
-
-    def on_workflow_node_execute_succeeded(self, event: NodeRunSucceededEvent) -> None:
-        """
-        Workflow node execute succeeded
-        """
-        route_node_state = event.route_node_state
-
-        self.print_text("\n[NodeRunSucceededEvent]", color="green")
-        self.print_text(f"Node ID: {event.node_id}", color="green")
-        self.print_text(f"Node Title: {event.node_data.title}", color="green")
-        self.print_text(f"Type: {event.node_type.value}", color="green")
-
-        if route_node_state.node_run_result:
-            node_run_result = route_node_state.node_run_result
-            self.print_text(
-                f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
-                color="green",
-            )
-            self.print_text(
-                f"Process Data: "
-                f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
-                color="green",
-            )
-            self.print_text(
-                f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
-                color="green",
-            )
-            self.print_text(
-                f"Metadata: {jsonable_encoder(node_run_result.metadata) if node_run_result.metadata else ''}",
-                color="green",
-            )
-
-    def on_workflow_node_execute_failed(self, event: NodeRunFailedEvent) -> None:
-        """
-        Workflow node execute failed
-        """
-        route_node_state = event.route_node_state
-
-        self.print_text("\n[NodeRunFailedEvent]", color="red")
-        self.print_text(f"Node ID: {event.node_id}", color="red")
-        self.print_text(f"Node Title: {event.node_data.title}", color="red")
-        self.print_text(f"Type: {event.node_type.value}", color="red")
-
-        if route_node_state.node_run_result:
-            node_run_result = route_node_state.node_run_result
-            self.print_text(f"Error: {node_run_result.error}", color="red")
-            self.print_text(
-                f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
-                color="red",
-            )
-            self.print_text(
-                f"Process Data: "
-                f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
-                color="red",
-            )
-            self.print_text(
-                f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
-                color="red",
-            )
-
-    def on_node_text_chunk(self, event: NodeRunStreamChunkEvent) -> None:
-        """
-        Publish text chunk
-        """
-        route_node_state = event.route_node_state
-        if not self.current_node_id or self.current_node_id != route_node_state.node_id:
-            self.current_node_id = route_node_state.node_id
-            self.print_text("\n[NodeRunStreamChunkEvent]")
-            self.print_text(f"Node ID: {route_node_state.node_id}")
-
-            node_run_result = route_node_state.node_run_result
-            if node_run_result:
-                self.print_text(
-                    f"Metadata: {jsonable_encoder(node_run_result.metadata) if node_run_result.metadata else ''}"
-                )
-
-        self.print_text(event.chunk_content, color="pink", end="")
-
-    def on_workflow_parallel_started(self, event: ParallelBranchRunStartedEvent) -> None:
-        """
-        Publish parallel started
-        """
-        self.print_text("\n[ParallelBranchRunStartedEvent]", color="blue")
-        self.print_text(f"Parallel ID: {event.parallel_id}", color="blue")
-        self.print_text(f"Branch ID: {event.parallel_start_node_id}", color="blue")
-        if event.in_iteration_id:
-            self.print_text(f"Iteration ID: {event.in_iteration_id}", color="blue")
-        if event.in_loop_id:
-            self.print_text(f"Loop ID: {event.in_loop_id}", color="blue")
-
-    def on_workflow_parallel_completed(
-        self, event: ParallelBranchRunSucceededEvent | ParallelBranchRunFailedEvent
-    ) -> None:
-        """
-        Publish parallel completed
-        """
-        if isinstance(event, ParallelBranchRunSucceededEvent):
-            color = "blue"
-        elif isinstance(event, ParallelBranchRunFailedEvent):
-            color = "red"
-
-        self.print_text(
-            "\n[ParallelBranchRunSucceededEvent]"
-            if isinstance(event, ParallelBranchRunSucceededEvent)
-            else "\n[ParallelBranchRunFailedEvent]",
-            color=color,
-        )
-        self.print_text(f"Parallel ID: {event.parallel_id}", color=color)
-        self.print_text(f"Branch ID: {event.parallel_start_node_id}", color=color)
-        if event.in_iteration_id:
-            self.print_text(f"Iteration ID: {event.in_iteration_id}", color=color)
-        if event.in_loop_id:
-            self.print_text(f"Loop ID: {event.in_loop_id}", color=color)
-
-        if isinstance(event, ParallelBranchRunFailedEvent):
-            self.print_text(f"Error: {event.error}", color=color)
-
-    def on_workflow_iteration_started(self, event: IterationRunStartedEvent) -> None:
-        """
-        Publish iteration started
-        """
-        self.print_text("\n[IterationRunStartedEvent]", color="blue")
-        self.print_text(f"Iteration Node ID: {event.iteration_id}", color="blue")
-
-    def on_workflow_iteration_next(self, event: IterationRunNextEvent) -> None:
-        """
-        Publish iteration next
-        """
-        self.print_text("\n[IterationRunNextEvent]", color="blue")
-        self.print_text(f"Iteration Node ID: {event.iteration_id}", color="blue")
-        self.print_text(f"Iteration Index: {event.index}", color="blue")
-
-    def on_workflow_iteration_completed(self, event: IterationRunSucceededEvent | IterationRunFailedEvent) -> None:
-        """
-        Publish iteration completed
-        """
-        self.print_text(
-            "\n[IterationRunSucceededEvent]"
-            if isinstance(event, IterationRunSucceededEvent)
-            else "\n[IterationRunFailedEvent]",
-            color="blue",
-        )
-        self.print_text(f"Node ID: {event.iteration_id}", color="blue")
-
-    def on_workflow_loop_started(self, event: LoopRunStartedEvent) -> None:
-        """
-        Publish loop started
-        """
-        self.print_text("\n[LoopRunStartedEvent]", color="blue")
-        self.print_text(f"Loop Node ID: {event.loop_node_id}", color="blue")
-
-    def on_workflow_loop_next(self, event: LoopRunNextEvent) -> None:
-        """
-        Publish loop next
-        """
-        self.print_text("\n[LoopRunNextEvent]", color="blue")
-        self.print_text(f"Loop Node ID: {event.loop_node_id}", color="blue")
-        self.print_text(f"Loop Index: {event.index}", color="blue")
-
-    def on_workflow_loop_completed(self, event: LoopRunSucceededEvent | LoopRunFailedEvent) -> None:
-        """
-        Publish loop completed
-        """
-        self.print_text(
-            "\n[LoopRunSucceededEvent]" if isinstance(event, LoopRunSucceededEvent) else "\n[LoopRunFailedEvent]",
-            color="blue",
-        )
-        self.print_text(f"Loop Node ID: {event.loop_node_id}", color="blue")
-
-    def print_text(self, text: str, color: Optional[str] = None, end: str = "\n") -> None:
-        """Print text with highlighting and no end characters."""
-        text_to_print = self._get_colored_text(text, color) if color else text
-        print(f"{text_to_print}", end=end)
-
-    def _get_colored_text(self, text: str, color: str) -> str:
-        """Get colored text."""
-        color_str = _TEXT_COLOR_MAPPING[color]
-        return f"\u001b[{color_str}m\033[1;3m{text}\u001b[0m"

api/core/workflow/docs/WORKER_POOL_CONFIG.md

@@ -0,0 +1,173 @@
+# GraphEngine Worker Pool Configuration
+
+## Overview
+
+The GraphEngine now supports **dynamic worker pool management** to optimize performance and resource usage. Instead of a fixed 10-worker pool, the engine can:
+
+1. **Start with optimal worker count** based on graph complexity
+1. **Scale up** when workload increases
+1. **Scale down** when workers are idle
+1. **Respect configurable min/max limits**
+
+## Benefits
+
+- **Resource Efficiency**: Uses fewer workers for simple sequential workflows
+- **Better Performance**: Scales up for parallel-heavy workflows
+- **Gevent Optimization**: Works efficiently with Gevent's greenlet model
+- **Memory Savings**: Reduces memory footprint for simple workflows
+
+## Configuration
+
+### Configuration Variables (via dify_config)
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `GRAPH_ENGINE_MIN_WORKERS` | 1 | Minimum number of workers per engine |
+| `GRAPH_ENGINE_MAX_WORKERS` | 10 | Maximum number of workers per engine |
+| `GRAPH_ENGINE_SCALE_UP_THRESHOLD` | 3 | Queue depth that triggers scale up |
+| `GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME` | 5.0 | Seconds of idle time before scaling down |
+
+### Example Configurations
+
+#### Low-Resource Environment
+
+```bash
+export GRAPH_ENGINE_MIN_WORKERS=1
+export GRAPH_ENGINE_MAX_WORKERS=3
+export GRAPH_ENGINE_SCALE_UP_THRESHOLD=2
+export GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME=3.0
+```
+
+#### High-Performance Environment
+
+```bash
+export GRAPH_ENGINE_MIN_WORKERS=2
+export GRAPH_ENGINE_MAX_WORKERS=20
+export GRAPH_ENGINE_SCALE_UP_THRESHOLD=5
+export GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME=10.0
+```
+
+#### Default (Balanced)
+
+```bash
+# Uses defaults: min=1, max=10, threshold=3, idle_time=5.0
+```
+
+## How It Works
+
+### Initial Worker Calculation
+
+The engine analyzes the graph structure at startup:
+
+- **Sequential graphs** (no branches): 1 worker
+- **Limited parallelism** (few branches): 2 workers
+- **Moderate parallelism**: 3 workers
+- **High parallelism** (many branches): 5 workers
+
+### Dynamic Scaling
+
+During execution:
+
+1. **Scale Up** triggers when:
+
+   - Queue depth exceeds `SCALE_UP_THRESHOLD`
+   - All workers are busy and queue has items
+   - Not at `MAX_WORKERS` limit
+
+1. **Scale Down** triggers when:
+
+   - Worker idle for more than `SCALE_DOWN_IDLE_TIME` seconds
+   - Above `MIN_WORKERS` limit
+
+### Gevent Compatibility
+
+Since Gevent patches threading to use greenlets:
+
+- Workers are lightweight coroutines, not OS threads
+- Dynamic scaling has minimal overhead
+- Can efficiently handle many concurrent workers
+
+## Migration Guide
+
+### Before (Fixed 10 Workers)
+
+```python
+# Every GraphEngine instance created 10 workers
+# Resource waste for simple workflows
+# No adaptation to workload
+```
+
+### After (Dynamic Workers)
+
+```python
+# GraphEngine creates 1-5 initial workers based on graph
+# Scales up/down based on workload
+# Configurable via environment variables
+```
+
+### Backward Compatibility
+
+The default configuration (`max=10`) maintains compatibility with existing deployments. To get the old behavior exactly:
+
+```bash
+export GRAPH_ENGINE_MIN_WORKERS=10
+export GRAPH_ENGINE_MAX_WORKERS=10
+```
+
+## Performance Impact
+
+### Memory Usage
+
+- **Simple workflows**: ~80% reduction (1 vs 10 workers)
+- **Complex workflows**: Similar or slightly better
+
+### Execution Time
+
+- **Sequential workflows**: No change
+- **Parallel workflows**: Improved with proper scaling
+- **Bursty workloads**: Better adaptation
+
+### Example Metrics
+
+| Workflow Type | Old (10 workers) | New (Dynamic) | Improvement |
+|--------------|------------------|---------------|-------------|
+| Sequential | 10 workers idle | 1 worker active | 90% fewer workers |
+| 3-way parallel | 7 workers idle | 3 workers active | 70% fewer workers |
+| Heavy parallel | 10 workers busy | 10+ workers (scales up) | Better throughput |
+
+## Monitoring
+
+Log messages indicate scaling activity:
+
+```shell
+INFO: GraphEngine initialized with 2 workers (min: 1, max: 10)
+INFO: Scaled up workers: 2 -> 3 (queue_depth: 4)
+INFO: Scaled down workers: 3 -> 2 (removed 1 idle workers)
+```
+
+## Best Practices
+
+1. **Start with defaults** - They work well for most cases
+1. **Monitor queue depth** - Adjust `SCALE_UP_THRESHOLD` if queues back up
+1. **Consider workload patterns**:
+   - Bursty: Lower `SCALE_DOWN_IDLE_TIME`
+   - Steady: Higher `SCALE_DOWN_IDLE_TIME`
+1. **Test with your workloads** - Measure and tune
+
+## Troubleshooting
+
+### Workers not scaling up
+
+- Check `GRAPH_ENGINE_MAX_WORKERS` limit
+- Verify queue depth exceeds threshold
+- Check logs for scaling messages
+
+### Workers scaling down too quickly
+
+- Increase `GRAPH_ENGINE_SCALE_DOWN_IDLE_TIME`
+- Consider workload patterns
+
+### Out of memory
+
+- Reduce `GRAPH_ENGINE_MAX_WORKERS`
+- Check for memory leaks in nodes

api/core/workflow/entities/__init__.py

@@ -0,0 +1,18 @@
+from .agent import AgentNodeStrategyInit
+from .graph_init_params import GraphInitParams
+from .graph_runtime_state import GraphRuntimeState
+from .run_condition import RunCondition
+from .variable_pool import VariablePool, VariableValue
+from .workflow_execution import WorkflowExecution
+from .workflow_node_execution import WorkflowNodeExecution
+
+__all__ = [
+    "AgentNodeStrategyInit",
+    "GraphInitParams",
+    "GraphRuntimeState",
+    "RunCondition",
+    "VariablePool",
+    "VariableValue",
+    "WorkflowExecution",
+    "WorkflowNodeExecution",
+]

api/core/workflow/entities/agent.py

@@ -0,0 +1,10 @@
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class AgentNodeStrategyInit(BaseModel):
+    """Agent node strategy initialization data."""
+
+    name: str
+    icon: Optional[str] = None

api/core/workflow/entities/graph_init_params.py

@@ -3,19 +3,18 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from core.app.entities.app_invoke_entities import InvokeFrom
-from models.enums import UserFrom
-from models.workflow import WorkflowType
-
 
 class GraphInitParams(BaseModel):
     # init params
     tenant_id: str = Field(..., description="tenant / workspace id")
     app_id: str = Field(..., description="app id")
-    workflow_type: WorkflowType = Field(..., description="workflow type")
     workflow_id: str = Field(..., description="workflow id")
     graph_config: Mapping[str, Any] = Field(..., description="graph config")
     user_id: str = Field(..., description="user id")
-    user_from: UserFrom = Field(..., description="user from, account or end-user")
-    invoke_from: InvokeFrom = Field(..., description="invoke from, service-api, web-app, explore or debugger")
+    user_from: str = Field(
+        ..., description="user from, account or end-user"
+    )  # Should be UserFrom enum: 'account' | 'end-user'
+    invoke_from: str = Field(
+        ..., description="invoke from, service-api, web-app, explore or debugger"
+    )  # Should be InvokeFrom enum: 'service-api' | 'web-app' | 'explore' | 'debugger'
     call_depth: int = Field(..., description="call depth")

api/core/workflow/entities/graph_runtime_state.py

@@ -3,8 +3,8 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from core.model_runtime.entities.llm_entities import LLMUsage
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.graph_engine.entities.runtime_route_state import RuntimeRouteState
+
+from .variable_pool import VariablePool
 
 
 class GraphRuntimeState(BaseModel):
@@ -26,6 +26,3 @@ class GraphRuntimeState(BaseModel):
 
     node_run_steps: int = 0
     """node run steps"""
-
-    node_run_state: RuntimeRouteState = RuntimeRouteState()
-    """node run state"""

api/core/workflow/entities/node_entities.py

@@ -1,34 +0,0 @@
-from collections.abc import Mapping
-from typing import Any, Optional
-
-from pydantic import BaseModel
-
-from core.model_runtime.entities.llm_entities import LLMUsage
-from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
-
-
-class NodeRunResult(BaseModel):
-    """
-    Node Run Result.
-    """
-
-    status: WorkflowNodeExecutionStatus = WorkflowNodeExecutionStatus.RUNNING
-
-    inputs: Optional[Mapping[str, Any]] = None  # node inputs
-    process_data: Optional[Mapping[str, Any]] = None  # process data
-    outputs: Optional[Mapping[str, Any]] = None  # node outputs
-    metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None  # node metadata
-    llm_usage: Optional[LLMUsage] = None  # llm usage
-
-    edge_source_handle: Optional[str] = None  # source handle id of node with multiple branches
-
-    error: Optional[str] = None  # error message if status is failed
-    error_type: Optional[str] = None  # error type if status is failed
-
-    # single step node run retry
-    retry_index: int = 0
-
-
-class AgentNodeStrategyInit(BaseModel):
-    name: str
-    icon: str | None = None

api/core/workflow/entities/variable_entities.py

@@ -1,12 +0,0 @@
-from collections.abc import Sequence
-
-from pydantic import BaseModel
-
-
-class VariableSelector(BaseModel):
-    """
-    Variable Selector.
-    """
-
-    variable: str
-    value_selector: Sequence[str]

api/core/workflow/entities/workflow_execution.py

@@ -7,31 +7,14 @@ implementation details like tenant_id, app_id, etc.
 
 from collections.abc import Mapping
 from datetime import datetime
-from enum import StrEnum
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
+from core.workflow.enums import WorkflowExecutionStatus, WorkflowType
 from libs.datetime_utils import naive_utc_now
 
 
-class WorkflowType(StrEnum):
-    """
-    Workflow Type Enum for domain layer
-    """
-
-    WORKFLOW = "workflow"
-    CHAT = "chat"
-
-
-class WorkflowExecutionStatus(StrEnum):
-    RUNNING = "running"
-    SUCCEEDED = "succeeded"
-    FAILED = "failed"
-    STOPPED = "stopped"
-    PARTIAL_SUCCEEDED = "partial-succeeded"
-
-
 class WorkflowExecution(BaseModel):
     """
     Domain model for workflow execution based on WorkflowRun but without

api/core/workflow/entities/workflow_node_execution.py

@@ -8,49 +8,11 @@ and don't contain implementation details like tenant_id, app_id, etc.
 
 from collections.abc import Mapping
 from datetime import datetime
-from enum import StrEnum
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
-from core.workflow.nodes.enums import NodeType
-
-
-class WorkflowNodeExecutionMetadataKey(StrEnum):
-    """
-    Node Run Metadata Key.
-    """
-
-    TOTAL_TOKENS = "total_tokens"
-    TOTAL_PRICE = "total_price"
-    CURRENCY = "currency"
-    TOOL_INFO = "tool_info"
-    AGENT_LOG = "agent_log"
-    ITERATION_ID = "iteration_id"
-    ITERATION_INDEX = "iteration_index"
-    LOOP_ID = "loop_id"
-    LOOP_INDEX = "loop_index"
-    PARALLEL_ID = "parallel_id"
-    PARALLEL_START_NODE_ID = "parallel_start_node_id"
-    PARENT_PARALLEL_ID = "parent_parallel_id"
-    PARENT_PARALLEL_START_NODE_ID = "parent_parallel_start_node_id"
-    PARALLEL_MODE_RUN_ID = "parallel_mode_run_id"
-    ITERATION_DURATION_MAP = "iteration_duration_map"  # single iteration duration if iteration node runs
-    LOOP_DURATION_MAP = "loop_duration_map"  # single loop duration if loop node runs
-    ERROR_STRATEGY = "error_strategy"  # node in continue on error mode return the field
-    LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
-
-
-class WorkflowNodeExecutionStatus(StrEnum):
-    """
-    Node Execution Status Enum.
-    """
-
-    RUNNING = "running"
-    SUCCEEDED = "succeeded"
-    FAILED = "failed"
-    EXCEPTION = "exception"
-    RETRY = "retry"
+from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 
 
 class WorkflowNodeExecution(BaseModel):

api/core/workflow/enums.py

@@ -1,4 +1,12 @@
-from enum import StrEnum
+from enum import Enum, StrEnum
+
+
+class NodeState(Enum):
+    """State of a node or edge during workflow execution."""
+
+    UNKNOWN = "unknown"
+    TAKEN = "taken"
+    SKIPPED = "skipped"
 
 
 class SystemVariableKey(StrEnum):
@@ -14,3 +22,104 @@ class SystemVariableKey(StrEnum):
     APP_ID = "app_id"
     WORKFLOW_ID = "workflow_id"
     WORKFLOW_EXECUTION_ID = "workflow_run_id"
+
+
+class NodeType(StrEnum):
+    START = "start"
+    END = "end"
+    ANSWER = "answer"
+    LLM = "llm"
+    KNOWLEDGE_RETRIEVAL = "knowledge-retrieval"
+    IF_ELSE = "if-else"
+    CODE = "code"
+    TEMPLATE_TRANSFORM = "template-transform"
+    QUESTION_CLASSIFIER = "question-classifier"
+    HTTP_REQUEST = "http-request"
+    TOOL = "tool"
+    VARIABLE_AGGREGATOR = "variable-aggregator"
+    LEGACY_VARIABLE_AGGREGATOR = "variable-assigner"  # TODO: Merge this into VARIABLE_AGGREGATOR in the database.
+    LOOP = "loop"
+    LOOP_START = "loop-start"
+    LOOP_END = "loop-end"
+    ITERATION = "iteration"
+    ITERATION_START = "iteration-start"  # Fake start node for iteration.
+    PARAMETER_EXTRACTOR = "parameter-extractor"
+    VARIABLE_ASSIGNER = "assigner"
+    DOCUMENT_EXTRACTOR = "document-extractor"
+    LIST_OPERATOR = "list-operator"
+    AGENT = "agent"
+
+
+class NodeExecutionType(StrEnum):
+    """Node execution type classification."""
+
+    EXECUTABLE = "executable"  # Regular nodes that execute and produce outputs
+    RESPONSE = "response"  # Response nodes that stream outputs (Answer, End)
+    BRANCH = "branch"  # Nodes that can choose different branches (if-else, question-classifier)
+    CONTAINER = "container"  # Container nodes that manage subgraphs (iteration, loop, graph)
+    ROOT = "root"  # Nodes that can serve as execution entry points
+
+
+class ErrorStrategy(StrEnum):
+    FAIL_BRANCH = "fail-branch"
+    DEFAULT_VALUE = "default-value"
+
+
+class FailBranchSourceHandle(StrEnum):
+    FAILED = "fail-branch"
+    SUCCESS = "success-branch"
+
+
+class WorkflowType(StrEnum):
+    """
+    Workflow Type Enum for domain layer
+    """
+
+    WORKFLOW = "workflow"
+    CHAT = "chat"
+
+
+class WorkflowExecutionStatus(StrEnum):
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+    STOPPED = "stopped"
+    PARTIAL_SUCCEEDED = "partial-succeeded"
+
+
+class WorkflowNodeExecutionMetadataKey(StrEnum):
+    """
+    Node Run Metadata Key.
+    """
+
+    TOTAL_TOKENS = "total_tokens"
+    TOTAL_PRICE = "total_price"
+    CURRENCY = "currency"
+    TOOL_INFO = "tool_info"
+    AGENT_LOG = "agent_log"
+    ITERATION_ID = "iteration_id"
+    ITERATION_INDEX = "iteration_index"
+    LOOP_ID = "loop_id"
+    LOOP_INDEX = "loop_index"
+    PARALLEL_ID = "parallel_id"
+    PARALLEL_START_NODE_ID = "parallel_start_node_id"
+    PARENT_PARALLEL_ID = "parent_parallel_id"
+    PARENT_PARALLEL_START_NODE_ID = "parent_parallel_start_node_id"
+    PARALLEL_MODE_RUN_ID = "parallel_mode_run_id"
+    ITERATION_DURATION_MAP = "iteration_duration_map"  # single iteration duration if iteration node runs
+    LOOP_DURATION_MAP = "loop_duration_map"  # single loop duration if loop node runs
+    ERROR_STRATEGY = "error_strategy"  # node in continue on error mode return the field
+    LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
+
+
+class WorkflowNodeExecutionStatus(StrEnum):
+    PENDING = "pending"  # Node is scheduled but not yet executing
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+    EXCEPTION = "exception"
+    STOPPED = "stopped"
+    PAUSED = "paused"
+
+    # Legacy statuses - kept for backward compatibility
+    RETRY = "retry"  # Legacy: replaced by retry mechanism in error handling

api/core/workflow/errors.py

@@ -1,8 +1,8 @@
-from core.workflow.nodes.base import BaseNode
+from core.workflow.nodes.base.node import Node
 
 
 class WorkflowNodeRunFailedError(Exception):
-    def __init__(self, node: BaseNode, err_msg: str):
+    def __init__(self, node: Node, err_msg: str):
         self._node = node
         self._error = err_msg
         super().__init__(f"Node {node.title} run failed: {err_msg}")

api/core/workflow/graph/__init__.py

@@ -0,0 +1,5 @@
+from .edge import Edge
+from .graph import Graph, NodeFactory
+from .graph_template import GraphTemplate
+
+__all__ = ["Edge", "Graph", "GraphTemplate", "NodeFactory"]

api/core/workflow/graph/edge.py

@@ -0,0 +1,15 @@
+import uuid
+from dataclasses import dataclass, field
+
+from core.workflow.enums import NodeState
+
+
+@dataclass
+class Edge:
+    """Edge connecting two nodes in a workflow graph."""
+
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    tail: str = ""  # tail node id (source)
+    head: str = ""  # head node id (target)
+    source_handle: str = "source"  # source handle for conditional branching
+    state: NodeState = field(default=NodeState.UNKNOWN)  # edge execution state

api/core/workflow/graph/graph.py

@@ -0,0 +1,346 @@
+import logging
+from collections import defaultdict
+from collections.abc import Mapping, Sequence
+from typing import Protocol, cast, final
+
+from core.workflow.enums import NodeExecutionType, NodeState, NodeType
+from core.workflow.nodes.base.node import Node
+from libs.typing import is_str, is_str_dict
+
+from .edge import Edge
+
+logger = logging.getLogger(__name__)
+
+
+class NodeFactory(Protocol):
+    """
+    Protocol for creating Node instances from node data dictionaries.
+
+    This protocol decouples the Graph class from specific node mapping implementations,
+    allowing for different node creation strategies while maintaining type safety.
+    """
+
+    def create_node(self, node_config: dict[str, object]) -> Node:
+        """
+        Create a Node instance from node configuration data.
+
+        :param node_config: node configuration dictionary containing type and other data
+        :return: initialized Node instance
+        :raises ValueError: if node type is unknown or configuration is invalid
+        """
+        ...
+
+
+@final
+class Graph:
+    """Graph representation with nodes and edges for workflow execution."""
+
+    def __init__(
+        self,
+        *,
+        nodes: dict[str, Node] | None = None,
+        edges: dict[str, Edge] | None = None,
+        in_edges: dict[str, list[str]] | None = None,
+        out_edges: dict[str, list[str]] | None = None,
+        root_node: Node,
+    ):
+        """
+        Initialize Graph instance.
+
+        :param nodes: graph nodes mapping (node id: node object)
+        :param edges: graph edges mapping (edge id: edge object)
+        :param in_edges: incoming edges mapping (node id: list of edge ids)
+        :param out_edges: outgoing edges mapping (node id: list of edge ids)
+        :param root_node: root node object
+        """
+        self.nodes = nodes or {}
+        self.edges = edges or {}
+        self.in_edges = in_edges or {}
+        self.out_edges = out_edges or {}
+        self.root_node = root_node
+
+    @classmethod
+    def _parse_node_configs(cls, node_configs: list[dict[str, object]]) -> dict[str, dict[str, object]]:
+        """
+        Parse node configurations and build a mapping of node IDs to configs.
+
+        :param node_configs: list of node configuration dictionaries
+        :return: mapping of node ID to node config
+        """
+        node_configs_map: dict[str, dict[str, object]] = {}
+
+        for node_config in node_configs:
+            node_id = node_config.get("id")
+            if not node_id or not isinstance(node_id, str):
+                continue
+
+            node_configs_map[node_id] = node_config
+
+        return node_configs_map
+
+    @classmethod
+    def _find_root_node_id(
+        cls,
+        node_configs_map: Mapping[str, Mapping[str, object]],
+        edge_configs: Sequence[Mapping[str, object]],
+        root_node_id: str | None = None,
+    ) -> str:
+        """
+        Find the root node ID if not specified.
+
+        :param node_configs_map: mapping of node ID to node config
+        :param edge_configs: list of edge configurations
+        :param root_node_id: explicitly specified root node ID
+        :return: determined root node ID
+        """
+        if root_node_id:
+            if root_node_id not in node_configs_map:
+                raise ValueError(f"Root node id {root_node_id} not found in the graph")
+            return root_node_id
+
+        # Find nodes with no incoming edges
+        nodes_with_incoming: set[str] = set()
+        for edge_config in edge_configs:
+            target = edge_config.get("target")
+            if isinstance(target, str):
+                nodes_with_incoming.add(target)
+
+        root_candidates = [nid for nid in node_configs_map if nid not in nodes_with_incoming]
+
+        # Prefer START node if available
+        start_node_id = None
+        for nid in root_candidates:
+            node_data = node_configs_map[nid].get("data")
+            if not is_str_dict(node_data):
+                continue
+            node_type = node_data.get("type")
+            if not isinstance(node_type, str):
+                continue
+            if node_type == NodeType.START:
+                start_node_id = nid
+                break
+
+        root_node_id = start_node_id or (root_candidates[0] if root_candidates else None)
+
+        if not root_node_id:
+            raise ValueError("Unable to determine root node ID")
+
+        return root_node_id
+
+    @classmethod
+    def _build_edges(
+        cls, edge_configs: list[dict[str, object]]
+    ) -> tuple[dict[str, Edge], dict[str, list[str]], dict[str, list[str]]]:
+        """
+        Build edge objects and mappings from edge configurations.
+
+        :param edge_configs: list of edge configurations
+        :return: tuple of (edges dict, in_edges dict, out_edges dict)
+        """
+        edges: dict[str, Edge] = {}
+        in_edges: dict[str, list[str]] = defaultdict(list)
+        out_edges: dict[str, list[str]] = defaultdict(list)
+
+        edge_counter = 0
+        for edge_config in edge_configs:
+            source = edge_config.get("source")
+            target = edge_config.get("target")
+
+            if not is_str(source) or not is_str(target):
+                continue
+
+            # Create edge
+            edge_id = f"edge_{edge_counter}"
+            edge_counter += 1
+
+            source_handle = edge_config.get("sourceHandle", "source")
+            if not is_str(source_handle):
+                continue
+
+            edge = Edge(
+                id=edge_id,
+                tail=source,
+                head=target,
+                source_handle=source_handle,
+            )
+
+            edges[edge_id] = edge
+            out_edges[source].append(edge_id)
+            in_edges[target].append(edge_id)
+
+        return edges, dict(in_edges), dict(out_edges)
+
+    @classmethod
+    def _create_node_instances(
+        cls,
+        node_configs_map: dict[str, dict[str, object]],
+        node_factory: "NodeFactory",
+    ) -> dict[str, Node]:
+        """
+        Create node instances from configurations using the node factory.
+
+        :param node_configs_map: mapping of node ID to node config
+        :param node_factory: factory for creating node instances
+        :return: mapping of node ID to node instance
+        """
+        nodes: dict[str, Node] = {}
+
+        for node_id, node_config in node_configs_map.items():
+            try:
+                node_instance = node_factory.create_node(node_config)
+            except ValueError as e:
+                logger.warning("Failed to create node instance: %s", str(e))
+                continue
+            nodes[node_id] = node_instance
+
+        return nodes
+
+    @classmethod
+    def _mark_inactive_root_branches(
+        cls,
+        nodes: dict[str, Node],
+        edges: dict[str, Edge],
+        in_edges: dict[str, list[str]],
+        out_edges: dict[str, list[str]],
+        active_root_id: str,
+    ) -> None:
+        """
+        Mark nodes and edges from inactive root branches as skipped.
+
+        Algorithm:
+        1. Mark inactive root nodes as skipped
+        2. For skipped nodes, mark all their outgoing edges as skipped
+        3. For each edge marked as skipped, check its target node:
+           - If ALL incoming edges are skipped, mark the node as skipped
+           - Otherwise, leave the node state unchanged
+
+        :param nodes: mapping of node ID to node instance
+        :param edges: mapping of edge ID to edge instance
+        :param in_edges: mapping of node ID to incoming edge IDs
+        :param out_edges: mapping of node ID to outgoing edge IDs
+        :param active_root_id: ID of the active root node
+        """
+        # Find all top-level root nodes (nodes with ROOT execution type and no incoming edges)
+        top_level_roots: list[str] = [
+            node.id for node in nodes.values() if node.execution_type == NodeExecutionType.ROOT
+        ]
+
+        # If there's only one root or the active root is not a top-level root, no marking needed
+        if len(top_level_roots) <= 1 or active_root_id not in top_level_roots:
+            return
+
+        # Mark inactive root nodes as skipped
+        inactive_roots: list[str] = [root_id for root_id in top_level_roots if root_id != active_root_id]
+        for root_id in inactive_roots:
+            if root_id in nodes:
+                nodes[root_id].state = NodeState.SKIPPED
+
+        # Recursively mark downstream nodes and edges
+        def mark_downstream(node_id: str) -> None:
+            """Recursively mark downstream nodes and edges as skipped."""
+            if nodes[node_id].state != NodeState.SKIPPED:
+                return
+            # If this node is skipped, mark all its outgoing edges as skipped
+            out_edge_ids = out_edges.get(node_id, [])
+            for edge_id in out_edge_ids:
+                edge = edges[edge_id]
+                edge.state = NodeState.SKIPPED
+
+                # Check the target node of this edge
+                target_node = nodes[edge.head]
+                in_edge_ids = in_edges.get(target_node.id, [])
+                in_edge_states = [edges[eid].state for eid in in_edge_ids]
+
+                # If all incoming edges are skipped, mark the node as skipped
+                if all(state == NodeState.SKIPPED for state in in_edge_states):
+                    target_node.state = NodeState.SKIPPED
+                    # Recursively process downstream nodes
+                    mark_downstream(target_node.id)
+
+        # Process each inactive root and its downstream nodes
+        for root_id in inactive_roots:
+            mark_downstream(root_id)
+
+    @classmethod
+    def init(
+        cls,
+        *,
+        graph_config: Mapping[str, object],
+        node_factory: "NodeFactory",
+        root_node_id: str | None = None,
+    ) -> "Graph":
+        """
+        Initialize graph
+
+        :param graph_config: graph config containing nodes and edges
+        :param node_factory: factory for creating node instances from config data
+        :param root_node_id: root node id
+        :return: graph instance
+        """
+        # Parse configs
+        edge_configs = graph_config.get("edges", [])
+        node_configs = graph_config.get("nodes", [])
+
+        edge_configs = cast(list[dict[str, object]], edge_configs)
+        node_configs = cast(list[dict[str, object]], node_configs)
+
+        if not node_configs:
+            raise ValueError("Graph must have at least one node")
+
+        node_configs = [node_config for node_config in node_configs if node_config.get("type", "") != "custom-note"]
+
+        # Parse node configurations
+        node_configs_map = cls._parse_node_configs(node_configs)
+
+        # Find root node
+        root_node_id = cls._find_root_node_id(node_configs_map, edge_configs, root_node_id)
+
+        # Build edges
+        edges, in_edges, out_edges = cls._build_edges(edge_configs)
+
+        # Create node instances
+        nodes = cls._create_node_instances(node_configs_map, node_factory)
+
+        # Get root node instance
+        root_node = nodes[root_node_id]
+
+        # Mark inactive root branches as skipped
+        cls._mark_inactive_root_branches(nodes, edges, in_edges, out_edges, root_node_id)
+
+        # Create and return the graph
+        return cls(
+            nodes=nodes,
+            edges=edges,
+            in_edges=in_edges,
+            out_edges=out_edges,
+            root_node=root_node,
+        )
+
+    @property
+    def node_ids(self) -> list[str]:
+        """
+        Get list of node IDs (compatibility property for existing code)
+
+        :return: list of node IDs
+        """
+        return list(self.nodes.keys())
+
+    def get_outgoing_edges(self, node_id: str) -> list[Edge]:
+        """
+        Get all outgoing edges from a node (V2 method)
+
+        :param node_id: node id
+        :return: list of outgoing edges
+        """
+        edge_ids = self.out_edges.get(node_id, [])
+        return [self.edges[eid] for eid in edge_ids if eid in self.edges]
+
+    def get_incoming_edges(self, node_id: str) -> list[Edge]:
+        """
+        Get all incoming edges to a node (V2 method)
+
+        :param node_id: node id
+        :return: list of incoming edges
+        """
+        edge_ids = self.in_edges.get(node_id, [])
+        return [self.edges[eid] for eid in edge_ids if eid in self.edges]

api/core/workflow/graph/graph_template.py

@@ -0,0 +1,20 @@
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class GraphTemplate(BaseModel):
+    """
+    Graph Template for container nodes and subgraph expansion
+
+    According to GraphEngine V2 spec, GraphTemplate contains:
+    - nodes: mapping of node definitions
+    - edges: mapping of edge definitions
+    - root_ids: list of root node IDs
+    - output_selectors: list of output selectors for the template
+    """
+
+    nodes: dict[str, dict[str, Any]] = Field(default_factory=dict, description="node definitions mapping")
+    edges: dict[str, dict[str, Any]] = Field(default_factory=dict, description="edge definitions mapping")
+    root_ids: list[str] = Field(default_factory=list, description="root node IDs")
+    output_selectors: list[str] = Field(default_factory=list, description="output selectors")

api/core/workflow/graph_engine/__init__.py

@@ -1,4 +1,3 @@
-from .entities import Graph, GraphInitParams, GraphRuntimeState, RuntimeRouteState
 from .graph_engine import GraphEngine
 
-__all__ = ["Graph", "GraphEngine", "GraphInitParams", "GraphRuntimeState", "RuntimeRouteState"]
+__all__ = ["GraphEngine"]

api/core/workflow/graph_engine/command_channels/README.md

@@ -0,0 +1,33 @@
+# Command Channels
+
+Channel implementations for external workflow control.
+
+## Components
+
+### InMemoryChannel
+
+Thread-safe in-memory queue for single-process deployments.
+
+- `fetch_commands()` - Get pending commands
+- `send_command()` - Add command to queue
+
+### RedisChannel
+
+Redis-based queue for distributed deployments.
+
+- `fetch_commands()` - Get commands with JSON deserialization
+- `send_command()` - Store commands with TTL
+
+## Usage
+
+```python
+# Local execution
+channel = InMemoryChannel()
+channel.send_command(AbortCommand(graph_id="workflow-123"))
+
+# Distributed execution
+redis_channel = RedisChannel(
+    redis_client=redis_client,
+    channel_key="workflow:123:commands"
+)
+```

api/core/workflow/graph_engine/command_channels/__init__.py

@@ -0,0 +1,6 @@
+"""Command channel implementations for GraphEngine."""
+
+from .in_memory_channel import InMemoryChannel
+from .redis_channel import RedisChannel
+
+__all__ = ["InMemoryChannel", "RedisChannel"]

api/core/workflow/graph_engine/command_channels/in_memory_channel.py

@@ -0,0 +1,53 @@
+"""
+In-memory implementation of CommandChannel for local/testing scenarios.
+
+This implementation uses a thread-safe queue for command communication
+within a single process. Each instance handles commands for one workflow execution.
+"""
+
+from queue import Queue
+from typing import final
+
+from ..entities.commands import GraphEngineCommand
+
+
+@final
+class InMemoryChannel:
+    """
+    In-memory command channel implementation using a thread-safe queue.
+
+    Each instance is dedicated to a single GraphEngine/workflow execution.
+    Suitable for local development, testing, and single-instance deployments.
+    """
+
+    def __init__(self) -> None:
+        """Initialize the in-memory channel with a single queue."""
+        self._queue: Queue[GraphEngineCommand] = Queue()
+
+    def fetch_commands(self) -> list[GraphEngineCommand]:
+        """
+        Fetch all pending commands from the queue.
+
+        Returns:
+            List of pending commands (drains the queue)
+        """
+        commands: list[GraphEngineCommand] = []
+
+        # Drain all available commands from the queue
+        while not self._queue.empty():
+            try:
+                command = self._queue.get_nowait()
+                commands.append(command)
+            except Exception:
+                break
+
+        return commands
+
+    def send_command(self, command: GraphEngineCommand) -> None:
+        """
+        Send a command to this channel's queue.
+
+        Args:
+            command: The command to send
+        """
+        self._queue.put(command)

api/core/workflow/graph_engine/command_channels/redis_channel.py

@@ -0,0 +1,110 @@
+"""
+Redis-based implementation of CommandChannel for distributed scenarios.
+
+This implementation uses Redis lists for command queuing, supporting
+multi-instance deployments and cross-server communication.
+Each instance uses a unique key for its command queue.
+"""
+
+import json
+from typing import TYPE_CHECKING, Any, final
+
+from ..entities.commands import AbortCommand, CommandType, GraphEngineCommand
+
+if TYPE_CHECKING:
+    from extensions.ext_redis import RedisClientWrapper
+
+
+@final
+class RedisChannel:
+    """
+    Redis-based command channel implementation for distributed systems.
+
+    Each instance uses a unique Redis key for its command queue.
+    Commands are JSON-serialized for transport.
+    """
+
+    def __init__(
+        self,
+        redis_client: "RedisClientWrapper",
+        channel_key: str,
+        command_ttl: int = 3600,
+    ) -> None:
+        """
+        Initialize the Redis channel.
+
+        Args:
+            redis_client: Redis client instance
+            channel_key: Unique key for this channel's command queue
+            command_ttl: TTL for command keys in seconds (default: 3600)
+        """
+        self._redis = redis_client
+        self._key = channel_key
+        self._command_ttl = command_ttl
+
+    def fetch_commands(self) -> list[GraphEngineCommand]:
+        """
+        Fetch all pending commands from Redis.
+
+        Returns:
+            List of pending commands (drains the Redis list)
+        """
+        commands: list[GraphEngineCommand] = []
+
+        # Use pipeline for atomic operations
+        with self._redis.pipeline() as pipe:
+            # Get all commands and clear the list atomically
+            pipe.lrange(self._key, 0, -1)
+            pipe.delete(self._key)
+            results = pipe.execute()
+
+        # Parse commands from JSON
+        if results[0]:
+            for command_json in results[0]:
+                try:
+                    command_data = json.loads(command_json)
+                    command = self._deserialize_command(command_data)
+                    if command:
+                        commands.append(command)
+                except (json.JSONDecodeError, ValueError):
+                    # Skip invalid commands
+                    continue
+
+        return commands
+
+    def send_command(self, command: GraphEngineCommand) -> None:
+        """
+        Send a command to Redis.
+
+        Args:
+            command: The command to send
+        """
+        command_json = json.dumps(command.model_dump())
+
+        # Push to list and set expiry
+        with self._redis.pipeline() as pipe:
+            pipe.rpush(self._key, command_json)
+            pipe.expire(self._key, self._command_ttl)
+            pipe.execute()
+
+    def _deserialize_command(self, data: dict[str, Any]) -> GraphEngineCommand | None:
+        """
+        Deserialize a command from dictionary data.
+
+        Args:
+            data: Command data dictionary
+
+        Returns:
+            Deserialized command or None if invalid
+        """
+        try:
+            command_type = CommandType(data.get("command_type"))
+
+            if command_type == CommandType.ABORT:
+                return AbortCommand(**data)
+            else:
+                # For other command types, use base class
+                return GraphEngineCommand(**data)
+
+        except (ValueError, TypeError):
+            return None

api/core/workflow/graph_engine/command_processing/__init__.py

@@ -0,0 +1,14 @@
+"""
+Command processing subsystem for graph engine.
+
+This package handles external commands sent to the engine
+during execution.
+"""
+
+from .command_handlers import AbortCommandHandler
+from .command_processor import CommandProcessor
+
+__all__ = [
+    "AbortCommandHandler",
+    "CommandProcessor",
+]

api/core/workflow/graph_engine/command_processing/command_handlers.py

@@ -0,0 +1,32 @@
+"""
+Command handler implementations.
+"""
+
+import logging
+from typing import final
+
+from typing_extensions import override
+
+from ..domain.graph_execution import GraphExecution
+from ..entities.commands import AbortCommand, GraphEngineCommand
+from .command_processor import CommandHandler
+
+logger = logging.getLogger(__name__)
+
+
+@final
+class AbortCommandHandler(CommandHandler):
+    """Handles abort commands."""
+
+    @override
+    def handle(self, command: GraphEngineCommand, execution: GraphExecution) -> None:
+        """
+        Handle an abort command.
+
+        Args:
+            command: The abort command
+            execution: Graph execution to abort
+        """
+        assert isinstance(command, AbortCommand)
+        logger.debug("Aborting workflow %s: %s", execution.workflow_id, command.reason)
+        execution.abort(command.reason or "User requested abort")

api/core/workflow/graph_engine/command_processing/command_processor.py

@@ -0,0 +1,79 @@
+"""
+Main command processor for handling external commands.
+"""
+
+import logging
+from typing import Protocol, final
+
+from ..domain.graph_execution import GraphExecution
+from ..entities.commands import GraphEngineCommand
+from ..protocols.command_channel import CommandChannel
+
+logger = logging.getLogger(__name__)
+
+
+class CommandHandler(Protocol):
+    """Protocol for command handlers."""
+
+    def handle(self, command: GraphEngineCommand, execution: GraphExecution) -> None: ...
+
+
+@final
+class CommandProcessor:
+    """
+    Processes external commands sent to the engine.
+
+    This polls the command channel and dispatches commands to
+    appropriate handlers.
+    """
+
+    def __init__(
+        self,
+        command_channel: CommandChannel,
+        graph_execution: GraphExecution,
+    ) -> None:
+        """
+        Initialize the command processor.
+
+        Args:
+            command_channel: Channel for receiving commands
+            graph_execution: Graph execution aggregate
+        """
+        self._command_channel = command_channel
+        self._graph_execution = graph_execution
+        self._handlers: dict[type[GraphEngineCommand], CommandHandler] = {}
+
+    def register_handler(self, command_type: type[GraphEngineCommand], handler: CommandHandler) -> None:
+        """
+        Register a handler for a command type.
+
+        Args:
+            command_type: Type of command to handle
+            handler: Handler for the command
+        """
+        self._handlers[command_type] = handler
+
+    def process_commands(self) -> None:
+        """Check for and process any pending commands."""
+        try:
+            commands = self._command_channel.fetch_commands()
+            for command in commands:
+                self._handle_command(command)
+        except Exception as e:
+            logger.warning("Error processing commands: %s", e)
+
+    def _handle_command(self, command: GraphEngineCommand) -> None:
+        """
+        Handle a single command.
+
+        Args:
+            command: The command to handle
+        """
+        handler = self._handlers.get(type(command))
+        if handler:
+            try:
+                handler.handle(command, self._graph_execution)
+            except Exception:
+                logger.exception("Error handling command %s", command.__class__.__name__)
+        else:
+            logger.warning("No handler registered for command: %s", command.__class__.__name__)

api/core/workflow/graph_engine/condition_handlers/base_handler.py

@@ -1,25 +0,0 @@
-from abc import ABC, abstractmethod
-
-from core.workflow.graph_engine.entities.graph import Graph
-from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
-from core.workflow.graph_engine.entities.run_condition import RunCondition
-from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
-
-
-class RunConditionHandler(ABC):
-    def __init__(self, init_params: GraphInitParams, graph: Graph, condition: RunCondition):
-        self.init_params = init_params
-        self.graph = graph
-        self.condition = condition
-
-    @abstractmethod
-    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState) -> bool:
-        """
-        Check if the condition can be executed
-
-        :param graph_runtime_state: graph runtime state
-        :param previous_route_node_state: previous route node state
-        :return: bool
-        """
-        raise NotImplementedError

api/core/workflow/graph_engine/condition_handlers/branch_identify_handler.py

@@ -1,25 +0,0 @@
-from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
-from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
-
-
-class BranchIdentifyRunConditionHandler(RunConditionHandler):
-    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState) -> bool:
-        """
-        Check if the condition can be executed
-
-        :param graph_runtime_state: graph runtime state
-        :param previous_route_node_state: previous route node state
-        :return: bool
-        """
-        if not self.condition.branch_identify:
-            raise Exception("Branch identify is required")
-
-        run_result = previous_route_node_state.node_run_result
-        if not run_result:
-            return False
-
-        if not run_result.edge_source_handle:
-            return False
-
-        return self.condition.branch_identify == run_result.edge_source_handle

api/core/workflow/graph_engine/condition_handlers/condition_handler.py

@@ -1,27 +0,0 @@
-from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
-from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
-from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
-from core.workflow.utils.condition.processor import ConditionProcessor
-
-
-class ConditionRunConditionHandlerHandler(RunConditionHandler):
-    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState):
-        """
-        Check if the condition can be executed
-
-        :param graph_runtime_state: graph runtime state
-        :param previous_route_node_state: previous route node state
-        :return: bool
-        """
-        if not self.condition.conditions:
-            return True
-
-        # process condition
-        condition_processor = ConditionProcessor()
-        _, _, final_result = condition_processor.process_conditions(
-            variable_pool=graph_runtime_state.variable_pool,
-            conditions=self.condition.conditions,
-            operator="and",
-        )
-
-        return final_result

api/core/workflow/graph_engine/condition_handlers/condition_manager.py

@@ -1,25 +0,0 @@
-from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
-from core.workflow.graph_engine.condition_handlers.branch_identify_handler import BranchIdentifyRunConditionHandler
-from core.workflow.graph_engine.condition_handlers.condition_handler import ConditionRunConditionHandlerHandler
-from core.workflow.graph_engine.entities.graph import Graph
-from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
-from core.workflow.graph_engine.entities.run_condition import RunCondition
-
-
-class ConditionManager:
-    @staticmethod
-    def get_condition_handler(
-        init_params: GraphInitParams, graph: Graph, run_condition: RunCondition
-    ) -> RunConditionHandler:
-        """
-        Get condition handler
-
-        :param init_params: init params
-        :param graph: graph
-        :param run_condition: run condition
-        :return: condition handler
-        """
-        if run_condition.type == "branch_identify":
-            return BranchIdentifyRunConditionHandler(init_params=init_params, graph=graph, condition=run_condition)
-        else:
-            return ConditionRunConditionHandlerHandler(init_params=init_params, graph=graph, condition=run_condition)

api/core/workflow/graph_engine/domain/__init__.py

@@ -0,0 +1,16 @@
+"""
+Domain models for graph engine.
+
+This package contains the core domain entities, value objects, and aggregates
+that represent the business concepts of workflow graph execution.
+"""
+
+from .execution_context import ExecutionContext
+from .graph_execution import GraphExecution
+from .node_execution import NodeExecution
+
+__all__ = [
+    "ExecutionContext",
+    "GraphExecution",
+    "NodeExecution",
+]

api/core/workflow/graph_engine/domain/execution_context.py

@@ -0,0 +1,37 @@
+"""
+ExecutionContext value object containing immutable execution parameters.
+"""
+
+from dataclasses import dataclass
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from models.enums import UserFrom
+
+
+@dataclass(frozen=True)
+class ExecutionContext:
+    """
+    Immutable value object containing the context for a graph execution.
+
+    This encapsulates all the contextual information needed to execute a workflow,
+    keeping it separate from the mutable execution state.
+    """
+
+    tenant_id: str
+    app_id: str
+    workflow_id: str
+    user_id: str
+    user_from: UserFrom
+    invoke_from: InvokeFrom
+    call_depth: int
+    max_execution_steps: int
+    max_execution_time: int
+
+    def __post_init__(self) -> None:
+        """Validate execution context parameters."""
+        if self.call_depth < 0:
+            raise ValueError("Call depth must be non-negative")
+        if self.max_execution_steps <= 0:
+            raise ValueError("Max execution steps must be positive")
+        if self.max_execution_time <= 0:
+            raise ValueError("Max execution time must be positive")

api/core/workflow/graph_engine/domain/graph_execution.py

@@ -0,0 +1,71 @@
+"""
+GraphExecution aggregate root managing the overall graph execution state.
+"""
+
+from dataclasses import dataclass, field
+
+from .node_execution import NodeExecution
+
+
+@dataclass
+class GraphExecution:
+    """
+    Aggregate root for graph execution.
+
+    This manages the overall execution state of a workflow graph,
+    coordinating between multiple node executions.
+    """
+
+    workflow_id: str
+    started: bool = False
+    completed: bool = False
+    aborted: bool = False
+    error: Exception | None = None
+    node_executions: dict[str, NodeExecution] = field(default_factory=dict)
+
+    def start(self) -> None:
+        """Mark the graph execution as started."""
+        if self.started:
+            raise RuntimeError("Graph execution already started")
+        self.started = True
+
+    def complete(self) -> None:
+        """Mark the graph execution as completed."""
+        if not self.started:
+            raise RuntimeError("Cannot complete execution that hasn't started")
+        if self.completed:
+            raise RuntimeError("Graph execution already completed")
+        self.completed = True
+
+    def abort(self, reason: str) -> None:
+        """Abort the graph execution."""
+        self.aborted = True
+        self.error = RuntimeError(f"Aborted: {reason}")
+
+    def fail(self, error: Exception) -> None:
+        """Mark the graph execution as failed."""
+        self.error = error
+        self.completed = True
+
+    def get_or_create_node_execution(self, node_id: str) -> NodeExecution:
+        """Get or create a node execution entity."""
+        if node_id not in self.node_executions:
+            self.node_executions[node_id] = NodeExecution(node_id=node_id)
+        return self.node_executions[node_id]
+
+    @property
+    def is_running(self) -> bool:
+        """Check if the execution is currently running."""
+        return self.started and not self.completed and not self.aborted
+
+    @property
+    def has_error(self) -> bool:
+        """Check if the execution has encountered an error."""
+        return self.error is not None
+
+    @property
+    def error_message(self) -> str | None:
+        """Get the error message if an error exists."""
+        if not self.error:
+            return None
+        return str(self.error)

api/core/workflow/graph_engine/domain/node_execution.py

@@ -0,0 +1,45 @@
+"""
+NodeExecution entity representing a node's execution state.
+"""
+
+from dataclasses import dataclass
+
+from core.workflow.enums import NodeState
+
+
+@dataclass
+class NodeExecution:
+    """
+    Entity representing the execution state of a single node.
+
+    This is a mutable entity that tracks the runtime state of a node
+    during graph execution.
+    """
+
+    node_id: str
+    state: NodeState = NodeState.UNKNOWN
+    retry_count: int = 0
+    execution_id: str | None = None
+    error: str | None = None
+
+    def mark_started(self, execution_id: str) -> None:
+        """Mark the node as started with an execution ID."""
+        self.state = NodeState.TAKEN
+        self.execution_id = execution_id
+
+    def mark_taken(self) -> None:
+        """Mark the node as successfully completed."""
+        self.state = NodeState.TAKEN
+        self.error = None
+
+    def mark_failed(self, error: str) -> None:
+        """Mark the node as failed with an error."""
+        self.error = error
+
+    def mark_skipped(self) -> None:
+        """Mark the node as skipped."""
+        self.state = NodeState.SKIPPED
+
+    def increment_retry(self) -> None:
+        """Increment the retry count for this node."""
+        self.retry_count += 1

api/core/workflow/graph_engine/entities/__init__.py

@@ -1,6 +0,0 @@
-from .graph import Graph
-from .graph_init_params import GraphInitParams
-from .graph_runtime_state import GraphRuntimeState
-from .runtime_route_state import RuntimeRouteState
-
-__all__ = ["Graph", "GraphInitParams", "GraphRuntimeState", "RuntimeRouteState"]

api/core/workflow/graph_engine/entities/commands.py

@@ -0,0 +1,33 @@
+"""
+GraphEngine command entities for external control.
+
+This module defines command types that can be sent to a running GraphEngine
+instance to control its execution flow.
+"""
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class CommandType(str, Enum):
+    """Types of commands that can be sent to GraphEngine."""
+
+    ABORT = "abort"
+    PAUSE = "pause"
+    RESUME = "resume"
+
+
+class GraphEngineCommand(BaseModel):
+    """Base class for all GraphEngine commands."""
+
+    command_type: CommandType = Field(..., description="Type of command")
+    payload: dict[str, Any] | None = Field(default=None, description="Optional command payload")
+
+
+class AbortCommand(GraphEngineCommand):
+    """Command to abort a running workflow execution."""
+
+    command_type: CommandType = Field(default=CommandType.ABORT, description="Type of command")
+    reason: str | None = Field(default=None, description="Optional reason for abort")