Merge 升级到1.11.4

# Conflicts:
#	.github/workflows/tool-test-sdks.yaml
#	api/.env.example
#	api/README.md
#	api/commands.py
#	api/controllers/console/explore/wraps.py
#	api/controllers/web/workflow.py
#	api/extensions/ext_commands.py
#	api/models/model.py
#	api/pyproject.toml
#	api/services/feature_service.py
#	web/README.md
#	web/app/components/explore/app-card/index.tsx
#	web/app/components/explore/app-list/index.tsx
#	web/app/components/explore/sidebar/index.tsx
#	web/app/signin/components/mail-and-password-auth.tsx
#	web/i18n/uk-UA/app-overview.json
#	web/i18n/uk-UA/app.json
#	web/i18n/uk-UA/billing.json
#	web/i18n/uk-UA/common.json
#	web/i18n/uk-UA/dataset-creation.json
#	web/i18n/uk-UA/dataset-documents.json
#	web/i18n/uk-UA/dataset-hit-testing.json
#	web/i18n/uk-UA/dataset-settings.json
#	web/i18n/uk-UA/dataset.json
#	web/i18n/uk-UA/explore.json
#	web/i18n/uk-UA/plugin.json
#	web/i18n/uk-UA/tools.json
#	web/next.config.js
#	web/package.json
#	web/pnpm-lock.yaml
#	web/service/common.ts
#	web/service/explore.ts
#	web/service/fetch.ts
#	web/service/use-explore.ts
#	web/types/feature.ts

api/core/agent/base_agent_runner.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import uuid
+from decimal import Decimal
 from typing import Union, cast
 
 from sqlalchemy import select
@@ -41,6 +42,7 @@ from core.tools.tool_manager import ToolManager
 from core.tools.utils.dataset_retriever_tool import DatasetRetrieverTool
 from extensions.ext_database import db
 from factories import file_factory
+from models.enums import CreatorUserRole
 from models.model import Conversation, Message, MessageAgentThought, MessageFile
 
 logger = logging.getLogger(__name__)
@@ -289,6 +291,7 @@ class BaseAgentRunner(AppRunner):
         thought = MessageAgentThought(
             message_id=message_id,
             message_chain_id=None,
+            tool_process_data=None,
             thought="",
             tool=tool_name,
             tool_labels_str="{}",
@@ -296,20 +299,20 @@ class BaseAgentRunner(AppRunner):
             tool_input=tool_input,
             message=message,
             message_token=0,
-            message_unit_price=0,
-            message_price_unit=0,
+            message_unit_price=Decimal(0),
+            message_price_unit=Decimal("0.001"),
             message_files=json.dumps(messages_ids) if messages_ids else "",
             answer="",
             observation="",
             answer_token=0,
-            answer_unit_price=0,
-            answer_price_unit=0,
+            answer_unit_price=Decimal(0),
+            answer_price_unit=Decimal("0.001"),
             tokens=0,
-            total_price=0,
+            total_price=Decimal(0),
             position=self.agent_thought_count + 1,
             currency="USD",
             latency=0,
-            created_by_role="account",
+            created_by_role=CreatorUserRole.ACCOUNT,
             created_by=self.user_id,
         )
 
@@ -342,7 +345,8 @@ class BaseAgentRunner(AppRunner):
             raise ValueError("agent thought not found")
 
         if thought:
-            agent_thought.thought += thought
+            existing_thought = agent_thought.thought or ""
+            agent_thought.thought = f"{existing_thought}{thought}"
 
         if tool_name:
             agent_thought.tool = tool_name
@@ -440,21 +444,30 @@ class BaseAgentRunner(AppRunner):
             agent_thoughts: list[MessageAgentThought] = message.agent_thoughts
             if agent_thoughts:
                 for agent_thought in agent_thoughts:
-                    tools = agent_thought.tool
-                    if tools:
-                        tools = tools.split(";")
+                    tool_names_raw = agent_thought.tool
+                    if tool_names_raw:
+                        tool_names = tool_names_raw.split(";")
                         tool_calls: list[AssistantPromptMessage.ToolCall] = []
                         tool_call_response: list[ToolPromptMessage] = []
-                        try:
-                            tool_inputs = json.loads(agent_thought.tool_input)
-                        except Exception:
-                            tool_inputs = {tool: {} for tool in tools}
-                        try:
-                            tool_responses = json.loads(agent_thought.observation)
-                        except Exception:
-                            tool_responses = dict.fromkeys(tools, agent_thought.observation)
+                        tool_input_payload = agent_thought.tool_input
+                        if tool_input_payload:
+                            try:
+                                tool_inputs = json.loads(tool_input_payload)
+                            except Exception:
+                                tool_inputs = {tool: {} for tool in tool_names}
+                        else:
+                            tool_inputs = {tool: {} for tool in tool_names}
 
-                        for tool in tools:
+                        observation_payload = agent_thought.observation
+                        if observation_payload:
+                            try:
+                                tool_responses = json.loads(observation_payload)
+                            except Exception:
+                                tool_responses = dict.fromkeys(tool_names, observation_payload)
+                        else:
+                            tool_responses = dict.fromkeys(tool_names, observation_payload)
+
+                        for tool in tool_names:
                             # generate a uuid for tool call
                             tool_call_id = str(uuid.uuid4())
                             tool_calls.append(
@@ -484,7 +497,7 @@ class BaseAgentRunner(AppRunner):
                                 *tool_call_response,
                             ]
                         )
-                    if not tools:
+                    if not tool_names_raw:
                         result.append(AssistantPromptMessage(content=agent_thought.thought))
             else:
                 if message.answer:

api/core/agent/fc_agent_runner.py

@@ -188,7 +188,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     ),
                 )
 
-            assistant_message = AssistantPromptMessage(content="", tool_calls=[])
+            assistant_message = AssistantPromptMessage(content=response, tool_calls=[])
             if tool_calls:
                 assistant_message.tool_calls = [
                     AssistantPromptMessage.ToolCall(
@@ -200,8 +200,6 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     )
                     for tool_call in tool_calls
                 ]
-            else:
-                assistant_message.content = response
 
             self._current_thoughts.append(assistant_message)
 

api/core/app/app_config/entities.py

@@ -1,4 +1,3 @@
-import json
 from collections.abc import Sequence
 from enum import StrEnum, auto
 from typing import Any, Literal
@@ -121,7 +120,7 @@ class VariableEntity(BaseModel):
     allowed_file_types: Sequence[FileType] | None = Field(default_factory=list)
     allowed_file_extensions: Sequence[str] | None = Field(default_factory=list)
     allowed_file_upload_methods: Sequence[FileTransferMethod] | None = Field(default_factory=list)
-    json_schema: str | None = Field(default=None)
+    json_schema: dict | None = Field(default=None)
 
     @field_validator("description", mode="before")
     @classmethod
@@ -135,17 +134,11 @@ class VariableEntity(BaseModel):
 
     @field_validator("json_schema")
     @classmethod
-    def validate_json_schema(cls, schema: str | None) -> str | None:
+    def validate_json_schema(cls, schema: dict | None) -> dict | None:
         if schema is None:
             return None
-
         try:
-            json_schema = json.loads(schema)
-        except json.JSONDecodeError:
-            raise ValueError(f"invalid json_schema value {schema}")
-
-        try:
-            Draft7Validator.check_schema(json_schema)
+            Draft7Validator.check_schema(schema)
         except SchemaError as e:
             raise ValueError(f"Invalid JSON schema: {e.message}")
         return schema

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

@@ -26,7 +26,6 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
     @classmethod
     def get_app_config(cls, app_model: App, workflow: Workflow) -> AdvancedChatAppConfig:
         features_dict = workflow.features_dict
-
         app_mode = AppMode.value_of(app_model.mode)
         app_config = AdvancedChatAppConfig(
             tenant_id=app_model.tenant_id,

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

@@ -24,7 +24,7 @@ from core.app.layers.conversation_variable_persist_layer import ConversationVari
 from core.db.session_factory import session_factory
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
-from core.variables.variables import VariableUnion
+from core.variables.variables import Variable
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -149,8 +149,8 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
                 system_variables=system_inputs,
                 user_inputs=inputs,
                 environment_variables=self._workflow.environment_variables,
-                # Based on the definition of `VariableUnion`,
-                # `list[Variable]` can be safely used as `list[VariableUnion]` since they are compatible.
+                # Based on the definition of `Variable`,
+                # `VariableBase` instances can be safely used as `Variable` since they are compatible.
                 conversation_variables=conversation_variables,
             )
 
@@ -319,7 +319,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
             trace_manager=app_generate_entity.trace_manager,
         )
 
-    def _initialize_conversation_variables(self) -> list[VariableUnion]:
+    def _initialize_conversation_variables(self) -> list[Variable]:
         """
         Initialize conversation variables for the current conversation.
 
@@ -344,7 +344,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
             conversation_variables = [var.to_variable() for var in existing_variables]
 
             session.commit()
-            return cast(list[VariableUnion], conversation_variables)
+            return cast(list[Variable], conversation_variables)
 
     def _load_existing_conversation_variables(self, session: Session) -> list[ConversationVariable]:
         """

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

@@ -374,25 +374,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if node_finish_resp:
             yield node_finish_resp
 
-        # For ANSWER nodes, check if we need to send a message_replace event
-        # Only send if the final output differs from the accumulated task_state.answer
-        # This happens when variables were updated by variable_assigner during workflow execution
-        if event.node_type == NodeType.ANSWER and event.outputs:
-            final_answer = event.outputs.get("answer")
-            if final_answer is not None and final_answer != self._task_state.answer:
-                logger.info(
-                    "ANSWER node final output '%s' differs from accumulated answer '%s', sending message_replace event",
-                    final_answer,
-                    self._task_state.answer,
-                )
-                # Update the task state answer
-                self._task_state.answer = str(final_answer)
-                # Send message_replace event to update the UI
-                yield self._message_cycle_manager.message_replace_to_stream_response(
-                    answer=str(final_answer),
-                    reason="variable_update",
-                )
-
     def _handle_node_failed_events(
         self,
         event: Union[QueueNodeFailedEvent, QueueNodeExceptionEvent],

api/core/app/apps/base_app_generator.py

@@ -1,4 +1,3 @@
-import json
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Union, final
 
@@ -76,12 +75,24 @@ class BaseAppGenerator:
         user_inputs = {**user_inputs, **files_inputs, **file_list_inputs}
 
         # Check if all files are converted to File
-        if any(filter(lambda v: isinstance(v, dict), user_inputs.values())):
-            raise ValueError("Invalid input type")
-        if any(
-            filter(lambda v: isinstance(v, dict), filter(lambda item: isinstance(item, list), user_inputs.values()))
-        ):
-            raise ValueError("Invalid input type")
+        invalid_dict_keys = [
+            k
+            for k, v in user_inputs.items()
+            if isinstance(v, dict)
+            and entity_dictionary[k].type not in {VariableEntityType.FILE, VariableEntityType.JSON_OBJECT}
+        ]
+        if invalid_dict_keys:
+            raise ValueError(f"Invalid input type for {invalid_dict_keys}")
+
+        invalid_list_dict_keys = [
+            k
+            for k, v in user_inputs.items()
+            if isinstance(v, list)
+            and any(isinstance(item, dict) for item in v)
+            and entity_dictionary[k].type != VariableEntityType.FILE_LIST
+        ]
+        if invalid_list_dict_keys:
+            raise ValueError(f"Invalid input type for {invalid_list_dict_keys}")
 
         return user_inputs
 
@@ -178,12 +189,8 @@ class BaseAppGenerator:
                     elif value == 0:
                         value = False
             case VariableEntityType.JSON_OBJECT:
-                if not isinstance(value, str):
-                    raise ValueError(f"{variable_entity.variable} in input form must be a string")
-                try:
-                    json.loads(value)
-                except json.JSONDecodeError:
-                    raise ValueError(f"{variable_entity.variable} in input form must be a valid JSON object")
+                if value and not isinstance(value, dict):
+                    raise ValueError(f"{variable_entity.variable} in input form must be a dict")
             case _:
                 raise AssertionError("this statement should be unreachable.")
 

api/core/app/apps/pipeline/pipeline_runner.py

@@ -9,13 +9,13 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
     RagPipelineGenerateEntity,
 )
+from core.app.workflow.node_factory import DifyNodeFactory
 from core.variables.variables import RAGPipelineVariable, RAGPipelineVariableInput
 from core.workflow.entities.graph_init_params import GraphInitParams
 from core.workflow.enums import WorkflowType
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.persistence import PersistenceWorkflowInfo, WorkflowPersistenceLayer
 from core.workflow.graph_events import GraphEngineEvent, GraphRunFailedEvent
-from core.workflow.nodes.node_factory import DifyNodeFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
 from core.workflow.runtime import GraphRuntimeState, VariablePool

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

@@ -10,7 +10,7 @@ from typing import Any, Literal, Union, cast, overload
 from flask import Flask, current_app
 from pydantic import ValidationError
 from sqlalchemy import select
-from sqlalchemy.orm import Session, sessionmaker
+from sqlalchemy.orm import sessionmaker
 
 import contexts
 from configs import dify_config
@@ -25,6 +25,7 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
+from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
@@ -492,7 +493,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :return:
         """
         with preserve_flask_contexts(flask_app, context_vars=context):
-            with Session(db.engine, expire_on_commit=False) as session:
+            with session_factory.create_session() as session:
                 workflow = session.scalar(
                     select(Workflow).where(
                         Workflow.tenant_id == application_generate_entity.app_config.tenant_id,

api/core/app/apps/workflow_app_runner.py

@@ -25,6 +25,7 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
+from core.app.workflow.node_factory import DifyNodeFactory
 from core.workflow.entities import GraphInitParams
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -53,7 +54,6 @@ from core.workflow.graph_events import (
 )
 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.runtime import GraphRuntimeState, VariablePool
 from core.workflow.system_variable import SystemVariable
@@ -166,18 +166,22 @@ class WorkflowBasedAppRunner:
 
         # Determine which type of single node execution and get graph/variable_pool
         if single_iteration_run:
-            graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
+            graph, variable_pool = self._get_graph_and_variable_pool_for_single_node_run(
                 workflow=workflow,
                 node_id=single_iteration_run.node_id,
                 user_inputs=dict(single_iteration_run.inputs),
                 graph_runtime_state=graph_runtime_state,
+                node_type_filter_key="iteration_id",
+                node_type_label="iteration",
             )
         elif single_loop_run:
-            graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
+            graph, variable_pool = self._get_graph_and_variable_pool_for_single_node_run(
                 workflow=workflow,
                 node_id=single_loop_run.node_id,
                 user_inputs=dict(single_loop_run.inputs),
                 graph_runtime_state=graph_runtime_state,
+                node_type_filter_key="loop_id",
+                node_type_label="loop",
             )
         else:
             raise ValueError("Neither single_iteration_run nor single_loop_run is specified")
@@ -314,44 +318,6 @@ class WorkflowBasedAppRunner:
 
         return graph, variable_pool
 
-    def _get_graph_and_variable_pool_of_single_iteration(
-        self,
-        workflow: Workflow,
-        node_id: str,
-        user_inputs: dict[str, Any],
-        graph_runtime_state: GraphRuntimeState,
-    ) -> tuple[Graph, VariablePool]:
-        """
-        Get variable pool of single iteration
-        """
-        return self._get_graph_and_variable_pool_for_single_node_run(
-            workflow=workflow,
-            node_id=node_id,
-            user_inputs=user_inputs,
-            graph_runtime_state=graph_runtime_state,
-            node_type_filter_key="iteration_id",
-            node_type_label="iteration",
-        )
-
-    def _get_graph_and_variable_pool_of_single_loop(
-        self,
-        workflow: Workflow,
-        node_id: str,
-        user_inputs: dict[str, Any],
-        graph_runtime_state: GraphRuntimeState,
-    ) -> tuple[Graph, VariablePool]:
-        """
-        Get variable pool of single loop
-        """
-        return self._get_graph_and_variable_pool_for_single_node_run(
-            workflow=workflow,
-            node_id=node_id,
-            user_inputs=user_inputs,
-            graph_runtime_state=graph_runtime_state,
-            node_type_filter_key="loop_id",
-            node_type_label="loop",
-        )
-
     def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent):
         """
         Handle event

api/core/app/layers/conversation_variable_persist_layer.py

@@ -1,6 +1,6 @@
 import logging
 
-from core.variables import Variable
+from core.variables import VariableBase
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
 from core.workflow.conversation_variable_updater import ConversationVariableUpdater
 from core.workflow.enums import NodeType
@@ -44,7 +44,7 @@ class ConversationVariablePersistenceLayer(GraphEngineLayer):
             if selector[0] != CONVERSATION_VARIABLE_NODE_ID:
                 continue
             variable = self.graph_runtime_state.variable_pool.get(selector)
-            if not isinstance(variable, Variable):
+            if not isinstance(variable, VariableBase):
                 logger.warning(
                     "Conversation variable not found in variable pool. selector=%s",
                     selector,

api/core/app/layers/trigger_post_layer.py

@@ -3,8 +3,8 @@ from datetime import UTC, datetime
 from typing import Any, ClassVar
 
 from pydantic import TypeAdapter
-from sqlalchemy.orm import Session, sessionmaker
 
+from core.db.session_factory import session_factory
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.graph_events.base import GraphEngineEvent
 from core.workflow.graph_events.graph import GraphRunFailedEvent, GraphRunPausedEvent, GraphRunSucceededEvent
@@ -31,13 +31,11 @@ class TriggerPostLayer(GraphEngineLayer):
         cfs_plan_scheduler_entity: AsyncWorkflowCFSPlanEntity,
         start_time: datetime,
         trigger_log_id: str,
-        session_maker: sessionmaker[Session],
     ):
         super().__init__()
         self.trigger_log_id = trigger_log_id
         self.start_time = start_time
         self.cfs_plan_scheduler_entity = cfs_plan_scheduler_entity
-        self.session_maker = session_maker
 
     def on_graph_start(self):
         pass
@@ -47,7 +45,7 @@ class TriggerPostLayer(GraphEngineLayer):
         Update trigger log with success or failure.
         """
         if isinstance(event, tuple(self._STATUS_MAP.keys())):
-            with self.session_maker() as session:
+            with session_factory.create_session() as session:
                 repo = SQLAlchemyWorkflowTriggerLogRepository(session)
                 trigger_log = repo.get_by_id(self.trigger_log_id)
                 if not trigger_log:

api/core/app/workflow/__init__.py

@@ -0,0 +1,3 @@
+from .node_factory import DifyNodeFactory
+
+__all__ = ["DifyNodeFactory"]

api/core/app/workflow/node_factory.py

@@ -1,16 +1,22 @@
-from collections.abc import Sequence
+from collections.abc import Callable, Sequence
 from typing import TYPE_CHECKING, final
 
 from typing_extensions import override
 
 from configs import dify_config
+from core.file import file_manager
+from core.helper import ssrf_proxy
 from core.helper.code_executor.code_executor import CodeExecutor
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
+from core.tools.tool_file_manager import ToolFileManager
 from core.workflow.enums import NodeType
 from core.workflow.graph import NodeFactory
 from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.code.code_node import CodeNode
 from core.workflow.nodes.code.limits import CodeNodeLimits
+from core.workflow.nodes.http_request.node import HttpRequestNode
+from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
+from core.workflow.nodes.protocols import FileManagerProtocol, HttpClientProtocol
 from core.workflow.nodes.template_transform.template_renderer import (
     CodeExecutorJinja2TemplateRenderer,
     Jinja2TemplateRenderer,
@@ -18,8 +24,6 @@ from core.workflow.nodes.template_transform.template_renderer import (
 from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
 from libs.typing import is_str, is_str_dict
 
-from .node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
-
 if TYPE_CHECKING:
     from core.workflow.entities import GraphInitParams
     from core.workflow.runtime import GraphRuntimeState
@@ -43,6 +47,9 @@ class DifyNodeFactory(NodeFactory):
         code_providers: Sequence[type[CodeNodeProvider]] | None = None,
         code_limits: CodeNodeLimits | None = None,
         template_renderer: Jinja2TemplateRenderer | None = None,
+        http_request_http_client: HttpClientProtocol = ssrf_proxy,
+        http_request_tool_file_manager_factory: Callable[[], ToolFileManager] = ToolFileManager,
+        http_request_file_manager: FileManagerProtocol = file_manager,
     ) -> None:
         self.graph_init_params = graph_init_params
         self.graph_runtime_state = graph_runtime_state
@@ -61,6 +68,9 @@ class DifyNodeFactory(NodeFactory):
             max_object_array_length=dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH,
         )
         self._template_renderer = template_renderer or CodeExecutorJinja2TemplateRenderer()
+        self._http_request_http_client = http_request_http_client
+        self._http_request_tool_file_manager_factory = http_request_tool_file_manager_factory
+        self._http_request_file_manager = http_request_file_manager
 
     @override
     def create_node(self, node_config: dict[str, object]) -> Node:
@@ -113,6 +123,7 @@ class DifyNodeFactory(NodeFactory):
                 code_providers=self._code_providers,
                 code_limits=self._code_limits,
             )
+
         if node_type == NodeType.TEMPLATE_TRANSFORM:
             return TemplateTransformNode(
                 id=node_id,
@@ -122,6 +133,17 @@ class DifyNodeFactory(NodeFactory):
                 template_renderer=self._template_renderer,
             )
 
+        if node_type == NodeType.HTTP_REQUEST:
+            return HttpRequestNode(
+                id=node_id,
+                config=node_config,
+                graph_init_params=self.graph_init_params,
+                graph_runtime_state=self.graph_runtime_state,
+                http_client=self._http_request_http_client,
+                tool_file_manager_factory=self._http_request_tool_file_manager_factory,
+                file_manager=self._http_request_file_manager,
+            )
+
         return node_class(
             id=node_id,
             config=node_config,

api/core/helper/ssrf_proxy.py

@@ -33,6 +33,10 @@ class MaxRetriesExceededError(ValueError):
     pass
 
 
+request_error = httpx.RequestError
+max_retries_exceeded_error = MaxRetriesExceededError
+
+
 def _create_proxy_mounts() -> dict[str, httpx.HTTPTransport]:
     return {
         "http://": httpx.HTTPTransport(

api/core/llm_generator/llm_generator.py

@@ -71,8 +71,8 @@ class LLMGenerator:
             response: LLMResult = model_instance.invoke_llm(
                 prompt_messages=list(prompts), model_parameters={"max_tokens": 500, "temperature": 1}, stream=False
             )
-        answer = cast(str, response.message.content)
-        if answer is None:
+        answer = response.message.get_text_content()
+        if answer == "":
             return ""
         try:
             result_dict = json.loads(answer)
@@ -184,7 +184,7 @@ class LLMGenerator:
                     prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
                 )
 
-                rule_config["prompt"] = cast(str, response.message.content)
+                rule_config["prompt"] = response.message.get_text_content()
 
             except InvokeError as e:
                 error = str(e)
@@ -237,13 +237,11 @@ class LLMGenerator:
 
                 return rule_config
 
-            rule_config["prompt"] = cast(str, prompt_content.message.content)
+            rule_config["prompt"] = prompt_content.message.get_text_content()
 
-            if not isinstance(prompt_content.message.content, str):
-                raise NotImplementedError("prompt content is not a string")
             parameter_generate_prompt = parameter_template.format(
                 inputs={
-                    "INPUT_TEXT": prompt_content.message.content,
+                    "INPUT_TEXT": prompt_content.message.get_text_content(),
                 },
                 remove_template_variables=False,
             )
@@ -253,7 +251,7 @@ class LLMGenerator:
             statement_generate_prompt = statement_template.format(
                 inputs={
                     "TASK_DESCRIPTION": instruction,
-                    "INPUT_TEXT": prompt_content.message.content,
+                    "INPUT_TEXT": prompt_content.message.get_text_content(),
                 },
                 remove_template_variables=False,
             )
@@ -263,7 +261,7 @@ class LLMGenerator:
                 parameter_content: LLMResult = model_instance.invoke_llm(
                     prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
                 )
-                rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
+                rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.get_text_content())
             except InvokeError as e:
                 error = str(e)
                 error_step = "generate variables"
@@ -272,7 +270,7 @@ class LLMGenerator:
                 statement_content: LLMResult = model_instance.invoke_llm(
                     prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
                 )
-                rule_config["opening_statement"] = cast(str, statement_content.message.content)
+                rule_config["opening_statement"] = statement_content.message.get_text_content()
             except InvokeError as e:
                 error = str(e)
                 error_step = "generate conversation opener"
@@ -315,7 +313,7 @@ class LLMGenerator:
                 prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
             )
 
-            generated_code = cast(str, response.message.content)
+            generated_code = response.message.get_text_content()
             return {"code": generated_code, "language": code_language, "error": ""}
 
         except InvokeError as e:
@@ -351,7 +349,7 @@ class LLMGenerator:
             raise TypeError("Expected LLMResult when stream=False")
         response = result
 
-        answer = cast(str, response.message.content)
+        answer = response.message.get_text_content()
         return answer.strip()
 
     @classmethod
@@ -375,10 +373,7 @@ class LLMGenerator:
                 prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
             )
 
-            raw_content = response.message.content
-
-            if not isinstance(raw_content, str):
-                raise ValueError(f"LLM response content must be a string, got: {type(raw_content)}")
+            raw_content = response.message.get_text_content()
 
             try:
                 parsed_content = json.loads(raw_content)

api/core/model_runtime/entities/message_entities.py

@@ -251,10 +251,7 @@ class AssistantPromptMessage(PromptMessage):
 
         :return: True if prompt message is empty, False otherwise
         """
-        if not super().is_empty() and not self.tool_calls:
-            return False
-
-        return True
+        return super().is_empty() and not self.tool_calls
 
 
 class SystemPromptMessage(PromptMessage):

api/core/ops/aliyun_trace/aliyun_trace.py

@@ -1,6 +1,7 @@
 import logging
 from collections.abc import Sequence
 
+from opentelemetry.trace import SpanKind
 from sqlalchemy.orm import sessionmaker
 
 from core.ops.aliyun_trace.data_exporter.traceclient import (
@@ -54,7 +55,7 @@ from core.ops.entities.trace_entity import (
     ToolTraceInfo,
     WorkflowTraceInfo,
 )
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.entities import WorkflowNodeExecution
 from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
@@ -151,6 +152,7 @@ class AliyunDataTrace(BaseTraceInstance):
             ),
             status=status,
             links=trace_metadata.links,
+            span_kind=SpanKind.SERVER,
         )
         self.trace_client.add_span(message_span)
 
@@ -273,7 +275,7 @@ class AliyunDataTrace(BaseTraceInstance):
         service_account = self.get_service_account_with_tenant(app_id)
 
         session_factory = sessionmaker(bind=db.engine)
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=service_account,
             app_id=app_id,
@@ -456,6 +458,7 @@ class AliyunDataTrace(BaseTraceInstance):
                 ),
                 status=status,
                 links=trace_metadata.links,
+                span_kind=SpanKind.SERVER,
             )
             self.trace_client.add_span(message_span)
 
@@ -475,6 +478,7 @@ class AliyunDataTrace(BaseTraceInstance):
             ),
             status=status,
             links=trace_metadata.links,
+            span_kind=SpanKind.SERVER if message_span_id is None else SpanKind.INTERNAL,
         )
         self.trace_client.add_span(workflow_span)
 

api/core/ops/aliyun_trace/data_exporter/traceclient.py

@@ -166,7 +166,7 @@ class SpanBuilder:
             attributes=span_data.attributes,
             events=span_data.events,
             links=span_data.links,
-            kind=trace_api.SpanKind.INTERNAL,
+            kind=span_data.span_kind,
             status=span_data.status,
             start_time=span_data.start_time,
             end_time=span_data.end_time,

api/core/ops/aliyun_trace/entities/aliyun_trace_entity.py

@@ -4,7 +4,7 @@ from typing import Any
 
 from opentelemetry import trace as trace_api
 from opentelemetry.sdk.trace import Event
-from opentelemetry.trace import Status, StatusCode
+from opentelemetry.trace import SpanKind, Status, StatusCode
 from pydantic import BaseModel, Field
 
 
@@ -34,3 +34,4 @@ class SpanData(BaseModel):
     status: Status = Field(default=Status(StatusCode.UNSET), description="The status of the span.")
     start_time: int | None = Field(..., description="The start time of the span in nanoseconds.")
     end_time: int | None = Field(..., description="The end time of the span in nanoseconds.")
+    span_kind: SpanKind = Field(default=SpanKind.INTERNAL, description="The OpenTelemetry SpanKind for this span.")

api/core/ops/ops_trace_manager.py

@@ -35,7 +35,6 @@ 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
-from repositories.factory import DifyAPIRepositoryFactory
 from tasks.ops_trace_task import process_trace_tasks
 
 if TYPE_CHECKING:
@@ -473,6 +472,9 @@ class TraceTask:
         if cls._workflow_run_repo is None:
             with cls._repo_lock:
                 if cls._workflow_run_repo is None:
+                    # Lazy import to avoid circular import during module initialization
+                    from repositories.factory import DifyAPIRepositoryFactory
+
                     session_maker = sessionmaker(bind=db.engine, expire_on_commit=False)
                     cls._workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
         return cls._workflow_run_repo

api/core/plugin/impl/base.py

@@ -320,18 +320,17 @@ class BasePluginClient:
             case PluginInvokeError.__name__:
                 error_object = json.loads(message)
                 invoke_error_type = error_object.get("error_type")
-                args = error_object.get("args")
                 match invoke_error_type:
                     case InvokeRateLimitError.__name__:
-                        raise InvokeRateLimitError(description=args.get("description"))
+                        raise InvokeRateLimitError(description=error_object.get("message"))
                     case InvokeAuthorizationError.__name__:
-                        raise InvokeAuthorizationError(description=args.get("description"))
+                        raise InvokeAuthorizationError(description=error_object.get("message"))
                     case InvokeBadRequestError.__name__:
-                        raise InvokeBadRequestError(description=args.get("description"))
+                        raise InvokeBadRequestError(description=error_object.get("message"))
                     case InvokeConnectionError.__name__:
-                        raise InvokeConnectionError(description=args.get("description"))
+                        raise InvokeConnectionError(description=error_object.get("message"))
                     case InvokeServerUnavailableError.__name__:
-                        raise InvokeServerUnavailableError(description=args.get("description"))
+                        raise InvokeServerUnavailableError(description=error_object.get("message"))
                     case CredentialsValidateFailedError.__name__:
                         raise CredentialsValidateFailedError(error_object.get("message"))
                     case EndpointSetupFailedError.__name__:
@@ -339,11 +338,11 @@ class BasePluginClient:
                     case TriggerProviderCredentialValidationError.__name__:
                         raise TriggerProviderCredentialValidationError(error_object.get("message"))
                     case TriggerPluginInvokeError.__name__:
-                        raise TriggerPluginInvokeError(description=error_object.get("description"))
+                        raise TriggerPluginInvokeError(description=error_object.get("message"))
                     case TriggerInvokeError.__name__:
                         raise TriggerInvokeError(error_object.get("message"))
                     case EventIgnoreError.__name__:
-                        raise EventIgnoreError(description=error_object.get("description"))
+                        raise EventIgnoreError(description=error_object.get("message"))
                     case _:
                         raise PluginInvokeError(description=message)
             case PluginDaemonInternalServerError.__name__:

api/core/plugin/impl/endpoint.py

@@ -1,5 +1,6 @@
 from core.plugin.entities.endpoint import EndpointEntityWithInstance
 from core.plugin.impl.base import BasePluginClient
+from core.plugin.impl.exc import PluginDaemonInternalServerError
 
 
 class PluginEndpointClient(BasePluginClient):
@@ -70,18 +71,27 @@ class PluginEndpointClient(BasePluginClient):
     def delete_endpoint(self, tenant_id: str, user_id: str, endpoint_id: str):
         """
         Delete the given endpoint.
+
+        This operation is idempotent: if the endpoint is already deleted (record not found),
+        it will return True instead of raising an error.
         """
-        return self._request_with_plugin_daemon_response(
-            "POST",
-            f"plugin/{tenant_id}/endpoint/remove",
-            bool,
-            data={
-                "endpoint_id": endpoint_id,
-            },
-            headers={
-                "Content-Type": "application/json",
-            },
-        )
+        try:
+            return self._request_with_plugin_daemon_response(
+                "POST",
+                f"plugin/{tenant_id}/endpoint/remove",
+                bool,
+                data={
+                    "endpoint_id": endpoint_id,
+                },
+                headers={
+                    "Content-Type": "application/json",
+                },
+            )
+        except PluginDaemonInternalServerError as e:
+            # Make delete idempotent: if record is not found, consider it a success
+            if "record not found" in str(e.description).lower():
+                return True
+            raise
 
     def enable_endpoint(self, tenant_id: str, user_id: str, endpoint_id: str):
         """

api/core/rag/datasource/vdb/iris/iris_vector.py

@@ -154,7 +154,7 @@ class IrisConnectionPool:
                 # Add to cache to skip future checks
                 self._schemas_initialized.add(schema)
 
-            except Exception as e:
+            except Exception:
                 conn.rollback()
                 logger.exception("Failed to ensure schema %s exists", schema)
                 raise
@@ -177,6 +177,9 @@ class IrisConnectionPool:
 class IrisVector(BaseVector):
     """IRIS vector database implementation using native VECTOR type and HNSW indexing."""
 
+    # Fallback score for full-text search when Rank function unavailable or TEXT_INDEX disabled
+    _FULL_TEXT_FALLBACK_SCORE = 0.5
+
     def __init__(self, collection_name: str, config: IrisVectorConfig) -> None:
         super().__init__(collection_name)
         self.config = config
@@ -272,41 +275,131 @@ class IrisVector(BaseVector):
             return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        """Search documents by full-text using iFind index or fallback to LIKE search."""
+        """Search documents by full-text using iFind index with BM25 relevance scoring.
+
+        When IRIS_TEXT_INDEX is enabled, this method uses the auto-generated Rank
+        function from %iFind.Index.Basic to calculate BM25 relevance scores. The Rank
+        function is automatically created with naming: {schema}.{table_name}_{index}Rank
+
+        Args:
+            query: Search query string
+            **kwargs: Optional parameters including top_k, document_ids_filter
+
+        Returns:
+            List of Document objects with relevance scores in metadata["score"]
+        """
         top_k = kwargs.get("top_k", 5)
+        document_ids_filter = kwargs.get("document_ids_filter")
 
         with self._get_cursor() as cursor:
             if self.config.IRIS_TEXT_INDEX:
-                # Use iFind full-text search with index
+                # Use iFind full-text search with auto-generated Rank function
                 text_index_name = f"idx_{self.table_name}_text"
+                # IRIS removes underscores from function names
+                table_no_underscore = self.table_name.replace("_", "")
+                index_no_underscore = text_index_name.replace("_", "")
+                rank_function = f"{self.schema}.{table_no_underscore}_{index_no_underscore}Rank"
+
+                # Build WHERE clause with document ID filter if provided
+                where_clause = f"WHERE %ID %FIND search_index({text_index_name}, ?)"
+                # First param for Rank function, second for FIND
+                params = [query, query]
+
+                if document_ids_filter:
+                    # Add document ID filter
+                    placeholders = ",".join("?" * len(document_ids_filter))
+                    where_clause += f" AND JSON_VALUE(meta, '$.document_id') IN ({placeholders})"
+                    params.extend(document_ids_filter)
+
                 sql = f"""
-                    SELECT TOP {top_k} id, text, meta
+                    SELECT TOP {top_k}
+                        id,
+                        text,
+                        meta,
+                        {rank_function}(%ID, ?) AS score
                     FROM {self.schema}.{self.table_name}
-                    WHERE %ID %FIND search_index({text_index_name}, ?)
+                    {where_clause}
+                    ORDER BY score DESC
                 """
-                cursor.execute(sql, (query,))
+
+                logger.debug(
+                    "iFind search: query='%s', index='%s', rank='%s'",
+                    query,
+                    text_index_name,
+                    rank_function,
+                )
+
+                try:
+                    cursor.execute(sql, params)
+                except Exception:  # pylint: disable=broad-exception-caught
+                    # Fallback to query without Rank function if it fails
+                    logger.warning(
+                        "Rank function '%s' failed, using fixed score",
+                        rank_function,
+                        exc_info=True,
+                    )
+                    sql_fallback = f"""
+                        SELECT TOP {top_k} id, text, meta, {self._FULL_TEXT_FALLBACK_SCORE} AS score
+                        FROM {self.schema}.{self.table_name}
+                        {where_clause}
+                    """
+                    # Skip first param (for Rank function)
+                    cursor.execute(sql_fallback, params[1:])
             else:
-                # Fallback to LIKE search (inefficient for large datasets)
-                # Escape special characters for LIKE clause to prevent SQL injection
-                from libs.helper import escape_like_pattern
+                # Fallback to LIKE search (IRIS_TEXT_INDEX disabled)
+                from libs.helper import (  # pylint: disable=import-outside-toplevel
+                    escape_like_pattern,
+                )
 
                 escaped_query = escape_like_pattern(query)
                 query_pattern = f"%{escaped_query}%"
+
+                # Build WHERE clause with document ID filter if provided
+                where_clause = "WHERE text LIKE ? ESCAPE '\\\\'"
+                params = [query_pattern]
+
+                if document_ids_filter:
+                    placeholders = ",".join("?" * len(document_ids_filter))
+                    where_clause += f" AND JSON_VALUE(meta, '$.document_id') IN ({placeholders})"
+                    params.extend(document_ids_filter)
+
                 sql = f"""
-                    SELECT TOP {top_k} id, text, meta
+                    SELECT TOP {top_k} id, text, meta, {self._FULL_TEXT_FALLBACK_SCORE} AS score
                     FROM {self.schema}.{self.table_name}
-                    WHERE text LIKE ? ESCAPE '\\'
+                    {where_clause}
+                    ORDER BY LENGTH(text) ASC
                 """
-                cursor.execute(sql, (query_pattern,))
+
+                logger.debug(
+                    "LIKE fallback (TEXT_INDEX disabled): query='%s'",
+                    query_pattern,
+                )
+                cursor.execute(sql, params)
 
             docs = []
             for row in cursor.fetchall():
-                if len(row) >= 3:
-                    metadata = json.loads(row[2]) if row[2] else {}
-                    docs.append(Document(page_content=row[1], metadata=metadata))
+                # Expecting 4 columns: id, text, meta, score
+                if len(row) >= 4:
+                    text_content = row[1]
+                    meta_str = row[2]
+                    score_value = row[3]
+
+                    metadata = json.loads(meta_str) if meta_str else {}
+                    # Add score to metadata for hybrid search compatibility
+                    score = float(score_value) if score_value is not None else 0.0
+                    metadata["score"] = score
+
+                    docs.append(Document(page_content=text_content, metadata=metadata))
+
+            logger.info(
+                "Full-text search completed: query='%s', results=%d/%d",
+                query,
+                len(docs),
+                top_k,
+            )
 
             if not docs:
-                logger.info("Full-text search for '%s' returned no results", query)
+                logger.warning("Full-text search for '%s' returned no results", query)
 
             return docs
 
@@ -370,7 +463,11 @@ class IrisVector(BaseVector):
                         AS %iFind.Index.Basic
                         (LANGUAGE = '{language}', LOWER = 1, INDEXOPTION = 0)
                     """
-                    logger.info("Creating text index: %s with language: %s", text_index_name, language)
+                    logger.info(
+                        "Creating text index: %s with language: %s",
+                        text_index_name,
+                        language,
+                    )
                     logger.info("SQL for text index: %s", sql_text_index)
                     cursor.execute(sql_text_index)
                     logger.info("Text index created successfully: %s", text_index_name)

api/core/tools/entities/tool_entities.py

@@ -130,7 +130,7 @@ class ToolInvokeMessage(BaseModel):
         text: str
 
     class JsonMessage(BaseModel):
-        json_object: dict
+        json_object: dict | list
         suppress_output: bool = Field(default=False, description="Whether to suppress JSON output in result string")
 
     class BlobMessage(BaseModel):
@@ -144,7 +144,14 @@ class ToolInvokeMessage(BaseModel):
         end: bool = Field(..., description="Whether the chunk is the last chunk")
 
     class FileMessage(BaseModel):
-        pass
+        file_marker: str = Field(default="file_marker")
+
+        @model_validator(mode="before")
+        @classmethod
+        def validate_file_message(cls, values):
+            if isinstance(values, dict) and "file_marker" not in values:
+                raise ValueError("Invalid FileMessage: missing file_marker")
+            return values
 
     class VariableMessage(BaseModel):
         variable_name: str = Field(..., description="The name of the variable")
@@ -234,10 +241,22 @@ class ToolInvokeMessage(BaseModel):
 
     @field_validator("message", mode="before")
     @classmethod
-    def decode_blob_message(cls, v):
+    def decode_blob_message(cls, v, info: ValidationInfo):
+        # 处理 blob 解码
         if isinstance(v, dict) and "blob" in v:
             with contextlib.suppress(Exception):
                 v["blob"] = base64.b64decode(v["blob"])
+
+        # Force correct message type based on type field
+        # Only wrap dict types to avoid wrapping already parsed Pydantic model objects
+        if info.data and isinstance(info.data, dict) and isinstance(v, dict):
+            msg_type = info.data.get("type")
+            if msg_type == cls.MessageType.JSON:
+                if "json_object" not in v:
+                    v = {"json_object": v}
+            elif msg_type == cls.MessageType.FILE:
+                v = {"file_marker": "file_marker"}
+
         return v
 
     @field_serializer("message")

api/core/tools/tool_engine.py

@@ -1,5 +1,6 @@
 import contextlib
 import json
+import logging
 from collections.abc import Generator, Iterable
 from copy import deepcopy
 from datetime import UTC, datetime
@@ -36,6 +37,8 @@ from extensions.ext_database import db
 from models.enums import CreatorUserRole
 from models.model import Message, MessageFile
 
+logger = logging.getLogger(__name__)
+
 
 class ToolEngine:
     """
@@ -123,25 +126,31 @@ class ToolEngine:
             # transform tool invoke message to get LLM friendly message
             return plain_text, message_files, meta
         except ToolProviderCredentialValidationError as e:
+            logger.error(e, exc_info=True)
             error_response = "Please check your tool provider credentials"
             agent_tool_callback.on_tool_error(e)
         except (ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError) as e:
             error_response = f"there is not a tool named {tool.entity.identity.name}"
+            logger.error(e, exc_info=True)
             agent_tool_callback.on_tool_error(e)
         except ToolParameterValidationError as e:
             error_response = f"tool parameters validation error: {e}, please check your tool parameters"
             agent_tool_callback.on_tool_error(e)
+            logger.error(e, exc_info=True)
         except ToolInvokeError as e:
             error_response = f"tool invoke error: {e}"
             agent_tool_callback.on_tool_error(e)
+            logger.error(e, exc_info=True)
         except ToolEngineInvokeError as e:
             meta = e.meta
             error_response = f"tool invoke error: {meta.error}"
             agent_tool_callback.on_tool_error(e)
+            logger.error(e, exc_info=True)
             return error_response, [], meta
         except Exception as e:
             error_response = f"unknown error: {e}"
             agent_tool_callback.on_tool_error(e)
+            logger.error(e, exc_info=True)
 
         return error_response, [], ToolInvokeMeta.error_instance(error_response)
 

api/core/tools/workflow_as_tool/tool.py

@@ -5,10 +5,9 @@ import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import Any, cast
 
-from flask import has_request_context
 from sqlalchemy import select
-from sqlalchemy.orm import Session
 
+from core.db.session_factory import session_factory
 from core.file import FILE_MODEL_IDENTITY, File, FileTransferMethod
 from core.model_runtime.entities.llm_entities import LLMUsage, LLMUsageMetadata
 from core.tools.__base.tool import Tool
@@ -20,9 +19,7 @@ from core.tools.entities.tool_entities import (
     ToolProviderType,
 )
 from core.tools.errors import ToolInvokeError
-from extensions.ext_database import db
 from factories.file_factory import build_from_mapping
-from libs.login import current_user
 from models import Account, Tenant
 from models.model import App, EndUser
 from models.workflow import Workflow
@@ -210,50 +207,38 @@ class WorkflowTool(Tool):
         Returns:
             Account | EndUser | None: The resolved user object, or None if resolution fails.
         """
-        if has_request_context():
-            return self._resolve_user_from_request()
-        else:
-            return self._resolve_user_from_database(user_id=user_id)
-
-    def _resolve_user_from_request(self) -> Account | EndUser | None:
-        """
-        Resolve user from Flask request context.
-        """
-        try:
-            # Note: `current_user` is a LocalProxy. Never compare it with None directly.
-            return getattr(current_user, "_get_current_object", lambda: current_user)()
-        except Exception as e:
-            logger.warning("Failed to resolve user from request context: %s", e)
-            return None
+        return self._resolve_user_from_database(user_id=user_id)
 
     def _resolve_user_from_database(self, user_id: str) -> Account | EndUser | None:
         """
         Resolve user from database (worker/Celery context).
         """
+        with session_factory.create_session() as session:
+            tenant_stmt = select(Tenant).where(Tenant.id == self.runtime.tenant_id)
+            tenant = session.scalar(tenant_stmt)
+            if not tenant:
+                return None
+
+            user_stmt = select(Account).where(Account.id == user_id)
+            user = session.scalar(user_stmt)
+            if user:
+                user.current_tenant = tenant
+                session.expunge(user)
+                return user
+
+            end_user_stmt = select(EndUser).where(EndUser.id == user_id, EndUser.tenant_id == tenant.id)
+            end_user = session.scalar(end_user_stmt)
+            if end_user:
+                session.expunge(end_user)
+                return end_user
 
-        tenant_stmt = select(Tenant).where(Tenant.id == self.runtime.tenant_id)
-        tenant = db.session.scalar(tenant_stmt)
-        if not tenant:
             return None
 
-        user_stmt = select(Account).where(Account.id == user_id)
-        user = db.session.scalar(user_stmt)
-        if user:
-            user.current_tenant = tenant
-            return user
-
-        end_user_stmt = select(EndUser).where(EndUser.id == user_id, EndUser.tenant_id == tenant.id)
-        end_user = db.session.scalar(end_user_stmt)
-        if end_user:
-            return end_user
-
-        return None
-
     def _get_workflow(self, app_id: str, version: str) -> Workflow:
         """
         get the workflow by app id and version
         """
-        with Session(db.engine, expire_on_commit=False) as session, session.begin():
+        with session_factory.create_session() as session, session.begin():
             if not version:
                 stmt = (
                     select(Workflow)
@@ -265,22 +250,24 @@ class WorkflowTool(Tool):
                 stmt = select(Workflow).where(Workflow.app_id == app_id, Workflow.version == version)
                 workflow = session.scalar(stmt)
 
-        if not workflow:
-            raise ValueError("workflow not found or not published")
+            if not workflow:
+                raise ValueError("workflow not found or not published")
 
-        return workflow
+            session.expunge(workflow)
+            return workflow
 
     def _get_app(self, app_id: str) -> App:
         """
         get the app by app id
         """
         stmt = select(App).where(App.id == app_id)
-        with Session(db.engine, expire_on_commit=False) as session, session.begin():
+        with session_factory.create_session() as session, session.begin():
             app = session.scalar(stmt)
-        if not app:
-            raise ValueError("app not found")
+            if not app:
+                raise ValueError("app not found")
 
-        return app
+            session.expunge(app)
+            return app
 
     def _transform_args(self, tool_parameters: dict) -> tuple[dict, list[dict]]:
         """

api/core/variables/__init__.py

@@ -30,6 +30,7 @@ from .variables import (
     SecretVariable,
     StringVariable,
     Variable,
+    VariableBase,
 )
 
 __all__ = [
@@ -62,4 +63,5 @@ __all__ = [
     "StringSegment",
     "StringVariable",
     "Variable",
+    "VariableBase",
 ]

api/core/variables/segments.py

@@ -232,7 +232,7 @@ def get_segment_discriminator(v: Any) -> SegmentType | None:
 # - All variants in `SegmentUnion` must inherit from the `Segment` class.
 # - The union must include all non-abstract subclasses of `Segment`, except:
 #   - `SegmentGroup`, which is not added to the variable pool.
-#   - `Variable` and its subclasses, which are handled by `VariableUnion`.
+#   - `VariableBase` and its subclasses, which are handled by `Variable`.
 SegmentUnion: TypeAlias = Annotated[
     (
         Annotated[NoneSegment, Tag(SegmentType.NONE)]

api/core/variables/variables.py

@@ -27,7 +27,7 @@ from .segments import (
 from .types import SegmentType
 
 
-class Variable(Segment):
+class VariableBase(Segment):
     """
     A variable is a segment that has a name.
 
@@ -45,23 +45,23 @@ class Variable(Segment):
     selector: Sequence[str] = Field(default_factory=list)
 
 
-class StringVariable(StringSegment, Variable):
+class StringVariable(StringSegment, VariableBase):
     pass
 
 
-class FloatVariable(FloatSegment, Variable):
+class FloatVariable(FloatSegment, VariableBase):
     pass
 
 
-class IntegerVariable(IntegerSegment, Variable):
+class IntegerVariable(IntegerSegment, VariableBase):
     pass
 
 
-class ObjectVariable(ObjectSegment, Variable):
+class ObjectVariable(ObjectSegment, VariableBase):
     pass
 
 
-class ArrayVariable(ArraySegment, Variable):
+class ArrayVariable(ArraySegment, VariableBase):
     pass
 
 
@@ -89,16 +89,16 @@ class SecretVariable(StringVariable):
         return encrypter.obfuscated_token(self.value)
 
 
-class NoneVariable(NoneSegment, Variable):
+class NoneVariable(NoneSegment, VariableBase):
     value_type: SegmentType = SegmentType.NONE
     value: None = None
 
 
-class FileVariable(FileSegment, Variable):
+class FileVariable(FileSegment, VariableBase):
     pass
 
 
-class BooleanVariable(BooleanSegment, Variable):
+class BooleanVariable(BooleanSegment, VariableBase):
     pass
 
 
@@ -139,13 +139,13 @@ class RAGPipelineVariableInput(BaseModel):
     value: Any
 
 
-# The `VariableUnion`` type is used to enable serialization and deserialization with Pydantic.
-# Use `Variable` for type hinting when serialization is not required.
+# The `Variable` type is used to enable serialization and deserialization with Pydantic.
+# Use `VariableBase` for type hinting when serialization is not required.
 #
 # Note:
-# - All variants in `VariableUnion` must inherit from the `Variable` class.
-# - The union must include all non-abstract subclasses of `Segment`, except:
-VariableUnion: TypeAlias = Annotated[
+# - All variants in `Variable` must inherit from the `VariableBase` class.
+# - The union must include all non-abstract subclasses of `VariableBase`.
+Variable: TypeAlias = Annotated[
     (
         Annotated[NoneVariable, Tag(SegmentType.NONE)]
         | Annotated[StringVariable, Tag(SegmentType.STRING)]

api/core/workflow/context/__init__.py

@@ -0,0 +1,34 @@
+"""
+Execution Context - Context management for workflow execution.
+
+This package provides Flask-independent context management for workflow
+execution in multi-threaded environments.
+"""
+
+from core.workflow.context.execution_context import (
+    AppContext,
+    ContextProviderNotFoundError,
+    ExecutionContext,
+    IExecutionContext,
+    NullAppContext,
+    capture_current_context,
+    read_context,
+    register_context,
+    register_context_capturer,
+    reset_context_provider,
+)
+from core.workflow.context.models import SandboxContext
+
+__all__ = [
+    "AppContext",
+    "ContextProviderNotFoundError",
+    "ExecutionContext",
+    "IExecutionContext",
+    "NullAppContext",
+    "SandboxContext",
+    "capture_current_context",
+    "read_context",
+    "register_context",
+    "register_context_capturer",
+    "reset_context_provider",
+]

api/core/workflow/context/execution_context.py

@@ -0,0 +1,284 @@
+"""
+Execution Context - Abstracted context management for workflow execution.
+"""
+
+import contextvars
+import threading
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator
+from contextlib import AbstractContextManager, contextmanager
+from typing import Any, Protocol, TypeVar, final, runtime_checkable
+
+from pydantic import BaseModel
+
+
+class AppContext(ABC):
+    """
+    Abstract application context interface.
+
+    This abstraction allows workflow execution to work with or without Flask
+    by providing a common interface for application context management.
+    """
+
+    @abstractmethod
+    def get_config(self, key: str, default: Any = None) -> Any:
+        """Get configuration value by key."""
+        pass
+
+    @abstractmethod
+    def get_extension(self, name: str) -> Any:
+        """Get Flask extension by name (e.g., 'db', 'cache')."""
+        pass
+
+    @abstractmethod
+    def enter(self) -> AbstractContextManager[None]:
+        """Enter the application context."""
+        pass
+
+
+@runtime_checkable
+class IExecutionContext(Protocol):
+    """
+    Protocol for execution context.
+
+    This protocol defines the interface that all execution contexts must implement,
+    allowing both ExecutionContext and FlaskExecutionContext to be used interchangeably.
+    """
+
+    def __enter__(self) -> "IExecutionContext":
+        """Enter the execution context."""
+        ...
+
+    def __exit__(self, *args: Any) -> None:
+        """Exit the execution context."""
+        ...
+
+    @property
+    def user(self) -> Any:
+        """Get user object."""
+        ...
+
+
+@final
+class ExecutionContext:
+    """
+    Execution context for workflow execution in worker threads.
+
+    This class encapsulates all context needed for workflow execution:
+    - Application context (Flask app or standalone)
+    - Context variables for Python contextvars
+    - User information (optional)
+
+    It is designed to be serializable and passable to worker threads.
+    """
+
+    def __init__(
+        self,
+        app_context: AppContext | None = None,
+        context_vars: contextvars.Context | None = None,
+        user: Any = None,
+    ) -> None:
+        """
+        Initialize execution context.
+
+        Args:
+            app_context: Application context (Flask or standalone)
+            context_vars: Python contextvars to preserve
+            user: User object (optional)
+        """
+        self._app_context = app_context
+        self._context_vars = context_vars
+        self._user = user
+        self._local = threading.local()
+
+    @property
+    def app_context(self) -> AppContext | None:
+        """Get application context."""
+        return self._app_context
+
+    @property
+    def context_vars(self) -> contextvars.Context | None:
+        """Get context variables."""
+        return self._context_vars
+
+    @property
+    def user(self) -> Any:
+        """Get user object."""
+        return self._user
+
+    @contextmanager
+    def enter(self) -> Generator[None, None, None]:
+        """
+        Enter this execution context.
+
+        This is a convenience method that creates a context manager.
+        """
+        # Restore context variables if provided
+        if self._context_vars:
+            for var, val in self._context_vars.items():
+                var.set(val)
+
+        # Enter app context if available
+        if self._app_context is not None:
+            with self._app_context.enter():
+                yield
+        else:
+            yield
+
+    def __enter__(self) -> "ExecutionContext":
+        """Enter the execution context."""
+        cm = self.enter()
+        self._local.cm = cm
+        cm.__enter__()
+        return self
+
+    def __exit__(self, *args: Any) -> None:
+        """Exit the execution context."""
+        cm = getattr(self._local, "cm", None)
+        if cm is not None:
+            cm.__exit__(*args)
+
+
+class NullAppContext(AppContext):
+    """
+    Null implementation of AppContext for non-Flask environments.
+
+    This is used when running without Flask (e.g., in tests or standalone mode).
+    """
+
+    def __init__(self, config: dict[str, Any] | None = None) -> None:
+        """
+        Initialize null app context.
+
+        Args:
+            config: Optional configuration dictionary
+        """
+        self._config = config or {}
+        self._extensions: dict[str, Any] = {}
+
+    def get_config(self, key: str, default: Any = None) -> Any:
+        """Get configuration value by key."""
+        return self._config.get(key, default)
+
+    def get_extension(self, name: str) -> Any:
+        """Get extension by name."""
+        return self._extensions.get(name)
+
+    def set_extension(self, name: str, extension: Any) -> None:
+        """Set extension by name."""
+        self._extensions[name] = extension
+
+    @contextmanager
+    def enter(self) -> Generator[None, None, None]:
+        """Enter null context (no-op)."""
+        yield
+
+
+class ExecutionContextBuilder:
+    """
+    Builder for creating ExecutionContext instances.
+
+    This provides a fluent API for building execution contexts.
+    """
+
+    def __init__(self) -> None:
+        self._app_context: AppContext | None = None
+        self._context_vars: contextvars.Context | None = None
+        self._user: Any = None
+
+    def with_app_context(self, app_context: AppContext) -> "ExecutionContextBuilder":
+        """Set application context."""
+        self._app_context = app_context
+        return self
+
+    def with_context_vars(self, context_vars: contextvars.Context) -> "ExecutionContextBuilder":
+        """Set context variables."""
+        self._context_vars = context_vars
+        return self
+
+    def with_user(self, user: Any) -> "ExecutionContextBuilder":
+        """Set user."""
+        self._user = user
+        return self
+
+    def build(self) -> ExecutionContext:
+        """Build the execution context."""
+        return ExecutionContext(
+            app_context=self._app_context,
+            context_vars=self._context_vars,
+            user=self._user,
+        )
+
+
+_capturer: Callable[[], IExecutionContext] | None = None
+
+# Tenant-scoped providers using tuple keys for clarity and constant-time lookup.
+# Key mapping:
+#   (name, tenant_id) -> provider
+# - name: namespaced identifier (recommend prefixing, e.g. "workflow.sandbox")
+# - tenant_id: tenant identifier string
+# Value:
+#   provider: Callable[[], BaseModel] returning the typed context value
+# Type-safety note:
+#   - This registry cannot enforce that all providers for a given name return the same BaseModel type.
+#   - Implementors SHOULD provide typed wrappers around register/read (like Go's context best practice),
+#     e.g. def register_sandbox_ctx(tenant_id: str, p: Callable[[], SandboxContext]) and
+#          def read_sandbox_ctx(tenant_id: str) -> SandboxContext.
+_tenant_context_providers: dict[tuple[str, str], Callable[[], BaseModel]] = {}
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class ContextProviderNotFoundError(KeyError):
+    """Raised when a tenant-scoped context provider is missing for a given (name, tenant_id)."""
+
+    pass
+
+
+def register_context_capturer(capturer: Callable[[], IExecutionContext]) -> None:
+    """Register a single enterable execution context capturer (e.g., Flask)."""
+    global _capturer
+    _capturer = capturer
+
+
+def register_context(name: str, tenant_id: str, provider: Callable[[], BaseModel]) -> None:
+    """Register a tenant-specific provider for a named context.
+
+    Tip: use a namespaced "name" (e.g., "workflow.sandbox") to avoid key collisions.
+    Consider adding a typed wrapper for this registration in your feature module.
+    """
+    _tenant_context_providers[(name, tenant_id)] = provider
+
+
+def read_context(name: str, *, tenant_id: str) -> BaseModel:
+    """
+    Read a context value for a specific tenant.
+
+    Raises KeyError if the provider for (name, tenant_id) is not registered.
+    """
+    prov = _tenant_context_providers.get((name, tenant_id))
+    if prov is None:
+        raise ContextProviderNotFoundError(f"Context provider '{name}' not registered for tenant '{tenant_id}'")
+    return prov()
+
+
+def capture_current_context() -> IExecutionContext:
+    """
+    Capture current execution context from the calling environment.
+
+    If a capturer is registered (e.g., Flask), use it. Otherwise, return a minimal
+    context with NullAppContext + copy of current contextvars.
+    """
+    if _capturer is None:
+        return ExecutionContext(
+            app_context=NullAppContext(),
+            context_vars=contextvars.copy_context(),
+        )
+    return _capturer()
+
+
+def reset_context_provider() -> None:
+    """Reset the capturer and all tenant-scoped context providers (primarily for tests)."""
+    global _capturer
+    _capturer = None
+    _tenant_context_providers.clear()

api/core/workflow/context/models.py

@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+from pydantic import AnyHttpUrl, BaseModel
+
+
+class SandboxContext(BaseModel):
+    """Typed context for sandbox integration. All fields optional by design."""
+
+    sandbox_url: AnyHttpUrl | None = None
+    sandbox_token: str | None = None  # optional, if later needed for auth
+
+
+__all__ = ["SandboxContext"]

api/core/workflow/conversation_variable_updater.py

@@ -1,7 +1,7 @@
 import abc
 from typing import Protocol
 
-from core.variables import Variable
+from core.variables import VariableBase
 
 
 class ConversationVariableUpdater(Protocol):
@@ -20,12 +20,12 @@ class ConversationVariableUpdater(Protocol):
     """
 
     @abc.abstractmethod
-    def update(self, conversation_id: str, variable: "Variable"):
+    def update(self, conversation_id: str, variable: "VariableBase"):
         """
         Updates the value of the specified conversation variable in the underlying storage.
 
         :param conversation_id: The ID of the conversation to update. Typically references `ConversationVariable.id`.
-        :param variable: The `Variable` instance containing the updated value.
+        :param variable: The `VariableBase` instance containing the updated value.
         """
         pass
 

api/core/workflow/enums.py

@@ -211,6 +211,10 @@ class WorkflowExecutionStatus(StrEnum):
     def is_ended(self) -> bool:
         return self in _END_STATE
 
+    @classmethod
+    def ended_values(cls) -> list[str]:
+        return [status.value for status in _END_STATE]
+
 
 _END_STATE = frozenset(
     [

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

@@ -11,7 +11,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from core.variables.variables import VariableUnion
+from core.variables.variables import Variable
 
 
 class CommandType(StrEnum):
@@ -46,7 +46,7 @@ class PauseCommand(GraphEngineCommand):
 class VariableUpdate(BaseModel):
     """Represents a single variable update instruction."""
 
-    value: VariableUnion = Field(description="New variable value")
+    value: Variable = Field(description="New variable value")
 
 
 class UpdateVariablesCommand(GraphEngineCommand):

api/core/workflow/graph_engine/graph_engine.py

@@ -7,15 +7,13 @@ Domain-Driven Design principles for improved maintainability and testability.
 
 from __future__ import annotations
 
-import contextvars
 import logging
 import queue
 import threading
 from collections.abc import Generator
 from typing import TYPE_CHECKING, cast, final
 
-from flask import Flask, current_app
-
+from core.workflow.context import capture_current_context
 from core.workflow.enums import NodeExecutionType
 from core.workflow.graph import Graph
 from core.workflow.graph_events import (
@@ -159,17 +157,8 @@ class GraphEngine:
         self._layers: list[GraphEngineLayer] = []
 
         # === Worker Pool Setup ===
-        # Capture Flask app context for worker threads
-        flask_app: Flask | None = None
-        try:
-            app = current_app._get_current_object()  # type: ignore
-            if isinstance(app, Flask):
-                flask_app = app
-        except RuntimeError:
-            pass
-
-        # Capture context variables for worker threads
-        context_vars = contextvars.copy_context()
+        # Capture execution context for worker threads
+        execution_context = capture_current_context()
 
         # Create worker pool for parallel node execution
         self._worker_pool = WorkerPool(
@@ -177,8 +166,7 @@ class GraphEngine:
             event_queue=self._event_queue,
             graph=self._graph,
             layers=self._layers,
-            flask_app=flask_app,
-            context_vars=context_vars,
+            execution_context=execution_context,
             min_workers=self._min_workers,
             max_workers=self._max_workers,
             scale_up_threshold=self._scale_up_threshold,

api/core/workflow/graph_engine/worker.py

@@ -5,26 +5,26 @@ Workers pull node IDs from the ready_queue, execute nodes, and push events
 to the event_queue for the dispatcher to process.
 """
 
-import contextvars
 import queue
 import threading
 import time
 from collections.abc import Sequence
 from datetime import datetime
-from typing import final
-from uuid import uuid4
+from typing import TYPE_CHECKING, final
 
-from flask import Flask
 from typing_extensions import override
 
+from core.workflow.context import IExecutionContext
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.graph_events import GraphNodeEventBase, NodeRunFailedEvent
 from core.workflow.nodes.base.node import Node
-from libs.flask_utils import preserve_flask_contexts
 
 from .ready_queue import ReadyQueue
 
+if TYPE_CHECKING:
+    pass
+
 
 @final
 class Worker(threading.Thread):
@@ -44,8 +44,7 @@ class Worker(threading.Thread):
         layers: Sequence[GraphEngineLayer],
         stop_event: threading.Event,
         worker_id: int = 0,
-        flask_app: Flask | None = None,
-        context_vars: contextvars.Context | None = None,
+        execution_context: IExecutionContext | None = None,
     ) -> None:
         """
         Initialize worker thread.
@@ -56,19 +55,17 @@ class Worker(threading.Thread):
             graph: Graph containing nodes to execute
             layers: Graph engine layers for node execution hooks
             worker_id: Unique identifier for this worker
-            flask_app: Optional Flask application for context preservation
-            context_vars: Optional context variables to preserve in worker thread
+            execution_context: Optional execution context for context preservation
         """
         super().__init__(name=f"GraphWorker-{worker_id}", daemon=True)
         self._ready_queue = ready_queue
         self._event_queue = event_queue
         self._graph = graph
         self._worker_id = worker_id
-        self._flask_app = flask_app
-        self._context_vars = context_vars
-        self._last_task_time = time.time()
+        self._execution_context = execution_context
         self._stop_event = stop_event
         self._layers = layers if layers is not None else []
+        self._last_task_time = time.time()
 
     def stop(self) -> None:
         """Worker is controlled via shared stop_event from GraphEngine.
@@ -115,7 +112,7 @@ class Worker(threading.Thread):
                 self._ready_queue.task_done()
             except Exception as e:
                 error_event = NodeRunFailedEvent(
-                    id=str(uuid4()),
+                    id=node.execution_id,
                     node_id=node.id,
                     node_type=node.node_type,
                     in_iteration_id=None,
@@ -135,11 +132,9 @@ class Worker(threading.Thread):
 
         error: Exception | None = None
 
-        if self._flask_app and self._context_vars:
-            with preserve_flask_contexts(
-                flask_app=self._flask_app,
-                context_vars=self._context_vars,
-            ):
+        # Execute the node with preserved context if execution context is provided
+        if self._execution_context is not None:
+            with self._execution_context:
                 self._invoke_node_run_start_hooks(node)
                 try:
                     node_events = node.run()

api/core/workflow/graph_engine/worker_management/worker_pool.py

@@ -8,9 +8,10 @@ DynamicScaler, and WorkerFactory into a single class.
 import logging
 import queue
 import threading
-from typing import TYPE_CHECKING, final
+from typing import final
 
 from configs import dify_config
+from core.workflow.context import IExecutionContext
 from core.workflow.graph import Graph
 from core.workflow.graph_events import GraphNodeEventBase
 
@@ -20,11 +21,6 @@ from ..worker import Worker
 
 logger = logging.getLogger(__name__)
 
-if TYPE_CHECKING:
-    from contextvars import Context
-
-    from flask import Flask
-
 
 @final
 class WorkerPool:
@@ -42,8 +38,7 @@ class WorkerPool:
         graph: Graph,
         layers: list[GraphEngineLayer],
         stop_event: threading.Event,
-        flask_app: "Flask | None" = None,
-        context_vars: "Context | None" = None,
+        execution_context: IExecutionContext | None = None,
         min_workers: int | None = None,
         max_workers: int | None = None,
         scale_up_threshold: int | None = None,
@@ -57,8 +52,7 @@ class WorkerPool:
             event_queue: Queue for worker events
             graph: The workflow graph
             layers: Graph engine layers for node execution hooks
-            flask_app: Optional Flask app for context preservation
-            context_vars: Optional context variables
+            execution_context: Optional execution context for context preservation
             min_workers: Minimum number of workers
             max_workers: Maximum number of workers
             scale_up_threshold: Queue depth to trigger scale up
@@ -67,8 +61,7 @@ class WorkerPool:
         self._ready_queue = ready_queue
         self._event_queue = event_queue
         self._graph = graph
-        self._flask_app = flask_app
-        self._context_vars = context_vars
+        self._execution_context = execution_context
         self._layers = layers
 
         # Scaling parameters with defaults
@@ -152,8 +145,7 @@ class WorkerPool:
             graph=self._graph,
             layers=self._layers,
             worker_id=worker_id,
-            flask_app=self._flask_app,
-            context_vars=self._context_vars,
+            execution_context=self._execution_context,
             stop_event=self._stop_event,
         )
 

api/core/workflow/nodes/agent/agent_node.py

@@ -235,7 +235,18 @@ class AgentNode(Node[AgentNodeData]):
                                 0,
                             ):
                                 value_param = param.get("value", {})
-                                params[key] = value_param.get("value", "") if value_param is not None else None
+                                if value_param and value_param.get("type", "") == "variable":
+                                    variable_selector = value_param.get("value")
+                                    if not variable_selector:
+                                        raise ValueError("Variable selector is missing for a variable-type parameter.")
+
+                                    variable = variable_pool.get(variable_selector)
+                                    if variable is None:
+                                        raise AgentVariableNotFoundError(str(variable_selector))
+
+                                    params[key] = variable.value
+                                else:
+                                    params[key] = value_param.get("value", "") if value_param is not None else None
                             else:
                                 params[key] = None
                         parameters = params
@@ -483,7 +494,7 @@ class AgentNode(Node[AgentNodeData]):
 
         text = ""
         files: list[File] = []
-        json_list: list[dict] = []
+        json_list: list[dict | list] = []
 
         agent_logs: list[AgentLogEvent] = []
         agent_execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] = {}
@@ -557,13 +568,18 @@ class AgentNode(Node[AgentNodeData]):
             elif message.type == ToolInvokeMessage.MessageType.JSON:
                 assert isinstance(message.message, ToolInvokeMessage.JsonMessage)
                 if node_type == NodeType.AGENT:
-                    msg_metadata: dict[str, Any] = message.message.json_object.pop("execution_metadata", {})
-                    llm_usage = LLMUsage.from_metadata(cast(LLMUsageMetadata, msg_metadata))
-                    agent_execution_metadata = {
-                        WorkflowNodeExecutionMetadataKey(key): value
-                        for key, value in msg_metadata.items()
-                        if key in WorkflowNodeExecutionMetadataKey.__members__.values()
-                    }
+                    if isinstance(message.message.json_object, dict):
+                        msg_metadata: dict[str, Any] = message.message.json_object.pop("execution_metadata", {})
+                        llm_usage = LLMUsage.from_metadata(cast(LLMUsageMetadata, msg_metadata))
+                        agent_execution_metadata = {
+                            WorkflowNodeExecutionMetadataKey(key): value
+                            for key, value in msg_metadata.items()
+                            if key in WorkflowNodeExecutionMetadataKey.__members__.values()
+                        }
+                    else:
+                        msg_metadata = {}
+                        llm_usage = LLMUsage.empty_usage()
+                        agent_execution_metadata = {}
                 if message.message.json_object:
                     json_list.append(message.message.json_object)
             elif message.type == ToolInvokeMessage.MessageType.LINK:
@@ -672,7 +688,7 @@ class AgentNode(Node[AgentNodeData]):
                 yield agent_log
 
         # Add agent_logs to outputs['json'] to ensure frontend can access thinking process
-        json_output: list[dict[str, Any]] = []
+        json_output: list[dict[str, Any] | list[Any]] = []
 
         # Step 1: append each agent log as its own dict.
         if agent_logs:

api/core/workflow/nodes/base/node.py

@@ -469,12 +469,8 @@ class Node(Generic[NodeDataT]):
         import core.workflow.nodes as _nodes_pkg
 
         for _, _modname, _ in pkgutil.walk_packages(_nodes_pkg.__path__, _nodes_pkg.__name__ + "."):
-            # Avoid importing modules that depend on the registry to prevent circular imports
-            # e.g. node_factory imports node_mapping which builds the mapping here.
-            if _modname in {
-                "core.workflow.nodes.node_factory",
-                "core.workflow.nodes.node_mapping",
-            }:
+            # Avoid importing modules that depend on the registry to prevent circular imports.
+            if _modname == "core.workflow.nodes.node_mapping":
                 continue
             importlib.import_module(_modname)
 

api/core/workflow/nodes/datasource/datasource_node.py

@@ -301,7 +301,7 @@ class DatasourceNode(Node[DatasourceNodeData]):
 
         text = ""
         files: list[File] = []
-        json: list[dict] = []
+        json: list[dict | list] = []
 
         variables: dict[str, Any] = {}
 

api/core/workflow/nodes/http_request/executor.py

@@ -17,6 +17,7 @@ from core.helper import ssrf_proxy
 from core.variables.segments import ArrayFileSegment, FileSegment
 from core.workflow.runtime import VariablePool
 
+from ..protocols import FileManagerProtocol, HttpClientProtocol
 from .entities import (
     HttpRequestNodeAuthorization,
     HttpRequestNodeData,
@@ -78,6 +79,8 @@ class Executor:
         timeout: HttpRequestNodeTimeout,
         variable_pool: VariablePool,
         max_retries: int = dify_config.SSRF_DEFAULT_MAX_RETRIES,
+        http_client: HttpClientProtocol = ssrf_proxy,
+        file_manager: FileManagerProtocol = file_manager,
     ):
         # If authorization API key is present, convert the API key using the variable pool
         if node_data.authorization.type == "api-key":
@@ -104,6 +107,8 @@ class Executor:
         self.data = None
         self.json = None
         self.max_retries = max_retries
+        self._http_client = http_client
+        self._file_manager = file_manager
 
         # init template
         self.variable_pool = variable_pool
@@ -200,7 +205,7 @@ class Executor:
                     if file_variable is None:
                         raise FileFetchError(f"cannot fetch file with selector {file_selector}")
                     file = file_variable.value
-                    self.content = file_manager.download(file)
+                    self.content = self._file_manager.download(file)
                 case "x-www-form-urlencoded":
                     form_data = {
                         self.variable_pool.convert_template(item.key).text: self.variable_pool.convert_template(
@@ -239,7 +244,7 @@ class Executor:
                             ):
                                 file_tuple = (
                                     file.filename,
-                                    file_manager.download(file),
+                                    self._file_manager.download(file),
                                     file.mime_type or "application/octet-stream",
                                 )
                                 if key not in files:
@@ -332,19 +337,18 @@ class Executor:
         do http request depending on api bundle
         """
         _METHOD_MAP = {
-            "get": ssrf_proxy.get,
-            "head": ssrf_proxy.head,
-            "post": ssrf_proxy.post,
-            "put": ssrf_proxy.put,
-            "delete": ssrf_proxy.delete,
-            "patch": ssrf_proxy.patch,
+            "get": self._http_client.get,
+            "head": self._http_client.head,
+            "post": self._http_client.post,
+            "put": self._http_client.put,
+            "delete": self._http_client.delete,
+            "patch": self._http_client.patch,
         }
         method_lc = self.method.lower()
         if method_lc not in _METHOD_MAP:
             raise InvalidHttpMethodError(f"Invalid http method {self.method}")
 
         request_args = {
-            "url": self.url,
             "data": self.data,
             "files": self.files,
             "json": self.json,
@@ -357,8 +361,12 @@ class Executor:
         }
         # request_args = {k: v for k, v in request_args.items() if v is not None}
         try:
-            response: httpx.Response = _METHOD_MAP[method_lc](**request_args, max_retries=self.max_retries)
-        except (ssrf_proxy.MaxRetriesExceededError, httpx.RequestError) as e:
+            response: httpx.Response = _METHOD_MAP[method_lc](
+                url=self.url,
+                **request_args,
+                max_retries=self.max_retries,
+            )
+        except (self._http_client.max_retries_exceeded_error, self._http_client.request_error) as e:
             raise HttpRequestNodeError(str(e)) from e
         # FIXME: fix type ignore, this maybe httpx type issue
         return response

api/core/workflow/nodes/http_request/node.py

@@ -1,10 +1,11 @@
 import logging
 import mimetypes
-from collections.abc import Mapping, Sequence
-from typing import Any
+from collections.abc import Callable, Mapping, Sequence
+from typing import TYPE_CHECKING, Any
 
 from configs import dify_config
-from core.file import File, FileTransferMethod
+from core.file import File, FileTransferMethod, file_manager
+from core.helper import ssrf_proxy
 from core.tools.tool_file_manager import ToolFileManager
 from core.variables.segments import ArrayFileSegment
 from core.workflow.enums import NodeType, WorkflowNodeExecutionStatus
@@ -13,6 +14,7 @@ from core.workflow.nodes.base import variable_template_parser
 from core.workflow.nodes.base.entities import VariableSelector
 from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.http_request.executor import Executor
+from core.workflow.nodes.protocols import FileManagerProtocol, HttpClientProtocol
 from factories import file_factory
 
 from .entities import (
@@ -30,10 +32,35 @@ HTTP_REQUEST_DEFAULT_TIMEOUT = HttpRequestNodeTimeout(
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from core.workflow.entities import GraphInitParams
+    from core.workflow.runtime import GraphRuntimeState
+
 
 class HttpRequestNode(Node[HttpRequestNodeData]):
     node_type = NodeType.HTTP_REQUEST
 
+    def __init__(
+        self,
+        id: str,
+        config: Mapping[str, Any],
+        graph_init_params: "GraphInitParams",
+        graph_runtime_state: "GraphRuntimeState",
+        *,
+        http_client: HttpClientProtocol = ssrf_proxy,
+        tool_file_manager_factory: Callable[[], ToolFileManager] = ToolFileManager,
+        file_manager: FileManagerProtocol = file_manager,
+    ) -> None:
+        super().__init__(
+            id=id,
+            config=config,
+            graph_init_params=graph_init_params,
+            graph_runtime_state=graph_runtime_state,
+        )
+        self._http_client = http_client
+        self._tool_file_manager_factory = tool_file_manager_factory
+        self._file_manager = file_manager
+
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
         return {
@@ -71,6 +98,8 @@ class HttpRequestNode(Node[HttpRequestNodeData]):
                 timeout=self._get_request_timeout(self.node_data),
                 variable_pool=self.graph_runtime_state.variable_pool,
                 max_retries=0,
+                http_client=self._http_client,
+                file_manager=self._file_manager,
             )
             process_data["request"] = http_executor.to_log()
 
@@ -199,7 +228,7 @@ class HttpRequestNode(Node[HttpRequestNodeData]):
         mime_type = (
             content_disposition_type or content_type or mimetypes.guess_type(filename)[0] or "application/octet-stream"
         )
-        tool_file_manager = ToolFileManager()
+        tool_file_manager = self._tool_file_manager_factory()
 
         tool_file = tool_file_manager.create_file_by_raw(
             user_id=self.user_id,

api/core/workflow/nodes/iteration/iteration_node.py

@@ -1,17 +1,15 @@
-import contextvars
 import logging
 from collections.abc import Generator, Mapping, Sequence
 from concurrent.futures import Future, ThreadPoolExecutor, as_completed
 from datetime import UTC, datetime
 from typing import TYPE_CHECKING, Any, NewType, cast
 
-from flask import Flask, current_app
 from typing_extensions import TypeIs
 
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.variables import IntegerVariable, NoneSegment
 from core.variables.segments import ArrayAnySegment, ArraySegment
-from core.variables.variables import VariableUnion
+from core.variables.variables import Variable
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
 from core.workflow.enums import (
     NodeExecutionType,
@@ -39,7 +37,6 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.iteration.entities import ErrorHandleMode, IterationNodeData
 from core.workflow.runtime import VariablePool
 from libs.datetime_utils import naive_utc_now
-from libs.flask_utils import preserve_flask_contexts
 
 from .exc import (
     InvalidIteratorValueError,
@@ -51,6 +48,7 @@ from .exc import (
 )
 
 if TYPE_CHECKING:
+    from core.workflow.context import IExecutionContext
     from core.workflow.graph_engine import GraphEngine
 
 logger = logging.getLogger(__name__)
@@ -240,7 +238,7 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
                         datetime,
                         list[GraphNodeEventBase],
                         object | None,
-                        dict[str, VariableUnion],
+                        dict[str, Variable],
                         LLMUsage,
                     ]
                 ],
@@ -252,8 +250,7 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
                     self._execute_single_iteration_parallel,
                     index=index,
                     item=item,
-                    flask_app=current_app._get_current_object(),  # type: ignore
-                    context_vars=contextvars.copy_context(),
+                    execution_context=self._capture_execution_context(),
                 )
                 future_to_index[future] = index
 
@@ -306,11 +303,10 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
         self,
         index: int,
         item: object,
-        flask_app: Flask,
-        context_vars: contextvars.Context,
-    ) -> tuple[datetime, list[GraphNodeEventBase], object | None, dict[str, VariableUnion], LLMUsage]:
+        execution_context: "IExecutionContext",
+    ) -> tuple[datetime, list[GraphNodeEventBase], object | None, dict[str, Variable], LLMUsage]:
         """Execute a single iteration in parallel mode and return results."""
-        with preserve_flask_contexts(flask_app=flask_app, context_vars=context_vars):
+        with execution_context:
             iter_start_at = datetime.now(UTC).replace(tzinfo=None)
             events: list[GraphNodeEventBase] = []
             outputs_temp: list[object] = []
@@ -339,6 +335,12 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
                 graph_engine.graph_runtime_state.llm_usage,
             )
 
+    def _capture_execution_context(self) -> "IExecutionContext":
+        """Capture current execution context for parallel iterations."""
+        from core.workflow.context import capture_current_context
+
+        return capture_current_context()
+
     def _handle_iteration_success(
         self,
         started_at: datetime,
@@ -515,11 +517,11 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
 
         return variable_mapping
 
-    def _extract_conversation_variable_snapshot(self, *, variable_pool: VariablePool) -> dict[str, VariableUnion]:
+    def _extract_conversation_variable_snapshot(self, *, variable_pool: VariablePool) -> dict[str, Variable]:
         conversation_variables = variable_pool.variable_dictionary.get(CONVERSATION_VARIABLE_NODE_ID, {})
         return {name: variable.model_copy(deep=True) for name, variable in conversation_variables.items()}
 
-    def _sync_conversation_variables_from_snapshot(self, snapshot: dict[str, VariableUnion]) -> None:
+    def _sync_conversation_variables_from_snapshot(self, snapshot: dict[str, Variable]) -> None:
         parent_pool = self.graph_runtime_state.variable_pool
         parent_conversations = parent_pool.variable_dictionary.get(CONVERSATION_VARIABLE_NODE_ID, {})
 
@@ -586,11 +588,11 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
 
     def _create_graph_engine(self, index: int, item: object):
         # Import dependencies
+        from core.app.workflow.node_factory import DifyNodeFactory
         from core.workflow.entities import GraphInitParams
         from core.workflow.graph import Graph
         from core.workflow.graph_engine import GraphEngine
         from core.workflow.graph_engine.command_channels import InMemoryChannel
-        from core.workflow.nodes.node_factory import DifyNodeFactory
         from core.workflow.runtime import GraphRuntimeState
 
         # Create GraphInitParams from node attributes

api/core/workflow/nodes/loop/loop_node.py

@@ -413,11 +413,11 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
 
     def _create_graph_engine(self, start_at: datetime, root_node_id: str):
         # Import dependencies
+        from core.app.workflow.node_factory import DifyNodeFactory
         from core.workflow.entities import GraphInitParams
         from core.workflow.graph import Graph
         from core.workflow.graph_engine import GraphEngine
         from core.workflow.graph_engine.command_channels import InMemoryChannel
-        from core.workflow.nodes.node_factory import DifyNodeFactory
         from core.workflow.runtime import GraphRuntimeState
 
         # Create GraphInitParams from node attributes

api/core/workflow/nodes/protocols.py

@@ -0,0 +1,29 @@
+from typing import Protocol
+
+import httpx
+
+from core.file import File
+
+
+class HttpClientProtocol(Protocol):
+    @property
+    def max_retries_exceeded_error(self) -> type[Exception]: ...
+
+    @property
+    def request_error(self) -> type[Exception]: ...
+
+    def get(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+    def head(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+    def post(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+    def put(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+    def delete(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+    def patch(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+
+
+class FileManagerProtocol(Protocol):
+    def download(self, f: File, /) -> bytes: ...

api/core/workflow/nodes/start/start_node.py

@@ -1,4 +1,3 @@
-import json
 from typing import Any
 
 from jsonschema import Draft7Validator, ValidationError
@@ -43,25 +42,22 @@ class StartNode(Node[StartNodeData]):
             if value is None and variable.required:
                 raise ValueError(f"{key} is required in input form")
 
+            # If no value provided, skip further processing for this key
+            if not value:
+                continue
+
+            if not isinstance(value, dict):
+                raise ValueError(f"JSON object for '{key}' must be an object")
+
+            # Overwrite with normalized dict to ensure downstream consistency
+            node_inputs[key] = value
+
+            # If schema exists, then validate against it
             schema = variable.json_schema
             if not schema:
                 continue
 
-            if not value:
-                continue
-
             try:
-                json_schema = json.loads(schema)
-            except json.JSONDecodeError as e:
-                raise ValueError(f"{schema} must be a valid JSON object")
-
-            try:
-                json_value = json.loads(value)
-            except json.JSONDecodeError as e:
-                raise ValueError(f"{value} must be a valid JSON object")
-
-            try:
-                Draft7Validator(json_schema).validate(json_value)
+                Draft7Validator(schema).validate(value)
             except ValidationError as e:
                 raise ValueError(f"JSON object for '{key}' does not match schema: {e.message}")
-            node_inputs[key] = json_value

api/core/workflow/nodes/tool/tool_node.py

@@ -244,7 +244,7 @@ class ToolNode(Node[ToolNodeData]):
 
         text = ""
         files: list[File] = []
-        json: list[dict] = []
+        json: list[dict | list] = []
 
         variables: dict[str, Any] = {}
 
@@ -400,7 +400,7 @@ class ToolNode(Node[ToolNodeData]):
                         message.message.metadata = dict_metadata
 
         # Add agent_logs to outputs['json'] to ensure frontend can access thinking process
-        json_output: list[dict[str, Any]] = []
+        json_output: list[dict[str, Any] | list[Any]] = []
 
         # Step 2: normalize JSON into {"data": [...]}.change json to list[dict]
         if json:

api/core/workflow/nodes/variable_assigner/v1/node.py

@@ -1,7 +1,7 @@
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
-from core.variables import SegmentType, Variable
+from core.variables import SegmentType, VariableBase
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
 from core.workflow.entities import GraphInitParams
 from core.workflow.enums import NodeType, WorkflowNodeExecutionStatus
@@ -33,6 +33,15 @@ class VariableAssignerNode(Node[VariableAssignerData]):
             graph_runtime_state=graph_runtime_state,
         )
 
+    def blocks_variable_output(self, variable_selectors: set[tuple[str, ...]]) -> bool:
+        """
+        Check if this Variable Assigner node blocks the output of specific variables.
+
+        Returns True if this node updates any of the requested conversation variables.
+        """
+        assigned_selector = tuple(self.node_data.assigned_variable_selector)
+        return assigned_selector in variable_selectors
+
     @classmethod
     def version(cls) -> str:
         return "1"
@@ -64,7 +73,7 @@ class VariableAssignerNode(Node[VariableAssignerData]):
         assigned_variable_selector = self.node_data.assigned_variable_selector
         # Should be String, Number, Object, ArrayString, ArrayNumber, ArrayObject
         original_variable = self.graph_runtime_state.variable_pool.get(assigned_variable_selector)
-        if not isinstance(original_variable, Variable):
+        if not isinstance(original_variable, VariableBase):
             raise VariableOperatorNodeError("assigned variable not found")
 
         match self.node_data.write_mode:

api/core/workflow/nodes/variable_assigner/v2/node.py

@@ -2,7 +2,7 @@ import json
 from collections.abc import Mapping, MutableMapping, Sequence
 from typing import TYPE_CHECKING, Any
 
-from core.variables import SegmentType, Variable
+from core.variables import SegmentType, VariableBase
 from core.variables.consts import SELECTORS_LENGTH
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
 from core.workflow.enums import NodeType, WorkflowNodeExecutionStatus
@@ -118,7 +118,7 @@ class VariableAssignerNode(Node[VariableAssignerNodeData]):
                 # ==================== Validation Part
 
                 # Check if variable exists
-                if not isinstance(variable, Variable):
+                if not isinstance(variable, VariableBase):
                     raise VariableNotFoundError(variable_selector=item.variable_selector)
 
                 # Check if operation is supported
@@ -192,7 +192,7 @@ class VariableAssignerNode(Node[VariableAssignerNodeData]):
 
         for selector in updated_variable_selectors:
             variable = self.graph_runtime_state.variable_pool.get(selector)
-            if not isinstance(variable, Variable):
+            if not isinstance(variable, VariableBase):
                 raise VariableNotFoundError(variable_selector=selector)
             process_data[variable.name] = variable.value
 
@@ -213,7 +213,7 @@ class VariableAssignerNode(Node[VariableAssignerNodeData]):
     def _handle_item(
         self,
         *,
-        variable: Variable,
+        variable: VariableBase,
         operation: Operation,
         value: Any,
     ):

api/core/workflow/runtime/variable_pool.py

@@ -9,10 +9,10 @@ from typing import Annotated, Any, Union, cast
 from pydantic import BaseModel, Field
 
 from core.file import File, FileAttribute, file_manager
-from core.variables import Segment, SegmentGroup, Variable
+from core.variables import Segment, SegmentGroup, VariableBase
 from core.variables.consts import SELECTORS_LENGTH
 from core.variables.segments import FileSegment, ObjectSegment
-from core.variables.variables import RAGPipelineVariableInput, VariableUnion
+from core.variables.variables import RAGPipelineVariableInput, Variable
 from core.workflow.constants import (
     CONVERSATION_VARIABLE_NODE_ID,
     ENVIRONMENT_VARIABLE_NODE_ID,
@@ -32,7 +32,7 @@ class VariablePool(BaseModel):
     # The first element of the selector is the node id, it's the first-level key in the dictionary.
     # Other elements of the selector are the keys in the second-level dictionary. To get the key, we hash the
     # elements of the selector except the first one.
-    variable_dictionary: defaultdict[str, Annotated[dict[str, VariableUnion], Field(default_factory=dict)]] = Field(
+    variable_dictionary: defaultdict[str, Annotated[dict[str, Variable], Field(default_factory=dict)]] = Field(
         description="Variables mapping",
         default=defaultdict(dict),
     )
@@ -46,13 +46,13 @@ class VariablePool(BaseModel):
         description="System variables",
         default_factory=SystemVariable.empty,
     )
-    environment_variables: Sequence[VariableUnion] = Field(
+    environment_variables: Sequence[Variable] = Field(
         description="Environment variables.",
-        default_factory=list[VariableUnion],
+        default_factory=list[Variable],
     )
-    conversation_variables: Sequence[VariableUnion] = Field(
+    conversation_variables: Sequence[Variable] = Field(
         description="Conversation variables.",
-        default_factory=list[VariableUnion],
+        default_factory=list[Variable],
     )
     rag_pipeline_variables: list[RAGPipelineVariableInput] = Field(
         description="RAG pipeline variables.",
@@ -105,7 +105,7 @@ class VariablePool(BaseModel):
                 f"got {len(selector)} elements"
             )
 
-        if isinstance(value, Variable):
+        if isinstance(value, VariableBase):
             variable = value
         elif isinstance(value, Segment):
             variable = variable_factory.segment_to_variable(segment=value, selector=selector)
@@ -114,9 +114,9 @@ class VariablePool(BaseModel):
             variable = variable_factory.segment_to_variable(segment=segment, selector=selector)
 
         node_id, name = self._selector_to_keys(selector)
-        # Based on the definition of `VariableUnion`,
-        # `list[Variable]` can be safely used as `list[VariableUnion]` since they are compatible.
-        self.variable_dictionary[node_id][name] = cast(VariableUnion, variable)
+        # Based on the definition of `Variable`,
+        # `VariableBase` instances can be safely used as `Variable` since they are compatible.
+        self.variable_dictionary[node_id][name] = cast(Variable, variable)
 
     @classmethod
     def _selector_to_keys(cls, selector: Sequence[str]) -> tuple[str, str]:

api/core/workflow/variable_loader.py

@@ -2,7 +2,7 @@ import abc
 from collections.abc import Mapping, Sequence
 from typing import Any, Protocol
 
-from core.variables import Variable
+from core.variables import VariableBase
 from core.variables.consts import SELECTORS_LENGTH
 from core.workflow.runtime import VariablePool
 
@@ -26,7 +26,7 @@ class VariableLoader(Protocol):
     """
 
     @abc.abstractmethod
-    def load_variables(self, selectors: list[list[str]]) -> list[Variable]:
+    def load_variables(self, selectors: list[list[str]]) -> list[VariableBase]:
         """Load variables based on the provided selectors. If the selectors are empty,
         this method should return an empty list.
 
@@ -36,7 +36,7 @@ class VariableLoader(Protocol):
         :param: selectors: a list of string list, each inner list should have at least two elements:
             - the first element is the node ID,
             - the second element is the variable name.
-        :return: a list of Variable objects that match the provided selectors.
+        :return: a list of VariableBase objects that match the provided selectors.
         """
         pass
 
@@ -46,7 +46,7 @@ class _DummyVariableLoader(VariableLoader):
     Serves as a placeholder when no variable loading is needed.
     """
 
-    def load_variables(self, selectors: list[list[str]]) -> list[Variable]:
+    def load_variables(self, selectors: list[list[str]]) -> list[VariableBase]:
         return []
 
 

api/core/workflow/workflow_entry.py

@@ -7,6 +7,7 @@ from typing import Any
 from configs import dify_config
 from core.app.apps.exc import GenerateTaskStoppedError
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.app.workflow.node_factory import DifyNodeFactory
 from core.file.models import File
 from core.workflow.constants import ENVIRONMENT_VARIABLE_NODE_ID
 from core.workflow.entities import GraphInitParams
@@ -136,13 +137,11 @@ class WorkflowEntry:
         :param user_inputs: user inputs
         :return:
         """
-        node_config = workflow.get_node_config_by_id(node_id)
+        node_config = dict(workflow.get_node_config_by_id(node_id))
         node_config_data = node_config.get("data", {})
 
-        # Get node class
+        # Get node type
         node_type = NodeType(node_config_data.get("type"))
-        node_version = node_config_data.get("version", "1")
-        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type][node_version]
 
         # init graph init params and runtime state
         graph_init_params = GraphInitParams(
@@ -158,12 +157,12 @@ class WorkflowEntry:
         graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
 
         # init workflow run state
-        node = node_cls(
-            id=str(uuid.uuid4()),
-            config=node_config,
+        node_factory = DifyNodeFactory(
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
+        node = node_factory.create_node(node_config)
+        node_cls = type(node)
 
         try:
             # variable selector to variable mapping
@@ -190,8 +189,7 @@ class WorkflowEntry:
             )
 
         try:
-            # run node
-            generator = node.run()
+            generator = cls._traced_node_run(node)
         except Exception as e:
             logger.exception(
                 "error while running node, workflow_id=%s, node_id=%s, node_type=%s, node_version=%s",
@@ -324,8 +322,7 @@ class WorkflowEntry:
                 tenant_id=tenant_id,
             )
 
-            # run node
-            generator = node.run()
+            generator = cls._traced_node_run(node)
 
             return node, generator
         except Exception as e:
@@ -431,3 +428,26 @@ class WorkflowEntry:
                         input_value = current_variable.value | input_value
 
                 variable_pool.add([variable_node_id] + variable_key_list, input_value)
+
+    @staticmethod
+    def _traced_node_run(node: Node) -> Generator[GraphNodeEventBase, None, None]:
+        """
+        Wraps a node's run method with OpenTelemetry tracing and returns a generator.
+        """
+        # Wrap node.run() with ObservabilityLayer hooks to produce node-level spans
+        layer = ObservabilityLayer()
+        layer.on_graph_start()
+        node.ensure_execution_id()
+
+        def _gen():
+            error: Exception | None = None
+            layer.on_node_run_start(node)
+            try:
+                yield from node.run()
+            except Exception as exc:
+                error = exc
+                raise
+            finally:
+                layer.on_node_run_end(node, error)
+
+        return _gen()