fix(api): restore advanced chat refresh_model contract

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

@@ -5,7 +5,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Literal, TypeVar, Union, overload
+from typing import TYPE_CHECKING, Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -47,7 +47,6 @@ from extensions.ext_database import db
 from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
-from models.base import Base
 from models.enums import WorkflowRunTriggeredFrom
 from services.conversation_service import ConversationService
 from services.workflow_draft_variable_service import (
@@ -522,8 +521,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
 
         # release database connection, because the following new thread operations may take a long time
         with Session(bind=db.engine, expire_on_commit=False) as session:
-            workflow = _refresh_model(session, workflow)
-            message = _refresh_model(session, message)
+            workflow = _refresh_model(session=session, model=workflow)
+            message = _refresh_model(session=session, model=message)
+        assert message is not None
         #     workflow_ = session.get(Workflow, workflow.id)
         #     assert workflow_ is not None
         #     workflow = workflow_
@@ -690,11 +690,21 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 raise e
 
 
-_T = TypeVar("_T", bound=Base)
+@overload
+def _refresh_model(*, session: Session | None = None, model: Workflow) -> Workflow: ...
 
 
-def _refresh_model(session, model: _T) -> _T:
-    with Session(bind=db.engine, expire_on_commit=False) as session:
-        detach_model = session.get(type(model), model.id)
-        assert detach_model is not None
-        return detach_model
+@overload
+def _refresh_model(*, session: Session | None = None, model: Message) -> Message: ...
+
+
+def _refresh_model(*, session: Session | None = None, model: Any) -> Any:
+    if session is not None:
+        detached_model = session.get(type(model), model.id)
+        assert detached_model is not None
+        return detached_model
+
+    with Session(bind=db.engine, expire_on_commit=False) as refresh_session:
+        detached_model = refresh_session.get(type(model), model.id)
+        assert detached_model is not None
+        return detached_model

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

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import Any, cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -56,8 +56,8 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
-    ) -> Generator[dict | str, Any, None]:
+        cls, stream_response: Iterator[AppStreamResponse]
+    ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -87,8 +87,8 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
-    ) -> Generator[dict | str, Any, None]:
+        cls, stream_response: Iterator[AppStreamResponse]
+    ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response

api/core/app/apps/agent_chat/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -55,7 +55,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -86,7 +86,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/base_app_generate_response_converter.py

@@ -1,7 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
-from collections.abc import Generator, Mapping
-from typing import Any, Union
+from collections.abc import Generator, Iterator, Mapping
+from typing import Any
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.task_entities import AppBlockingResponse, AppStreamResponse
@@ -16,24 +16,26 @@ class AppGenerateResponseConverter(ABC):
 
     @classmethod
     def convert(
-        cls, response: Union[AppBlockingResponse, Generator[AppStreamResponse, Any, None]], invoke_from: InvokeFrom
-    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], Any, None]:
+        cls, response: AppBlockingResponse | Iterator[AppStreamResponse], invoke_from: InvokeFrom
+    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], None, None]:
         if invoke_from in {InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API}:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_full_response(response)
             else:
+                stream_response = response
 
-                def _generate_full_response() -> Generator[dict | str, Any, None]:
-                    yield from cls.convert_stream_full_response(response)
+                def _generate_full_response() -> Generator[dict[str, Any] | str, None, None]:
+                    yield from cls.convert_stream_full_response(stream_response)
 
                 return _generate_full_response()
         else:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_simple_response(response)
             else:
+                stream_response = response
 
-                def _generate_simple_response() -> Generator[dict | str, Any, None]:
-                    yield from cls.convert_stream_simple_response(response)
+                def _generate_simple_response() -> Generator[dict[str, Any] | str, None, None]:
+                    yield from cls.convert_stream_simple_response(stream_response)
 
                 return _generate_simple_response()
 
@@ -50,14 +52,14 @@ class AppGenerateResponseConverter(ABC):
     @classmethod
     @abstractmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         raise NotImplementedError
 
     @classmethod
     @abstractmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         raise NotImplementedError
 

api/core/app/apps/base_app_generator.py

@@ -224,6 +224,7 @@ class BaseAppGenerator:
     def _get_draft_var_saver_factory(invoke_from: InvokeFrom, account: Account | EndUser) -> DraftVariableSaverFactory:
         if invoke_from == InvokeFrom.DEBUGGER:
             assert isinstance(account, Account)
+            debug_account = account
 
             def draft_var_saver_factory(
                 session: Session,
@@ -240,7 +241,7 @@ class BaseAppGenerator:
                     node_type=node_type,
                     node_execution_id=node_execution_id,
                     enclosing_node_id=enclosing_node_id,
-                    user=account,
+                    user=debug_account,
                 )
         else:
 

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

@@ -166,15 +166,19 @@ class ChatAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
+        assert conversation is not None
+        assert message is not None
+        generated_conversation_id = str(conversation.id)
+        generated_message_id = str(message.id)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
             task_id=application_generate_entity.task_id,
             user_id=application_generate_entity.user_id,
             invoke_from=application_generate_entity.invoke_from,
-            conversation_id=conversation.id,
+            conversation_id=generated_conversation_id,
             app_mode=conversation.mode,
-            message_id=message.id,
+            message_id=generated_message_id,
         )
 
         # new thread with request context
@@ -184,8 +188,8 @@ class ChatAppGenerator(MessageBasedAppGenerator):
                 flask_app=current_app._get_current_object(),  # type: ignore
                 application_generate_entity=application_generate_entity,
                 queue_manager=queue_manager,
-                conversation_id=conversation.id,
-                message_id=message.id,
+                conversation_id=generated_conversation_id,
+                message_id=generated_message_id,
             )
 
         worker_thread = threading.Thread(target=worker_with_context)

api/core/app/apps/chat/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -55,7 +55,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -86,7 +86,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

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

@@ -149,6 +149,8 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity)
+        assert conversation is not None
+        assert message is not None
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -312,15 +314,19 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity)
+        assert conversation is not None
+        assert message is not None
+        conversation_id = str(conversation.id)
+        message_id = str(message.id)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
             task_id=application_generate_entity.task_id,
             user_id=application_generate_entity.user_id,
             invoke_from=application_generate_entity.invoke_from,
-            conversation_id=conversation.id,
+            conversation_id=conversation_id,
             app_mode=conversation.mode,
-            message_id=message.id,
+            message_id=message_id,
         )
 
         # new thread with request context
@@ -330,7 +336,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
                 flask_app=current_app._get_current_object(),  # type: ignore
                 application_generate_entity=application_generate_entity,
                 queue_manager=queue_manager,
-                message_id=message.id,
+                message_id=message_id,
             )
 
         worker_thread = threading.Thread(target=worker_with_context)

api/core/app/apps/completion/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -54,7 +54,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -84,7 +84,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

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

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -36,7 +36,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -65,7 +65,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

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

@@ -1,4 +1,4 @@
-from collections.abc import Generator
+from collections.abc import Generator, Iterator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -36,7 +36,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -65,7 +65,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Generator[AppStreamResponse, None, None]
+        cls, stream_response: Iterator[AppStreamResponse]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/workflow_app_runner.py

@@ -1,13 +1,17 @@
 import logging
 import time
 from collections.abc import Mapping, Sequence
-from typing import Any, cast
+from typing import Protocol, TypeAlias
 
 from pydantic import ValidationError
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.agent_strategy import AgentStrategyInfo
-from core.app.entities.app_invoke_entities import InvokeFrom, UserFrom, build_dify_run_context
+from core.app.entities.app_invoke_entities import (
+    InvokeFrom,
+    UserFrom,
+    build_dify_run_context,
+)
 from core.app.entities.queue_entities import (
     AppQueueEvent,
     QueueAgentLogEvent,
@@ -36,7 +40,7 @@ from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id, resolve_workflow_node_class
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities import GraphInitParams
-from dify_graph.entities.graph_config import NodeConfigDictAdapter
+from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
 from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.graph import Graph
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
@@ -75,6 +79,14 @@ from tasks.mail_human_input_delivery_task import dispatch_human_input_email_task
 
 logger = logging.getLogger(__name__)
 
+GraphConfigObject: TypeAlias = dict[str, object]
+GraphConfigMapping: TypeAlias = Mapping[str, object]
+
+
+class SingleNodeRunEntity(Protocol):
+    node_id: str
+    inputs: Mapping[str, object]
+
 
 class WorkflowBasedAppRunner:
     def __init__(
@@ -98,7 +110,7 @@ class WorkflowBasedAppRunner:
 
     def _init_graph(
         self,
-        graph_config: Mapping[str, Any],
+        graph_config: GraphConfigMapping,
         graph_runtime_state: GraphRuntimeState,
         user_from: UserFrom,
         invoke_from: InvokeFrom,
@@ -154,8 +166,8 @@ class WorkflowBasedAppRunner:
     def _prepare_single_node_execution(
         self,
         workflow: Workflow,
-        single_iteration_run: Any | None = None,
-        single_loop_run: Any | None = None,
+        single_iteration_run: SingleNodeRunEntity | None = None,
+        single_loop_run: SingleNodeRunEntity | None = None,
     ) -> tuple[Graph, VariablePool, GraphRuntimeState]:
         """
         Prepare graph, variable pool, and runtime state for single node execution
@@ -208,11 +220,88 @@ class WorkflowBasedAppRunner:
         # This ensures all nodes in the graph reference the same GraphRuntimeState instance
         return graph, variable_pool, graph_runtime_state
 
+    @staticmethod
+    def _get_graph_items(graph_config: GraphConfigMapping) -> tuple[list[GraphConfigMapping], list[GraphConfigMapping]]:
+        nodes = graph_config.get("nodes")
+        edges = graph_config.get("edges")
+        if not isinstance(nodes, list):
+            raise ValueError("nodes in workflow graph must be a list")
+        if not isinstance(edges, list):
+            raise ValueError("edges in workflow graph must be a list")
+
+        validated_nodes: list[GraphConfigMapping] = []
+        for node in nodes:
+            if not isinstance(node, Mapping):
+                raise ValueError("nodes in workflow graph must be mappings")
+            validated_nodes.append(node)
+
+        validated_edges: list[GraphConfigMapping] = []
+        for edge in edges:
+            if not isinstance(edge, Mapping):
+                raise ValueError("edges in workflow graph must be mappings")
+            validated_edges.append(edge)
+
+        return validated_nodes, validated_edges
+
+    @staticmethod
+    def _extract_start_node_id(node_config: GraphConfigMapping | None) -> str | None:
+        if node_config is None:
+            return None
+        node_data = node_config.get("data")
+        if not isinstance(node_data, Mapping):
+            return None
+        start_node_id = node_data.get("start_node_id")
+        return start_node_id if isinstance(start_node_id, str) else None
+
+    @classmethod
+    def _build_single_node_graph_config(
+        cls,
+        *,
+        graph_config: GraphConfigMapping,
+        node_id: str,
+        node_type_filter_key: str,
+    ) -> tuple[GraphConfigObject, NodeConfigDict]:
+        node_configs, edge_configs = cls._get_graph_items(graph_config)
+        main_node_config = next((node for node in node_configs if node.get("id") == node_id), None)
+        start_node_id = cls._extract_start_node_id(main_node_config)
+
+        filtered_node_configs = [
+            dict(node)
+            for node in node_configs
+            if node.get("id") == node_id
+            or (isinstance(node_data := node.get("data"), Mapping) and node_data.get(node_type_filter_key) == node_id)
+            or (start_node_id and node.get("id") == start_node_id)
+        ]
+        if not filtered_node_configs:
+            raise ValueError(f"node id {node_id} not found in workflow graph")
+
+        filtered_node_ids = {
+            str(node_id_value) for node in filtered_node_configs if isinstance((node_id_value := node.get("id")), str)
+        }
+        filtered_edge_configs = [
+            dict(edge)
+            for edge in edge_configs
+            if (edge.get("source") is None or edge.get("source") in filtered_node_ids)
+            and (edge.get("target") is None or edge.get("target") in filtered_node_ids)
+        ]
+
+        target_node_config = next((node for node in filtered_node_configs if node.get("id") == node_id), None)
+        if target_node_config is None:
+            raise ValueError(f"node id {node_id} not found in workflow graph")
+
+        return (
+            {
+                "nodes": filtered_node_configs,
+                "edges": filtered_edge_configs,
+            },
+            NodeConfigDictAdapter.validate_python(target_node_config),
+        )
+
     def _get_graph_and_variable_pool_for_single_node_run(
         self,
         workflow: Workflow,
         node_id: str,
-        user_inputs: dict[str, Any],
+        user_inputs: Mapping[str, object],
         graph_runtime_state: GraphRuntimeState,
         node_type_filter_key: str,  # 'iteration_id' or 'loop_id'
         node_type_label: str = "node",  # 'iteration' or 'loop' for error messages
@@ -236,41 +325,14 @@ class WorkflowBasedAppRunner:
         if not graph_config:
             raise ValueError("workflow graph not found")
 
-        graph_config = cast(dict[str, Any], graph_config)
-
         if "nodes" not in graph_config or "edges" not in graph_config:
             raise ValueError("nodes or edges not found in workflow graph")
 
-        if not isinstance(graph_config.get("nodes"), list):
-            raise ValueError("nodes in workflow graph must be a list")
-
-        if not isinstance(graph_config.get("edges"), list):
-            raise ValueError("edges in workflow graph must be a list")
-
-        # filter nodes only in the specified node type (iteration or loop)
-        main_node_config = next((n for n in graph_config.get("nodes", []) if n.get("id") == node_id), None)
-        start_node_id = main_node_config.get("data", {}).get("start_node_id") if main_node_config else None
-        node_configs = [
-            node
-            for node in graph_config.get("nodes", [])
-            if node.get("id") == node_id
-            or node.get("data", {}).get(node_type_filter_key, "") == node_id
-            or (start_node_id and node.get("id") == start_node_id)
-        ]
-
-        graph_config["nodes"] = node_configs
-
-        node_ids = [node.get("id") for node in node_configs]
-
-        # filter edges only in the specified node type
-        edge_configs = [
-            edge
-            for edge in graph_config.get("edges", [])
-            if (edge.get("source") is None or edge.get("source") in node_ids)
-            and (edge.get("target") is None or edge.get("target") in node_ids)
-        ]
-
-        graph_config["edges"] = edge_configs
+        graph_config, target_node_config = self._build_single_node_graph_config(
+            graph_config=graph_config,
+            node_id=node_id,
+            node_type_filter_key=node_type_filter_key,
+        )
 
         # Create required parameters for Graph.init
         graph_init_params = GraphInitParams(
@@ -299,18 +361,6 @@ class WorkflowBasedAppRunner:
         if not graph:
             raise ValueError("graph not found in workflow")
 
-        # fetch node config from node id
-        target_node_config = None
-        for node in node_configs:
-            if node.get("id") == node_id:
-                target_node_config = node
-                break
-
-        if not target_node_config:
-            raise ValueError(f"{node_type_label} node id not found in workflow graph")
-
-        target_node_config = NodeConfigDictAdapter.validate_python(target_node_config)
-
         # Get node class
         node_type = target_node_config["data"].type
         node_version = str(target_node_config["data"].version)

api/core/app/entities/app_invoke_entities.py

@@ -213,7 +213,7 @@ class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping
+        inputs: Mapping[str, object]
 
     single_iteration_run: SingleIterationRunEntity | None = None
 
@@ -223,7 +223,7 @@ class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping
+        inputs: Mapping[str, object]
 
     single_loop_run: SingleLoopRunEntity | None = None
 
@@ -243,7 +243,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         """
 
         node_id: str
-        inputs: dict
+        inputs: Mapping[str, object]
 
     single_iteration_run: SingleIterationRunEntity | None = None
 
@@ -253,7 +253,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         """
 
         node_id: str
-        inputs: dict
+        inputs: Mapping[str, object]
 
     single_loop_run: SingleLoopRunEntity | None = None
 

api/core/tools/tool_manager.py

@@ -1045,9 +1045,10 @@ class ToolManager:
                         continue
                     tool_input = ToolNodeData.ToolInput.model_validate(tool_configurations.get(parameter.name, {}))
                     if tool_input.type == "variable":
-                        variable = variable_pool.get(tool_input.value)
+                        variable_selector = tool_input.require_variable_selector()
+                        variable = variable_pool.get(variable_selector)
                         if variable is None:
-                            raise ToolParameterError(f"Variable {tool_input.value} does not exist")
+                            raise ToolParameterError(f"Variable {variable_selector} does not exist")
                         parameter_value = variable.value
                     elif tool_input.type == "constant":
                         parameter_value = tool_input.value

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

@@ -1,13 +1,24 @@
-from enum import IntEnum, StrEnum, auto
-from typing import Any, Literal, Union
+from __future__ import annotations
 
-from pydantic import BaseModel
+from enum import IntEnum, StrEnum, auto
+from typing import Literal, TypeAlias
+
+from pydantic import BaseModel, TypeAdapter, field_validator
+from pydantic_core.core_schema import ValidationInfo
 
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.tools.entities.tool_entities import ToolSelector
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
 
+AgentInputConstantValue: TypeAlias = (
+    list[ToolSelector] | str | int | float | bool | dict[str, object] | list[object] | None
+)
+VariableSelector: TypeAlias = list[str]
+
+_AGENT_INPUT_VALUE_ADAPTER: TypeAdapter[AgentInputConstantValue] = TypeAdapter(AgentInputConstantValue)
+_AGENT_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
+
 
 class AgentNodeData(BaseNodeData):
     type: NodeType = BuiltinNodeTypes.AGENT
@@ -21,8 +32,20 @@ class AgentNodeData(BaseNodeData):
     tool_node_version: str | None = None
 
     class AgentInput(BaseModel):
-        value: Union[list[str], list[ToolSelector], Any]
         type: Literal["mixed", "variable", "constant"]
+        value: AgentInputConstantValue | VariableSelector
+
+        @field_validator("value", mode="before")
+        @classmethod
+        def validate_value(
+            cls, value: object, validation_info: ValidationInfo
+        ) -> AgentInputConstantValue | VariableSelector:
+            input_type = validation_info.data.get("type")
+            if input_type == "variable":
+                return _AGENT_VARIABLE_SELECTOR_ADAPTER.validate_python(value)
+            if input_type in {"mixed", "constant"}:
+                return _AGENT_INPUT_VALUE_ADAPTER.validate_python(value)
+            raise ValueError(f"Unknown agent input type: {input_type}")
 
     agent_parameters: dict[str, AgentInput]
 

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

@@ -1,16 +1,17 @@
 from __future__ import annotations
 
 import json
-from collections.abc import Sequence
-from typing import Any, cast
+from collections.abc import Mapping, Sequence
+from typing import TypeAlias
 
 from packaging.version import Version
-from pydantic import ValidationError
+from pydantic import TypeAdapter, ValidationError
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.plugin.entities.request import InvokeCredentials
@@ -28,6 +29,14 @@ from .entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGen
 from .exceptions import AgentInputTypeError, AgentVariableNotFoundError
 from .strategy_protocols import ResolvedAgentStrategy
 
+JsonObject: TypeAlias = dict[str, object]
+JsonObjectList: TypeAlias = list[JsonObject]
+VariableSelector: TypeAlias = list[str]
+
+_JSON_OBJECT_ADAPTER = TypeAdapter(JsonObject)
+_JSON_OBJECT_LIST_ADAPTER = TypeAdapter(JsonObjectList)
+_VARIABLE_SELECTOR_ADAPTER = TypeAdapter(VariableSelector)
+
 
 class AgentRuntimeSupport:
     def build_parameters(
@@ -39,12 +48,12 @@ class AgentRuntimeSupport:
         strategy: ResolvedAgentStrategy,
         tenant_id: str,
         app_id: str,
-        invoke_from: Any,
+        invoke_from: InvokeFrom,
         for_log: bool = False,
-    ) -> dict[str, Any]:
+    ) -> dict[str, object]:
         agent_parameters_dictionary = {parameter.name: parameter for parameter in agent_parameters}
 
-        result: dict[str, Any] = {}
+        result: dict[str, object] = {}
         for parameter_name in node_data.agent_parameters:
             parameter = agent_parameters_dictionary.get(parameter_name)
             if not parameter:
@@ -54,9 +63,10 @@ class AgentRuntimeSupport:
             agent_input = node_data.agent_parameters[parameter_name]
             match agent_input.type:
                 case "variable":
-                    variable = variable_pool.get(agent_input.value)  # type: ignore[arg-type]
+                    variable_selector = _VARIABLE_SELECTOR_ADAPTER.validate_python(agent_input.value)
+                    variable = variable_pool.get(variable_selector)
                     if variable is None:
-                        raise AgentVariableNotFoundError(str(agent_input.value))
+                        raise AgentVariableNotFoundError(str(variable_selector))
                     parameter_value = variable.value
                 case "mixed" | "constant":
                     try:
@@ -79,60 +89,38 @@ class AgentRuntimeSupport:
 
             value = parameter_value
             if parameter.type == "array[tools]":
-                value = cast(list[dict[str, Any]], value)
-                value = [tool for tool in value if tool.get("enabled", False)]
-                value = self._filter_mcp_type_tool(strategy, value)
-                for tool in value:
-                    if "schemas" in tool:
-                        tool.pop("schemas")
-                    parameters = tool.get("parameters", {})
-                    if all(isinstance(v, dict) for _, v in parameters.items()):
-                        params = {}
-                        for key, param in parameters.items():
-                            if param.get("auto", ParamsAutoGenerated.OPEN) in (
-                                ParamsAutoGenerated.CLOSE,
-                                0,
-                            ):
-                                value_param = param.get("value", {})
-                                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
-                    tool["settings"] = {k: v.get("value", None) for k, v in tool.get("settings", {}).items()}
-                    tool["parameters"] = parameters
+                tool_payloads = _JSON_OBJECT_LIST_ADAPTER.validate_python(value)
+                value = self._normalize_tool_payloads(
+                    strategy=strategy,
+                    tools=tool_payloads,
+                    variable_pool=variable_pool,
+                )
 
             if not for_log:
                 if parameter.type == "array[tools]":
-                    value = cast(list[dict[str, Any]], value)
+                    value = _JSON_OBJECT_LIST_ADAPTER.validate_python(value)
                     tool_value = []
                     for tool in value:
-                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN))
-                        setting_params = tool.get("settings", {})
-                        parameters = tool.get("parameters", {})
+                        provider_type = self._coerce_tool_provider_type(tool.get("type"))
+                        setting_params = self._coerce_json_object(tool.get("settings")) or {}
+                        parameters = self._coerce_json_object(tool.get("parameters")) or {}
                         manual_input_params = [key for key, value in parameters.items() if value is not None]
 
                         parameters = {**parameters, **setting_params}
+                        provider_id = self._coerce_optional_string(tool.get("provider_name")) or ""
+                        tool_name = self._coerce_optional_string(tool.get("tool_name")) or ""
+                        plugin_unique_identifier = self._coerce_optional_string(tool.get("plugin_unique_identifier"))
+                        credential_id = self._coerce_optional_string(tool.get("credential_id"))
                         entity = AgentToolEntity(
-                            provider_id=tool.get("provider_name", ""),
+                            provider_id=provider_id,
                             provider_type=provider_type,
-                            tool_name=tool.get("tool_name", ""),
+                            tool_name=tool_name,
                             tool_parameters=parameters,
-                            plugin_unique_identifier=tool.get("plugin_unique_identifier", None),
-                            credential_id=tool.get("credential_id", None),
+                            plugin_unique_identifier=plugin_unique_identifier,
+                            credential_id=credential_id,
                         )
 
-                        extra = tool.get("extra", {})
+                        extra = self._coerce_json_object(tool.get("extra")) or {}
 
                         runtime_variable_pool: VariablePool | None = None
                         if node_data.version != "1" or node_data.tool_node_version is not None:
@@ -145,8 +133,9 @@ class AgentRuntimeSupport:
                             runtime_variable_pool,
                         )
                         if tool_runtime.entity.description:
+                            description_override = self._coerce_optional_string(extra.get("description"))
                             tool_runtime.entity.description.llm = (
-                                extra.get("description", "") or tool_runtime.entity.description.llm
+                                description_override or tool_runtime.entity.description.llm
                             )
                         for tool_runtime_params in tool_runtime.entity.parameters:
                             tool_runtime_params.form = (
@@ -167,13 +156,13 @@ class AgentRuntimeSupport:
                             {
                                 **tool_runtime.entity.model_dump(mode="json"),
                                 "runtime_parameters": runtime_parameters,
-                                "credential_id": tool.get("credential_id", None),
+                                "credential_id": credential_id,
                                 "provider_type": provider_type.value,
                             }
                         )
                     value = tool_value
                 if parameter.type == AgentStrategyParameter.AgentStrategyParameterType.MODEL_SELECTOR:
-                    value = cast(dict[str, Any], value)
+                    value = _JSON_OBJECT_ADAPTER.validate_python(value)
                     model_instance, model_schema = self.fetch_model(tenant_id=tenant_id, value=value)
                     history_prompt_messages = []
                     if node_data.memory:
@@ -199,17 +188,27 @@ class AgentRuntimeSupport:
 
         return result
 
-    def build_credentials(self, *, parameters: dict[str, Any]) -> InvokeCredentials:
+    def build_credentials(self, *, parameters: Mapping[str, object]) -> InvokeCredentials:
         credentials = InvokeCredentials()
         credentials.tool_credentials = {}
-        for tool in parameters.get("tools", []):
+        tools = parameters.get("tools")
+        if not isinstance(tools, list):
+            return credentials
+
+        for raw_tool in tools:
+            tool = self._coerce_json_object(raw_tool)
+            if tool is None:
+                continue
             if not tool.get("credential_id"):
                 continue
             try:
                 identity = ToolIdentity.model_validate(tool.get("identity", {}))
             except ValidationError:
                 continue
-            credentials.tool_credentials[identity.provider] = tool.get("credential_id", None)
+            credential_id = self._coerce_optional_string(tool.get("credential_id"))
+            if credential_id is None:
+                continue
+            credentials.tool_credentials[identity.provider] = credential_id
         return credentials
 
     def fetch_memory(
@@ -232,14 +231,14 @@ class AgentRuntimeSupport:
 
         return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
-    def fetch_model(self, *, tenant_id: str, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
+    def fetch_model(self, *, tenant_id: str, value: Mapping[str, object]) -> tuple[ModelInstance, AIModelEntity | None]:
         provider_manager = ProviderManager()
         provider_model_bundle = provider_manager.get_provider_model_bundle(
             tenant_id=tenant_id,
-            provider=value.get("provider", ""),
+            provider=str(value.get("provider", "")),
             model_type=ModelType.LLM,
         )
-        model_name = value.get("model", "")
+        model_name = str(value.get("model", ""))
         model_credentials = provider_model_bundle.configuration.get_current_credentials(
             model_type=ModelType.LLM,
             model=model_name,
@@ -249,7 +248,7 @@ class AgentRuntimeSupport:
         model_instance = ModelManager().get_model_instance(
             tenant_id=tenant_id,
             provider=provider_name,
-            model_type=ModelType(value.get("model_type", "")),
+            model_type=ModelType(str(value.get("model_type", ""))),
             model=model_name,
         )
         model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
@@ -268,9 +267,88 @@ class AgentRuntimeSupport:
     @staticmethod
     def _filter_mcp_type_tool(
         strategy: ResolvedAgentStrategy,
-        tools: list[dict[str, Any]],
-    ) -> list[dict[str, Any]]:
+        tools: JsonObjectList,
+    ) -> JsonObjectList:
         meta_version = strategy.meta_version
         if meta_version and Version(meta_version) > Version("0.0.1"):
             return tools
         return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]
+
+    def _normalize_tool_payloads(
+        self,
+        *,
+        strategy: ResolvedAgentStrategy,
+        tools: JsonObjectList,
+        variable_pool: VariablePool,
+    ) -> JsonObjectList:
+        enabled_tools = [dict(tool) for tool in tools if bool(tool.get("enabled", False))]
+        normalized_tools = self._filter_mcp_type_tool(strategy, enabled_tools)
+        for tool in normalized_tools:
+            tool.pop("schemas", None)
+            tool["parameters"] = self._resolve_tool_parameters(tool=tool, variable_pool=variable_pool)
+            tool["settings"] = self._resolve_tool_settings(tool)
+        return normalized_tools
+
+    def _resolve_tool_parameters(self, *, tool: Mapping[str, object], variable_pool: VariablePool) -> JsonObject:
+        parameter_configs = self._coerce_named_json_objects(tool.get("parameters"))
+        if parameter_configs is None:
+            raw_parameters = self._coerce_json_object(tool.get("parameters"))
+            return raw_parameters or {}
+
+        resolved_parameters: JsonObject = {}
+        for key, parameter_config in parameter_configs.items():
+            if parameter_config.get("auto", ParamsAutoGenerated.OPEN) in (ParamsAutoGenerated.CLOSE, 0):
+                value_param = self._coerce_json_object(parameter_config.get("value"))
+                if value_param and value_param.get("type") == "variable":
+                    variable_selector = _VARIABLE_SELECTOR_ADAPTER.validate_python(value_param.get("value"))
+                    variable = variable_pool.get(variable_selector)
+                    if variable is None:
+                        raise AgentVariableNotFoundError(str(variable_selector))
+                    resolved_parameters[key] = variable.value
+                else:
+                    resolved_parameters[key] = value_param.get("value", "") if value_param is not None else None
+            else:
+                resolved_parameters[key] = None
+
+        return resolved_parameters
+
+    @staticmethod
+    def _resolve_tool_settings(tool: Mapping[str, object]) -> JsonObject:
+        settings = AgentRuntimeSupport._coerce_named_json_objects(tool.get("settings"))
+        if settings is None:
+            return {}
+        return {key: setting.get("value") for key, setting in settings.items()}
+
+    @staticmethod
+    def _coerce_json_object(value: object) -> JsonObject | None:
+        try:
+            return _JSON_OBJECT_ADAPTER.validate_python(value)
+        except ValidationError:
+            return None
+
+    @staticmethod
+    def _coerce_optional_string(value: object) -> str | None:
+        return value if isinstance(value, str) else None
+
+    @staticmethod
+    def _coerce_tool_provider_type(value: object) -> ToolProviderType:
+        if isinstance(value, ToolProviderType):
+            return value
+        if isinstance(value, str):
+            return ToolProviderType(value)
+        return ToolProviderType.BUILT_IN
+
+    @classmethod
+    def _coerce_named_json_objects(cls, value: object) -> dict[str, JsonObject] | None:
+        if not isinstance(value, dict):
+            return None
+
+        coerced: dict[str, JsonObject] = {}
+        for key, item in value.items():
+            if not isinstance(key, str):
+                return None
+            json_object = cls._coerce_json_object(item)
+            if json_object is None:
+                return None
+            coerced[key] = json_object
+        return coerced

api/core/workflow/workflow_entry.py

@@ -1,7 +1,7 @@
 import logging
 import time
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, cast
+from typing import Any, TypeAlias, cast
 
 from configs import dify_config
 from core.app.apps.exc import GenerateTaskStoppedError
@@ -32,6 +32,13 @@ from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
+SpecialValueScalar: TypeAlias = str | int | float | bool | None
+SpecialValue: TypeAlias = SpecialValueScalar | File | Mapping[str, "SpecialValue"] | list["SpecialValue"]
+SerializedSpecialValue: TypeAlias = (
+    SpecialValueScalar | dict[str, "SerializedSpecialValue"] | list["SerializedSpecialValue"]
+)
+SingleNodeGraphConfig: TypeAlias = dict[str, list[dict[str, object]]]
+
 
 class _WorkflowChildEngineBuilder:
     @staticmethod
@@ -276,10 +283,10 @@ class WorkflowEntry:
     @staticmethod
     def _create_single_node_graph(
         node_id: str,
-        node_data: dict[str, Any],
+        node_data: Mapping[str, object],
         node_width: int = 114,
         node_height: int = 514,
-    ) -> dict[str, Any]:
+    ) -> SingleNodeGraphConfig:
         """
         Create a minimal graph structure for testing a single node in isolation.
 
@@ -289,14 +296,14 @@ class WorkflowEntry:
         :param node_height: height for UI layout (default: 100)
         :return: graph dictionary with start node and target node
         """
-        node_config = {
+        node_config: dict[str, object] = {
             "id": node_id,
             "width": node_width,
             "height": node_height,
             "type": "custom",
-            "data": node_data,
+            "data": dict(node_data),
         }
-        start_node_config = {
+        start_node_config: dict[str, object] = {
             "id": "start",
             "width": node_width,
             "height": node_height,
@@ -321,7 +328,12 @@ class WorkflowEntry:
 
     @classmethod
     def run_free_node(
-        cls, node_data: dict[str, Any], node_id: str, tenant_id: str, user_id: str, user_inputs: dict[str, Any]
+        cls,
+        node_data: Mapping[str, object],
+        node_id: str,
+        tenant_id: str,
+        user_id: str,
+        user_inputs: Mapping[str, object],
     ) -> tuple[Node, Generator[GraphNodeEventBase, None, None]]:
         """
         Run free node
@@ -339,6 +351,8 @@ class WorkflowEntry:
         graph_dict = cls._create_single_node_graph(node_id, node_data)
 
         node_type = node_data.get("type", "")
+        if not isinstance(node_type, str):
+            raise ValueError("Node type must be a string")
         if node_type not in {BuiltinNodeTypes.PARAMETER_EXTRACTOR, BuiltinNodeTypes.QUESTION_CLASSIFIER}:
             raise ValueError(f"Node type {node_type} not supported")
 
@@ -369,7 +383,7 @@ class WorkflowEntry:
         graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
 
         # init workflow run state
-        node_config = NodeConfigDictAdapter.validate_python({"id": node_id, "data": node_data})
+        node_config = NodeConfigDictAdapter.validate_python({"id": node_id, "data": dict(node_data)})
         node_factory = DifyNodeFactory(
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
@@ -405,30 +419,34 @@ class WorkflowEntry:
             raise WorkflowNodeRunFailedError(node=node, err_msg=str(e))
 
     @staticmethod
-    def handle_special_values(value: Mapping[str, Any] | None) -> Mapping[str, Any] | None:
+    def handle_special_values(value: Mapping[str, SpecialValue] | None) -> dict[str, SerializedSpecialValue] | None:
         # NOTE(QuantumGhost): Avoid using this function in new code.
         # Keep values structured as long as possible and only convert to dict
         # immediately before serialization (e.g., JSON serialization) to maintain
         # data integrity and type information.
         result = WorkflowEntry._handle_special_values(value)
-        return result if isinstance(result, Mapping) or result is None else dict(result)
+        if result is None:
+            return None
+        if isinstance(result, dict):
+            return result
+        raise TypeError("handle_special_values expects a mapping input")
 
     @staticmethod
-    def _handle_special_values(value: Any):
+    def _handle_special_values(value: SpecialValue) -> SerializedSpecialValue:
         if value is None:
             return value
-        if isinstance(value, dict):
-            res = {}
+        if isinstance(value, Mapping):
+            res: dict[str, SerializedSpecialValue] = {}
             for k, v in value.items():
                 res[k] = WorkflowEntry._handle_special_values(v)
             return res
         if isinstance(value, list):
-            res_list = []
+            res_list: list[SerializedSpecialValue] = []
             for item in value:
                 res_list.append(WorkflowEntry._handle_special_values(item))
             return res_list
         if isinstance(value, File):
-            return value.to_dict()
+            return dict(value.to_dict())
         return value
 
     @classmethod

api/dify_graph/file/file_manager.py

@@ -112,6 +112,8 @@ def _get_encoded_string(f: File, /) -> str:
             data = _download_file_content(f.storage_key)
         case FileTransferMethod.DATASOURCE_FILE:
             data = _download_file_content(f.storage_key)
+        case _:
+            raise ValueError(f"Unsupported transfer method: {f.transfer_method}")
 
     return base64.b64encode(data).decode("utf-8")
 

api/dify_graph/graph_engine/layers/execution_limits.py

@@ -133,6 +133,8 @@ class ExecutionLimitsLayer(GraphEngineLayer):
         elif limit_type == LimitType.TIME_LIMIT:
             elapsed_time = time.time() - self.start_time if self.start_time else 0
             reason = f"Maximum execution time exceeded: {elapsed_time:.2f}s > {self.max_time}s"
+        else:
+            return
 
         self.logger.warning("Execution limit exceeded: %s", reason)
 

api/dify_graph/nodes/base/node.py

@@ -336,12 +336,7 @@ class Node(Generic[NodeDataT]):
 
     def _restore_execution_id_from_runtime_state(self) -> str | None:
         graph_execution = self.graph_runtime_state.graph_execution
-        try:
-            node_executions = graph_execution.node_executions
-        except AttributeError:
-            return None
-        if not isinstance(node_executions, dict):
-            return None
+        node_executions = graph_execution.node_executions
         node_execution = node_executions.get(self._node_id)
         if node_execution is None:
             return None
@@ -395,8 +390,7 @@ class Node(Generic[NodeDataT]):
                 if isinstance(event, NodeEventBase):  # pyright: ignore[reportUnnecessaryIsInstance]
                     yield self._dispatch(event)
                 elif isinstance(event, GraphNodeEventBase) and not event.in_iteration_id and not event.in_loop_id:  # pyright: ignore[reportUnnecessaryIsInstance]
-                    event.id = self.execution_id
-                    yield event
+                    yield event.model_copy(update={"id": self.execution_id})
                 else:
                     yield event
         except Exception as e:

api/dify_graph/nodes/document_extractor/node.py

@@ -443,7 +443,10 @@ def _extract_text_from_docx(file_content: bytes) -> str:
         # Keep track of paragraph and table positions
         content_items: list[tuple[int, str, Table | Paragraph]] = []
 
-        it = iter(doc.element.body)
+        doc_body = getattr(doc.element, "body", None)
+        if doc_body is None:
+            raise TextExtractionError("DOCX body not found")
+        it = iter(doc_body)
         part = next(it, None)
         i = 0
         while part is not None:

api/dify_graph/nodes/llm/entities.py

@@ -1,7 +1,8 @@
 from collections.abc import Mapping, Sequence
-from typing import Any, Literal
+from typing import Literal, NotRequired
 
 from pydantic import BaseModel, Field, field_validator
+from typing_extensions import TypedDict
 
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
 from dify_graph.entities.base_node_data import BaseNodeData
@@ -10,11 +11,17 @@ from dify_graph.model_runtime.entities import ImagePromptMessageContent, LLMMode
 from dify_graph.nodes.base.entities import VariableSelector
 
 
+class StructuredOutputConfig(TypedDict):
+    schema: Mapping[str, object]
+    name: NotRequired[str]
+    description: NotRequired[str]
+
+
 class ModelConfig(BaseModel):
     provider: str
     name: str
     mode: LLMMode
-    completion_params: dict[str, Any] = Field(default_factory=dict)
+    completion_params: dict[str, object] = Field(default_factory=dict)
 
 
 class ContextConfig(BaseModel):
@@ -33,7 +40,7 @@ class VisionConfig(BaseModel):
 
     @field_validator("configs", mode="before")
     @classmethod
-    def convert_none_configs(cls, v: Any):
+    def convert_none_configs(cls, v: object):
         if v is None:
             return VisionConfigOptions()
         return v
@@ -44,7 +51,7 @@ class PromptConfig(BaseModel):
 
     @field_validator("jinja2_variables", mode="before")
     @classmethod
-    def convert_none_jinja2_variables(cls, v: Any):
+    def convert_none_jinja2_variables(cls, v: object):
         if v is None:
             return []
         return v
@@ -67,7 +74,7 @@ class LLMNodeData(BaseNodeData):
     memory: MemoryConfig | None = None
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
-    structured_output: Mapping[str, Any] | None = None
+    structured_output: StructuredOutputConfig | None = None
     # We used 'structured_output_enabled' in the past, but it's not a good name.
     structured_output_switch_on: bool = Field(False, alias="structured_output_enabled")
     reasoning_format: Literal["separated", "tagged"] = Field(
@@ -90,11 +97,30 @@ class LLMNodeData(BaseNodeData):
 
     @field_validator("prompt_config", mode="before")
     @classmethod
-    def convert_none_prompt_config(cls, v: Any):
+    def convert_none_prompt_config(cls, v: object):
         if v is None:
             return PromptConfig()
         return v
 
+    @field_validator("structured_output", mode="before")
+    @classmethod
+    def convert_legacy_structured_output(cls, v: object) -> StructuredOutputConfig | None | object:
+        if not isinstance(v, Mapping):
+            return v
+
+        schema = v.get("schema")
+        if schema is None:
+            return None
+
+        normalized: StructuredOutputConfig = {"schema": schema}
+        name = v.get("name")
+        description = v.get("description")
+        if isinstance(name, str):
+            normalized["name"] = name
+        if isinstance(description, str):
+            normalized["description"] = description
+        return normalized
+
     @property
     def structured_output_enabled(self) -> bool:
         return self.structured_output_switch_on and self.structured_output is not None

api/dify_graph/nodes/llm/node.py

@@ -9,6 +9,7 @@ import time
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal
 
+from pydantic import TypeAdapter
 from sqlalchemy import select
 
 from core.llm_generator.output_parser.errors import OutputParserError
@@ -74,6 +75,7 @@ from .entities import (
     LLMNodeChatModelMessage,
     LLMNodeCompletionModelPromptTemplate,
     LLMNodeData,
+    StructuredOutputConfig,
 )
 from .exc import (
     InvalidContextStructureError,
@@ -88,6 +90,7 @@ if TYPE_CHECKING:
     from dify_graph.runtime import GraphRuntimeState
 
 logger = logging.getLogger(__name__)
+_JSON_OBJECT_ADAPTER = TypeAdapter(dict[str, object])
 
 
 class LLMNode(Node[LLMNodeData]):
@@ -354,7 +357,7 @@ class LLMNode(Node[LLMNodeData]):
         stop: Sequence[str] | None = None,
         user_id: str,
         structured_output_enabled: bool,
-        structured_output: Mapping[str, Any] | None = None,
+        structured_output: StructuredOutputConfig | None = None,
         file_saver: LLMFileSaver,
         file_outputs: list[File],
         node_id: str,
@@ -367,8 +370,10 @@ class LLMNode(Node[LLMNodeData]):
         model_schema = llm_utils.fetch_model_schema(model_instance=model_instance)
 
         if structured_output_enabled:
+            if structured_output is None:
+                raise LLMNodeError("Please provide a valid structured output schema")
             output_schema = LLMNode.fetch_structured_output_schema(
-                structured_output=structured_output or {},
+                structured_output=structured_output,
             )
             request_start_time = time.perf_counter()
 
@@ -920,6 +925,12 @@ class LLMNode(Node[LLMNodeData]):
             # Extract clean text and reasoning from <think> tags
             clean_text, reasoning_content = LLMNode._split_reasoning(full_text, reasoning_format)
 
+        structured_output = (
+            dict(invoke_result.structured_output)
+            if isinstance(invoke_result, LLMResultWithStructuredOutput) and invoke_result.structured_output is not None
+            else None
+        )
+
         event = ModelInvokeCompletedEvent(
             # Use clean_text for separated mode, full_text for tagged mode
             text=clean_text if reasoning_format == "separated" else full_text,
@@ -928,7 +939,7 @@ class LLMNode(Node[LLMNodeData]):
             # Reasoning content for workflow variables and downstream nodes
             reasoning_content=reasoning_content,
             # Pass structured output if enabled
-            structured_output=getattr(invoke_result, "structured_output", None),
+            structured_output=structured_output,
         )
         if request_latency is not None:
             event.usage.latency = round(request_latency, 3)
@@ -962,27 +973,18 @@ class LLMNode(Node[LLMNodeData]):
     @staticmethod
     def fetch_structured_output_schema(
         *,
-        structured_output: Mapping[str, Any],
-    ) -> dict[str, Any]:
+        structured_output: StructuredOutputConfig,
+    ) -> dict[str, object]:
         """
         Fetch the structured output schema from the node data.
 
         Returns:
-            dict[str, Any]: The structured output schema
+            dict[str, object]: The structured output schema
         """
-        if not structured_output:
+        schema = structured_output.get("schema")
+        if not schema:
             raise LLMNodeError("Please provide a valid structured output schema")
-        structured_output_schema = json.dumps(structured_output.get("schema", {}), ensure_ascii=False)
-        if not structured_output_schema:
-            raise LLMNodeError("Please provide a valid structured output schema")
-
-        try:
-            schema = json.loads(structured_output_schema)
-            if not isinstance(schema, dict):
-                raise LLMNodeError("structured_output_schema must be a JSON object")
-            return schema
-        except json.JSONDecodeError:
-            raise LLMNodeError("structured_output_schema is not valid JSON format")
+        return _JSON_OBJECT_ADAPTER.validate_python(schema)
 
     @staticmethod
     def _save_multimodal_output_and_convert_result_to_markdown(

api/dify_graph/nodes/loop/entities.py

@@ -1,7 +1,10 @@
-from enum import StrEnum
-from typing import Annotated, Any, Literal
+from __future__ import annotations
 
-from pydantic import AfterValidator, BaseModel, Field, field_validator
+from enum import StrEnum
+from typing import Annotated, Any, Literal, TypeAlias, cast
+
+from pydantic import AfterValidator, BaseModel, Field, TypeAdapter, field_validator
+from pydantic_core.core_schema import ValidationInfo
 
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
@@ -9,6 +12,12 @@ from dify_graph.nodes.base import BaseLoopNodeData, BaseLoopState
 from dify_graph.utils.condition.entities import Condition
 from dify_graph.variables.types import SegmentType
 
+LoopValue: TypeAlias = str | int | float | bool | None | dict[str, Any] | list[Any]
+LoopValueMapping: TypeAlias = dict[str, LoopValue]
+VariableSelector: TypeAlias = list[str]
+
+_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
+
 _VALID_VAR_TYPE = frozenset(
     [
         SegmentType.STRING,
@@ -29,6 +38,36 @@ def _is_valid_var_type(seg_type: SegmentType) -> SegmentType:
     return seg_type
 
 
+def _validate_loop_value(value: object) -> LoopValue:
+    if value is None or isinstance(value, (str, int, float, bool)):
+        return cast(LoopValue, value)
+
+    if isinstance(value, list):
+        return [_validate_loop_value(item) for item in value]
+
+    if isinstance(value, dict):
+        normalized: dict[str, LoopValue] = {}
+        for key, item in value.items():
+            if not isinstance(key, str):
+                raise TypeError("Loop values only support string object keys")
+            normalized[key] = _validate_loop_value(item)
+        return normalized
+
+    raise TypeError("Loop values must be JSON-like primitives, arrays, or objects")
+
+
+def _validate_loop_value_mapping(value: object) -> LoopValueMapping:
+    if not isinstance(value, dict):
+        raise TypeError("Loop outputs must be an object")
+
+    normalized: LoopValueMapping = {}
+    for key, item in value.items():
+        if not isinstance(key, str):
+            raise TypeError("Loop output keys must be strings")
+        normalized[key] = _validate_loop_value(item)
+    return normalized
+
+
 class LoopVariableData(BaseModel):
     """
     Loop Variable Data.
@@ -37,7 +76,29 @@ class LoopVariableData(BaseModel):
     label: str
     var_type: Annotated[SegmentType, AfterValidator(_is_valid_var_type)]
     value_type: Literal["variable", "constant"]
-    value: Any | list[str] | None = None
+    value: LoopValue | VariableSelector | None = None
+
+    @field_validator("value", mode="before")
+    @classmethod
+    def validate_value(cls, value: object, validation_info: ValidationInfo) -> LoopValue | VariableSelector | None:
+        value_type = validation_info.data.get("value_type")
+        if value_type == "variable":
+            if value is None:
+                raise ValueError("Variable loop inputs require a selector")
+            return _VARIABLE_SELECTOR_ADAPTER.validate_python(value)
+        if value_type == "constant":
+            return _validate_loop_value(value)
+        raise ValueError(f"Unknown loop variable value type: {value_type}")
+
+    def require_variable_selector(self) -> VariableSelector:
+        if self.value_type != "variable":
+            raise ValueError(f"Expected variable loop input, got {self.value_type}")
+        return _VARIABLE_SELECTOR_ADAPTER.validate_python(self.value)
+
+    def require_constant_value(self) -> LoopValue:
+        if self.value_type != "constant":
+            raise ValueError(f"Expected constant loop input, got {self.value_type}")
+        return _validate_loop_value(self.value)
 
 
 class LoopNodeData(BaseLoopNodeData):
@@ -46,14 +107,14 @@ class LoopNodeData(BaseLoopNodeData):
     break_conditions: list[Condition]  # Conditions to break the loop
     logical_operator: Literal["and", "or"]
     loop_variables: list[LoopVariableData] | None = Field(default_factory=list[LoopVariableData])
-    outputs: dict[str, Any] = Field(default_factory=dict)
+    outputs: LoopValueMapping = Field(default_factory=dict)
 
     @field_validator("outputs", mode="before")
     @classmethod
-    def validate_outputs(cls, v):
-        if v is None:
+    def validate_outputs(cls, value: object) -> LoopValueMapping:
+        if value is None:
             return {}
-        return v
+        return _validate_loop_value_mapping(value)
 
 
 class LoopStartNodeData(BaseNodeData):
@@ -77,8 +138,8 @@ class LoopState(BaseLoopState):
     Loop State.
     """
 
-    outputs: list[Any] = Field(default_factory=list)
-    current_output: Any = None
+    outputs: list[LoopValue] = Field(default_factory=list)
+    current_output: LoopValue | None = None
 
     class MetaData(BaseLoopState.MetaData):
         """
@@ -87,7 +148,7 @@ class LoopState(BaseLoopState):
 
         loop_length: int
 
-    def get_last_output(self) -> Any:
+    def get_last_output(self) -> LoopValue | None:
         """
         Get last output.
         """
@@ -95,7 +156,7 @@ class LoopState(BaseLoopState):
             return self.outputs[-1]
         return None
 
-    def get_current_output(self) -> Any:
+    def get_current_output(self) -> LoopValue | None:
         """
         Get current output.
         """

api/dify_graph/nodes/loop/loop_node.py

@@ -3,7 +3,7 @@ import json
 import logging
 from collections.abc import Callable, Generator, Mapping, Sequence
 from datetime import datetime
-from typing import TYPE_CHECKING, Any, Literal, cast
+from typing import TYPE_CHECKING, Literal, cast
 
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
 from dify_graph.enums import (
@@ -29,7 +29,7 @@ from dify_graph.node_events import (
 )
 from dify_graph.nodes.base import LLMUsageTrackingMixin
 from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.loop.entities import LoopCompletedReason, LoopNodeData, LoopVariableData
+from dify_graph.nodes.loop.entities import LoopCompletedReason, LoopNodeData, LoopValue, LoopVariableData
 from dify_graph.utils.condition.processor import ConditionProcessor
 from dify_graph.variables import Segment, SegmentType
 from factories.variable_factory import TypeMismatchError, build_segment_with_type, segment_to_variable
@@ -60,7 +60,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         break_conditions = self.node_data.break_conditions
         logical_operator = self.node_data.logical_operator
 
-        inputs = {"loop_count": loop_count}
+        inputs: dict[str, object] = {"loop_count": loop_count}
 
         if not self.node_data.start_node_id:
             raise ValueError(f"field start_node_id in loop {self._node_id} not found")
@@ -68,12 +68,14 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         root_node_id = self.node_data.start_node_id
 
         # Initialize loop variables in the original variable pool
-        loop_variable_selectors = {}
+        loop_variable_selectors: dict[str, list[str]] = {}
         if self.node_data.loop_variables:
             value_processor: dict[Literal["constant", "variable"], Callable[[LoopVariableData], Segment | None]] = {
-                "constant": lambda var: self._get_segment_for_constant(var.var_type, var.value),
+                "constant": lambda var: self._get_segment_for_constant(var.var_type, var.require_constant_value()),
                 "variable": lambda var: (
-                    self.graph_runtime_state.variable_pool.get(var.value) if isinstance(var.value, list) else None
+                    self.graph_runtime_state.variable_pool.get(var.require_variable_selector())
+                    if var.value is not None
+                    else None
                 ),
             }
             for loop_variable in self.node_data.loop_variables:
@@ -95,7 +97,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         condition_processor = ConditionProcessor()
 
         loop_duration_map: dict[str, float] = {}
-        single_loop_variable_map: dict[str, dict[str, Any]] = {}  # single loop variable output
+        single_loop_variable_map: dict[str, dict[str, LoopValue]] = {}  # single loop variable output
         loop_usage = LLMUsage.empty_usage()
         loop_node_ids = self._extract_loop_node_ids_from_config(self.graph_config, self._node_id)
 
@@ -146,7 +148,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
                 loop_usage = self._merge_usage(loop_usage, graph_engine.graph_runtime_state.llm_usage)
 
                 # Collect loop variable values after iteration
-                single_loop_variable = {}
+                single_loop_variable: dict[str, LoopValue] = {}
                 for key, selector in loop_variable_selectors.items():
                     segment = self.graph_runtime_state.variable_pool.get(selector)
                     single_loop_variable[key] = segment.value if segment else None
@@ -297,20 +299,29 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
     def _extract_variable_selector_to_variable_mapping(
         cls,
         *,
-        graph_config: Mapping[str, Any],
+        graph_config: Mapping[str, object],
         node_id: str,
         node_data: LoopNodeData,
     ) -> Mapping[str, Sequence[str]]:
-        variable_mapping = {}
+        variable_mapping: dict[str, Sequence[str]] = {}
 
         # Extract loop node IDs statically from graph_config
 
         loop_node_ids = cls._extract_loop_node_ids_from_config(graph_config, node_id)
 
         # Get node configs from graph_config
-        node_configs = {node["id"]: node for node in graph_config.get("nodes", []) if "id" in node}
+        raw_nodes = graph_config.get("nodes")
+        node_configs: dict[str, Mapping[str, object]] = {}
+        if isinstance(raw_nodes, list):
+            for raw_node in raw_nodes:
+                if not isinstance(raw_node, dict):
+                    continue
+                raw_node_id = raw_node.get("id")
+                if isinstance(raw_node_id, str):
+                    node_configs[raw_node_id] = raw_node
         for sub_node_id, sub_node_config in node_configs.items():
-            if sub_node_config.get("data", {}).get("loop_id") != node_id:
+            sub_node_data = sub_node_config.get("data")
+            if not isinstance(sub_node_data, dict) or sub_node_data.get("loop_id") != node_id:
                 continue
 
             # variable selector to variable mapping
@@ -341,9 +352,8 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
 
         for loop_variable in node_data.loop_variables or []:
             if loop_variable.value_type == "variable":
-                assert loop_variable.value is not None, "Loop variable value must be provided for variable type"
                 # add loop variable to variable mapping
-                selector = loop_variable.value
+                selector = loop_variable.require_variable_selector()
                 variable_mapping[f"{node_id}.{loop_variable.label}"] = selector
 
         # remove variable out from loop
@@ -352,7 +362,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         return variable_mapping
 
     @classmethod
-    def _extract_loop_node_ids_from_config(cls, graph_config: Mapping[str, Any], loop_node_id: str) -> set[str]:
+    def _extract_loop_node_ids_from_config(cls, graph_config: Mapping[str, object], loop_node_id: str) -> set[str]:
         """
         Extract node IDs that belong to a specific loop from graph configuration.
 
@@ -363,12 +373,19 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         :param loop_node_id: the ID of the loop node
         :return: set of node IDs that belong to the loop
         """
-        loop_node_ids = set()
+        loop_node_ids: set[str] = set()
 
         # Find all nodes that belong to this loop
-        nodes = graph_config.get("nodes", [])
-        for node in nodes:
-            node_data = node.get("data", {})
+        raw_nodes = graph_config.get("nodes")
+        if not isinstance(raw_nodes, list):
+            return loop_node_ids
+
+        for node in raw_nodes:
+            if not isinstance(node, dict):
+                continue
+            node_data = node.get("data")
+            if not isinstance(node_data, dict):
+                continue
             if node_data.get("loop_id") == loop_node_id:
                 node_id = node.get("id")
                 if node_id:
@@ -377,7 +394,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         return loop_node_ids
 
     @staticmethod
-    def _get_segment_for_constant(var_type: SegmentType, original_value: Any) -> Segment:
+    def _get_segment_for_constant(var_type: SegmentType, original_value: LoopValue | None) -> Segment:
         """Get the appropriate segment type for a constant value."""
         # TODO: Refactor for maintainability:
         # 1. Ensure type handling logic stays synchronized with _VALID_VAR_TYPE (entities.py)

api/dify_graph/nodes/parameter_extractor/entities.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Any, Literal
+from typing import Annotated, Literal
 
 from pydantic import (
     BaseModel,
@@ -6,6 +6,7 @@ from pydantic import (
     Field,
     field_validator,
 )
+from typing_extensions import TypedDict
 
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from dify_graph.entities.base_node_data import BaseNodeData
@@ -55,7 +56,7 @@ class ParameterConfig(BaseModel):
 
     @field_validator("name", mode="before")
     @classmethod
-    def validate_name(cls, value) -> str:
+    def validate_name(cls, value: object) -> str:
         if not value:
             raise ValueError("Parameter name is required")
         if value in {"__reason", "__is_success"}:
@@ -79,6 +80,23 @@ class ParameterConfig(BaseModel):
         return element_type
 
 
+class JsonSchemaArrayItems(TypedDict):
+    type: str
+
+
+class ParameterJsonSchemaProperty(TypedDict, total=False):
+    description: str
+    type: str
+    items: JsonSchemaArrayItems
+    enum: list[str]
+
+
+class ParameterJsonSchema(TypedDict):
+    type: Literal["object"]
+    properties: dict[str, ParameterJsonSchemaProperty]
+    required: list[str]
+
+
 class ParameterExtractorNodeData(BaseNodeData):
     """
     Parameter Extractor Node Data.
@@ -95,19 +113,19 @@ class ParameterExtractorNodeData(BaseNodeData):
 
     @field_validator("reasoning_mode", mode="before")
     @classmethod
-    def set_reasoning_mode(cls, v) -> str:
-        return v or "function_call"
+    def set_reasoning_mode(cls, v: object) -> str:
+        return str(v) if v else "function_call"
 
-    def get_parameter_json_schema(self):
+    def get_parameter_json_schema(self) -> ParameterJsonSchema:
         """
         Get parameter json schema.
 
         :return: parameter json schema
         """
-        parameters: dict[str, Any] = {"type": "object", "properties": {}, "required": []}
+        parameters: ParameterJsonSchema = {"type": "object", "properties": {}, "required": []}
 
         for parameter in self.parameters:
-            parameter_schema: dict[str, Any] = {"description": parameter.description}
+            parameter_schema: ParameterJsonSchemaProperty = {"description": parameter.description}
 
             if parameter.type == SegmentType.STRING:
                 parameter_schema["type"] = "string"
@@ -118,7 +136,7 @@ class ParameterExtractorNodeData(BaseNodeData):
                     raise AssertionError("element type should not be None.")
                 parameter_schema["items"] = {"type": element_type.value}
             else:
-                parameter_schema["type"] = parameter.type
+                parameter_schema["type"] = parameter.type.value
 
             if parameter.options:
                 parameter_schema["enum"] = parameter.options

api/dify_graph/nodes/parameter_extractor/parameter_extractor_node.py

@@ -5,6 +5,8 @@ import uuid
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, cast
 
+from pydantic import TypeAdapter
+
 from core.model_manager import ModelInstance
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
@@ -63,6 +65,7 @@ from .prompts import (
 )
 
 logger = logging.getLogger(__name__)
+_JSON_OBJECT_ADAPTER = TypeAdapter(dict[str, object])
 
 if TYPE_CHECKING:
     from dify_graph.entities import GraphInitParams
@@ -70,7 +73,7 @@ if TYPE_CHECKING:
     from dify_graph.runtime import GraphRuntimeState
 
 
-def extract_json(text):
+def extract_json(text: str) -> str | None:
     """
     From a given JSON started from '{' or '[' extract the complete JSON object.
     """
@@ -392,10 +395,15 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         )
 
         # generate tool
+        parameter_schema = node_data.get_parameter_json_schema()
         tool = PromptMessageTool(
             name=FUNCTION_CALLING_EXTRACTOR_NAME,
             description="Extract parameters from the natural language text",
-            parameters=node_data.get_parameter_json_schema(),
+            parameters={
+                "type": parameter_schema["type"],
+                "properties": dict(parameter_schema["properties"]),
+                "required": list(parameter_schema["required"]),
+            },
         )
 
         return prompt_messages, [tool]
@@ -602,19 +610,21 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         else:
             return None
 
-    def _transform_result(self, data: ParameterExtractorNodeData, result: dict):
+    def _transform_result(self, data: ParameterExtractorNodeData, result: Mapping[str, object]) -> dict[str, object]:
         """
         Transform result into standard format.
         """
-        transformed_result: dict[str, Any] = {}
+        transformed_result: dict[str, object] = {}
         for parameter in data.parameters:
             if parameter.name in result:
                 param_value = result[parameter.name]
                 # transform value
                 if parameter.type == SegmentType.NUMBER:
-                    transformed = self._transform_number(param_value)
-                    if transformed is not None:
-                        transformed_result[parameter.name] = transformed
+                    if isinstance(param_value, (bool, int, float, str)):
+                        numeric_value: bool | int | float | str = param_value
+                        transformed = self._transform_number(numeric_value)
+                        if transformed is not None:
+                            transformed_result[parameter.name] = transformed
                 elif parameter.type == SegmentType.BOOLEAN:
                     if isinstance(result[parameter.name], (bool, int)):
                         transformed_result[parameter.name] = bool(result[parameter.name])
@@ -661,7 +671,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
 
         return transformed_result
 
-    def _extract_complete_json_response(self, result: str) -> dict | None:
+    def _extract_complete_json_response(self, result: str) -> dict[str, object] | None:
         """
         Extract complete json response.
         """
@@ -672,11 +682,11 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
                 json_str = extract_json(result[idx:])
                 if json_str:
                     with contextlib.suppress(Exception):
-                        return cast(dict, json.loads(json_str))
+                        return _JSON_OBJECT_ADAPTER.validate_python(json.loads(json_str))
         logger.info("extra error: %s", result)
         return None
 
-    def _extract_json_from_tool_call(self, tool_call: AssistantPromptMessage.ToolCall) -> dict | None:
+    def _extract_json_from_tool_call(self, tool_call: AssistantPromptMessage.ToolCall) -> dict[str, object] | None:
         """
         Extract json from tool call.
         """
@@ -690,16 +700,16 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
                 json_str = extract_json(result[idx:])
                 if json_str:
                     with contextlib.suppress(Exception):
-                        return cast(dict, json.loads(json_str))
+                        return _JSON_OBJECT_ADAPTER.validate_python(json.loads(json_str))
 
         logger.info("extra error: %s", result)
         return None
 
-    def _generate_default_result(self, data: ParameterExtractorNodeData):
+    def _generate_default_result(self, data: ParameterExtractorNodeData) -> dict[str, object]:
         """
         Generate default result.
         """
-        result: dict[str, Any] = {}
+        result: dict[str, object] = {}
         for parameter in data.parameters:
             if parameter.type == "number":
                 result[parameter.name] = 0

api/dify_graph/nodes/tool/entities.py

@@ -1,12 +1,66 @@
-from typing import Any, Literal, Union
+from __future__ import annotations
 
-from pydantic import BaseModel, field_validator
+from typing import Literal, TypeAlias, cast
+
+from pydantic import BaseModel, TypeAdapter, field_validator
 from pydantic_core.core_schema import ValidationInfo
+from typing_extensions import TypedDict
 
 from core.tools.entities.tool_entities import ToolProviderType
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
 
+ToolConfigurationValue: TypeAlias = str | int | float | bool
+ToolInputConstantValue: TypeAlias = str | int | float | bool | dict[str, object] | list[object] | None
+VariableSelector: TypeAlias = list[str]
+
+_TOOL_INPUT_MIXED_ADAPTER: TypeAdapter[str] = TypeAdapter(str)
+_TOOL_INPUT_CONSTANT_ADAPTER: TypeAdapter[ToolInputConstantValue] = TypeAdapter(ToolInputConstantValue)
+_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
+
+
+class WorkflowToolInputValue(TypedDict):
+    type: Literal["mixed", "variable", "constant"]
+    value: ToolInputConstantValue | VariableSelector
+
+
+ToolConfigurationEntry: TypeAlias = ToolConfigurationValue | WorkflowToolInputValue
+ToolConfigurations: TypeAlias = dict[str, ToolConfigurationEntry]
+
+
+class ToolInputPayload(BaseModel):
+    type: Literal["mixed", "variable", "constant"]
+    value: ToolInputConstantValue | VariableSelector
+
+    @field_validator("value", mode="before")
+    @classmethod
+    def validate_value(
+        cls, value: object, validation_info: ValidationInfo
+    ) -> ToolInputConstantValue | VariableSelector:
+        input_type = validation_info.data.get("type")
+        if input_type == "mixed":
+            return _TOOL_INPUT_MIXED_ADAPTER.validate_python(value)
+        if input_type == "variable":
+            return _VARIABLE_SELECTOR_ADAPTER.validate_python(value)
+        if input_type == "constant":
+            return _TOOL_INPUT_CONSTANT_ADAPTER.validate_python(value)
+        raise ValueError(f"Unknown tool input type: {input_type}")
+
+    def require_variable_selector(self) -> VariableSelector:
+        if self.type != "variable":
+            raise ValueError(f"Expected variable tool input, got {self.type}")
+        return _VARIABLE_SELECTOR_ADAPTER.validate_python(self.value)
+
+
+def _validate_tool_configuration_entry(value: object) -> ToolConfigurationEntry:
+    if isinstance(value, (str, int, float, bool)):
+        return cast(ToolConfigurationEntry, value)
+
+    if isinstance(value, dict):
+        return cast(ToolConfigurationEntry, ToolInputPayload.model_validate(value).model_dump())
+
+    raise TypeError("Tool configuration values must be primitives or workflow tool input objects")
+
 
 class ToolEntity(BaseModel):
     provider_id: str
@@ -14,52 +68,29 @@ class ToolEntity(BaseModel):
     provider_name: str  # redundancy
     tool_name: str
     tool_label: str  # redundancy
-    tool_configurations: dict[str, Any]
+    tool_configurations: ToolConfigurations
     credential_id: str | None = None
     plugin_unique_identifier: str | None = None  # redundancy
 
     @field_validator("tool_configurations", mode="before")
     @classmethod
-    def validate_tool_configurations(cls, value, values: ValidationInfo):
+    def validate_tool_configurations(cls, value: object, _validation_info: ValidationInfo) -> ToolConfigurations:
         if not isinstance(value, dict):
-            raise ValueError("tool_configurations must be a dictionary")
+            raise TypeError("tool_configurations must be a dictionary")
 
-        for key in values.data.get("tool_configurations", {}):
-            value = values.data.get("tool_configurations", {}).get(key)
-            if not isinstance(value, str | int | float | bool):
-                raise ValueError(f"{key} must be a string")
-
-        return value
+        normalized: ToolConfigurations = {}
+        for key, item in value.items():
+            if not isinstance(key, str):
+                raise TypeError("tool_configurations keys must be strings")
+            normalized[key] = _validate_tool_configuration_entry(item)
+        return normalized
 
 
 class ToolNodeData(BaseNodeData, ToolEntity):
     type: NodeType = BuiltinNodeTypes.TOOL
 
-    class ToolInput(BaseModel):
-        # TODO: check this type
-        value: Union[Any, list[str]]
-        type: Literal["mixed", "variable", "constant"]
-
-        @field_validator("type", mode="before")
-        @classmethod
-        def check_type(cls, value, validation_info: ValidationInfo):
-            typ = value
-            value = validation_info.data.get("value")
-
-            if value is None:
-                return typ
-
-            if typ == "mixed" and not isinstance(value, str):
-                raise ValueError("value must be a string")
-            elif typ == "variable":
-                if not isinstance(value, list):
-                    raise ValueError("value must be a list")
-                for val in value:
-                    if not isinstance(val, str):
-                        raise ValueError("value must be a list of strings")
-            elif typ == "constant" and not isinstance(value, (allowed_types := (str, int, float, bool, dict, list))):
-                raise ValueError(f"value must be one of: {', '.join(t.__name__ for t in allowed_types)}")
-            return typ
+    class ToolInput(ToolInputPayload):
+        pass
 
     tool_parameters: dict[str, ToolInput]
     # The version of the tool parameter.
@@ -69,7 +100,7 @@ class ToolNodeData(BaseNodeData, ToolEntity):
 
     @field_validator("tool_parameters", mode="before")
     @classmethod
-    def filter_none_tool_inputs(cls, value):
+    def filter_none_tool_inputs(cls, value: object) -> object:
         if not isinstance(value, dict):
             return value
 
@@ -80,8 +111,10 @@ class ToolNodeData(BaseNodeData, ToolEntity):
         }
 
     @staticmethod
-    def _has_valid_value(tool_input):
+    def _has_valid_value(tool_input: object) -> bool:
         """Check if the value is valid"""
         if isinstance(tool_input, dict):
             return tool_input.get("value") is not None
-        return getattr(tool_input, "value", None) is not None
+        if isinstance(tool_input, ToolNodeData.ToolInput):
+            return tool_input.value is not None
+        return False

api/dify_graph/nodes/tool/tool_node.py

@@ -225,10 +225,11 @@ class ToolNode(Node[ToolNodeData]):
                 continue
             tool_input = node_data.tool_parameters[parameter_name]
             if tool_input.type == "variable":
-                variable = variable_pool.get(tool_input.value)
+                variable_selector = tool_input.require_variable_selector()
+                variable = variable_pool.get(variable_selector)
                 if variable is None:
                     if parameter.required:
-                        raise ToolParameterError(f"Variable {tool_input.value} does not exist")
+                        raise ToolParameterError(f"Variable {variable_selector} does not exist")
                     continue
                 parameter_value = variable.value
             elif tool_input.type in {"mixed", "constant"}:
@@ -510,8 +511,9 @@ class ToolNode(Node[ToolNodeData]):
                     for selector in selectors:
                         result[selector.variable] = selector.value_selector
                 case "variable":
-                    selector_key = ".".join(input.value)
-                    result[f"#{selector_key}#"] = input.value
+                    variable_selector = input.require_variable_selector()
+                    selector_key = ".".join(variable_selector)
+                    result[f"#{selector_key}#"] = variable_selector
                 case "constant":
                     pass
 

api/dify_graph/nodes/variable_assigner/v1/node.py

@@ -9,7 +9,7 @@ from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.variable_assigner.common import helpers as common_helpers
 from dify_graph.nodes.variable_assigner.common.exc import VariableOperatorNodeError
-from dify_graph.variables import SegmentType, VariableBase
+from dify_graph.variables import Segment, SegmentType, VariableBase
 
 from .node_data import VariableAssignerData, WriteMode
 
@@ -74,23 +74,29 @@ class VariableAssignerNode(Node[VariableAssignerData]):
         if not isinstance(original_variable, VariableBase):
             raise VariableOperatorNodeError("assigned variable not found")
 
+        income_value: Segment
+        updated_variable: VariableBase
         match self.node_data.write_mode:
             case WriteMode.OVER_WRITE:
-                income_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
-                if not income_value:
+                input_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
+                if input_value is None:
                     raise VariableOperatorNodeError("input value not found")
+                income_value = input_value
                 updated_variable = original_variable.model_copy(update={"value": income_value.value})
 
             case WriteMode.APPEND:
-                income_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
-                if not income_value:
+                input_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
+                if input_value is None:
                     raise VariableOperatorNodeError("input value not found")
+                income_value = input_value
                 updated_value = original_variable.value + [income_value.value]
                 updated_variable = original_variable.model_copy(update={"value": updated_value})
 
             case WriteMode.CLEAR:
                 income_value = SegmentType.get_zero_value(original_variable.value_type)
                 updated_variable = original_variable.model_copy(update={"value": income_value.to_object()})
+            case _:
+                raise VariableOperatorNodeError(f"unsupported write mode: {self.node_data.write_mode}")
 
         # Over write the variable.
         self.graph_runtime_state.variable_pool.add(assigned_variable_selector, updated_variable)

api/dify_graph/runtime/graph_runtime_state.py

@@ -66,6 +66,11 @@ class GraphExecutionProtocol(Protocol):
     exceptions_count: int
     pause_reasons: list[PauseReason]
 
+    @property
+    def node_executions(self) -> Mapping[str, NodeExecutionProtocol]:
+        """Return node execution state keyed by node id for resume support."""
+        ...
+
     def start(self) -> None:
         """Transition execution into the running state."""
         ...
@@ -91,6 +96,12 @@ class GraphExecutionProtocol(Protocol):
         ...
 
 
+class NodeExecutionProtocol(Protocol):
+    """Structural interface for per-node execution state used during resume."""
+
+    execution_id: str | None
+
+
 class ResponseStreamCoordinatorProtocol(Protocol):
     """Structural interface for response stream coordinator."""
 

api/pyrefly-local-excludes.txt

@@ -13,21 +13,6 @@ controllers/console/workspace/trigger_providers.py
 controllers/service_api/app/annotation.py
 controllers/web/workflow_events.py
 core/agent/fc_agent_runner.py
-core/app/apps/advanced_chat/app_generator.py
-core/app/apps/advanced_chat/app_runner.py
-core/app/apps/advanced_chat/generate_task_pipeline.py
-core/app/apps/agent_chat/app_generator.py
-core/app/apps/base_app_generate_response_converter.py
-core/app/apps/base_app_generator.py
-core/app/apps/chat/app_generator.py
-core/app/apps/common/workflow_response_converter.py
-core/app/apps/completion/app_generator.py
-core/app/apps/pipeline/pipeline_generator.py
-core/app/apps/pipeline/pipeline_runner.py
-core/app/apps/workflow/app_generator.py
-core/app/apps/workflow/app_runner.py
-core/app/apps/workflow/generate_task_pipeline.py
-core/app/apps/workflow_app_runner.py
 core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
 core/datasource/datasource_manager.py
 core/external_data_tool/api/api.py
@@ -108,35 +93,6 @@ core/tools/workflow_as_tool/provider.py
 core/trigger/debug/event_selectors.py
 core/trigger/entities/entities.py
 core/trigger/provider.py
-core/workflow/workflow_entry.py
-dify_graph/entities/workflow_execution.py
-dify_graph/file/file_manager.py
-dify_graph/graph_engine/error_handler.py
-dify_graph/graph_engine/layers/execution_limits.py
-dify_graph/nodes/agent/agent_node.py
-dify_graph/nodes/base/node.py
-dify_graph/nodes/code/code_node.py
-dify_graph/nodes/datasource/datasource_node.py
-dify_graph/nodes/document_extractor/node.py
-dify_graph/nodes/human_input/human_input_node.py
-dify_graph/nodes/if_else/if_else_node.py
-dify_graph/nodes/iteration/iteration_node.py
-dify_graph/nodes/knowledge_index/knowledge_index_node.py
-core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
-dify_graph/nodes/list_operator/node.py
-dify_graph/nodes/llm/node.py
-dify_graph/nodes/loop/loop_node.py
-dify_graph/nodes/parameter_extractor/parameter_extractor_node.py
-dify_graph/nodes/question_classifier/question_classifier_node.py
-dify_graph/nodes/start/start_node.py
-dify_graph/nodes/template_transform/template_transform_node.py
-dify_graph/nodes/tool/tool_node.py
-dify_graph/nodes/trigger_plugin/trigger_event_node.py
-dify_graph/nodes/trigger_schedule/trigger_schedule_node.py
-dify_graph/nodes/trigger_webhook/node.py
-dify_graph/nodes/variable_aggregator/variable_aggregator_node.py
-dify_graph/nodes/variable_assigner/v1/node.py
-dify_graph/nodes/variable_assigner/v2/node.py
 extensions/logstore/repositories/logstore_api_workflow_run_repository.py
 extensions/otel/instrumentation.py
 extensions/otel/runtime.py

api/tests/unit_tests/core/app/apps/advanced_chat/test_app_generator.py

@@ -1013,7 +1013,7 @@ class TestAdvancedChatAppGeneratorInternals:
         monkeypatch.setattr("core.app.apps.advanced_chat.app_generator.Session", _Session)
         monkeypatch.setattr("core.app.apps.advanced_chat.app_generator.db", SimpleNamespace(engine=object()))
 
-        refreshed = _refresh_model(session=SimpleNamespace(), model=source_model)
+        refreshed = _refresh_model(session=None, model=source_model)
 
         assert refreshed is detached_model
 

api/tests/unit_tests/core/app/apps/test_workflow_app_runner_single_node.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any
+from copy import deepcopy
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -33,8 +33,8 @@ def _make_graph_state():
     ],
 )
 def test_run_uses_single_node_execution_branch(
-    single_iteration_run: Any,
-    single_loop_run: Any,
+    single_iteration_run: WorkflowAppGenerateEntity.SingleIterationRunEntity | None,
+    single_loop_run: WorkflowAppGenerateEntity.SingleLoopRunEntity | None,
 ) -> None:
     app_config = MagicMock()
     app_config.app_id = "app"
@@ -130,10 +130,23 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
                     "break_conditions": [],
                     "logical_operator": "and",
                 },
+            },
+            {
+                "id": "other-node",
+                "data": {
+                    "type": "answer",
+                    "title": "Answer",
+                },
+            },
+        ],
+        "edges": [
+            {
+                "source": "other-node",
+                "target": "loop-node",
             }
         ],
-        "edges": [],
     }
+    original_graph_dict = deepcopy(workflow.graph_dict)
 
     _, _, graph_runtime_state = _make_graph_state()
     seen_configs: list[object] = []
@@ -143,13 +156,19 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
         seen_configs.append(value)
         return original_validate_python(value)
 
+    class FakeNodeClass:
+        @staticmethod
+        def extract_variable_selector_to_variable_mapping(**_kwargs):
+            return {}
+
     monkeypatch.setattr(NodeConfigDictAdapter, "validate_python", record_validate_python)
 
     with (
         patch("core.app.apps.workflow_app_runner.DifyNodeFactory"),
-        patch("core.app.apps.workflow_app_runner.Graph.init", return_value=MagicMock()),
+        patch("core.app.apps.workflow_app_runner.Graph.init", return_value=MagicMock()) as graph_init,
         patch("core.app.apps.workflow_app_runner.load_into_variable_pool"),
         patch("core.app.apps.workflow_app_runner.WorkflowEntry.mapping_user_inputs_to_variable_pool"),
+        patch("core.app.apps.workflow_app_runner.resolve_workflow_node_class", return_value=FakeNodeClass),
     ):
         runner._get_graph_and_variable_pool_for_single_node_run(
             workflow=workflow,
@@ -161,3 +180,8 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
         )
 
     assert seen_configs == [workflow.graph_dict["nodes"][0]]
+    assert workflow.graph_dict == original_graph_dict
+    graph_config = graph_init.call_args.kwargs["graph_config"]
+    assert graph_config is not workflow.graph_dict
+    assert graph_config["nodes"] == [workflow.graph_dict["nodes"][0]]
+    assert graph_config["edges"] == []