Merge remote-tracking branch 'origin/feat/pull-a-variable' into feat/pull-a-variable

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

@@ -82,7 +82,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -110,7 +110,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -120,6 +120,6 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

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

@@ -81,7 +81,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

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

@@ -81,7 +81,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

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

@@ -70,6 +70,8 @@ class _NodeSnapshot:
     """Empty string means the node is not executing inside an iteration."""
     loop_id: str = ""
     """Empty string means the node is not executing inside a loop."""
+    mention_parent_id: str = ""
+    """Empty string means the node is not an extractor node."""
 
 
 class WorkflowResponseConverter:
@@ -131,6 +133,7 @@ class WorkflowResponseConverter:
             start_at=event.start_at,
             iteration_id=event.in_iteration_id or "",
             loop_id=event.in_loop_id or "",
+            mention_parent_id=event.in_mention_parent_id or "",
         )
         node_execution_id = NodeExecutionId(event.node_execution_id)
         self._node_snapshots[node_execution_id] = snapshot
@@ -287,6 +290,7 @@ class WorkflowResponseConverter:
                 created_at=int(snapshot.start_at.timestamp()),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
                 agent_strategy=event.agent_strategy,
             ),
         )
@@ -373,6 +377,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
             ),
         )
 
@@ -422,6 +427,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                mention_parent_id=event.in_mention_parent_id,
                 retry_index=event.retry_index,
             ),
         )

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

@@ -79,7 +79,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -106,7 +106,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 if not isinstance(metadata, dict):
                     metadata = {}
@@ -116,6 +116,6 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

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

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(cast(dict, data))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(cast(dict, sub_stream_response.to_ignore_detail_dict()))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk

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

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk

api/core/app/apps/workflow_app_runner.py

@@ -385,6 +385,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -405,6 +406,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                     agent_strategy=event.agent_strategy,
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
@@ -428,6 +430,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -444,6 +447,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -460,6 +464,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
@@ -469,6 +474,7 @@ class WorkflowBasedAppRunner:
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
@@ -477,6 +483,7 @@ class WorkflowBasedAppRunner:
                     retriever_resources=event.retriever_resources,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunAgentLogEvent):

api/core/app/entities/queue_entities.py

@@ -190,6 +190,8 @@ class QueueTextChunkEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -229,6 +231,8 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -306,6 +310,8 @@ class QueueNodeStartedEvent(AppQueueEvent):
     node_run_index: int = 1  # FIXME(-LAN-): may not used
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
     agent_strategy: AgentNodeStrategyInit | None = None
 
@@ -328,6 +334,8 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -383,6 +391,8 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -407,6 +417,8 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)

api/core/app/entities/task_entities.py

@@ -262,6 +262,7 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
         agent_strategy: AgentNodeStrategyInit | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
@@ -285,6 +286,7 @@ class NodeStartStreamResponse(StreamResponse):
                 "extras": {},
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -320,6 +322,7 @@ class NodeFinishStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -349,6 +352,7 @@ class NodeFinishStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -384,6 +388,7 @@ class NodeRetryStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        mention_parent_id: str | None = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -414,6 +419,7 @@ class NodeRetryStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "mention_parent_id": self.data.mention_parent_id,
                 "retry_index": self.data.retry_index,
             },
         }

api/core/prompt/advanced_prompt_transform.py

@@ -5,7 +5,7 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.file import file_manager
 from core.file.models import File
 from core.helper.code_executor.jinja2.jinja2_formatter import Jinja2Formatter
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_runtime.entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -43,7 +43,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -84,7 +84,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -145,7 +145,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -270,7 +270,7 @@ class AdvancedPromptTransform(PromptTransform):
 
     def _set_histories_variable(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         raw_prompt: str,
         role_prefix: MemoryConfig.RolePrefix,

api/core/prompt/prompt_transform.py

@@ -1,7 +1,7 @@
 from typing import Any
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
@@ -11,7 +11,7 @@ from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 class PromptTransform:
     def _append_chat_histories(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         prompt_messages: list[PromptMessage],
         model_config: ModelConfigWithCredentialsEntity,
@@ -52,7 +52,7 @@ class PromptTransform:
 
     def _get_history_messages_from_memory(
         self,
-        memory: TokenBufferMemory,
+        memory: BaseMemory,
         memory_config: MemoryConfig,
         max_token_limit: int,
         human_prefix: str | None = None,
@@ -73,7 +73,7 @@ class PromptTransform:
         return memory.get_history_prompt_text(**kwargs)
 
     def _get_history_messages_list_from_memory(
-        self, memory: TokenBufferMemory, memory_config: MemoryConfig, max_token_limit: int
+        self, memory: BaseMemory, memory_config: MemoryConfig, max_token_limit: int
     ) -> list[PromptMessage]:
         """Get memory messages."""
         return list(

api/core/workflow/enums.py

@@ -253,6 +253,7 @@ class WorkflowNodeExecutionMetadataKey(StrEnum):
     LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
     DATASOURCE_INFO = "datasource_info"
     COMPLETED_REASON = "completed_reason"  # completed reason for loop node
+    MENTION_PARENT_ID = "mention_parent_id"  # parent node id for extractor nodes
 
 
 class WorkflowNodeExecutionStatus(StrEnum):

api/core/workflow/graph_engine/event_management/event_handlers.py

@@ -93,8 +93,8 @@ class EventHandler:
         Args:
             event: The event to handle
         """
-        # Events in loops or iterations are always collected
+        # Events in loops, iterations, or extractor groups are always collected
-        if event.in_loop_id or event.in_iteration_id:
+        if event.in_loop_id or event.in_iteration_id or event.in_mention_parent_id:
             self._event_collector.collect(event)
             return
         return self._dispatch(event)

api/core/workflow/graph_engine/layers/persistence.py

@@ -68,6 +68,7 @@ class _NodeRuntimeSnapshot:
     predecessor_node_id: str | None
     iteration_id: str | None
     loop_id: str | None
+    mention_parent_id: str | None
     created_at: datetime
 
 
@@ -230,6 +231,7 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
         metadata = {
             WorkflowNodeExecutionMetadataKey.ITERATION_ID: event.in_iteration_id,
             WorkflowNodeExecutionMetadataKey.LOOP_ID: event.in_loop_id,
+            WorkflowNodeExecutionMetadataKey.MENTION_PARENT_ID: event.in_mention_parent_id,
         }
 
         domain_execution = WorkflowNodeExecution(
@@ -256,6 +258,7 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
             predecessor_node_id=event.predecessor_node_id,
             iteration_id=event.in_iteration_id,
             loop_id=event.in_loop_id,
+            mention_parent_id=event.in_mention_parent_id,
             created_at=event.start_at,
         )
         self._node_snapshots[event.id] = snapshot

api/core/workflow/graph_events/base.py

@@ -21,6 +21,12 @@ class GraphNodeEventBase(GraphEngineEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_mention_parent_id: str | None = None
+    """Parent node id if this is an extractor node event.
+    
+    When set, indicates this event belongs to an extractor node that
+    is extracting values for the specified parent node.
+    """
 
     # The version of the node, or "1" if not specified.
     node_version: str = "1"

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

@@ -12,11 +12,14 @@ from sqlalchemy.orm import Session
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
 from core.file import File, FileTransferMethod
+from core.memory.base import BaseMemory
+from core.memory.node_token_buffer_memory import NodeTokenBufferMemory
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.llm_entities import LLMUsage, LLMUsageMetadata
 from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
 from core.model_runtime.utils.encoders import jsonable_encoder
+from core.prompt.entities.advanced_prompt_entities import MemoryMode
 from core.provider_manager import ProviderManager
 from core.tools.entities.tool_entities import (
     ToolIdentity,
@@ -136,6 +139,9 @@ class AgentNode(Node[AgentNodeData]):
             )
             return
 
+        # Fetch memory for node memory saving
+        memory = self._fetch_memory_for_save()
+
         try:
             yield from self._transform_message(
                 messages=message_stream,
@@ -149,6 +155,7 @@ class AgentNode(Node[AgentNodeData]):
                 node_type=self.node_type,
                 node_id=self._node_id,
                 node_execution_id=self.id,
+                memory=memory,
             )
         except PluginDaemonClientSideError as e:
             transform_error = AgentMessageTransformError(
@@ -395,8 +402,20 @@ class AgentNode(Node[AgentNodeData]):
             icon = None
         return icon
 
-    def _fetch_memory(self, model_instance: ModelInstance) -> TokenBufferMemory | None:
+    def _fetch_memory(self, model_instance: ModelInstance) -> BaseMemory | None:
-        # get conversation id
+        """
+        Fetch memory based on configuration mode.
+
+        Returns TokenBufferMemory for conversation mode (default),
+        or NodeTokenBufferMemory for node mode (Chatflow only).
+        """
+        node_data = self.node_data
+        memory_config = node_data.memory
+
+        if not memory_config:
+            return None
+
+        # get conversation id (required for both modes in Chatflow)
         conversation_id_variable = self.graph_runtime_state.variable_pool.get(
             ["sys", SystemVariableKey.CONVERSATION_ID]
         )
@@ -404,16 +423,26 @@ class AgentNode(Node[AgentNodeData]):
             return None
         conversation_id = conversation_id_variable.value
 
-        with Session(db.engine, expire_on_commit=False) as session:
+        # Return appropriate memory type based on mode
-            stmt = select(Conversation).where(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
+        if memory_config.mode == MemoryMode.NODE:
-            conversation = session.scalar(stmt)
+            # Node-level memory (Chatflow only)
-
+            return NodeTokenBufferMemory(
-            if not conversation:
+                app_id=self.app_id,
-                return None
+                conversation_id=conversation_id,
-
+                node_id=self._node_id,
-        memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+                tenant_id=self.tenant_id,
-
+                model_instance=model_instance,
-        return memory
+            )
+        else:
+            # Conversation-level memory (default)
+            with Session(db.engine, expire_on_commit=False) as session:
+                stmt = select(Conversation).where(
+                    Conversation.app_id == self.app_id, Conversation.id == conversation_id
+                )
+                conversation = session.scalar(stmt)
+                if not conversation:
+                    return None
+            return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
     def _fetch_model(self, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
         provider_manager = ProviderManager()
@@ -457,6 +486,47 @@ class AgentNode(Node[AgentNodeData]):
         else:
             return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]
 
+    def _fetch_memory_for_save(self) -> BaseMemory | None:
+        """
+        Fetch memory instance for saving node memory.
+        This is a simplified version that doesn't require model_instance.
+        """
+        from core.model_manager import ModelManager
+        from core.model_runtime.entities.model_entities import ModelType
+
+        node_data = self.node_data
+        if not node_data.memory:
+            return None
+
+        # Get conversation_id
+        conversation_id_var = self.graph_runtime_state.variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
+        if not isinstance(conversation_id_var, StringSegment):
+            return None
+        conversation_id = conversation_id_var.value
+
+        # Return appropriate memory type based on mode
+        if node_data.memory.mode == MemoryMode.NODE:
+            # For node memory, we need a model_instance for token counting
+            # Use a simple default model for this purpose
+            try:
+                model_instance = ModelManager().get_default_model_instance(
+                    tenant_id=self.tenant_id,
+                    model_type=ModelType.LLM,
+                )
+            except Exception:
+                return None
+
+            return NodeTokenBufferMemory(
+                app_id=self.app_id,
+                conversation_id=conversation_id,
+                node_id=self._node_id,
+                tenant_id=self.tenant_id,
+                model_instance=model_instance,
+            )
+        else:
+            # Conversation-level memory doesn't need saving here
+            return None
+
     def _transform_message(
         self,
         messages: Generator[ToolInvokeMessage, None, None],
@@ -467,6 +537,7 @@ class AgentNode(Node[AgentNodeData]):
         node_type: NodeType,
         node_id: str,
         node_execution_id: str,
+        memory: BaseMemory | None = None,
     ) -> Generator[NodeEventBase, None, None]:
         """
         Convert ToolInvokeMessages into tuple[plain_text, files]
@@ -711,6 +782,21 @@ class AgentNode(Node[AgentNodeData]):
                 is_final=True,
             )
 
+        # Save to node memory if in node memory mode
+        from core.workflow.nodes.llm import llm_utils
+
+        # Get user query from sys.query
+        user_query_var = self.graph_runtime_state.variable_pool.get(["sys", SystemVariableKey.QUERY])
+        user_query = user_query_var.text if user_query_var else ""
+
+        llm_utils.save_node_memory(
+            memory=memory,
+            variable_pool=self.graph_runtime_state.variable_pool,
+            user_query=user_query,
+            assistant_response=text,
+            assistant_files=files,
+        )
+
         yield StreamCompletedEvent(
             node_run_result=NodeRunResult(
                 status=WorkflowNodeExecutionStatus.SUCCEEDED,

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

@@ -332,12 +332,17 @@ class Node(Generic[NodeDataT]):
 
             # Execute and process extractor node events
             for event in extractor_node.run():
+                # Tag event with parent node id for stream ordering and history tracking
+                if isinstance(event, GraphNodeEventBase):
+                    event.in_mention_parent_id = self._node_id
+
                 if isinstance(event, NodeRunSucceededEvent):
                     # Store extractor node outputs in variable pool
-                    outputs = event.node_run_result.outputs
+                    outputs: Mapping[str, Any] = event.node_run_result.outputs
                     for variable_name, variable_value in outputs.items():
                         self.graph_runtime_state.variable_pool.add((node_id, variable_name), variable_value)
-                yield event
+                if not isinstance(event, NodeRunStreamChunkEvent):
+                    yield event
 
     def run(self) -> Generator[GraphNodeEventBase, None, None]:
         execution_id = self.ensure_execution_id()

api/core/workflow/nodes/llm/llm_utils.py

@@ -139,6 +139,50 @@ def fetch_memory(
         return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
 
+def save_node_memory(
+    memory: BaseMemory | None,
+    variable_pool: VariablePool,
+    user_query: str,
+    assistant_response: str,
+    user_files: Sequence["File"] | None = None,
+    assistant_files: Sequence["File"] | None = None,
+) -> None:
+    """
+    Save dialogue turn to node memory if applicable.
+
+    This function handles the storage logic for NodeTokenBufferMemory.
+    For TokenBufferMemory (conversation-level), no action is taken as it uses
+    the Message table which is managed elsewhere.
+
+    :param memory: Memory instance (NodeTokenBufferMemory or TokenBufferMemory)
+    :param variable_pool: Variable pool containing system variables
+    :param user_query: User's input text
+    :param assistant_response: Assistant's response text
+    :param user_files: Files attached by user (optional)
+    :param assistant_files: Files generated by assistant (optional)
+    """
+    if not isinstance(memory, NodeTokenBufferMemory):
+        return
+
+    # Get workflow_run_id as the key for this execution
+    workflow_run_id_var = variable_pool.get(["sys", SystemVariableKey.WORKFLOW_EXECUTION_ID])
+    if not isinstance(workflow_run_id_var, StringSegment):
+        return
+
+    workflow_run_id = workflow_run_id_var.value
+    if not workflow_run_id:
+        return
+
+    memory.add_messages(
+        workflow_run_id=workflow_run_id,
+        user_content=user_query,
+        user_files=list(user_files) if user_files else None,
+        assistant_content=assistant_response,
+        assistant_files=list(assistant_files) if assistant_files else None,
+    )
+    memory.flush()
+
+
 def deduct_llm_quota(tenant_id: str, model_instance: ModelInstance, usage: LLMUsage):
     provider_model_bundle = model_instance.provider_model_bundle
     provider_configuration = provider_model_bundle.configuration

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

@@ -17,7 +17,6 @@ from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.llm_generator.output_parser.errors import OutputParserError
 from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
 from core.memory.base import BaseMemory
-from core.memory.node_token_buffer_memory import NodeTokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities import (
     ImagePromptMessageContent,
@@ -334,32 +333,16 @@ class LLMNode(Node[LLMNodeData]):
                 outputs["files"] = ArrayFileSegment(value=self._file_outputs)
 
             # Write to Node Memory if in node memory mode
-            if isinstance(memory, NodeTokenBufferMemory):
+            # Resolve the query template to get actual user content
-                # Get workflow_run_id as the key for this execution
+            actual_query = variable_pool.convert_template(query or "").text
-                workflow_run_id_var = variable_pool.get(["sys", SystemVariableKey.WORKFLOW_EXECUTION_ID])
+            llm_utils.save_node_memory(
-                workflow_run_id = workflow_run_id_var.value if isinstance(workflow_run_id_var, StringSegment) else ""
+                memory=memory,
-
+                variable_pool=variable_pool,
-                if workflow_run_id:
+                user_query=actual_query,
-                    # Resolve the query template to get actual user content
+                assistant_response=clean_text,
-                    # query may be a template like "{{#sys.query#}}" or "{{#node_id.output#}}"
+                user_files=files,
-                    actual_query = variable_pool.convert_template(query or "").text
+                assistant_files=self._file_outputs,
-
+            )
-                    # Get user files from sys.files
-                    user_files_var = variable_pool.get(["sys", SystemVariableKey.FILES])
-                    user_files: list[File] = []
-                    if isinstance(user_files_var, ArrayFileSegment):
-                        user_files = list(user_files_var.value)
-                    elif isinstance(user_files_var, FileSegment):
-                        user_files = [user_files_var.value]
-
-                    memory.add_messages(
-                        workflow_run_id=workflow_run_id,
-                        user_content=actual_query,
-                        user_files=user_files,
-                        assistant_content=clean_text,
-                        assistant_files=self._file_outputs,
-                    )
-                    memory.flush()
 
             # Send final chunk event to indicate streaming is complete
             yield StreamChunkEvent(

api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py

@@ -7,7 +7,7 @@ from typing import Any, cast
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.file import File
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import ImagePromptMessageContent
 from core.model_runtime.entities.llm_entities import LLMUsage
@@ -145,8 +145,10 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         memory = llm_utils.fetch_memory(
             variable_pool=variable_pool,
             app_id=self.app_id,
+            tenant_id=self.tenant_id,
             node_data_memory=node_data.memory,
             model_instance=model_instance,
+            node_id=self._node_id,
         )
 
         if (
@@ -244,6 +246,14 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         # transform result into standard format
         result = self._transform_result(data=node_data, result=result or {})
 
+        # Save to node memory if in node memory mode
+        llm_utils.save_node_memory(
+            memory=memory,
+            variable_pool=variable_pool,
+            user_query=query,
+            assistant_response=json.dumps(result, ensure_ascii=False),
+        )
+
         return NodeRunResult(
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs=inputs,
@@ -299,7 +309,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
@@ -381,7 +391,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -419,7 +429,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -453,7 +463,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -681,7 +691,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         max_token_limit: int = 2000,
     ) -> list[ChatModelMessage]:
         model_mode = ModelMode(node_data.model.mode)
@@ -708,7 +718,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -4,7 +4,7 @@ from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.memory.token_buffer_memory import TokenBufferMemory
+from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import LLMUsage, ModelPropertyKey, PromptMessageRole
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -96,8 +96,10 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         memory = llm_utils.fetch_memory(
             variable_pool=variable_pool,
             app_id=self.app_id,
+            tenant_id=self.tenant_id,
             node_data_memory=node_data.memory,
             model_instance=model_instance,
+            node_id=self._node_id,
         )
         # fetch instruction
         node_data.instruction = node_data.instruction or ""
@@ -203,6 +205,14 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
                 "usage": jsonable_encoder(usage),
             }
 
+            # Save to node memory if in node memory mode
+            llm_utils.save_node_memory(
+                memory=memory,
+                variable_pool=variable_pool,
+                user_query=query or "",
+                assistant_response=f"class_name: {category_name}, class_id: {category_id}",
+            )
+
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.SUCCEEDED,
                 inputs=variables,
@@ -312,7 +322,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         self,
         node_data: QuestionClassifierNodeData,
         query: str,
-        memory: TokenBufferMemory | None,
+        memory: BaseMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)

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

@@ -1,22 +1,26 @@
+import re
 from collections.abc import Sequence
-from typing import Any, Literal, Union
+from typing import Any, Literal, Self, Union
 
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel, field_validator, model_validator
 from pydantic_core.core_schema import ValidationInfo
 
 from core.tools.entities.tool_entities import ToolProviderType
 from core.workflow.nodes.base.entities import BaseNodeData
 
+# Pattern to match a single variable reference like {{#llm.context#}}
+SINGLE_VARIABLE_PATTERN = re.compile(r"^\s*\{\{#[a-zA-Z0-9_]+(?:\.[a-zA-Z_][a-zA-Z0-9_]*)+#\}\}\s*$")
 
-class MentionValue(BaseModel):
-    """Value structure for mention type parameters.
 
-    Used when a tool parameter needs to be extracted from conversation context
+class MentionConfig(BaseModel):
-    using an extractor LLM node.
+    """Configuration for extracting value from context variable.
+
+    Used when a tool parameter needs to be extracted from list[PromptMessage]
+    context using an extractor LLM node.
     """
 
-    # Variable selector for list[PromptMessage] input to extractor
+    # Instruction for the extractor LLM to extract the value
-    variable_selector: Sequence[str]
+    instruction: str
 
     # ID of the extractor LLM node
     extractor_node_id: str
@@ -60,8 +64,10 @@ class ToolEntity(BaseModel):
 class ToolNodeData(BaseNodeData, ToolEntity):
     class ToolInput(BaseModel):
         # TODO: check this type
-        value: Union[Any, list[str], MentionValue]
+        value: Union[Any, list[str]]
         type: Literal["mixed", "variable", "constant", "mention"]
+        # Required config for mention type, extracting value from context variable
+        mention_config: MentionConfig | None = None
 
         @field_validator("type", mode="before")
         @classmethod
@@ -74,6 +80,9 @@ class ToolNodeData(BaseNodeData, ToolEntity):
 
             if typ == "mixed" and not isinstance(value, str):
                 raise ValueError("value must be a string")
+            elif typ == "mention":
+                # Skip here, will be validated in model_validator
+                pass
             elif typ == "variable":
                 if not isinstance(value, list):
                     raise ValueError("value must be a list")
@@ -82,19 +91,31 @@ class ToolNodeData(BaseNodeData, ToolEntity):
                         raise ValueError("value must be a list of strings")
             elif typ == "constant" and not isinstance(value, str | int | float | bool | dict):
                 raise ValueError("value must be a string, int, float, bool or dict")
-            elif typ == "mention":
-                # Mention type: value should be a MentionValue or dict with required fields
-                if isinstance(value, MentionValue):
-                    pass  # Already validated by Pydantic
-                elif isinstance(value, dict):
-                    if "extractor_node_id" not in value:
-                        raise ValueError("value must contain extractor_node_id for mention type")
-                    if "output_selector" not in value:
-                        raise ValueError("value must contain output_selector for mention type")
-                else:
-                    raise ValueError("value must be a MentionValue or dict for mention type")
             return typ
 
+        @model_validator(mode="after")
+        def check_mention_type(self) -> Self:
+            """Validate mention type with mention_config."""
+            if self.type != "mention":
+                return self
+
+            value = self.value
+            if value is None:
+                return self
+
+            if not isinstance(value, str):
+                raise ValueError("value must be a string for mention type")
+            # For mention type, value must be a single variable reference
+            if not SINGLE_VARIABLE_PATTERN.match(value):
+                raise ValueError(
+                    "For mention type, value must be a single variable reference "
+                    "like {{#node.variable#}}, cannot contain other content"
+                )
+            # mention_config is required for mention type
+            if self.mention_config is None:
+                raise ValueError("mention_config is required for mention type")
+            return self
+
     tool_parameters: dict[str, ToolInput]
     # The version of the tool parameter.
     # If this value is None, it indicates this is a previous version

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

@@ -214,16 +214,11 @@ class ToolNode(Node[ToolNodeData]):
                 parameter_value = variable.value
             elif tool_input.type == "mention":
                 # Mention type: get value from extractor node's output
-                from .entities import MentionValue
+                if tool_input.mention_config is None:
-
+                    raise ToolParameterError(
-                mention_value = tool_input.value
+                        f"mention_config is required for mention type parameter '{parameter_name}'"
-                if isinstance(mention_value, MentionValue):
+                    )
-                    mention_config = mention_value.model_dump()
+                mention_config = tool_input.mention_config.model_dump()
-                elif isinstance(mention_value, dict):
-                    mention_config = mention_value
-                else:
-                    raise ToolParameterError(f"Invalid mention value for parameter '{parameter_name}'")
-
                 try:
                     parameter_value, found = variable_pool.resolve_mention(
                         mention_config, parameter_name=parameter_name
@@ -524,7 +519,7 @@ class ToolNode(Node[ToolNodeData]):
                     selector_key = ".".join(input.value)
                     result[f"#{selector_key}#"] = input.value
             elif input.type == "mention":
-                # Mention type handled by extractor node, no direct variable reference
+                # Mention type: value is handled by extractor node, no direct variable reference
                 pass
             elif input.type == "constant":
                 pass

api/tests/fixtures/pav-test-extraction.yml

@@ -11,6 +11,11 @@ dependencies:
   value:
     marketplace_plugin_unique_identifier: langgenius/google:0.0.8@3efcf55ffeef9d0f77715e0afb23534952ae0cb385c051d0637e86d71199d1a6
     version: null
+- current_identifier: null
+  type: marketplace
+  value:
+    marketplace_plugin_unique_identifier: langgenius/openai:0.2.3@5a7f82fa86e28332ad51941d0b491c1e8a38ead539656442f7bf4c6129cd15fa
+    version: null
 - current_identifier: null
   type: marketplace
   value:
@@ -115,7 +120,8 @@ workflow:
           enabled: false
           variable_selector: []
         memory:
-          query_prompt_template: ''
+          mode: node
+          query_prompt_template: '{{#sys.query#}}'
           role_prefix:
             assistant: ''
             user: ''
@@ -201,29 +207,17 @@ workflow:
         tool_node_version: '2'
         tool_parameters:
           query:
-            type: variable
+            mention_config:
-            value:
+              default_value: ''
-            - ext_1
+              extractor_node_id: 1767773709491_ext_query
-            - text
+              instruction: 请从对话历史中提取用户想要搜索的关键词，只返回关键词本身
+              null_strategy: use_default
+              output_selector:
+              - structured_output
+              - query
+            type: mention
+            value: '{{#llm.context#}}'
         type: tool
-        virtual_nodes:
-        - data:
-            model:
-              completion_params:
-                temperature: 0.7
-              mode: chat
-              name: qwen-max
-              provider: langgenius/tongyi/tongyi
-            context:
-              enabled: false
-            prompt_template:
-            - role: user
-              text: '{{#llm.context#}}'
-            - role: user
-              text: 请从对话历史中提取用户想要搜索的关键词，只返回关键词本身，不要返回其他内容
-            title: 提取搜索关键词
-          id: ext_1
-          type: llm
       height: 52
       id: '1767773709491'
       position:
@@ -237,6 +231,54 @@ workflow:
       targetPosition: left
       type: custom
       width: 242
+    - data:
+        context:
+          enabled: false
+          variable_selector: []
+        model:
+          completion_params:
+            temperature: 0.7
+          mode: chat
+          name: gpt-4o-mini
+          provider: langgenius/openai/openai
+        parent_node_id: '1767773709491'
+        prompt_template:
+        - $context:
+          - llm
+          - context
+          id: 75d58e22-dc59-40c8-ba6f-aeb28f4f305a
+        - id: 18ba6710-77f5-47f4-b144-9191833bb547
+          role: user
+          text: 请从对话历史中提取用户想要搜索的关键词，只返回关键词本身，不要返回其他内容
+        selected: false
+        structured_output:
+          schema:
+            additionalProperties: false
+            properties:
+              query:
+                description: 搜索的关键词
+                type: string
+            required:
+            - query
+            type: object
+        structured_output_enabled: true
+        title: 提取搜索关键词
+        type: llm
+        vision:
+          enabled: false
+      height: 88
+      id: 1767773709491_ext_query
+      position:
+        x: 531
+        y: 382
+      positionAbsolute:
+        x: 531
+        y: 382
+      selected: true
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      width: 242
     - data:
         answer: '搜索结果：
 
@@ -254,13 +296,13 @@ workflow:
       positionAbsolute:
         x: 984
         y: 282
-      selected: true
+      selected: false
       sourcePosition: right
       targetPosition: left
       type: custom
       width: 242
     viewport:
-      x: 151
+      x: -151
-      y: 141.5
+      y: 123
       zoom: 1
   rag_pipeline_variables: []