refactor: inject workflow memory via protocol

api/.importlinter

@@ -112,7 +112,6 @@ ignore_imports =
     core.workflow.nodes.llm.llm_utils -> core.model_manager
     core.workflow.nodes.llm.protocols -> core.model_manager
     core.workflow.nodes.llm.llm_utils -> core.model_runtime.model_providers.__base.large_language_model
-    core.workflow.nodes.llm.llm_utils -> models.model
     core.workflow.nodes.llm.llm_utils -> models.provider
     core.workflow.nodes.llm.llm_utils -> services.credit_pool_service
     core.workflow.nodes.llm.node -> core.tools.signature
@@ -150,7 +149,6 @@ ignore_imports =
     core.workflow.nodes.llm.node -> core.model_manager
     core.workflow.nodes.agent.entities -> core.prompt.entities.advanced_prompt_entities
     core.workflow.nodes.llm.entities -> core.prompt.entities.advanced_prompt_entities
-    core.workflow.nodes.llm.llm_utils -> core.prompt.entities.advanced_prompt_entities
     core.workflow.nodes.llm.node -> core.prompt.entities.advanced_prompt_entities
     core.workflow.nodes.llm.node -> core.prompt.utils.prompt_message_util
     core.workflow.nodes.parameter_extractor.entities -> core.prompt.entities.advanced_prompt_entities

api/core/app/workflow/node_factory.py

@@ -1,6 +1,8 @@
 from collections.abc import Mapping
 from typing import TYPE_CHECKING, Any, cast, final
 
+from sqlalchemy import select
+from sqlalchemy.orm import Session
 from typing_extensions import override
 
 from configs import dify_config
@@ -11,6 +13,7 @@ from core.helper.code_executor.code_executor import (
     CodeExecutor,
 )
 from core.helper.ssrf_proxy import ssrf_proxy
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
@@ -18,7 +21,7 @@ from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from core.tools.tool_file_manager import ToolFileManager
 from core.workflow.entities.graph_config import NodeConfigDict
-from core.workflow.enums import NodeType
+from core.workflow.enums import NodeType, SystemVariableKey
 from core.workflow.file.file_manager import file_manager
 from core.workflow.graph.graph import NodeFactory
 from core.workflow.nodes.base.node import Node
@@ -29,7 +32,6 @@ from core.workflow.nodes.datasource import DatasourceNode
 from core.workflow.nodes.document_extractor import DocumentExtractorNode, UnstructuredApiConfig
 from core.workflow.nodes.http_request import HttpRequestNode, build_http_request_config
 from core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node import KnowledgeRetrievalNode
-from core.workflow.nodes.llm import llm_utils
 from core.workflow.nodes.llm.entities import ModelConfig
 from core.workflow.nodes.llm.exc import LLMModeRequiredError, ModelNotExistError
 from core.workflow.nodes.llm.node import LLMNode
@@ -41,12 +43,34 @@ from core.workflow.nodes.template_transform.template_renderer import (
     CodeExecutorJinja2TemplateRenderer,
 )
 from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
+from core.workflow.variables.segments import StringSegment
+from extensions.ext_database import db
+from models.model import Conversation
 
 if TYPE_CHECKING:
     from core.workflow.entities import GraphInitParams
     from core.workflow.runtime import GraphRuntimeState
 
 
+def fetch_memory(
+    *,
+    conversation_id: str | None,
+    app_id: str,
+    node_data_memory: MemoryConfig | None,
+    model_instance: ModelInstance,
+) -> TokenBufferMemory | None:
+    if not node_data_memory or not conversation_id:
+        return None
+
+    with Session(db.engine, expire_on_commit=False) as session:
+        stmt = select(Conversation).where(Conversation.app_id == app_id, Conversation.id == conversation_id)
+        conversation = session.scalar(stmt)
+        if not conversation:
+            return None
+
+    return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+
+
 class DefaultWorkflowCodeExecutor:
     def execute(
         self,
@@ -221,6 +245,7 @@ class DifyNodeFactory(NodeFactory):
 
         if node_type == NodeType.QUESTION_CLASSIFIER:
             model_instance = self._build_model_instance_for_llm_node(node_data)
+            memory = self._build_memory_for_llm_node(node_data=node_data, model_instance=model_instance)
             return QuestionClassifierNode(
                 id=node_id,
                 config=node_config,
@@ -229,10 +254,12 @@ class DifyNodeFactory(NodeFactory):
                 credentials_provider=self._llm_credentials_provider,
                 model_factory=self._llm_model_factory,
                 model_instance=model_instance,
+                memory=memory,
             )
 
         if node_type == NodeType.PARAMETER_EXTRACTOR:
             model_instance = self._build_model_instance_for_llm_node(node_data)
+            memory = self._build_memory_for_llm_node(node_data=node_data, model_instance=model_instance)
             return ParameterExtractorNode(
                 id=node_id,
                 config=node_config,
@@ -241,6 +268,7 @@ class DifyNodeFactory(NodeFactory):
                 credentials_provider=self._llm_credentials_provider,
                 model_factory=self._llm_model_factory,
                 model_instance=model_instance,
+                memory=memory,
             )
 
         return node_class(
@@ -295,8 +323,14 @@ class DifyNodeFactory(NodeFactory):
             return None
 
         node_memory = MemoryConfig.model_validate(raw_memory_config)
-        return llm_utils.fetch_memory(
-            variable_pool=self.graph_runtime_state.variable_pool,
+        conversation_id_variable = self.graph_runtime_state.variable_pool.get(
+            ["sys", SystemVariableKey.CONVERSATION_ID]
+        )
+        conversation_id = (
+            conversation_id_variable.value if isinstance(conversation_id_variable, StringSegment) else None
+        )
+        return fetch_memory(
+            conversation_id=conversation_id,
             app_id=self.graph_init_params.app_id,
             node_data_memory=node_memory,
             model_instance=model_instance,

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

@@ -1,28 +1,31 @@
 from collections.abc import Sequence
 from typing import cast
 
-from sqlalchemy import select, update
+from sqlalchemy import update
 from sqlalchemy.orm import Session
 
 from configs import dify_config
 from core.entities.provider_entities import ProviderQuotaType, QuotaUnit
-from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
+from core.model_runtime.entities import PromptMessageRole
 from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import (
+    ImagePromptMessageContent,
+    PromptMessage,
+    TextPromptMessageContent,
+)
 from core.model_runtime.entities.model_entities import AIModelEntity
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.prompt.entities.advanced_prompt_entities import MemoryConfig
-from core.workflow.enums import SystemVariableKey
 from core.workflow.file.models import File
 from core.workflow.runtime import VariablePool
-from core.workflow.variables.segments import ArrayAnySegment, ArrayFileSegment, FileSegment, NoneSegment, StringSegment
+from core.workflow.variables.segments import ArrayAnySegment, ArrayFileSegment, FileSegment, NoneSegment
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import Conversation
 from models.provider import Provider, ProviderType
 from models.provider_ids import ModelProviderID
 
 from .exc import InvalidVariableTypeError
+from .protocols import PromptMessageMemory
 
 
 def fetch_model_schema(*, model_instance: ModelInstance) -> AIModelEntity:
@@ -48,26 +51,54 @@ def fetch_files(variable_pool: VariablePool, selector: Sequence[str]) -> Sequenc
     raise InvalidVariableTypeError(f"Invalid variable type: {type(variable)}")
 
 
-def fetch_memory(
-    variable_pool: VariablePool, app_id: str, node_data_memory: MemoryConfig | None, model_instance: ModelInstance
-) -> TokenBufferMemory | None:
-    if not node_data_memory:
-        return None
+def convert_history_messages_to_text(
+    *,
+    history_messages: Sequence[PromptMessage],
+    human_prefix: str,
+    ai_prefix: str,
+) -> str:
+    string_messages: list[str] = []
+    for message in history_messages:
+        if message.role == PromptMessageRole.USER:
+            role = human_prefix
+        elif message.role == PromptMessageRole.ASSISTANT:
+            role = ai_prefix
+        else:
+            continue
 
-    # get conversation id
-    conversation_id_variable = variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
-    if not isinstance(conversation_id_variable, StringSegment):
-        return None
-    conversation_id = conversation_id_variable.value
+        if isinstance(message.content, list):
+            content_parts = []
+            for content in message.content:
+                if isinstance(content, TextPromptMessageContent):
+                    content_parts.append(content.data)
+                elif isinstance(content, ImagePromptMessageContent):
+                    content_parts.append("[image]")
 
-    with Session(db.engine, expire_on_commit=False) as session:
-        stmt = select(Conversation).where(Conversation.app_id == app_id, Conversation.id == conversation_id)
-        conversation = session.scalar(stmt)
-        if not conversation:
-            return None
+            inner_msg = "\n".join(content_parts)
+            string_messages.append(f"{role}: {inner_msg}")
+        else:
+            string_messages.append(f"{role}: {message.content}")
 
-    memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
-    return memory
+    return "\n".join(string_messages)
+
+
+def fetch_memory_text(
+    *,
+    memory: PromptMessageMemory,
+    max_token_limit: int,
+    message_limit: int | None = None,
+    human_prefix: str = "Human",
+    ai_prefix: str = "Assistant",
+) -> str:
+    history_messages = memory.get_history_prompt_messages(
+        max_token_limit=max_token_limit,
+        message_limit=message_limit,
+    )
+    return convert_history_messages_to_text(
+        history_messages=history_messages,
+        human_prefix=human_prefix,
+        ai_prefix=ai_prefix,
+    )
 
 
 def deduct_llm_quota(tenant_id: str, model_instance: ModelInstance, usage: LLMUsage):

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

@@ -1336,48 +1336,16 @@ def _handle_memory_completion_mode(
         )
         if not memory_config.role_prefix:
             raise MemoryRolePrefixRequiredError("Memory role prefix is required for completion model.")
-        memory_messages = memory.get_history_prompt_messages(
+        memory_text = llm_utils.fetch_memory_text(
+            memory=memory,
             max_token_limit=rest_tokens,
             message_limit=memory_config.window.size if memory_config.window.enabled else None,
-        )
-        memory_text = _convert_history_messages_to_text(
-            history_messages=memory_messages,
             human_prefix=memory_config.role_prefix.user,
             ai_prefix=memory_config.role_prefix.assistant,
         )
     return memory_text
 
 
-def _convert_history_messages_to_text(
-    *,
-    history_messages: Sequence[PromptMessage],
-    human_prefix: str,
-    ai_prefix: str,
-) -> str:
-    string_messages: list[str] = []
-    for message in history_messages:
-        if message.role == PromptMessageRole.USER:
-            role = human_prefix
-        elif message.role == PromptMessageRole.ASSISTANT:
-            role = ai_prefix
-        else:
-            continue
-
-        if isinstance(message.content, list):
-            content_parts = []
-            for content in message.content:
-                if isinstance(content, TextPromptMessageContent):
-                    content_parts.append(content.data)
-                elif isinstance(content, ImagePromptMessageContent):
-                    content_parts.append("[image]")
-
-            inner_msg = "\n".join(content_parts)
-            string_messages.append(f"{role}: {inner_msg}")
-        else:
-            string_messages.append(f"{role}: {message.content}")
-    return "\n".join(string_messages)
-
-
 def _handle_completion_template(
     *,
     template: LLMNodeCompletionModelPromptTemplate,

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

@@ -5,7 +5,6 @@ import uuid
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, cast
 
-from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import ImagePromptMessageContent
 from core.model_runtime.entities.llm_entities import LLMUsage
@@ -24,12 +23,17 @@ from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
 from core.prompt.simple_prompt_transform import ModelMode
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
-from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from core.workflow.enums import (
+    NodeType,
+    WorkflowNodeExecutionMetadataKey,
+    WorkflowNodeExecutionStatus,
+)
 from core.workflow.file import File
 from core.workflow.node_events import NodeRunResult
 from core.workflow.nodes.base import variable_template_parser
 from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.llm import llm_utils
+from core.workflow.nodes.llm.protocols import PromptMessageMemory
 from core.workflow.runtime import VariablePool
 from core.workflow.variables.types import ArrayValidation, SegmentType
 from factories.variable_factory import build_segment_with_type
@@ -97,6 +101,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
     _model_instance: ModelInstance
     _credentials_provider: "CredentialsProvider"
     _model_factory: "ModelFactory"
+    _memory: PromptMessageMemory | None
 
     def __init__(
         self,
@@ -108,6 +113,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         credentials_provider: "CredentialsProvider",
         model_factory: "ModelFactory",
         model_instance: ModelInstance,
+        memory: PromptMessageMemory | None = None,
     ) -> None:
         super().__init__(
             id=id,
@@ -118,6 +124,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         self._credentials_provider = credentials_provider
         self._model_factory = model_factory
         self._model_instance = model_instance
+        self._memory = memory
 
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
@@ -163,13 +170,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
             model_schema = llm_utils.fetch_model_schema(model_instance=model_instance)
         except ValueError as exc:
             raise ModelSchemaNotFoundError("Model schema not found") from exc
-        # fetch memory
-        memory = llm_utils.fetch_memory(
-            variable_pool=variable_pool,
-            app_id=self.app_id,
-            node_data_memory=node_data.memory,
-            model_instance=model_instance,
-        )
+        memory = self._memory
 
         if (
             set(model_schema.features or []) & {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}
@@ -319,7 +320,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_instance: ModelInstance,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
@@ -407,7 +408,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_instance: ModelInstance,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -445,7 +446,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_instance: ModelInstance,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -470,7 +471,8 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
             files=files,
             context="",
             memory_config=node_data.memory,
-            memory=memory,
+            # AdvancedPromptTransform is still typed against TokenBufferMemory.
+            memory=cast(Any, memory),
             model_instance=model_instance,
             image_detail_config=vision_detail,
         )
@@ -483,7 +485,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_instance: ModelInstance,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -715,7 +717,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         max_token_limit: int = 2000,
     ) -> list[ChatModelMessage]:
         model_mode = ModelMode(node_data.model.mode)
@@ -724,8 +726,8 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         instruction = variable_pool.convert_template(node_data.instruction or "").text
 
         if memory and node_data.memory and node_data.memory.window:
-            memory_str = memory.get_history_prompt_text(
-                max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
+            memory_str = llm_utils.fetch_memory_text(
+                memory=memory, max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
             )
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = ChatModelMessage(
@@ -742,7 +744,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)
@@ -751,8 +753,8 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         instruction = variable_pool.convert_template(node_data.instruction or "").text
 
         if memory and node_data.memory and node_data.memory.window:
-            memory_str = memory.get_history_prompt_text(
-                max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
+            memory_str = llm_utils.fetch_memory_text(
+                memory=memory, max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
             )
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = ChatModelMessage(

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

@@ -3,7 +3,6 @@ import re
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
-from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import LLMUsage, ModelPropertyKey, PromptMessageRole
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -27,7 +26,7 @@ from core.workflow.nodes.llm import (
     llm_utils,
 )
 from core.workflow.nodes.llm.file_saver import FileSaverImpl, LLMFileSaver
-from core.workflow.nodes.llm.protocols import CredentialsProvider, ModelFactory
+from core.workflow.nodes.llm.protocols import CredentialsProvider, ModelFactory, PromptMessageMemory
 from libs.json_in_md_parser import parse_and_check_json_markdown
 
 from .entities import QuestionClassifierNodeData
@@ -56,6 +55,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
     _credentials_provider: "CredentialsProvider"
     _model_factory: "ModelFactory"
     _model_instance: ModelInstance
+    _memory: PromptMessageMemory | None
 
     def __init__(
         self,
@@ -67,6 +67,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         credentials_provider: "CredentialsProvider",
         model_factory: "ModelFactory",
         model_instance: ModelInstance,
+        memory: PromptMessageMemory | None = None,
         llm_file_saver: LLMFileSaver | None = None,
     ):
         super().__init__(
@@ -81,6 +82,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         self._credentials_provider = credentials_provider
         self._model_factory = model_factory
         self._model_instance = model_instance
+        self._memory = memory
 
         if llm_file_saver is None:
             llm_file_saver = FileSaverImpl(
@@ -103,13 +105,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         variables = {"query": query}
         # fetch model instance
         model_instance = self._model_instance
-        # fetch memory
-        memory = llm_utils.fetch_memory(
-            variable_pool=variable_pool,
-            app_id=self.app_id,
-            node_data_memory=node_data.memory,
-            model_instance=model_instance,
-        )
+        memory = self._memory
         # fetch instruction
         node_data.instruction = node_data.instruction or ""
         node_data.instruction = variable_pool.convert_template(node_data.instruction).text
@@ -323,7 +319,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         self,
         node_data: QuestionClassifierNodeData,
         query: str,
-        memory: TokenBufferMemory | None,
+        memory: PromptMessageMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)
@@ -336,7 +332,8 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         input_text = query
         memory_str = ""
         if memory:
-            memory_str = memory.get_history_prompt_text(
+            memory_str = llm_utils.fetch_memory_text(
+                memory=memory,
                 max_token_limit=max_token_limit,
                 message_limit=node_data.memory.window.size if node_data.memory and node_data.memory.window else None,
             )

api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py

@@ -5,7 +5,7 @@ from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.model_manager import ModelInstance
-from core.model_runtime.entities import AssistantPromptMessage
+from core.model_runtime.entities import AssistantPromptMessage, UserPromptMessage
 from core.workflow.entities import GraphInitParams
 from core.workflow.enums import WorkflowNodeExecutionStatus
 from core.workflow.nodes.llm.protocols import CredentialsProvider, ModelFactory
@@ -22,19 +22,17 @@ from tests.integration_tests.model_runtime.__mock.plugin_daemon import setup_mod
 
 def get_mocked_fetch_memory(memory_text: str):
     class MemoryMock:
-        def get_history_prompt_text(
+        def get_history_prompt_messages(
             self,
-            human_prefix: str = "Human",
-            ai_prefix: str = "Assistant",
             max_token_limit: int = 2000,
             message_limit: int | None = None,
         ):
-            return memory_text
+            return [UserPromptMessage(content=memory_text), AssistantPromptMessage(content="mocked answer")]
 
     return MagicMock(return_value=MemoryMock())
 
 
-def init_parameter_extractor_node(config: dict):
+def init_parameter_extractor_node(config: dict, memory=None):
     graph_config = {
         "edges": [
             {
@@ -79,6 +77,7 @@ def init_parameter_extractor_node(config: dict):
         credentials_provider=MagicMock(spec=CredentialsProvider),
         model_factory=MagicMock(spec=ModelFactory),
         model_instance=MagicMock(spec=ModelInstance),
+        memory=memory,
     )
     return node
 
@@ -350,7 +349,7 @@ def test_extract_json_from_tool_call():
     assert result["location"] == "kawaii"
 
 
-def test_chat_parameter_extractor_with_memory(setup_model_mock, monkeypatch):
+def test_chat_parameter_extractor_with_memory(setup_model_mock):
     """
     Test chat parameter extractor with memory.
     """
@@ -373,6 +372,7 @@ def test_chat_parameter_extractor_with_memory(setup_model_mock, monkeypatch):
                 "memory": {"window": {"enabled": True, "size": 50}},
             },
         },
+        memory=get_mocked_fetch_memory("customized memory")(),
     )
 
     node._model_instance = get_mocked_fetch_model_instance(
@@ -381,8 +381,6 @@ def test_chat_parameter_extractor_with_memory(setup_model_mock, monkeypatch):
         mode="chat",
         credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
     )()
-    # Test the mock before running the actual test
-    monkeypatch.setattr("core.workflow.nodes.llm.llm_utils.fetch_memory", get_mocked_fetch_memory("customized memory"))
     db.session.close = MagicMock()
 
     result = node._run()