refactor(memory): restructure memory modules and add versioned personal memory agents

benchmark/halumem/eval_reme.py

@@ -42,6 +42,7 @@ class EvalConfig:
     batch_size: int = 20
     output_dir: str = "bench_results/reme"
     eval_model_name: str = "qwen3-max"
+    algo_version: str = "v1"
 
 
 # ==================== Utilities ====================
@@ -217,7 +218,7 @@ async def answer_question_with_memories(
 
     result = await reme.llm.simple_request_for_json(
         prompt=prompt,
-        model_name=model_name
+        model_name="qwen3-30b-a3b-instruct-2507"
     )
 
     return result
@@ -269,9 +270,10 @@ async def evaluation_for_question(
 class MemoryProcessor:
     """Handles ReMe memory operations."""
 
-    def __init__(self, reme: ReMe, eval_model_name: str = "qwen3-max"):
+    def __init__(self, reme: ReMe, eval_model_name: str = "qwen3-max", algo_version: str = "v1"):
         self.reme = reme
         self.eval_model_name = eval_model_name
+        self.algo_version = algo_version
 
     async def add_memories(
             self,
@@ -297,7 +299,7 @@ class MemoryProcessor:
             result = await self.reme.summary_memory(
                 messages=batch,
                 user_name=user_id,
-                version="default",
+                version=self.algo_version,
                 return_dict=True,
             )
 
@@ -305,7 +307,7 @@ class MemoryProcessor:
             total_duration_ms += duration_ms
 
             extracted_memories.extend([m.model_dump(exclude_none=True) for m in result["answer"]])
-            summary_messages.extend([m.simple_dump() for m in result["messages"]])
+            summary_messages.extend([m.simple_dump(enable_argument_dict=True) for m in result["messages"]])
 
         return extracted_memories, summary_messages, total_duration_ms
 
@@ -329,13 +331,13 @@ class MemoryProcessor:
             query=query,
             retrieve_top_k=top_k,
             user_name=user_id,
-            version="default",
+            version=self.algo_version,
             return_dict=True,
         )
 
         # Extract memories from response
         memories = result["answer"]
-        agent_messages = [x.model_dump(exclude_none=True) for x in result["messages"]]
+        agent_messages = [x.simple_dump(enable_argument_dict=True) for x in result["messages"]]
         retrieved_nodes = [x.model_dump(exclude_none=True) for x in result["retrieved_nodes"]]
 
         # Use LLM to generate structured answer from memories
@@ -521,7 +523,11 @@ class HaluMemEvaluator:
         self.reme.prompt_handler.load_prompt_by_file(prompts_yaml_path)
 
         self.file_manager = FileManager(config.output_dir)
-        self.memory_processor = MemoryProcessor(self.reme, config.eval_model_name)
+        self.memory_processor = MemoryProcessor(
+            self.reme,
+            config.eval_model_name,
+            config.algo_version
+        )
         self.qa_evaluator = QuestionAnsweringEvaluator(
             self.memory_processor,
             self.reme,
@@ -763,7 +769,8 @@ async def main_async(
         top_k: int,
         user_num: int,
         max_concurrency: int,
-        eval_model_name: str = "qwen3-max"
+        eval_model_name: str = "qwen3-max",
+        algo_version: str = "v1"
 ):
     """Main async entry point for ReMe evaluation with proper resource cleanup."""
     config = EvalConfig(
@@ -771,7 +778,8 @@ async def main_async(
         top_k=top_k,
         user_num=user_num,
         max_concurrency=max_concurrency,
-        eval_model_name=eval_model_name
+        eval_model_name=eval_model_name,
+        algo_version=algo_version
     )
 
     # Use async context manager for automatic cleanup
@@ -784,7 +792,8 @@ def main(
         top_k: int,
         user_num: int,
         max_concurrency: int,
-        eval_model_name: str = "qwen3-max"
+        eval_model_name: str = "qwen3-max",
+        algo_version: str = "v1"
 ):
     """Main entry point for ReMe evaluation."""
     asyncio.run(main_async(
@@ -792,7 +801,8 @@ def main(
         top_k=top_k,
         user_num=user_num,
         max_concurrency=max_concurrency,
-        eval_model_name=eval_model_name
+        eval_model_name=eval_model_name,
+        algo_version=algo_version
     ))
 
 
@@ -833,6 +843,12 @@ if __name__ == "__main__":
         # default="qwen3-235b-a22b-instruct-2507",
         help="Model name for evaluation (default: qwen3-max)"
     )
+    parser.add_argument(
+        "--algo_version",
+        type=str,
+        default="v1",
+        help="Algorithm version for summary and retrieval (default: v1)"
+    )
 
     args = parser.parse_args()
 
@@ -841,5 +857,6 @@ if __name__ == "__main__":
         top_k=args.top_k,
         user_num=args.user_num,
         max_concurrency=args.max_concurrency,
-        eval_model_name=args.eval_model_name
+        eval_model_name=args.eval_model_name,
+        algo_version=args.algo_version
     )

benchmark/halumem/scripts.sh

@@ -0,0 +1 @@
+cat bench_results/reme/Martin\ Mark/session* | grep '"result_type": "' | awk -F'"' '{total++; if($4=="Correct") count++} END {printf "Correct Rate: %.2f%% (%d/%d)\n", (count/total)*100, count, total}'

reme/agent/memory/__init__.py

@@ -1,9 +1,32 @@
 """memory agent"""
 
-from . import default
 from .base_memory_agent import BaseMemoryAgent
+from .personal.personal_retriever import PersonalRetriever
+from .personal.personal_summarizer import PersonalSummarizer
+from .personal.personal_v1_retriever import PersonalV1Retriever
+from .personal.personal_v1_summarizer import PersonalV1Summarizer
+from .procedural.procedural_retriever import ProceduralRetriever
+from .procedural.procedural_summarizer import ProceduralSummarizer
+from .reme_retriever import ReMeRetriever
+from .reme_summarizer import ReMeSummarizer
+from .tool.tool_retriever import ToolRetriever
+from .tool.tool_summarizer import ToolSummarizer
+from ...core import R
 
 __all__ = [
-    "default",
     "BaseMemoryAgent",
+    "PersonalRetriever",
+    "PersonalSummarizer",
+    "PersonalV1Retriever",
+    "PersonalV1Summarizer",
+    "ProceduralRetriever",
+    "ProceduralSummarizer",
+    "ReMeRetriever",
+    "ReMeSummarizer",
+    "ToolRetriever",
+    "ToolSummarizer",
 ]
+
+for name in __all__:
+    tool_class = globals()[name]
+    R.op.register()(tool_class)

reme/agent/memory/default/__init__.py

@@ -1,26 +0,0 @@
-"""Default memory agents for personal, procedural, tool and ReMe memory operations."""
-
-from .personal_retriever import PersonalRetriever
-from .personal_summarizer import PersonalSummarizer
-from .procedural_retriever import ProceduralRetriever
-from .procedural_summarizer import ProceduralSummarizer
-from .reme_retriever import ReMeRetriever
-from .reme_summarizer import ReMeSummarizer
-from .tool_retriever import ToolRetriever
-from .tool_summarizer import ToolSummarizer
-from ....core import R
-
-__all__ = [
-    "PersonalRetriever",
-    "PersonalSummarizer",
-    "ProceduralRetriever",
-    "ProceduralSummarizer",
-    "ReMeRetriever",
-    "ReMeSummarizer",
-    "ToolRetriever",
-    "ToolSummarizer",
-]
-
-for name in __all__:
-    tool_class = globals()[name]
-    R.op.register()(tool_class)

reme/agent/memory/default/tool_retriever.py

@@ -1,10 +0,0 @@
-"""Tool memory retriever agent implementation."""
-
-from ..base_memory_agent import BaseMemoryAgent
-from ....core.enumeration import MemoryType
-
-
-class ToolRetriever(BaseMemoryAgent):
-    """Agent responsible for retrieving tool-related memories."""
-
-    memory_type: MemoryType = MemoryType.TOOL

reme/agent/memory/default/tool_summarizer.py

@@ -1,10 +0,0 @@
-"""Tool memory summarizer agent implementation."""
-
-from ..base_memory_agent import BaseMemoryAgent
-from ....core.enumeration import MemoryType
-
-
-class ToolSummarizer(BaseMemoryAgent):
-    """Agent responsible for summarizing tool-related memories."""
-
-    memory_type: MemoryType = MemoryType.TOOL

reme/agent/memory/personal/personal_v1_retriever.py

@@ -0,0 +1,86 @@
+"""Personal memory retriever agent for retrieving personal memories through vector search."""
+
+from ..base_memory_agent import BaseMemoryAgent
+from ....core.enumeration import Role, MemoryType
+from ....core.op import BaseTool
+from ....core.schema import Message
+from ....core.utils import format_messages
+
+
+class PersonalV1Retriever(BaseMemoryAgent):
+    """Retrieve personal memories through vector search and history reading."""
+
+    memory_type: MemoryType = MemoryType.PERSONAL
+
+    async def build_messages(self) -> list[Message]:
+        if self.context.get("query"):
+            context = self.context.query
+        elif self.context.get("messages"):
+            context = self.description + "\n" + format_messages(self.context.messages)
+        else:
+            raise ValueError("input must have either `query` or `messages`")
+
+        read_all_profiles_tool: BaseTool | None = self.pop_tool("read_all_profiles")
+        if read_all_profiles_tool is not None:
+            all_profiles = await read_all_profiles_tool.call(
+                memory_target=self.memory_target,
+                service_context=self.service_context,
+            )
+        else:
+            all_profiles = ""
+
+        return [
+            Message(
+                role=Role.SYSTEM,
+                content=self.prompt_format(
+                    prompt_name="system_prompt",
+                    memory_type=self.memory_type.value,
+                    memory_target=self.memory_target,
+                    user_profile=all_profiles,
+                    context=context.strip(),
+                ),
+            ),
+            Message(
+                role=Role.USER,
+                content=self.get_prompt("user_message"),
+            ),
+        ]
+
+    async def _acting_step(
+        self,
+        assistant_message: Message,
+        tools: list[BaseTool],
+        step: int,
+        stage: str = "",
+        **kwargs,
+    ) -> tuple[list[BaseTool], list[Message]]:
+        """Execute tool calls with memory context."""
+        return await super()._acting_step(
+            assistant_message,
+            tools,
+            step,
+            memory_type=self.memory_type.value,
+            memory_target=self.memory_target,
+            retrieved_nodes=self.retrieved_nodes,
+            **kwargs,
+        )
+
+    async def execute(self):
+        result = await super().execute()
+        answer = result["answer"]
+        if "MEMORY_NOT_FOUND" in answer:
+            result["answer"] = "\n".join(
+                [
+                    n.format(
+                        include_memory_id=False,
+                        include_when_to_use=False,
+                        include_content=True,
+                        include_message_time=False,
+                        ref_memory_id_key="",
+                    )
+                    for n in self.retrieved_nodes
+                ],
+            )
+
+        result["retrieved_nodes"] = self.retrieved_nodes
+        return result

reme/agent/memory/personal/personal_v1_summarizer.py

@@ -0,0 +1,117 @@
+"""Personal memory summarizer agent for two-phase personal memory processing."""
+
+from loguru import logger
+
+from ..base_memory_agent import BaseMemoryAgent
+from ....core.enumeration import Role, MemoryType
+from ....core.op import BaseTool
+from ....core.schema import Message
+
+
+class PersonalV1Summarizer(BaseMemoryAgent):
+    """Two-phase personal memory processor: retrieve/add memories then update profile."""
+
+    memory_type: MemoryType = MemoryType.PERSONAL
+
+    async def _build_s1_messages(self) -> list[Message]:
+        return [
+            Message(
+                role=Role.SYSTEM,
+                content=self.prompt_format(
+                    prompt_name="system_prompt_s1",
+                    context=self.context.history_node.content,
+                    memory_type=self.memory_type.value,
+                    memory_target=self.memory_target,
+                ),
+            ),
+            Message(
+                role=Role.USER,
+                content=self.get_prompt("user_message_s1"),
+            ),
+        ]
+
+    async def _build_s2_messages(self) -> list[Message]:
+        return [
+            Message(
+                role=Role.SYSTEM,
+                content=self.prompt_format(
+                    prompt_name="system_prompt_s2",
+                    context=self.context.history_node.content,
+                    memory_type=self.memory_type.value,
+                    memory_target=self.memory_target,
+                ),
+            ),
+            Message(
+                role=Role.USER,
+                content=self.get_prompt("user_message_s2"),
+            ),
+        ]
+
+    async def _acting_step(
+        self,
+        assistant_message: Message,
+        tools: list[BaseTool],
+        step: int,
+        stage: str = "",
+        **kwargs,
+    ) -> tuple[list[BaseTool], list[Message]]:
+        """Execute tool calls with memory context."""
+        return await super()._acting_step(
+            assistant_message,
+            tools,
+            step,
+            stage=stage,
+            memory_type=self.memory_type.value,
+            memory_target=self.memory_target,
+            history_node=self.history_node,
+            author=self.author,
+            retrieved_nodes=self.retrieved_nodes,
+            **kwargs,
+        )
+
+    async def execute(self):
+        memory_tools = []
+        profile_tools = []
+        for i, tool in enumerate(self.tools):
+            tool_name = tool.tool_call.name
+            if "_memory" in tool_name:
+                memory_tools.append(tool)
+            elif "_profile" in tool_name:
+                profile_tools.append(tool)
+            else:
+                raise ValueError(f"[{self.__class__.__name__}] unknown tool_name={tool_name}")
+            logger.info(f"[{self.__class__.__name__}] tool_call[{i}]={tool.tool_call.simple_input_dump(as_dict=False)}")
+
+        stage = "s1-memory"
+        messages_s1 = await self._build_s1_messages()
+        for i, message in enumerate(messages_s1):
+            role = message.name or message.role
+            logger.info(f"[{self.__class__.__name__} {stage}] role={role} {message.simple_dump(as_dict=False)}")
+        tools_s1, messages_s1, success_s1 = await self.react(messages_s1, memory_tools, stage=stage)
+
+        if profile_tools:
+            stage = "s2-profile"
+            messages_s2 = await self._build_s2_messages()
+            for i, message in enumerate(messages_s2):
+                role = message.name or message.role
+                logger.info(f"[{self.__class__.__name__} {stage}] role={role} {message.simple_dump(as_dict=False)}")
+            tools_s2, messages_s2, success_s2 = await self.react(messages_s2, profile_tools, stage=stage)
+        else:
+            tools_s2, messages_s2, success_s2 = [], [], True
+
+        answer = (messages_s1[-1].content if success_s1 else "") + (messages_s2[-1].content if success_s2 else "")
+        success = success_s1 and success_s2
+        messages = messages_s1 + messages_s2
+        tools = tools_s1 + tools_s2
+        memory_nodes = []
+        for tool in tools:
+            if tool.memory_nodes:
+                memory_nodes.extend(tool.memory_nodes)
+
+        return {
+            "answer": answer,
+            "success": success,
+            "messages": messages,
+            "tools": tools,
+            "memory_nodes": memory_nodes,
+        }

reme/agent/memory/personal/personal_v1_summarizer.yaml

@@ -0,0 +1,65 @@
+system_prompt_s1: |
+  You are a Memory Agent responsible for managing {memory_type} memories about {memory_target}.
+
+  ## Latest Conversation
+  Format: round<index> [<timestamp>] <role/name>: <content>
+  {context}
+
+  ## Task
+  ### Step 1: Create Memory Draft
+  Create a memory draft in `add_draft_and_retrieve_similar_memory` based on the latest conversation.
+  - Use actual names from the conversation (e.g., "Bob likes apples") instead of generic references (e.g., "user likes apples")
+  - Always record memories with real names
+  - The tool will retrieve similar historical memories via vector search to help you consolidate in Step 2
+
+  ### Step 2: Update Memory Store
+  Update the vector store using `update_memory` to keep it well-organized and consolidated:
+
+  **What to Delete** (via `memory_ids_to_delete`):
+  - Duplicate memories with identical or highly similar content
+  - Memories that should be merged into a single consolidated entry
+
+  **What to Add** (via `memories_to_add` with message_time and memory_content):
+  - For each topic with changes: add ONE consolidated memory that merges related information
+  - New distinct memories that don't overlap with existing ones
+  - Updated memories that capture the latest state while preserving temporal evolution
+
+  ## Requirements
+  - Extract only what's explicitly stated—no inferences, assumptions, or fabrications
+  - Preserve temporal evolution: capture how things change over time within the same topic
+  - Maintain organization: group related memories by topic and eliminate all redundancy
+
+user_message_s1: |
+  Complete the task by following Step 1 and Step 2 in order
+
+system_prompt_s2: |
+  You are a Profile Agent responsible for managing profiles about {memory_target}.
+
+  ## Latest Conversation
+  Format: round<index> [<timestamp>] <role/name>: <content>
+  {context}
+
+  ## Task
+  ### Step 1: Create Profile Draft
+  Create a profile draft in `add_draft_and_read_all_profiles` based on the latest conversation.
+  - The tool will return all existing profiles to help you maintain the profile store in Step 2
+
+  ### Step 2: Update Profile Store
+  Update the profile store using `update_profile` to keep it well-organized and consolidated:
+
+  **What to Delete** (via `profile_ids_to_delete`):
+  - Duplicate profiles with identical keys or values
+  - Conflicting profiles that contradict the new information
+  - Profiles that should be merged into a single consolidated entry
+
+  **What to Add** (via `profiles_to_add` with message_time, profile_key, and profile_value):
+  - For each profile key with changes: add ONE consolidated profile that merges related information
+  - New distinct profiles that don't overlap with existing ones
+  - Updated profiles that capture the latest state
+
+  ## Requirements
+  - Extract only what's explicitly stated—no inferences, assumptions, or fabrications
+  - Maintain organization: group related profiles by key and eliminate all redundancy
+
+user_message_s2: |
+  Complete the task by following Step 1 and Step 2 in order

reme/agent/memory/reme_retriever.py

@@ -1,10 +1,10 @@
 """ReMe retriever agent that orchestrates multiple memory agents to retrieve information."""
 
-from ..base_memory_agent import BaseMemoryAgent
-from ....core.enumeration import Role
-from ....core.op import BaseTool
-from ....core.schema import Message
-from ....core.utils import format_messages
+from .base_memory_agent import BaseMemoryAgent
+from ...core.enumeration import Role
+from ...core.op import BaseTool
+from ...core.schema import Message
+from ...core.utils import format_messages
 
 
 class ReMeRetriever(BaseMemoryAgent):

reme/agent/memory/reme_retriever.yaml

@@ -23,7 +23,5 @@ system_prompt: |
      - Each memory_target should be assigned **only once** - do not duplicate assignments
   3. Use the `delegate_task` tool **once** with all relevant memory_target(s) to enable parallel processing by specialized agents
 
-  Note: If the context contains no memorable information (e.g., simple greetings), return `<NO_MEMORY_NEEDED>`.
-
 user_message: |
   Please analyze the context and delegate retrieval tasks to the appropriate specialized agents.

reme/agent/memory/reme_summarizer.py

@@ -1,10 +1,10 @@
 """ReMe summarizer agent that orchestrates multiple memory agents to summarize information."""
 
-from ..base_memory_agent import BaseMemoryAgent
-from ....core.enumeration import Role
-from ....core.op import BaseTool
-from ....core.schema import Message
-from ....core.utils import format_messages
+from .base_memory_agent import BaseMemoryAgent
+from ...core.enumeration import Role
+from ...core.op import BaseTool
+from ...core.schema import Message
+from ...core.utils import format_messages
 
 
 class ReMeSummarizer(BaseMemoryAgent):

reme/core/schema/message.py

@@ -84,6 +84,7 @@ class Message(BaseModel):
         add_reasoning: bool = True,
         add_time_created: bool = False,
         add_metadata: bool = False,
+        enable_argument_dict: bool = False,
         as_dict: bool = True,
     ) -> dict | str:
         """Transforms the message into a simplified dictionary for standard APIs."""
@@ -98,7 +99,13 @@ class Message(BaseModel):
             result["reasoning_content"] = self.reasoning_content
 
         if self.tool_calls:
-            result["tool_calls"] = [tc.simple_output_dump() for tc in self.tool_calls]
+            result["tool_calls"] = [
+                tc.simple_output_dump(
+                    as_dict=True,
+                    enable_argument_dict=enable_argument_dict,
+                )
+                for tc in self.tool_calls
+            ]
 
         if self.tool_call_id:
             result["tool_call_id"] = self.tool_call_id

reme/core/schema/tool_call.py

@@ -145,17 +145,13 @@ class ToolCall(BaseModel):
         }
         return result if as_dict else json.dumps(result, ensure_ascii=False)
 
-    def simple_output_dump(self, as_dict: bool = True) -> dict | str:
-        """Convert ToolCall to output format dictionary or JSON string for API responses.
-
-        Args:
-            as_dict: If True, returns dict; if False, returns JSON string.
-        """
+    def simple_output_dump(self, as_dict: bool = True, enable_argument_dict: bool = False) -> dict | str:
+        """Convert ToolCall to output format dictionary or JSON string for API responses."""
         result = {
             "index": self.index,
             "id": self.id,
             self.type: {
-                "arguments": self.arguments,
+                "arguments": self.argument_dict if enable_argument_dict else self.arguments,
                 "name": self.name,
             },
             "type": self.type,

reme/reme.py

@@ -3,15 +3,17 @@
 import sys
 from pathlib import Path
 
-from reme.agent.memory import BaseMemoryAgent
-from .agent.memory.default import (
+from .agent.memory import (
+    BaseMemoryAgent,
     ReMeSummarizer,
+    ReMeRetriever,
+    PersonalV1Summarizer,
+    PersonalV1Retriever,
     PersonalSummarizer,
     PersonalRetriever,
-    ReMeRetriever,
     ProceduralSummarizer,
-    ToolSummarizer,
     ProceduralRetriever,
+    ToolSummarizer,
     ToolRetriever,
 )
 from .config import ReMeConfigParser
@@ -116,7 +118,7 @@ class ReMe(Application):
             format_messages.append(message)
 
         personal_summarizer: BaseMemoryAgent
-        if version:
+        if version == "default":
             personal_summarizer = PersonalSummarizer(
                 tools=[
                     AddDraftAndRetrieveSimilarMemory(
@@ -134,17 +136,33 @@ class ReMe(Application):
                     ),
                 ],
             )
+
+        elif version == "v1":
+            personal_summarizer = PersonalV1Summarizer(
+                tools=[
+                    AddDraftAndRetrieveSimilarMemory(
+                        enable_thinking_params=enable_thinking_params,
+                        top_k=retrieve_top_k,
+                    ),
+                    UpdateMemoryV2(enable_thinking_params=enable_thinking_params),
+                    AddDraftAndReadAllProfiles(
+                        enable_thinking_params=enable_thinking_params,
+                        profile_dir=self.profile_dir,
+                    ),
+                ],
+            )
+
         else:
             raise NotImplementedError
 
         procedural_summarizer: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             procedural_summarizer = ProceduralSummarizer(tools=[])
         else:
             raise NotImplementedError
 
         tool_summarizer: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             tool_summarizer = ToolSummarizer(tools=[])
         else:
             raise NotImplementedError
@@ -186,7 +204,7 @@ class ReMe(Application):
             memory_agents = [personal_summarizer, procedural_summarizer, tool_summarizer]
 
         reme_summarizer: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             reme_summarizer = ReMeSummarizer(tools=[AddHistory(), DelegateTask(memory_agents=memory_agents)])
         else:
             raise NotImplementedError
@@ -221,7 +239,7 @@ class ReMe(Application):
         """Retrieve relevant personal, procedural and tool memories for a query."""
 
         personal_retriever: BaseMemoryAgent
-        if version:
+        if version == "default":
             personal_retriever = PersonalRetriever(
                 tools=[
                     ReadAllProfiles(
@@ -236,17 +254,33 @@ class ReMe(Application):
                     ReadHistory(enable_thinking_params=enable_thinking_params),
                 ],
             )
+
+        elif version == "v1":
+            personal_retriever = PersonalV1Retriever(
+                tools=[
+                    ReadAllProfiles(
+                        enable_thinking_params=enable_thinking_params,
+                        profile_dir=self.profile_dir,
+                    ),
+                    RetrieveMemory(
+                        enable_thinking_params=enable_thinking_params,
+                        top_k=retrieve_top_k,
+                        enable_time_filter=enable_time_filter,
+                    ),
+                ],
+            )
+
         else:
             raise NotImplementedError
 
         procedural_retriever: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             procedural_retriever = ProceduralRetriever(tools=[])
         else:
             raise NotImplementedError
 
         tool_retriever: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             tool_retriever = ToolRetriever(tools=[])
         else:
             raise NotImplementedError
@@ -286,7 +320,7 @@ class ReMe(Application):
             memory_agents = [personal_retriever, procedural_retriever, tool_retriever]
 
         reme_retriever: BaseMemoryAgent
-        if version == "default":
+        if version in ["default", "v1"]:
             reme_retriever = ReMeRetriever(tools=[DelegateTask(memory_agents=memory_agents)])
         else:
             raise NotImplementedError

reme/tool/memory/__init__.py

@@ -1,42 +1,48 @@
 """memory tools"""
 
-from .add_draft_and_read_all_profiles import AddDraftAndReadAllProfiles
-from .add_draft_and_retrieve_similar_memory import AddDraftAndRetrieveSimilarMemory
-from .add_history import AddHistory
-from .add_memory import AddMemory
 from .base_memory_tool import BaseMemoryTool
 from .delegate_task import DelegateTask
-from .delete_memory import DeleteMemory
-from .memory_handler import MemoryHandler
-from .profile_handler import ProfileHandler
-from .read_all_profiles import ReadAllProfiles
-from .read_history import ReadHistory
-from .retrieve_memory import RetrieveMemory
-from .retrieve_recent_memory import RetrieveRecentMemory
-from .update_memory import UpdateMemory
-from .update_memory_v2 import UpdateMemoryV2
-from .update_profile import UpdateProfile
+from .history.add_history import AddHistory
+from .history.read_history import ReadHistory
+from .profiles.add_draft_and_read_all_profiles import AddDraftAndReadAllProfiles
+from .profiles.profile_handler import ProfileHandler
+from .profiles.read_all_profiles import ReadAllProfiles
+from .profiles.update_profile import UpdateProfile
+from .vector.add_draft_and_retrieve_similar_memory import AddDraftAndRetrieveSimilarMemory
+from .vector.add_memory import AddMemory
+from .vector.delete_memory import DeleteMemory
+from .vector.memory_handler import MemoryHandler
+from .vector.retrieve_memory import RetrieveMemory
+from .vector.retrieve_recent_memory import RetrieveRecentMemory
+from .vector.update_memory import UpdateMemory
+from .vector.update_memory_v2 import UpdateMemoryV2
 from ...core import R
 
 __all__ = [
-    "AddDraftAndReadAllProfiles",
-    "AddDraftAndRetrieveSimilarMemory",
-    "AddHistory",
-    "AddMemory",
+    # Base
     "BaseMemoryTool",
     "DelegateTask",
-    "DeleteMemory",
-    "MemoryHandler",
+    # History
+    "AddHistory",
+    "ReadHistory",
+    # Profiles
+    "AddDraftAndReadAllProfiles",
     "ProfileHandler",
     "ReadAllProfiles",
-    "ReadHistory",
+    "UpdateProfile",
+    # Vector
+    "AddDraftAndRetrieveSimilarMemory",
+    "AddMemory",
+    "DeleteMemory",
+    "MemoryHandler",
     "RetrieveMemory",
     "RetrieveRecentMemory",
     "UpdateMemory",
     "UpdateMemoryV2",
-    "UpdateProfile",
 ]
 
 for name in __all__:
     tool_class = globals()[name]
-    R.op.register()(tool_class)
+    # Only register classes that inherit from BaseMemoryTool
+    if isinstance(tool_class, type) and issubclass(tool_class, BaseMemoryTool) and tool_class is not BaseMemoryTool:
+        R.op.register()(tool_class)

reme/tool/memory/history/add_history.py

@@ -2,10 +2,10 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
-from ...core.enumeration import MemoryType
-from ...core.schema import ToolCall, MemoryNode, Message
-from ...core.utils import format_messages
+from ..base_memory_tool import BaseMemoryTool
+from ....core.enumeration import MemoryType
+from ....core.schema import ToolCall, MemoryNode, Message
+from ....core.utils import format_messages
 
 
 class AddHistory(BaseMemoryTool):

reme/tool/memory/history/read_history.py

@@ -2,8 +2,8 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
-from ...core.schema import MemoryNode, ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import MemoryNode, ToolCall
 
 
 class ReadHistory(BaseMemoryTool):

reme/tool/memory/profiles/add_draft_and_read_all_profiles.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .profile_handler import ProfileHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class AddDraftAndReadAllProfiles(BaseMemoryTool):

reme/tool/memory/profiles/profile_handler.py

@@ -4,9 +4,9 @@ from pathlib import Path
 
 from loguru import logger
 
-from ...core.enumeration import MemoryType
-from ...core.schema import MemoryNode
-from ...core.utils import CacheHandler, deduplicate_memories
+from ....core.enumeration import MemoryType
+from ....core.schema import MemoryNode
+from ....core.utils import CacheHandler, deduplicate_memories
 
 
 class ProfileHandler:

reme/tool/memory/profiles/read_all_profiles.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .profile_handler import ProfileHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class ReadAllProfiles(BaseMemoryTool):

reme/tool/memory/profiles/update_profile.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .profile_handler import ProfileHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class UpdateProfile(BaseMemoryTool):

reme/tool/memory/vector/add_draft_and_retrieve_similar_memory.py

@@ -2,10 +2,10 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall, MemoryNode
-from ...core.utils import deduplicate_memories
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall, MemoryNode
+from ....core.utils import deduplicate_memories
 
 
 class AddDraftAndRetrieveSimilarMemory(BaseMemoryTool):

reme/tool/memory/vector/add_memory.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class AddMemory(BaseMemoryTool):

reme/tool/memory/vector/delete_memory.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class DeleteMemory(BaseMemoryTool):

reme/tool/memory/vector/memory_handler.py

@@ -1,9 +1,9 @@
 """Memory handler"""
 
-from ...core.context import ServiceContext
-from ...core.enumeration import MemoryType
-from ...core.schema import MemoryNode
-from ...core.vector_store import BaseVectorStore
+from ....core.context import ServiceContext
+from ....core.enumeration import MemoryType
+from ....core.schema import MemoryNode
+from ....core.vector_store import BaseVectorStore
 
 
 class MemoryHandler:

reme/tool/memory/vector/retrieve_memory.py

@@ -2,10 +2,10 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall, MemoryNode
-from ...core.utils import deduplicate_memories
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall, MemoryNode
+from ....core.utils import deduplicate_memories
 
 
 class RetrieveMemory(BaseMemoryTool):

reme/tool/memory/vector/retrieve_recent_memory.py

@@ -2,10 +2,10 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall, MemoryNode
-from ...core.utils import deduplicate_memories
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall, MemoryNode
+from ....core.utils import deduplicate_memories
 
 
 class RetrieveRecentMemory(BaseMemoryTool):

reme/tool/memory/vector/update_memory.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class UpdateMemory(BaseMemoryTool):

reme/tool/memory/vector/update_memory_v2.py

@@ -2,9 +2,9 @@
 
 from loguru import logger
 
-from .base_memory_tool import BaseMemoryTool
 from .memory_handler import MemoryHandler
-from ...core.schema import ToolCall
+from ..base_memory_tool import BaseMemoryTool
+from ....core.schema import ToolCall
 
 
 class UpdateMemoryV2(BaseMemoryTool):

tests/test_reme.py

@@ -17,34 +17,42 @@ async def test_reme():
         {
             "role": "user",
             "content": "你好，我是张伟，今年28岁，是一名软件工程师。",
+            "time_created": "2026-01-29 10:00:00",
         },
         {
             "role": "assistant",
             "content": "你好张伟！很高兴认识你。作为一名软件工程师，你主要从事什么方向的开发工作呢？",
+            "time_created": "2026-01-29 10:00:05",
         },
         {
             "role": "user",
             "content": "我主要做后端开发，擅长Python和Go语言。最近在研究AI Agent相关的技术。",
+            "time_created": "2026-01-29 10:00:30",
         },
         {
             "role": "assistant",
             "content": "很棒！Python和Go都是非常实用的语言。AI Agent是当前很热门的方向，你在这方面有什么具体的研究重点吗？",
+            "time_created": "2026-01-29 10:00:35",
         },
         {
             "role": "user",
             "content": "我特别关注记忆系统的设计，希望能让AI Agent具有长期记忆能力。我的工作地点在北京，平时喜欢看技术博客和参加技术分享会。",
+            "time_created": "2026-01-29 10:01:00",
         },
         {
             "role": "assistant",
             "content": "记忆系统确实是AI Agent的核心能力之一。北京有很多优秀的技术社区和活动，相信你能找到很多志同道合的朋友。",
+            "time_created": "2026-01-29 10:01:05",
         },
         {
             "role": "user",
             "content": "是的，我每周末都会去参加一些技术沙龙。对了，我的邮箱是zhangwei@example.com，如果有好的技术资料可以发给我。",
+            "time_created": "2026-01-29 10:01:30",
         },
         {
             "role": "assistant",
             "content": "好的，我记下了。保持学习的热情很重要，祝你在AI Agent领域的研究越来越深入！",
+            "time_created": "2026-01-29 10:01:35",
         },
     ]