feat：重构maisaka的消息类型，添加打断功能

src/chat/replyer/maisaka_generator.py

@@ -8,7 +8,6 @@ import time
 from sqlmodel import select
 
 from src.chat.message_receive.chat_manager import BotChatSession
-from src.chat.message_receive.message import SessionMessage
 from src.common.database.database import get_db_session
 from src.common.database.database_model import Expression
 from src.common.data_models.reply_generation_data_models import (
@@ -22,15 +21,11 @@ from src.config.config import global_config
 from src.core.types import ActionInfo
 from src.services.llm_service import LLMServiceClient
 
-from src.maisaka.message_adapter import (
-    get_message_kind,
-    get_message_role,
-    get_message_source,
-    get_message_text,
-    parse_speaker_content,
-)
+from src.chat.message_receive.message import SessionMessage
+from src.maisaka.context_messages import AssistantMessage, LLMContextMessage, ReferenceMessage, SessionBackedMessage, ToolResultMessage
+from src.maisaka.message_adapter import parse_speaker_content
 
-logger = get_logger("maisaka_replyer")
+logger = get_logger("replyer")
 
 
 @dataclass
@@ -96,16 +91,16 @@ class MaisakaReplyGenerator:
         return normalized
 
     @staticmethod
-    def _format_message_time(message: SessionMessage) -> str:
+    def _format_message_time(message: LLMContextMessage) -> str:
         return message.timestamp.strftime("%H:%M:%S")
 
     @staticmethod
-    def _extract_visible_assistant_reply(message: SessionMessage) -> str:
+    def _extract_visible_assistant_reply(message: AssistantMessage) -> str:
         del message
         return ""
 
-    def _extract_guided_bot_reply(self, message: SessionMessage) -> str:
-        speaker_name, body = parse_speaker_content(get_message_text(message).strip())
+    def _extract_guided_bot_reply(self, message: SessionBackedMessage) -> str:
+        speaker_name, body = parse_speaker_content(message.processed_plain_text.strip())
         bot_nickname = global_config.bot.nickname.strip() or "Bot"
         if speaker_name == bot_nickname:
             return self._normalize_content(body.strip())
@@ -134,25 +129,24 @@ class MaisakaReplyGenerator:
 
         return segments
 
-    def _format_chat_history(self, messages: List[SessionMessage]) -> str:
+    def _format_chat_history(self, messages: List[LLMContextMessage]) -> str:
         """格式化 replyer 使用的可见聊天记录。"""
         bot_nickname = global_config.bot.nickname.strip() or "Bot"
         parts: List[str] = []
 
         for message in messages:
-            role = get_message_role(message)
             timestamp = self._format_message_time(message)
 
-            if get_message_source(message) == "user_reference":
+            if isinstance(message, (ReferenceMessage, ToolResultMessage)):
                 continue
 
-            if role == "user":
+            if isinstance(message, SessionBackedMessage):
                 guided_reply = self._extract_guided_bot_reply(message)
                 if guided_reply:
                     parts.append(f"{timestamp} {bot_nickname}(you): {guided_reply}")
                     continue
 
-                raw_content = get_message_text(message)
+                raw_content = message.processed_plain_text
                 for speaker_name, content_body in self._split_user_message_segments(raw_content):
                     content = self._normalize_content(content_body)
                     if not content:
@@ -161,7 +155,7 @@ class MaisakaReplyGenerator:
                     parts.append(f"{timestamp} {visible_speaker}: {content}")
                 continue
 
-            if role == "assistant":
+            if isinstance(message, AssistantMessage):
                 visible_reply = self._extract_visible_assistant_reply(message)
                 if visible_reply:
                     parts.append(f"{timestamp} {bot_nickname}(you): {visible_reply}")
@@ -170,7 +164,7 @@ class MaisakaReplyGenerator:
 
     def _build_prompt(
         self,
-        chat_history: List[SessionMessage],
+        chat_history: List[LLMContextMessage],
         reply_reason: str,
         expression_habits: str = "",
     ) -> str:
@@ -182,6 +176,7 @@ class MaisakaReplyGenerator:
             system_prompt = load_prompt(
                 "maidairy_replyer",
                 bot_name=global_config.bot.nickname,
+                time_block=f"当前时间：{current_time}",
                 identity=self._personality_prompt,
                 reply_style=global_config.personality.reply_style,
             )
@@ -214,7 +209,7 @@ class MaisakaReplyGenerator:
 
     async def _build_reply_context(
         self,
-        chat_history: List[SessionMessage],
+        chat_history: List[LLMContextMessage],
         reply_message: Optional[SessionMessage],
         reply_reason: str,
         stream_id: Optional[str],
@@ -239,7 +234,7 @@ class MaisakaReplyGenerator:
     def _build_expression_habits(
         self,
         session_id: str,
-        chat_history: List[SessionMessage],
+        chat_history: List[LLMContextMessage],
         reply_message: Optional[SessionMessage],
         reply_reason: str,
     ) -> tuple[str, List[int]]:
@@ -301,7 +296,7 @@ class MaisakaReplyGenerator:
         think_level: int = 1,
         unknown_words: Optional[List[str]] = None,
         log_reply: bool = True,
-        chat_history: Optional[List[SessionMessage]] = None,
+        chat_history: Optional[List[LLMContextMessage]] = None,
         expression_habits: str = "",
         selected_expression_ids: Optional[List[int]] = None,
     ) -> Tuple[bool, ReplyGenerationResult]:
@@ -330,9 +325,7 @@ class MaisakaReplyGenerator:
         filtered_history = [
             message
             for message in chat_history
-            if get_message_role(message) != "system"
-            and get_message_kind(message) != "perception"
-            and get_message_source(message) != "user_reference"
+            if not isinstance(message, (ReferenceMessage, ToolResultMessage))
         ]
 
         logger.debug(f"Maisaka replyer: filtered_history size={len(filtered_history)}")

src/cli/maisaka_cli.py

@@ -23,7 +23,13 @@ from src.config.config import config_manager, global_config
 from src.mcp_module import MCPManager
 
 from src.maisaka.chat_loop_service import MaisakaChatLoopService
-from src.maisaka.message_adapter import build_message, format_speaker_content, remove_last_perception
+from src.maisaka.context_messages import (
+    AssistantMessage,
+    LLMContextMessage,
+    SessionBackedMessage,
+    ToolResultMessage,
+)
+from src.maisaka.message_adapter import format_speaker_content
 from src.maisaka.tool_handlers import (
     ToolHandlerContext,
     handle_mcp_tool,
@@ -43,7 +49,7 @@ class BufferCLI:
         self._chat_loop_service: Optional[MaisakaChatLoopService] = None
         self._reply_generator = MaisakaReplyGenerator()
         self._reader = InputReader()
-        self._chat_history: Optional[list[SessionMessage]] = None
+        self._chat_history: Optional[list[LLMContextMessage]] = None
         self._knowledge_store = get_knowledge_store()
         self._knowledge_learner = KnowledgeLearner("maisaka_cli")
         self._knowledge_min_messages_for_extraction = 10
@@ -118,22 +124,78 @@ class BufferCLI:
             self._chat_start_time = now
             self._last_assistant_response_time = None
             self._chat_history = self._chat_loop_service.build_chat_context(user_text)
-            self._trigger_knowledge_learning([self._chat_history[-1]])
+            self._trigger_knowledge_learning([self._build_cli_session_message(user_text, now)])
         else:
             self._chat_history.append(
-                build_message(
-                    role="user",
-                    content=format_speaker_content(
-                        global_config.maisaka.user_name.strip() or "User",
-                        user_text,
-                        now,
-                    ),
+                self._build_cli_context_message(
+                    user_text=user_text,
+                    timestamp=now,
+                    source_kind="user",
                 )
             )
-            self._trigger_knowledge_learning([self._chat_history[-1]])
+            self._trigger_knowledge_learning([self._build_cli_session_message(user_text, now)])
 
         await self._run_llm_loop(self._chat_history)
 
+    @staticmethod
+    def _build_cli_context_message(
+        user_text: str,
+        timestamp: datetime,
+        source_kind: str = "user",
+        speaker_name: Optional[str] = None,
+    ) -> SessionBackedMessage:
+        """为 CLI 构造新的上下文消息。"""
+        resolved_speaker_name = speaker_name or global_config.maisaka.user_name.strip() or "User"
+        visible_text = format_speaker_content(
+            resolved_speaker_name,
+            user_text,
+            timestamp,
+        )
+        planner_prefix = (
+            f"[时间]{timestamp.strftime('%H:%M:%S')}\n"
+            f"[用户]{resolved_speaker_name}\n"
+            "[用户群昵称]\n"
+            "[msg_id]\n"
+            "[发言内容]"
+        )
+        from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
+
+        return SessionBackedMessage(
+            raw_message=MessageSequence([TextComponent(f"{planner_prefix}{user_text}")]),
+            visible_text=visible_text,
+            timestamp=timestamp,
+            source_kind=source_kind,
+        )
+
+    @staticmethod
+    def _build_cli_session_message(user_text: str, timestamp: datetime) -> SessionMessage:
+        """为 CLI 的知识学习构造兼容 SessionMessage。"""
+        from src.common.data_models.mai_message_data_model import MessageInfo, UserInfo
+        from src.common.data_models.message_component_data_model import MessageSequence
+
+        message = SessionMessage(message_id=f"maisaka_cli_{int(timestamp.timestamp() * 1000)}", timestamp=timestamp, platform="maisaka")
+        message.message_info = MessageInfo(
+            user_info=UserInfo(
+                user_id="maisaka_user",
+                user_nickname=global_config.maisaka.user_name.strip() or "User",
+                user_cardname=None,
+            ),
+            group_info=None,
+            additional_config={},
+        )
+        message.session_id = "maisaka_cli"
+        message.raw_message = MessageSequence([])
+        visible_text = format_speaker_content(
+            global_config.maisaka.user_name.strip() or "User",
+            user_text,
+            timestamp,
+        )
+        message.raw_message.text(visible_text)
+        message.processed_plain_text = visible_text
+        message.display_message = visible_text
+        message.initialized = True
+        return message
+
     def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
         """在 CLI 会话中按批次触发 knowledge 学习。"""
         if not global_config.maisaka.enable_knowledge_module:
@@ -161,7 +223,7 @@ class BufferCLI:
         except Exception as exc:
             console.print(f"[warning]Knowledge learning failed: {exc}[/warning]")
 
-    async def _run_llm_loop(self, chat_history: list[SessionMessage]) -> None:
+    async def _run_llm_loop(self, chat_history: list[LLMContextMessage]) -> None:
         """
         Main inner loop for the Maisaka planner.
 
@@ -210,7 +272,8 @@ class BufferCLI:
                                 )
                             )
 
-                remove_last_perception(chat_history)
+                if chat_history and isinstance(chat_history[-1], AssistantMessage) and chat_history[-1].source == "perception":
+                    chat_history.pop()
 
                 perception_parts = []
                 if knowledge_analysis:
@@ -218,11 +281,10 @@ class BufferCLI:
 
                 if perception_parts:
                     chat_history.append(
-                        build_message(
-                            role="assistant",
+                        AssistantMessage(
                             content="\n\n".join(perception_parts),
-                            message_kind="perception",
-                            source="assistant",
+                            timestamp=datetime.now(),
+                            source_kind="perception",
                         )
                     )
             elif global_config.maisaka.show_thinking:
@@ -273,22 +335,19 @@ class BufferCLI:
                 elif tool_call.func_name == "reply":
                     reply = await self._generate_visible_reply(chat_history, response.content)
                     chat_history.append(
-                        build_message(
-                            role="tool",
+                        ToolResultMessage(
                             content="Visible reply generated and recorded.",
-                            source="tool",
+                            timestamp=datetime.now(),
                             tool_call_id=tool_call.call_id,
+                            tool_name=tool_call.func_name,
                         )
                     )
                     chat_history.append(
-                        build_message(
-                            role="user",
-                            content=format_speaker_content(
-                                global_config.bot.nickname.strip() or "MaiSaka",
-                                reply,
-                                datetime.now(),
-                            ),
-                            source="guided_reply",
+                        self._build_cli_context_message(
+                            user_text=reply,
+                            timestamp=datetime.now(),
+                            source_kind="guided_reply",
+                            speaker_name=global_config.bot.nickname.strip() or "MaiSaka",
                         )
                     )
 
@@ -296,11 +355,11 @@ class BufferCLI:
                     if global_config.maisaka.show_thinking:
                         console.print("[muted]No visible reply this round.[/muted]")
                     chat_history.append(
-                        build_message(
-                            role="tool",
+                        ToolResultMessage(
                             content="No visible reply was sent for this round.",
-                            source="tool",
+                            timestamp=datetime.now(),
                             tool_call_id=tool_call.call_id,
+                            tool_name=tool_call.func_name,
                         )
                     )
 
@@ -342,7 +401,7 @@ class BufferCLI:
                     )
                 )
 
-    async def _generate_visible_reply(self, chat_history: list[SessionMessage], latest_thought: str) -> str:
+    async def _generate_visible_reply(self, chat_history: list[LLMContextMessage], latest_thought: str) -> str:
         """根据最新思考生成并输出可见回复。"""
         if not latest_thought:
             return ""

src/know_u/knowledge.py

@@ -11,10 +11,11 @@ from src.chat.message_receive.message import SessionMessage
 from src.chat.utils.utils import is_bot_self
 from src.common.data_models.llm_service_data_models import LLMGenerationOptions
 from src.common.logger import get_logger
+from src.maisaka.context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage
 from src.services.llm_service import LLMServiceClient
 
 from src.know_u.knowledge_store import KNOWLEDGE_CATEGORIES, get_knowledge_store
-from src.maisaka.message_adapter import get_message_role, get_message_text, parse_speaker_content
+from src.maisaka.message_adapter import parse_speaker_content
 
 logger = get_logger("maisaka_knowledge")
 
@@ -53,7 +54,7 @@ def extract_category_ids_from_result(result: str) -> List[str]:
 
 async def retrieve_relevant_knowledge(
     knowledge_analyzer: Any,
-    chat_history: List[SessionMessage],
+    chat_history: List[LLMContextMessage],
 ) -> str:
     """Retrieve formatted knowledge snippets relevant to the current chat history."""
     store = get_knowledge_store()
@@ -156,14 +157,26 @@ class KnowledgeLearner:
         """
         lines: List[str] = []
         for message in self._messages_cache[-30:]:
-            if get_message_role(message) == "assistant":
-                continue
-            if get_message_role(message) == "tool":
-                continue
-            if is_bot_self(message.platform, message.message_info.user_info.user_id):
+            if isinstance(message, (AssistantMessage, ToolResultMessage)):
                 continue
+            if isinstance(message, SessionBackedMessage):
+                if message.original_message and is_bot_self(
+                    message.original_message.platform,
+                    message.original_message.message_info.user_info.user_id,
+                ):
+                    continue
+                raw_text = message.processed_plain_text.strip()
+                fallback_speaker = (
+                    message.original_message.message_info.user_info.user_nickname
+                    if message.original_message is not None
+                    else "用户"
+                )
+            else:
+                if is_bot_self(message.platform, message.message_info.user_info.user_id):
+                    continue
+                raw_text = message.processed_plain_text.strip()
+                fallback_speaker = message.message_info.user_info.user_nickname or "用户"
 
-            raw_text = get_message_text(message).strip()
             if not raw_text:
                 continue
 
@@ -172,7 +185,7 @@ class KnowledgeLearner:
             if not visible_text:
                 continue
 
-            speaker = speaker_name or message.message_info.user_info.user_nickname or "用户"
+            speaker = speaker_name or fallback_speaker
             lines.append(f"{speaker}: {visible_text}")
 
         return "\n".join(lines)

src/llm_models/model_client/adapter_base.py

@@ -3,6 +3,7 @@ from typing import Any, Callable, Coroutine, Generic, Tuple, TypeVar, cast
 
 import asyncio
 
+from src.common.logger import get_logger
 from src.config.model_configs import ModelInfo
 
 from .base_client import (
@@ -33,12 +34,14 @@ ProviderStreamResponseHandler = Callable[
 ProviderResponseParser = Callable[[RawResponseT], Tuple[APIResponse, UsageTuple | None]]
 """Provider 专用非流式响应解析函数类型。"""
 
+logger = get_logger("llm_adapter_base")
+
 
 async def await_task_with_interrupt(
     task: asyncio.Task[TaskResultT],
     interrupt_flag: asyncio.Event | None,
     *,
-    interval_seconds: float = 0.1,
+    interval_seconds: float = 0.02,
 ) -> TaskResultT:
     """在支持外部中断的前提下等待异步任务完成。
 
@@ -55,8 +58,11 @@ async def await_task_with_interrupt(
     """
     from src.llm_models.exceptions import ReqAbortException
 
+    started_at = asyncio.get_running_loop().time()
     while not task.done():
         if interrupt_flag and interrupt_flag.is_set():
+            elapsed = asyncio.get_running_loop().time() - started_at
+            logger.info(f"LLM 请求检测到中断信号，准备取消底层任务，elapsed={elapsed:.3f}s")
             task.cancel()
             raise ReqAbortException("请求被外部信号中断")
         await asyncio.sleep(interval_seconds)

src/llm_models/utils_model.py

@@ -22,6 +22,7 @@ from src.llm_models.exceptions import (
     EmptyResponseException,
     ModelAttemptFailed,
     NetworkConnectionError,
+    ReqAbortException,
     RespNotOkException,
     RespParseException,
 )
@@ -326,16 +327,7 @@ class LLMOrchestrator:
         del raise_when_empty
         self._refresh_task_config()
         start_time = time.time()
-        if self.request_type.startswith("maisaka_"):
-            logger.info(
-                f"LLMOrchestrator[{self.request_type}] 开始执行 generate_response_with_message_async "
-                f"(temperature={temperature}, max_tokens={max_tokens}, tools={len(tools or [])})"
-            )
 
-        if self.request_type.startswith("maisaka_"):
-            logger.info(
-                f"LLMOrchestrator[{self.request_type}] 正在根据 {len(tools or [])} 个工具构建内部工具选项"
-            )
         tool_built = self._build_tool_options(tools)
         if self.request_type.startswith("maisaka_"):
             logger.info(f"LLMOrchestrator[{self.request_type}] 已构建 {len(tool_built or [])} 个内部工具选项")
@@ -777,6 +769,9 @@ class LLMOrchestrator:
                 )
                 await asyncio.sleep(api_provider.retry_interval)
 
+            except ReqAbortException:
+                raise
+
             except Exception as e:
                 logger.error(traceback.format_exc())
 
@@ -881,6 +876,15 @@ class LLMOrchestrator:
                 self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1)
                 return LLMExecutionResult(api_response=response, model_info=model_info)
 
+            except ReqAbortException as e:
+                total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
+                self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1)
+                if self.request_type.startswith("maisaka_"):
+                    logger.info(
+                        f"LLMOrchestrator[{self.request_type}] 模型 model={model_info.name} 的请求已被外部信号中断"
+                    )
+                raise e
+
             except ModelAttemptFailed as e:
                 last_exception = e.original_exception or e
                 logger.warning(f"模型 '{model_info.name}' 尝试失败，切换到下一个模型。原因: {e}")

src/maisaka/chat_loop_service.py

@@ -14,9 +14,9 @@ from rich.panel import Panel
 from rich.pretty import Pretty
 from rich.text import Text
 
-from src.chat.message_receive.message import SessionMessage
 from src.cli.console import console
 from src.common.data_models.llm_service_data_models import LLMGenerationOptions
+from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
 from src.common.logger import get_logger
 from src.common.prompt_i18n import load_prompt
 from src.config.config import global_config
@@ -27,12 +27,8 @@ from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionI
 from src.services.llm_service import LLMServiceClient
 
 from .builtin_tools import get_builtin_tools
-from .message_adapter import (
-    build_message,
-    format_speaker_content,
-    get_message_role,
-    to_llm_message,
-)
+from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage
+from .message_adapter import format_speaker_content
 
 
 @dataclass(slots=True)
@@ -41,7 +37,7 @@ class ChatResponse:
 
     content: Optional[str]
     tool_calls: List[ToolCall]
-    raw_message: SessionMessage
+    raw_message: AssistantMessage
 
 
 logger = get_logger("maisaka_chat_loop")
@@ -59,6 +55,7 @@ class MaisakaChatLoopService:
         self._temperature = temperature
         self._max_tokens = max_tokens
         self._extra_tools: List[ToolOption] = []
+        self._interrupt_flag: asyncio.Event | None = None
         self._prompts_loaded = False
         self._prompt_load_lock = asyncio.Lock()
         self._personality_prompt = self._build_personality_prompt()
@@ -117,18 +114,21 @@ class MaisakaChatLoopService:
     def set_extra_tools(self, tools: List[ToolDefinitionInput]) -> None:
         self._extra_tools = normalize_tool_options(tools) or []
 
+    def set_interrupt_flag(self, interrupt_flag: asyncio.Event | None) -> None:
+        """设置当前 planner 请求使用的中断标记。"""
+        self._interrupt_flag = interrupt_flag
+
     async def analyze_knowledge_need(
         self,
-        chat_history: List[SessionMessage],
+        chat_history: List[LLMContextMessage],
         categories_summary: str,
     ) -> List[str]:
         """分析当前对话是否需要检索知识库分类。"""
         visible_history: List[str] = []
         for message in chat_history[-8:]:
-            if not message.content:
+            if not message.processed_plain_text:
                 continue
-            role = getattr(message, "role", "")
-            visible_history.append(f"{role}: {message.content}")
+            visible_history.append(f"{message.role}: {message.processed_plain_text}")
 
         if not visible_history or not categories_summary.strip():
             return []
@@ -302,7 +302,7 @@ class MaisakaChatLoopService:
             padding=(0, 1),
         )
 
-    async def chat_loop_step(self, chat_history: List[SessionMessage]) -> ChatResponse:
+    async def chat_loop_step(self, chat_history: List[LLMContextMessage]) -> ChatResponse:
         await self.ensure_chat_prompt_loaded()
         selected_history, selection_reason = self._select_llm_context_messages(chat_history)
 
@@ -313,7 +313,7 @@ class MaisakaChatLoopService:
             messages.append(system_msg.build())
 
             for msg in selected_history:
-                llm_message = to_llm_message(msg)
+                llm_message = msg.to_llm_message()
                 if llm_message is not None:
                     messages.append(llm_message)
 
@@ -342,15 +342,24 @@ class MaisakaChatLoopService:
             )
 
         request_started_at = perf_counter()
+        logger.info(
+            "planner 请求开始: "
+            f"selected_history={len(selected_history)} "
+            f"llm_messages={len(built_messages)} "
+            f"tool_count={len(all_tools)} "
+            f"interrupt_enabled={self._interrupt_flag is not None}"
+        )
         generation_result = await self._llm_chat.generate_response_with_messages(
             message_factory=message_factory,
             options=LLMGenerationOptions(
                 tool_options=all_tools if all_tools else None,
                 temperature=self._temperature,
                 max_tokens=self._max_tokens,
+                interrupt_flag=self._interrupt_flag,
             ),
         )
-        _ = perf_counter() - request_started_at
+        request_elapsed = perf_counter() - request_started_at
+        logger.info(f"planner 请求完成，elapsed={request_elapsed:.3f}s")
 
         tool_call_summaries = [
             {
@@ -365,11 +374,10 @@ class MaisakaChatLoopService:
             f"tool_calls={tool_call_summaries}"
         )
 
-        raw_message = build_message(
-            role=RoleType.Assistant.value,
+        raw_message = AssistantMessage(
             content=generation_result.response or "",
-            source="assistant",
-            tool_calls=generation_result.tool_calls or None,
+            timestamp=datetime.now(),
+            tool_calls=generation_result.tool_calls or [],
         )
         return ChatResponse(
             content=generation_result.response,
@@ -378,20 +386,19 @@ class MaisakaChatLoopService:
         )
 
     @staticmethod
-    def _select_llm_context_messages(chat_history: List[SessionMessage]) -> tuple[List[SessionMessage], str]:
+    def _select_llm_context_messages(chat_history: List[LLMContextMessage]) -> tuple[List[LLMContextMessage], str]:
         """选择真正发送给 LLM 的上下文消息。"""
         max_context_size = max(1, int(global_config.chat.max_context_size))
-        counted_roles = {"user", "assistant"}
         selected_indices: List[int] = []
         counted_message_count = 0
 
         for index in range(len(chat_history) - 1, -1, -1):
             message = chat_history[index]
-            if to_llm_message(message) is None:
+            if message.to_llm_message() is None:
                 continue
 
             selected_indices.append(index)
-            if get_message_role(message) in counted_roles:
+            if message.count_in_context:
                 counted_message_count += 1
                 if counted_message_count >= max_context_size:
                     break
@@ -410,15 +417,25 @@ class MaisakaChatLoopService:
         )
 
     @staticmethod
-    def build_chat_context(user_text: str) -> List[SessionMessage]:
+    def build_chat_context(user_text: str) -> List[LLMContextMessage]:
+        timestamp = datetime.now()
+        visible_text = format_speaker_content(
+            global_config.maisaka.user_name.strip() or "用户",
+            user_text,
+            timestamp,
+        )
+        planner_prefix = (
+            f"[时间]{timestamp.strftime('%H:%M:%S')}\n"
+            f"[用户]{global_config.maisaka.user_name.strip() or '用户'}\n"
+            "[用户群昵称]\n"
+            "[msg_id]\n"
+            "[发言内容]"
+        )
         return [
-            build_message(
-                role=RoleType.User.value,
-                content=format_speaker_content(
-                    global_config.maisaka.user_name.strip() or "用户",
-                    user_text,
-                    datetime.now(),
-                ),
-                source="user",
+            SessionBackedMessage(
+                raw_message=MessageSequence([TextComponent(f"{planner_prefix}{user_text}")]),
+                visible_text=visible_text,
+                timestamp=timestamp,
+                source_kind="user",
             )
         ]

src/maisaka/context_messages.py

@@ -0,0 +1,275 @@
+"""Maisaka 内部上下文消息抽象。"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+from io import BytesIO
+from typing import Optional
+import base64
+
+from PIL import Image as PILImage
+
+from src.chat.message_receive.message import SessionMessage
+from src.common.data_models.message_component_data_model import EmojiComponent, ImageComponent, MessageSequence, TextComponent
+from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
+from src.llm_models.payload_content.tool_option import ToolCall
+
+
+def _guess_image_format(image_bytes: bytes) -> Optional[str]:
+    if not image_bytes:
+        return None
+
+    try:
+        with PILImage.open(BytesIO(image_bytes)) as image:
+            return image.format.lower() if image.format else None
+    except Exception:
+        return None
+
+
+def _build_message_from_sequence(
+    role: RoleType,
+    message_sequence: MessageSequence,
+    fallback_text: str,
+    *,
+    tool_call_id: Optional[str] = None,
+    tool_calls: Optional[list[ToolCall]] = None,
+) -> Optional[Message]:
+    """根据消息片段构造统一 LLM 消息。"""
+    builder = MessageBuilder().set_role(role)
+    if role == RoleType.Assistant and tool_calls:
+        builder.set_tool_calls(tool_calls)
+    if role == RoleType.Tool and tool_call_id:
+        builder.add_tool_call(tool_call_id)
+
+    has_content = False
+    for component in message_sequence.components:
+        if isinstance(component, TextComponent):
+            if component.text:
+                builder.add_text_content(component.text)
+                has_content = True
+            continue
+
+        if isinstance(component, (EmojiComponent, ImageComponent)):
+            image_format = _guess_image_format(component.binary_data)
+            if image_format and component.binary_data:
+                builder.add_image_content(image_format, base64.b64encode(component.binary_data).decode("utf-8"))
+                has_content = True
+                continue
+
+            if component.content:
+                builder.add_text_content(component.content)
+                has_content = True
+
+    if not has_content and fallback_text:
+        builder.add_text_content(fallback_text)
+        has_content = True
+
+    if not has_content and not (role == RoleType.Assistant and tool_calls):
+        return None
+    return builder.build()
+
+
+class ReferenceMessageType(str, Enum):
+    """参考消息类型。"""
+
+    CUSTOM = "custom"
+    JARGON = "jargon"
+    KNOWLEDGE = "knowledge"
+    MEMORY = "memory"
+    TOOL_HINT = "tool_hint"
+
+
+class LLMContextMessage(ABC):
+    """Maisaka 内部用于组织 LLM 上下文的统一消息抽象。"""
+
+    timestamp: datetime
+
+    @property
+    @abstractmethod
+    def role(self) -> str:
+        """返回 LLM 消息角色。"""
+
+    @property
+    @abstractmethod
+    def processed_plain_text(self) -> str:
+        """返回可读的纯文本内容。"""
+
+    @property
+    def count_in_context(self) -> bool:
+        """是否占用普通 user/assistant 上下文窗口。"""
+        return True
+
+    @property
+    def source(self) -> str:
+        """返回消息来源。"""
+        return self.__class__.__name__
+
+    @abstractmethod
+    def to_llm_message(self) -> Optional[Message]:
+        """转换为统一 LLM 消息。"""
+
+    def consume_once(self) -> bool:
+        """消费一次生命周期，返回是否继续保留。"""
+        return True
+
+
+@dataclass(slots=True)
+class SessionBackedMessage(LLMContextMessage):
+    """真实会话上下文消息。"""
+
+    raw_message: MessageSequence
+    visible_text: str
+    timestamp: datetime
+    message_id: Optional[str] = None
+    original_message: Optional[SessionMessage] = None
+    source_kind: str = "user"
+
+    @property
+    def role(self) -> str:
+        return RoleType.User.value
+
+    @property
+    def processed_plain_text(self) -> str:
+        return self.visible_text
+
+    @property
+    def source(self) -> str:
+        return self.source_kind
+
+    def to_llm_message(self) -> Optional[Message]:
+        return _build_message_from_sequence(
+            RoleType.User,
+            self.raw_message,
+            self.processed_plain_text,
+        )
+
+    @classmethod
+    def from_session_message(
+        cls,
+        session_message: SessionMessage,
+        *,
+        raw_message: MessageSequence,
+        visible_text: str,
+        source_kind: str = "user",
+    ) -> "SessionBackedMessage":
+        """从真实 SessionMessage 构造上下文消息。"""
+        return cls(
+            raw_message=raw_message,
+            visible_text=visible_text,
+            timestamp=session_message.timestamp,
+            message_id=session_message.message_id,
+            original_message=session_message,
+            source_kind=source_kind,
+        )
+
+
+@dataclass(slots=True)
+class ReferenceMessage(LLMContextMessage):
+    """参考消息。"""
+
+    content: str
+    timestamp: datetime
+    reference_type: ReferenceMessageType = ReferenceMessageType.CUSTOM
+    remaining_uses_value: Optional[int] = 1
+    display_prefix: str = "[参考消息]"
+
+    @property
+    def role(self) -> str:
+        return RoleType.User.value
+
+    @property
+    def processed_plain_text(self) -> str:
+        return f"{self.display_prefix}\n{self.content}".strip()
+
+    @property
+    def count_in_context(self) -> bool:
+        return False
+
+    @property
+    def source(self) -> str:
+        return self.reference_type.value
+
+    def to_llm_message(self) -> Optional[Message]:
+        message_sequence = MessageSequence([TextComponent(self.processed_plain_text)])
+        return _build_message_from_sequence(RoleType.User, message_sequence, self.processed_plain_text)
+
+    def consume_once(self) -> bool:
+        if self.remaining_uses_value is None:
+            return True
+
+        self.remaining_uses_value -= 1
+        return self.remaining_uses_value > 0
+
+
+@dataclass(slots=True)
+class AssistantMessage(LLMContextMessage):
+    """内部 assistant 消息。"""
+
+    content: str
+    timestamp: datetime
+    tool_calls: list[ToolCall] = field(default_factory=list)
+    source_kind: str = "assistant"
+
+    @property
+    def role(self) -> str:
+        return RoleType.Assistant.value
+
+    @property
+    def processed_plain_text(self) -> str:
+        return self.content
+
+    @property
+    def count_in_context(self) -> bool:
+        return self.source_kind != "perception"
+
+    @property
+    def source(self) -> str:
+        return self.source_kind
+
+    def to_llm_message(self) -> Optional[Message]:
+        message_sequence = MessageSequence([])
+        if self.content:
+            message_sequence.text(self.content)
+        return _build_message_from_sequence(
+            RoleType.Assistant,
+            message_sequence,
+            self.content,
+            tool_calls=self.tool_calls or None,
+        )
+
+
+@dataclass(slots=True)
+class ToolResultMessage(LLMContextMessage):
+    """工具返回结果消息。"""
+
+    content: str
+    timestamp: datetime
+    tool_call_id: str
+    tool_name: str = ""
+    success: bool = True
+
+    @property
+    def role(self) -> str:
+        return RoleType.Tool.value
+
+    @property
+    def processed_plain_text(self) -> str:
+        return self.content
+
+    @property
+    def count_in_context(self) -> bool:
+        return False
+
+    @property
+    def source(self) -> str:
+        return self.tool_name or "tool"
+
+    def to_llm_message(self) -> Optional[Message]:
+        message_sequence = MessageSequence([TextComponent(self.content)])
+        return _build_message_from_sequence(
+            RoleType.Tool,
+            message_sequence,
+            self.content,
+            tool_call_id=self.tool_call_id,
+        )

src/maisaka/message_adapter.py

@@ -1,148 +1,32 @@
-"""
-MaiSaka 内部消息适配器。
-"""
+"""Maisaka 文本与消息片段适配工具。"""
 
 from copy import deepcopy
 from datetime import datetime
-from io import BytesIO
 from typing import Optional
-from uuid import uuid4
-import base64
 import re
 
-from PIL import Image as PILImage
-
-from src.chat.message_receive.message import SessionMessage
-from src.common.data_models.mai_message_data_model import GroupInfo, MessageInfo, UserInfo
 from src.common.data_models.message_component_data_model import EmojiComponent, ImageComponent, MessageSequence, TextComponent
-from src.config.config import global_config
-from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
-from src.llm_models.payload_content.tool_option import ToolCall
 
-MAISAKA_PLATFORM = "maisaka"
-MAISAKA_SESSION_ID = "maisaka_cli"
-MESSAGE_KIND_KEY = "maisaka_message_kind"
-SOURCE_KEY = "maisaka_source"
-LLM_ROLE_KEY = "maisaka_llm_role"
-TOOL_CALL_ID_KEY = "maisaka_tool_call_id"
-TOOL_CALLS_KEY = "maisaka_tool_calls"
 SPEAKER_PREFIX_PATTERN = re.compile(
     r"^(?:(?P<timestamp>\d{2}:\d{2}:\d{2}))?(?:\[msg_id:(?P<message_id>[^\]]+)\])?\[(?P<speaker>[^\]]+)\](?P<content>.*)$",
     re.DOTALL,
 )
 
 
-def _build_user_info_for_role(role: str) -> UserInfo:
-    if role == RoleType.User.value:
-        return UserInfo(
-            user_id="maisaka_user",
-            user_nickname=global_config.maisaka.user_name.strip() or "用户",
-            user_cardname=None,
-        )
-    if role == RoleType.Tool.value:
-        return UserInfo(user_id="maisaka_tool", user_nickname="tool", user_cardname=None)
-    return UserInfo(
-        user_id="maisaka_assistant",
-        user_nickname=global_config.bot.nickname.strip() or "MaiSaka",
-        user_cardname=None,
-    )
-
-
-def _serialize_tool_call(tool_call: ToolCall) -> dict:
-    return {
-        "call_id": tool_call.call_id,
-        "func_name": tool_call.func_name,
-        "args": tool_call.args or {},
-    }
-
-
-def _deserialize_tool_call(data: dict) -> ToolCall:
-    return ToolCall(
-        call_id=str(data.get("call_id", "")),
-        func_name=str(data.get("func_name", "")),
-        args=data.get("args", {}) or {},
-    )
-
-
-def _ensure_message_id_in_speaker_content(content: str, message_id: str) -> str:
-    """Ensure speaker-formatted visible text carries a msg_id marker."""
-    match = SPEAKER_PREFIX_PATTERN.match(content or "")
-    if not match:
-        return content
-
-    existing_message_id = match.group("message_id")
-    if existing_message_id:
-        return content
-
-    timestamp_text = match.group("timestamp")
-    speaker_name = match.group("speaker")
-    visible_content = match.group("content")
-    timestamp = datetime.strptime(timestamp_text, "%H:%M:%S") if timestamp_text else None
-    return format_speaker_content(speaker_name, visible_content, timestamp, message_id)
-
-
-def build_message(
-    role: str,
-    content: str = "",
-    *,
-    message_kind: str = "normal",
-    source: Optional[str] = None,
-    tool_call_id: Optional[str] = None,
-    tool_calls: Optional[list[ToolCall]] = None,
-    timestamp: Optional[datetime] = None,
-    message_id: Optional[str] = None,
-    platform: str = MAISAKA_PLATFORM,
-    session_id: str = MAISAKA_SESSION_ID,
-    user_info: Optional[UserInfo] = None,
-    group_info: Optional[GroupInfo] = None,
-    raw_message: Optional[MessageSequence] = None,
-    display_text: Optional[str] = None,
-) -> SessionMessage:
-    """为 MaiSaka 会话历史构建内部 ``SessionMessage``。"""
-    resolved_timestamp = timestamp or datetime.now()
-    resolved_role = role.value if isinstance(role, RoleType) else role
-    message = SessionMessage(
-        message_id=message_id or f"maisaka_{uuid4().hex}",
-        timestamp=resolved_timestamp,
-        platform=platform,
-    )
-    normalized_content = _ensure_message_id_in_speaker_content(content, message.message_id) if content else content
-    message.message_info = MessageInfo(
-        user_info=user_info or _build_user_info_for_role(resolved_role),
-        group_info=group_info,
-        additional_config={
-            LLM_ROLE_KEY: resolved_role,
-            MESSAGE_KIND_KEY: message_kind,
-            SOURCE_KEY: source or resolved_role,
-            TOOL_CALL_ID_KEY: tool_call_id,
-            TOOL_CALLS_KEY: [_serialize_tool_call(tool_call) for tool_call in (tool_calls or [])],
-        },
-    )
-    message.session_id = session_id
-    message.raw_message = raw_message if raw_message is not None else MessageSequence([])
-    if raw_message is None and normalized_content:
-        message.raw_message.text(normalized_content)
-    visible_text = display_text if display_text is not None else normalized_content
-    message.processed_plain_text = visible_text
-    message.display_message = visible_text
-    message.initialized = True
-    return message
-
-
 def format_speaker_content(
     speaker_name: str,
     content: str,
     timestamp: Optional[datetime] = None,
     message_id: Optional[str] = None,
 ) -> str:
-    """Format visible conversation content with an explicit speaker label."""
+    """将可见文本格式化为带说话人前缀的样式。"""
     time_prefix = timestamp.strftime("%H:%M:%S") if timestamp is not None else ""
     message_id_prefix = f"[msg_id:{message_id}]" if message_id else ""
     return f"{time_prefix}{message_id_prefix}[{speaker_name}]{content}"
 
 
 def parse_speaker_content(content: str) -> tuple[Optional[str], str]:
-    """Parse content formatted as [speaker]message."""
+    """解析形如 [speaker]message 的可见文本。"""
     match = SPEAKER_PREFIX_PATTERN.match(content or "")
     if not match:
         return None, content or ""
@@ -150,12 +34,12 @@ def parse_speaker_content(content: str) -> tuple[Optional[str], str]:
 
 
 def clone_message_sequence(message_sequence: MessageSequence) -> MessageSequence:
-    """Create a detached copy of a message sequence."""
+    """复制消息片段序列。"""
     return MessageSequence([deepcopy(component) for component in message_sequence.components])
 
 
 def build_visible_text_from_sequence(message_sequence: MessageSequence) -> str:
-    """Extract visible text from a message sequence without forcing image descriptions."""
+    """从消息片段序列提取可见文本。"""
     parts: list[str] = []
     for component in message_sequence.components:
         if isinstance(component, TextComponent):
@@ -181,112 +65,5 @@ def build_visible_text_from_sequence(message_sequence: MessageSequence) -> str:
 
         if isinstance(component, ImageComponent):
             parts.append("[图片]")
+
     return "".join(parts)
-
-
-def _guess_image_format(image_bytes: bytes) -> Optional[str]:
-    if not image_bytes:
-        return None
-
-    try:
-        with PILImage.open(BytesIO(image_bytes)) as image:
-            return image.format.lower() if image.format else None
-    except Exception:
-        return None
-
-
-def get_message_text(message: SessionMessage) -> str:
-    if message.processed_plain_text is not None:
-        return message.processed_plain_text
-    if message.display_message is not None:
-        return message.display_message
-
-    parts: list[str] = []
-    for component in message.raw_message.components:
-        text = getattr(component, "text", None)
-        if isinstance(text, str):
-            parts.append(text)
-    return "".join(parts)
-
-
-def get_message_role(message: SessionMessage) -> str:
-    return str(message.message_info.additional_config.get(LLM_ROLE_KEY, RoleType.User.value))
-
-
-def get_message_kind(message: SessionMessage) -> str:
-    return str(message.message_info.additional_config.get(MESSAGE_KIND_KEY, "normal"))
-
-
-def get_message_source(message: SessionMessage) -> str:
-    return str(message.message_info.additional_config.get(SOURCE_KEY, get_message_role(message)))
-
-
-def is_perception_message(message: SessionMessage) -> bool:
-    return get_message_kind(message) == "perception"
-
-
-def get_tool_call_id(message: SessionMessage) -> Optional[str]:
-    value = message.message_info.additional_config.get(TOOL_CALL_ID_KEY)
-    return str(value) if value else None
-
-
-def get_tool_calls(message: SessionMessage) -> list[ToolCall]:
-    raw_tool_calls = message.message_info.additional_config.get(TOOL_CALLS_KEY, [])
-    if not isinstance(raw_tool_calls, list):
-        return []
-    return [_deserialize_tool_call(item) for item in raw_tool_calls if isinstance(item, dict)]
-
-
-def remove_last_perception(messages: list[SessionMessage]) -> None:
-    for index in range(len(messages) - 1, -1, -1):
-        if is_perception_message(messages[index]):
-            messages.pop(index)
-            break
-
-
-def to_llm_message(message: SessionMessage) -> Optional[Message]:
-    role = get_message_role(message)
-    tool_call_id = get_tool_call_id(message)
-    tool_calls = get_tool_calls(message)
-
-    if role == RoleType.System.value:
-        role_type = RoleType.System
-    elif role == RoleType.User.value:
-        role_type = RoleType.User
-    elif role == RoleType.Assistant.value:
-        role_type = RoleType.Assistant
-    elif role == RoleType.Tool.value:
-        role_type = RoleType.Tool
-    else:
-        return None
-
-    builder = MessageBuilder().set_role(role_type)
-    if role_type == RoleType.Assistant and tool_calls:
-        builder.set_tool_calls(tool_calls)
-    if role_type == RoleType.Tool and tool_call_id:
-        builder.add_tool_call(tool_call_id)
-
-    has_content = False
-    for component in message.raw_message.components:
-        if isinstance(component, TextComponent):
-            if component.text:
-                builder.add_text_content(component.text)
-                has_content = True
-            continue
-
-        if isinstance(component, (ImageComponent, EmojiComponent)):
-            image_format = _guess_image_format(component.binary_data)
-            if image_format and component.binary_data:
-                builder.add_image_content(image_format, base64.b64encode(component.binary_data).decode("utf-8"))
-                has_content = True
-                continue
-
-            if component.content:
-                builder.add_text_content(component.content)
-                has_content = True
-
-    if not has_content:
-        content = get_message_text(message)
-        if content:
-            builder.add_text_content(content)
-    return builder.build()

src/maisaka/reasoning_engine.py

@@ -6,33 +6,32 @@ from typing import TYPE_CHECKING, Optional
 import asyncio
 import difflib
 import json
-import re
 import time
+import traceback
 
-from sqlmodel import select
 
 from src.chat.heart_flow.heartFC_utils import CycleDetail
 from src.chat.message_receive.message import SessionMessage
 from src.chat.replyer.replyer_manager import replyer_manager
-from src.chat.utils.utils import get_bot_account, process_llm_response
-from src.common.database.database import get_db_session
-from src.common.database.database_model import Jargon
-from src.common.data_models.mai_message_data_model import UserInfo
+from src.chat.utils.utils import process_llm_response
 from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
 from src.common.logger import get_logger
 from src.config.config import global_config
 from src.learners.jargon_explainer import search_jargon
+from src.llm_models.exceptions import ReqAbortException
 from src.llm_models.payload_content.tool_option import ToolCall
 from src.services import database_service as database_api, send_service
 
+from .context_messages import (
+    AssistantMessage,
+    LLMContextMessage,
+    SessionBackedMessage,
+    ToolResultMessage,
+)
 from .message_adapter import (
-    build_message,
     build_visible_text_from_sequence,
     clone_message_sequence,
     format_speaker_content,
-    get_message_source,
-    get_message_text,
-    get_message_role,
 )
 from .tool_handlers import (
     handle_mcp_tool,
@@ -51,7 +50,6 @@ class MaisakaReasoningEngine:
     def __init__(self, runtime: "MaisakaHeartFlowChatting") -> None:
         self._runtime = runtime
         self._last_reasoning_content: str = ""
-        self._shown_jargons: set[str] = set()  # 已在参考消息中展示过的 jargon
 
     async def run_loop(self) -> None:
         """独立消费消息批次，并执行对应的内部思考轮次。"""
@@ -65,6 +63,7 @@ class MaisakaReasoningEngine:
 
                 self._runtime._agent_state = self._runtime._STATE_RUNNING
                 if cached_messages:
+                    self._append_wait_interrupted_message_if_needed()
                     await self._ingest_messages(cached_messages)
                     anchor_message = cached_messages[-1]
                 else:
@@ -76,26 +75,35 @@ class MaisakaReasoningEngine:
                         self._runtime._internal_turn_queue.task_done()
                         continue
                     logger.info(f"{self._runtime.log_prefix} wait 超时后开始新一轮思考")
-                    self._runtime._chat_history.append(self._build_wait_timeout_message(anchor_message))
+                    self._runtime._chat_history.append(self._build_wait_timeout_message())
                     self._trim_chat_history()
                 try:
                     for round_index in range(self._runtime._max_internal_rounds):
                         cycle_detail = self._start_cycle()
                         self._runtime._log_cycle_started(cycle_detail, round_index)
                         try:
-                            # 每次LLM生成前，动态添加参考消息到最新位置
-                            reference_added = self._append_jargon_reference_message()
                             planner_started_at = time.time()
-                            response = await self._runtime._chat_loop_service.chat_loop_step(self._runtime._chat_history)
+                            logger.info(
+                                f"{self._runtime.log_prefix} planner 开始: "
+                                f"round={round_index + 1} "
+                                f"history_size={len(self._runtime._chat_history)} "
+                                f"started_at={planner_started_at:.3f}"
+                            )
+                            interrupt_flag = asyncio.Event()
+                            self._runtime._planner_interrupt_flag = interrupt_flag
+                            self._runtime._chat_loop_service.set_interrupt_flag(interrupt_flag)
+                            try:
+                                response = await self._runtime._chat_loop_service.chat_loop_step(self._runtime._chat_history)
+                            finally:
+                                if self._runtime._planner_interrupt_flag is interrupt_flag:
+                                    self._runtime._planner_interrupt_flag = None
+                                self._runtime._chat_loop_service.set_interrupt_flag(None)
                             cycle_detail.time_records["planner"] = time.time() - planner_started_at
-
-                            # LLM调用后，移除刚才添加的参考消息（一次性使用）
-                            if reference_added and self._runtime._chat_history:
-                                # 从末尾往前查找并移除参考消息
-                                for i in range(len(self._runtime._chat_history) - 1, -1, -1):
-                                    if get_message_source(self._runtime._chat_history[i]) == "user_reference":
-                                        self._runtime._chat_history.pop(i)
-                                        break
+                            logger.info(
+                                f"{self._runtime.log_prefix} planner 完成: "
+                                f"round={round_index + 1} "
+                                f"elapsed={cycle_detail.time_records['planner']:.3f}s"
+                            )
 
                             reasoning_content = response.content or ""
                             if self._should_replace_reasoning(reasoning_content):
@@ -104,9 +112,6 @@ class MaisakaReasoningEngine:
                                 logger.info(f"{self._runtime.log_prefix} reasoning content replaced due to high similarity")
 
                             self._last_reasoning_content = reasoning_content
-                            response.raw_message.platform = anchor_message.platform
-                            response.raw_message.session_id = self._runtime.session_id
-                            response.raw_message.message_info.group_info = self._runtime._build_group_info(anchor_message)
                             self._runtime._chat_history.append(response.raw_message)
 
                             if response.tool_calls:
@@ -124,6 +129,16 @@ class MaisakaReasoningEngine:
                             if response.content:
                                 continue
 
+                            break
+                        except ReqAbortException:
+                            interrupted_at = time.time()
+                            logger.info(
+                                f"{self._runtime.log_prefix} planner 打断成功: "
+                                f"round={round_index + 1} "
+                                f"started_at={planner_started_at:.3f} "
+                                f"interrupted_at={interrupted_at:.3f} "
+                                f"elapsed={interrupted_at - planner_started_at:.3f}s"
+                            )
                             break
                         finally:
                             self._end_cycle(cycle_detail)
@@ -136,6 +151,7 @@ class MaisakaReasoningEngine:
             raise
         except Exception:
             logger.exception("%s Maisaka internal loop crashed", self._runtime.log_prefix)
+            logger.error(traceback.format_exc())
             raise
 
     def _get_timeout_anchor_message(self) -> Optional[SessionMessage]:
@@ -144,16 +160,31 @@ class MaisakaReasoningEngine:
             return self._runtime.message_cache[-1]
         return None
 
-    def _build_wait_timeout_message(self, anchor_message: SessionMessage) -> SessionMessage:
-        """构造 wait 超时后的工具结果消息，用于触发下一轮思考。"""
-        return build_message(
-            role="tool",
+    def _build_wait_timeout_message(self) -> ToolResultMessage:
+        """构造 wait 超时后的工具结果消息。"""
+        tool_call_id = self._runtime._pending_wait_tool_call_id or "wait_timeout"
+        self._runtime._pending_wait_tool_call_id = None
+        return ToolResultMessage(
             content="wait 已超时，期间没有收到新的用户输入。请基于现有上下文继续下一轮思考。",
-            source="tool",
-            platform=anchor_message.platform,
-            session_id=self._runtime.session_id,
-            group_info=self._runtime._build_group_info(anchor_message),
-            user_info=UserInfo(user_id="maisaka_tool", user_nickname="tool", user_cardname=None),
+            timestamp=datetime.now(),
+            tool_call_id=tool_call_id,
+            tool_name="wait",
+        )
+
+    def _append_wait_interrupted_message_if_needed(self) -> None:
+        """如果 wait 被新消息打断，则补一条对应的工具结果消息。"""
+        tool_call_id = self._runtime._pending_wait_tool_call_id
+        if not tool_call_id:
+            return
+
+        self._runtime._pending_wait_tool_call_id = None
+        self._runtime._chat_history.append(
+            ToolResultMessage(
+                content="wait 被新的用户输入打断，已继续处理最新消息。",
+                timestamp=datetime.now(),
+                tool_call_id=tool_call_id,
+                tool_name="wait",
+            )
         )
 
     async def _ingest_messages(self, messages: list[SessionMessage]) -> None:
@@ -164,17 +195,11 @@ class MaisakaReasoningEngine:
             if not user_sequence.components:
                 continue
 
-            history_message = build_message(
-                role="user",
-                content=visible_text,
-                source="user",
-                timestamp=message.timestamp,
-                platform=message.platform,
-                session_id=self._runtime.session_id,
-                group_info=self._runtime._build_group_info(message),
-                user_info=self._runtime._build_runtime_user_info(),
+            history_message = SessionBackedMessage.from_session_message(
+                message,
                 raw_message=user_sequence,
-                display_text=visible_text,
+                visible_text=visible_text,
+                source_kind="user",
             )
             self._insert_chat_history_message(history_message)
             self._trim_chat_history()
@@ -239,141 +264,10 @@ class MaisakaReasoningEngine:
         speaker_name = user_info.user_cardname or user_info.user_nickname or user_info.user_id
         return format_speaker_content(speaker_name, content, message.timestamp, message.message_id).strip()
 
-    def _insert_chat_history_message(self, message: SessionMessage) -> int:
-        """按时间顺序将消息插入聊天历史，同时保留 system 消息在最前。"""
-        if not self._runtime._chat_history:
-            self._runtime._chat_history.append(message)
-            return 0
-
-        insert_at = len(self._runtime._chat_history)
-        for index, existing_message in enumerate(self._runtime._chat_history):
-            if get_message_role(existing_message) == "system":
-                continue
-            if existing_message.timestamp > message.timestamp:
-                insert_at = index
-                break
-
-        self._runtime._chat_history.insert(insert_at, message)
-        return insert_at
-
-    def _append_jargon_reference_message(self) -> bool:
-        """每次LLM生成前，如果命中了黑话词条，则添加一条参考信息消息到聊天历史末尾。
-
-        Returns:
-            bool: 是否添加了参考消息
-        """
-        content = self._build_user_history_corpus()
-        if not content:
-            return False
-
-        matched_words = self._find_jargon_words_in_text(content)
-        if not matched_words:
-            return False
-
-        # 记录已展示的 jargon
-        for word in matched_words:
-            self._shown_jargons.add(word.lower())
-
-        reference_text = (
-            "[参考信息]\n"
-            f"{','.join(matched_words)}可能是jargon，可以使用query_jargon来查看其含义"
-        )
-        reference_sequence = MessageSequence([TextComponent(reference_text)])
-
-        # 使用当前时间作为时间戳
-        reference_message = build_message(
-            role="user",
-            content="",
-            source="user_reference",
-            timestamp=datetime.now(),
-            platform=self._runtime.chat_stream.platform,
-            session_id=self._runtime.session_id,
-            group_info=self._runtime._build_group_info(),
-            user_info=self._runtime._build_runtime_user_info(),
-            raw_message=reference_sequence,
-            display_text=reference_text,
-        )
-        self._runtime._chat_history.append(reference_message)
-        return True
-
-    def _build_user_history_corpus(self) -> str:
-        """拼接当前聊天记录内所有用户消息的正文，用于统一匹配黑话。"""
-        parts: list[str] = []
-        for history_message in self._runtime._chat_history:
-            if get_message_role(history_message) != "user":
-                continue
-            if get_message_source(history_message) != "user":
-                continue
-            text = (get_message_text(history_message) or "").strip()
-            if not text:
-                continue
-            parts.append(text)
-
-        return "\n".join(parts)
-
-    def _find_jargon_words_in_text(self, content: str) -> list[str]:
-        """匹配正文中出现的 jargon 词条。"""
-        lowered_content = content.lower()
-        matched_entries: list[tuple[int, int, int, str]] = []
-        seen_words: set[str] = set()
-
-        with get_db_session(auto_commit=False) as session:
-            query = (
-                select(Jargon)
-                .where(Jargon.is_jargon.is_(True))
-                .order_by(Jargon.count.desc())  # type: ignore[attr-defined]
-            )
-            jargons = session.exec(query).all()
-
-            for jargon in jargons:
-                jargon_content = str(jargon.content or "").strip()
-                if not jargon_content:
-                    continue
-                # meaning 为空的不匹配
-                if not str(jargon.meaning or "").strip():
-                    continue
-                normalized_content = jargon_content.lower()
-                if normalized_content in seen_words:
-                    continue
-                # 跳过已经展示过的 jargon
-                if normalized_content in self._shown_jargons:
-                    continue
-                if not self._is_visible_jargon(jargon):
-                    continue
-                match_position = self._get_jargon_match_position(jargon_content, lowered_content, content)
-                if match_position is None:
-                    continue
-
-                seen_words.add(normalized_content)
-                matched_entries.append((match_position, -len(jargon_content), -int(jargon.count or 0), jargon_content))
-
-        matched_entries.sort()
-        return [matched_content for _, _, _, matched_content in matched_entries[:8]]
-
-    def _is_visible_jargon(self, jargon: Jargon) -> bool:
-        """判断当前会话是否可见该 jargon。"""
-        if global_config.expression.all_global_jargon or bool(jargon.is_global):
-            return True
-
-        try:
-            session_id_dict = json.loads(jargon.session_id_dict or "{}")
-        except (TypeError, json.JSONDecodeError):
-            logger.warning(f"Failed to parse jargon.session_id_dict: jargon_id={jargon.id}")
-            return False
-        return self._runtime.session_id in session_id_dict
-
-    @staticmethod
-    def _get_jargon_match_position(jargon_content: str, lowered_content: str, original_content: str) -> Optional[int]:
-        """返回 jargon 在文本中的首次命中位置，未命中时返回 `None`。"""
-        if re.search(r"[\u4e00-\u9fff]", jargon_content):
-            match_index = original_content.lower().find(jargon_content.lower())
-            return match_index if match_index >= 0 else None
-
-        pattern = rf"\b{re.escape(jargon_content.lower())}\b"
-        match = re.search(pattern, lowered_content)
-        if match is None:
-            return None
-        return match.start()
+    def _insert_chat_history_message(self, message: LLMContextMessage) -> int:
+        """将消息按处理顺序追加到聊天历史末尾。"""
+        self._runtime._chat_history.append(message)
+        return len(self._runtime._chat_history) - 1
 
     def _start_cycle(self) -> CycleDetail:
         """开始一轮 Maisaka 思考循环。"""
@@ -397,10 +291,7 @@ class MaisakaReasoningEngine:
 
     def _trim_chat_history(self) -> None:
         """裁剪聊天历史，保证用户消息数量不超过配置限制。"""
-        counted_roles = {"user", "assistant"}
-        conversation_message_count = sum(
-            1 for message in self._runtime._chat_history if get_message_role(message) in counted_roles
-        )
+        conversation_message_count = sum(1 for message in self._runtime._chat_history if message.count_in_context)
         if conversation_message_count <= self._runtime._max_context_size:
             return
 
@@ -410,7 +301,7 @@ class MaisakaReasoningEngine:
         while conversation_message_count >= self._runtime._max_context_size and trimmed_history:
             removed_message = trimmed_history.pop(0)
             removed_count += 1
-            if get_message_role(removed_message) in counted_roles:
+            if removed_message.count_in_context:
                 conversation_message_count -= 1
 
         self._runtime._chat_history = trimmed_history
@@ -441,6 +332,11 @@ class MaisakaReasoningEngine:
             bool: 是否需要替换
         """
         if not self._last_reasoning_content or not current_content:
+            logger.info(
+                f"{self._runtime.log_prefix} reasoning similarity skipped: "
+                f"last_empty={not bool(self._last_reasoning_content)} "
+                f"current_empty={not bool(current_content)} similarity=0.00"
+            )
             return False
 
         similarity = self._calculate_similarity(current_content, self._last_reasoning_content)
@@ -495,13 +391,7 @@ class MaisakaReasoningEngine:
                 except (TypeError, ValueError):
                     wait_seconds = 30
                 wait_seconds = max(0, wait_seconds)
-                self._runtime._chat_history.append(
-                    self._build_tool_message(
-                        tool_call,
-                        f"Waiting for future input for up to {wait_seconds} seconds.",
-                    )
-                )
-                self._runtime._enter_wait_state(seconds=wait_seconds)
+                self._runtime._enter_wait_state(seconds=wait_seconds, tool_call_id=tool_call.call_id)
                 return True
 
             if tool_call.func_name == "stop":
@@ -743,33 +633,27 @@ class MaisakaReasoningEngine:
                 tool_reasoning=latest_thought,
             )
 
-        target_platform = target_message.platform or anchor_message.platform
         bot_name = global_config.bot.nickname.strip() or "MaiSaka"
-        bot_user_info = UserInfo(
-            user_id=get_bot_account(target_platform) or "maisaka_assistant",
-            user_nickname=bot_name,
-            user_cardname=None,
+        reply_timestamp = datetime.now()
+        planner_prefix = (
+            f"[时间]{reply_timestamp.strftime('%H:%M:%S')}\n"
+            f"[用户]{bot_name}\n"
+            "[用户群昵称]\n"
+            "[msg_id]\n"
+            "[发言内容]"
         )
-        history_message = build_message(
-            role="user",
-            content="",
-            source="guided_reply",
-            platform=target_platform,
-            session_id=self._runtime.session_id,
-            group_info=self._runtime._build_group_info(target_message),
-            user_info=bot_user_info,
-        )
-        history_message.raw_message = MessageSequence(
-            [TextComponent(f"{self._build_planner_user_prefix(history_message)}{combined_reply_text}")]
+        history_message = SessionBackedMessage(
+            raw_message=MessageSequence([TextComponent(f"{planner_prefix}{combined_reply_text}")]),
+            visible_text="",
+            timestamp=reply_timestamp,
+            source_kind="guided_reply",
         )
         visible_reply_text = format_speaker_content(
             bot_name,
             combined_reply_text,
-            history_message.timestamp,
-            history_message.message_id,
+            reply_timestamp,
         )
-        history_message.display_message = visible_reply_text
-        history_message.processed_plain_text = visible_reply_text
+        history_message.visible_text = visible_reply_text
         self._runtime._chat_history.append(history_message)
         return True
 
@@ -871,14 +755,10 @@ class MaisakaReasoningEngine:
                 self._build_tool_message(tool_call, "Failed to send emoji.")
             )
 
-    def _build_tool_message(self, tool_call: ToolCall, content: str) -> SessionMessage:
-        return build_message(
-            role="tool",
+    def _build_tool_message(self, tool_call: ToolCall, content: str) -> ToolResultMessage:
+        return ToolResultMessage(
             content=content,
-            source="tool",
+            timestamp=datetime.now(),
             tool_call_id=tool_call.call_id,
-            platform=self._runtime.chat_stream.platform,
-            session_id=self._runtime.session_id,
-            group_info=self._runtime._build_group_info(),
-            user_info=UserInfo(user_id="maisaka_tool", user_nickname="tool", user_cardname=None),
+            tool_name=tool_call.func_name,
         )

src/maisaka/runtime.py

@@ -19,6 +19,7 @@ from src.learners.jargon_miner import JargonMiner
 from src.mcp_module import MCPManager
 
 from .chat_loop_service import MaisakaChatLoopService
+from .context_messages import LLMContextMessage
 from .reasoning_engine import MaisakaReasoningEngine
 
 logger = get_logger("maisaka_runtime")
@@ -40,7 +41,7 @@ class MaisakaHeartFlowChatting:
         session_name = chat_manager.get_session_name(session_id) or session_id
         self.log_prefix = f"[{session_name}]"
         self._chat_loop_service = MaisakaChatLoopService()
-        self._chat_history: list[SessionMessage] = []
+        self._chat_history: list[LLMContextMessage] = []
         self.history_loop: list[CycleDetail] = []
 
         # Keep all original messages for batching and later learning.
@@ -60,6 +61,8 @@ class MaisakaHeartFlowChatting:
         self._max_context_size = max(1, int(global_config.chat.max_context_size))
         self._agent_state: Literal["running", "wait", "stop"] = self._STATE_STOP
         self._wait_until: Optional[float] = None
+        self._pending_wait_tool_call_id: Optional[str] = None
+        self._planner_interrupt_flag: Optional[asyncio.Event] = None
 
         expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(session_id)
         self._enable_expression_use = expr_use
@@ -78,14 +81,14 @@ class MaisakaHeartFlowChatting:
     async def start(self) -> None:
         """Start the runtime loop."""
         if self._running:
+            self._ensure_background_tasks_running()
             return
 
         if global_config.maisaka.enable_mcp:
             await self._init_mcp()
 
         self._running = True
-        self._internal_loop_task = asyncio.create_task(self._reasoning_engine.run_loop())
-        self._loop_task = asyncio.create_task(self._main_loop())
+        self._ensure_background_tasks_running()
         logger.info(f"{self.log_prefix} Maisaka runtime started")
 
     async def stop(self) -> None:
@@ -128,12 +131,48 @@ class MaisakaHeartFlowChatting:
 
     async def register_message(self, message: SessionMessage) -> None:
         """Cache a new message and wake the main loop."""
+        if self._running:
+            self._ensure_background_tasks_running()
         self.message_cache.append(message)
         self._source_messages_by_id[message.message_id] = message
+        if self._agent_state == self._STATE_RUNNING and self._planner_interrupt_flag is not None:
+            logger.info(
+                f"{self.log_prefix} 收到新消息，发起 planner 打断; "
+                f"msg_id={message.message_id} cache_size={len(self.message_cache)} "
+                f"timestamp={time.time():.3f}"
+            )
+            self._planner_interrupt_flag.set()
         if self._agent_state in (self._STATE_WAIT, self._STATE_STOP):
             self._agent_state = self._STATE_RUNNING
         self._new_message_event.set()
 
+    def _ensure_background_tasks_running(self) -> None:
+        """确保后台任务仍在运行，若崩溃则自动拉起。"""
+        if not self._running:
+            return
+
+        if self._internal_loop_task is None or self._internal_loop_task.done():
+            if self._internal_loop_task is not None and not self._internal_loop_task.cancelled():
+                try:
+                    exc = self._internal_loop_task.exception()
+                except Exception:
+                    exc = None
+                if exc is not None:
+                    logger.error(f"{self.log_prefix} internal loop task exited unexpectedly: {exc}")
+            self._internal_loop_task = asyncio.create_task(self._reasoning_engine.run_loop())
+            logger.warning(f"{self.log_prefix} restarted Maisaka internal loop task")
+
+        if self._loop_task is None or self._loop_task.done():
+            if self._loop_task is not None and not self._loop_task.cancelled():
+                try:
+                    exc = self._loop_task.exception()
+                except Exception:
+                    exc = None
+                if exc is not None:
+                    logger.error(f"{self.log_prefix} main loop task exited unexpectedly: {exc}")
+            self._loop_task = asyncio.create_task(self._main_loop())
+            logger.warning(f"{self.log_prefix} restarted Maisaka main loop task")
+
     async def _main_loop(self) -> None:
         try:
             while self._running:
@@ -222,15 +261,17 @@ class MaisakaHeartFlowChatting:
             self._wait_until = None
             return "timeout"
 
-    def _enter_wait_state(self, seconds: Optional[float] = None) -> None:
+    def _enter_wait_state(self, seconds: Optional[float] = None, tool_call_id: Optional[str] = None) -> None:
         """Enter wait state."""
         self._agent_state = self._STATE_WAIT
         self._wait_until = None if seconds is None else time.time() + seconds
+        self._pending_wait_tool_call_id = tool_call_id
 
     def _enter_stop_state(self) -> None:
         """Enter stop state."""
         self._agent_state = self._STATE_STOP
         self._wait_until = None
+        self._pending_wait_tool_call_id = None
 
     async def _trigger_batch_learning(self, messages: list[SessionMessage]) -> None:
         """按同一批消息触发表达方式、黑话和 knowledge 学习。"""

src/maisaka/tool_handlers.py

@@ -9,12 +9,11 @@ import json as _json
 
 from rich.panel import Panel
 
-from src.chat.message_receive.message import SessionMessage
 from src.cli.console import console
 from src.cli.input_reader import InputReader
 from src.llm_models.payload_content.tool_option import ToolCall
 
-from .message_adapter import build_message
+from .context_messages import LLMContextMessage, ToolResultMessage
 
 if TYPE_CHECKING:
     from src.mcp_module import MCPManager
@@ -33,22 +32,34 @@ class ToolHandlerContext:
         self.last_user_input_time: Optional[datetime] = None
 
 
-async def handle_stop(tc: ToolCall, chat_history: list[SessionMessage]) -> None:
+async def handle_stop(tc: ToolCall, chat_history: list[LLMContextMessage]) -> None:
     """处理 stop 工具。"""
     console.print("[accent]调用工具: stop()[/accent]")
     chat_history.append(
-        build_message(role="tool", content="当前轮次结束后将停止对话循环。", tool_call_id=tc.call_id)
+        ToolResultMessage(
+            content="当前轮次结束后将停止对话循环。",
+            timestamp=datetime.now(),
+            tool_call_id=tc.call_id,
+            tool_name=tc.func_name,
+        )
     )
 
 
-async def handle_wait(tc: ToolCall, chat_history: list[SessionMessage], ctx: ToolHandlerContext) -> str:
+async def handle_wait(tc: ToolCall, chat_history: list[LLMContextMessage], ctx: ToolHandlerContext) -> str:
     """处理 wait 工具。"""
     seconds = (tc.args or {}).get("seconds", 30)
     seconds = max(5, min(seconds, 300))
     console.print(f"[accent]调用工具: wait({seconds})[/accent]")
 
     tool_result = await _do_wait(seconds, ctx)
-    chat_history.append(build_message(role="tool", content=tool_result, tool_call_id=tc.call_id))
+    chat_history.append(
+        ToolResultMessage(
+            content=tool_result,
+            timestamp=datetime.now(),
+            tool_call_id=tc.call_id,
+            tool_name=tc.func_name,
+        )
+    )
     return tool_result
 
 
@@ -78,7 +89,7 @@ async def _do_wait(seconds: int, ctx: ToolHandlerContext) -> str:
     return f"已收到用户输入: {user_input}"
 
 
-async def handle_mcp_tool(tc: ToolCall, chat_history: list[SessionMessage], mcp_manager: "MCPManager") -> None:
+async def handle_mcp_tool(tc: ToolCall, chat_history: list[LLMContextMessage], mcp_manager: "MCPManager") -> None:
     """处理 MCP 工具调用。"""
     args_str = _json.dumps(tc.args or {}, ensure_ascii=False)
     args_preview = args_str if len(args_str) <= 120 else args_str[:120] + "..."
@@ -96,10 +107,24 @@ async def handle_mcp_tool(tc: ToolCall, chat_history: list[SessionMessage], mcp_
             padding=(0, 1),
         )
     )
-    chat_history.append(build_message(role="tool", content=result, tool_call_id=tc.call_id))
+    chat_history.append(
+        ToolResultMessage(
+            content=result,
+            timestamp=datetime.now(),
+            tool_call_id=tc.call_id,
+            tool_name=tc.func_name,
+        )
+    )
 
 
-async def handle_unknown_tool(tc: ToolCall, chat_history: list[SessionMessage]) -> None:
+async def handle_unknown_tool(tc: ToolCall, chat_history: list[LLMContextMessage]) -> None:
     """处理未知工具调用。"""
     console.print(f"[accent]调用未知工具: {tc.func_name}({tc.args})[/accent]")
-    chat_history.append(build_message(role="tool", content=f"未知工具: {tc.func_name}", tool_call_id=tc.call_id))
+    chat_history.append(
+        ToolResultMessage(
+            content=f"未知工具: {tc.func_name}",
+            timestamp=datetime.now(),
+            tool_call_id=tc.call_id,
+            tool_name=tc.func_name,
+        )
+    )