Merge branch 'MaiM-with-u:dev' into dev

docs/plugins/quick-start.md

@@ -67,7 +67,7 @@ class HelloWorldPlugin(BasePlugin):
 
 - 首先，我们在plugin.py中定义了一个HelloWorldPulgin插件类，继承自 `BasePlugin` ，提供基本功能。
 - 通过给类加上，`@register_plugin` 装饰器，我们告诉系统"这是一个插件"
-- `plugin_name` 等是插件的基本信息，必须填写
+- `plugin_name` 等是插件的基本信息，必须填写，**此部分必须与目录名称相同，否则插件无法使用**
 - `get_plugin_components()` 返回插件的功能组件，现在我们没有定义任何action（动作）或者command(指令)，是空的
 
 ### 3. 测试基础插件

src/chat/focus_chat/planners/modify_actions.py

@@ -58,6 +58,8 @@ class ActionModifier:
         logger.debug(f"{self.log_prefix}开始完整动作修改流程")
 
         # === 第一阶段：传统观察处理 ===
+        chat_content = None
+
         if observations:
             hfc_obs = None
             chat_obs = None
@@ -78,7 +80,7 @@ class ActionModifier:
             if hfc_obs:
                 obs = hfc_obs
                 # 获取适用于FOCUS模式的动作
-                all_actions = self.action_manager.get_using_actions_for_mode("focus")
+                all_actions = self.all_actions
                 action_changes = await self.analyze_loop_actions(obs)
                 if action_changes["add"] or action_changes["remove"]:
                     # 合并动作变更
@@ -94,9 +96,8 @@ class ActionModifier:
 
             # 处理ChattingObservation - 传统的类型匹配检查
             if chat_obs:
-                obs = chat_obs
                 # 检查动作的关联类型
-                chat_context = get_chat_manager().get_stream(obs.chat_id).context
+                chat_context = get_chat_manager().get_stream(chat_obs.chat_id).context
                 type_mismatched_actions = []
 
                 for action_name in all_actions.keys():
@@ -128,26 +129,13 @@ class ActionModifier:
                 f"{self.log_prefix}传统动作修改完成，当前使用动作: {list(self.action_manager.get_using_actions().keys())}"
             )
 
-        # === chat_mode检查：强制移除非auto模式下的exit_focus_chat ===
+        # 注释：已移除exit_focus_chat动作，现在由no_reply动作处理频率检测退出专注模式
-        if global_config.chat.chat_mode != "auto":
-            if "exit_focus_chat" in self.action_manager.get_using_actions():
-                self.action_manager.remove_action_from_using("exit_focus_chat")
-                logger.info(
-                    f"{self.log_prefix}移除动作: exit_focus_chat，原因: chat_mode不为auto（当前模式: {global_config.chat.chat_mode}）"
-                )
 
         # === 第二阶段：激活类型判定 ===
         # 如果提供了聊天上下文，则进行激活类型判定
         if chat_content is not None:
             logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
 
-            # 保存exit_focus_chat动作（如果存在）
-            exit_focus_action = None
-            if "exit_focus_chat" in self.action_manager.get_using_actions():
-                exit_focus_action = self.action_manager.get_using_actions()["exit_focus_chat"]
-                self.action_manager.remove_action_from_using("exit_focus_chat")
-                logger.debug(f"{self.log_prefix}临时移除exit_focus_chat动作以进行激活类型判定")
-
             # 获取当前使用的动作集（经过第一阶段处理，且适用于FOCUS模式）
             current_using_actions = self.action_manager.get_using_actions()
             all_registered_actions = self.action_manager.get_registered_actions()
@@ -197,16 +185,7 @@ class ActionModifier:
                 reason = removal_reasons.get(action_name, "未知原因")
                 logger.info(f"{self.log_prefix}移除动作: {action_name}，原因: {reason}")
 
-            # 恢复exit_focus_chat动作（如果之前存在）
+            # 注释：已完全移除exit_focus_chat动作
-            if exit_focus_action:
-                # 只有在auto模式下才恢复exit_focus_chat动作
-                if global_config.chat.chat_mode == "auto":
-                    self.action_manager.add_action_to_using("exit_focus_chat")
-                    logger.debug(f"{self.log_prefix}恢复exit_focus_chat动作")
-                else:
-                    logger.debug(
-                        f"{self.log_prefix}跳过恢复exit_focus_chat动作，原因: chat_mode不为auto（当前模式: {global_config.chat.chat_mode}）"
-                    )
 
             logger.info(f"{self.log_prefix}激活类型判定完成，最终可用动作: {list(final_activated_actions.keys())}")
 
@@ -576,30 +555,13 @@ class ActionModifier:
         if not recent_cycles:
             return result
 
-        # 统计no_reply的数量
-        no_reply_count = 0
         reply_sequence = []  # 记录最近的动作序列
 
         for cycle in recent_cycles:
             action_result = cycle.loop_plan_info.get("action_result", {})
             action_type = action_result.get("action_type", "unknown")
-            if action_type == "no_reply":
-                no_reply_count += 1
             reply_sequence.append(action_type == "reply")
 
-        # 检查no_reply比例
-        if len(recent_cycles) >= (4 * global_config.chat.exit_focus_threshold) and (
-            no_reply_count / len(recent_cycles)
-        ) >= (0.7 * global_config.chat.exit_focus_threshold):
-            if global_config.chat.chat_mode == "auto":
-                result["add"].append("exit_focus_chat")
-                result["remove"].append("no_reply")
-                result["remove"].append("reply")
-                no_reply_ratio = no_reply_count / len(recent_cycles)
-                logger.info(
-                    f"{self.log_prefix}检测到高no_reply比例: {no_reply_ratio:.2f}，达到退出聊天阈值，将添加exit_focus_chat并移除no_reply/reply动作"
-                )
-
         # 计算连续回复的相关阈值
 
         max_reply_num = int(global_config.focus_chat.consecutive_replies * 3.2)
@@ -613,7 +575,7 @@ class ActionModifier:
             last_max_reply_num = reply_sequence[:]
 
         # 详细打印阈值和序列信息，便于调试
-        logger.debug(
+        logger.info(
             f"连续回复阈值: max={max_reply_num}, sec={sec_thres_reply_num}, one={one_thres_reply_num}，"
             f"最近reply序列: {last_max_reply_num}"
         )

src/chat/message_receive/message_sender.py

@@ -8,7 +8,7 @@ from src.common.message.api import get_global_api
 # from ...common.database import db # 数据库依赖似乎不需要了，注释掉
 from .message import MessageSending, MessageThinking, MessageSet
 
-from .storage import MessageStorage
+from src.chat.message_receive.storage import MessageStorage
 from ...config.config import global_config
 from ..utils.utils import truncate_message, calculate_typing_time, count_messages_between
 

src/chat/message_receive/storage.py

@@ -18,7 +18,12 @@ class MessageStorage:
             # 莫越权 救世啊
             pattern = r"<MainRule>.*?</MainRule>|<schedule>.*?</schedule>|<UserMessage>.*?</UserMessage>"
 
+            # print(message)
+
             processed_plain_text = message.processed_plain_text
+
+            # print(processed_plain_text)
+
             if processed_plain_text:
                 filtered_processed_plain_text = re.sub(pattern, "", processed_plain_text, flags=re.DOTALL)
             else:

src/chat/normal_chat/normal_chat.py

@@ -563,21 +563,21 @@ class NormalChat:
                                     self.interest_dict.pop(msg_id, None)
 
                             # 创建并行任务列表
-                            tasks = []
+                            coroutines = []
                             for msg_id, (message, interest_value, is_mentioned) in items_to_process:
-                                task = process_single_message(msg_id, message, interest_value, is_mentioned)
+                                coroutine = process_single_message(msg_id, message, interest_value, is_mentioned)
-                                tasks.append(task)
+                                coroutines.append(coroutine)
 
                             # 并行执行所有任务，限制并发数量避免资源过度消耗
-                            if tasks:
+                            if coroutines:
                                 # 使用信号量控制并发数，最多同时处理5个消息
                                 semaphore = asyncio.Semaphore(5)
 
-                                async def limited_process(task, sem):
+                                async def limited_process(coroutine, sem):
                                     async with sem:
-                                        await task
+                                        await coroutine
 
-                                limited_tasks = [limited_process(task, semaphore) for task in tasks]
+                                limited_tasks = [limited_process(coroutine, semaphore) for coroutine in coroutines]
                                 await asyncio.gather(*limited_tasks, return_exceptions=True)
 
                     except asyncio.CancelledError:

src/person_info/relationship_manager.py

@@ -423,8 +423,7 @@ class RelationshipManager:
 
 请根据你对ta过去的了解，和ta最近的行为，修改，整合，原有的了解，总结出对用户 {person_name}(昵称:{nickname})新的了解。
 
-了解可以包含性格，关系，感受，态度，你推测的ta的性别，年龄，外貌，身份，习惯，爱好，重要事件，重要经历等等内容。也可以包含其他点。
+了解请包含性格，对你的态度，你推测的ta的年龄，身份，习惯，爱好，重要事件和其他重要属性这几方面内容。
-关注友好和不友好的因素，不要忽略。
 请严格按照以下给出的信息，不要新增额外内容。
 
 你之前对他的了解是：
@@ -467,23 +466,23 @@ class RelationshipManager:
 
                 relation_value_prompt = f"""
 你的名字是{global_config.bot.nickname}。
-你对{person_name}的了解如下：
+你最近对{person_name}的了解如下：
-{compressed_summary}
+{points_text}
 
 请根据以上信息，评估你和{person_name}的关系，给出两个维度的值：熟悉度和好感度。
-1.  **熟悉度 (familiarity_value)**: 0-100的整数，表示你对ta的熟悉程度。
+1.  了解度 (familiarity_value): 0-100的整数，表示这些信息让你对ta的了解增进程度。
-    - 0: 完全陌生
+    - 0: 没有任何进一步了解
-    - 25: 有点眼熟
+    - 25: 有点进一步了解
-    - 50: 比较熟悉
+    - 50: 有进一步了解
-    - 75: 很熟悉
+    - 75: 有更多了解
-    - 100: 非常熟悉，了如指掌
+    - 100: 有了更多重要的了解
 
-2.  **好感度 (liking_value)**: 0-100的整数，表示你对ta的喜好程度。
+2.  **好感度 (liking_value)**: 0-100的整数，表示这些信息让你对ta的喜。
     - 0: 非常厌恶
     - 25: 有点反感
     - 50: 中立/无感
     - 75: 有点喜欢
-    - 100: 非常喜欢/挚友
+    - 100: 非常喜欢/开心对这个人
 
 请严格按照json格式输出，不要有其他多余内容：
 {{
@@ -501,19 +500,20 @@ class RelationshipManager:
                     new_familiarity_value = int(relation_value_json.get("familiarity_value", 0))
                     new_liking_value = int(relation_value_json.get("liking_value", 50))
 
-                    # 获取数据库中的旧值，如果不存在则使用默认值
+                    if new_familiarity_value > 25:
-                    old_familiarity_value = await person_info_manager.get_value(person_id, "familiarity_value") or 0
+                        old_familiarity_value = await person_info_manager.get_value(person_id, "familiarity_value") or 0
-                    old_liking_value = await person_info_manager.get_value(person_id, "liking_value") or 50
+                        old_familiarity_value += new_familiarity_value - 25 / 75
 
-                    # 计算平均值
+                    if new_liking_value > 50:
-                    final_familiarity_value = (old_familiarity_value + new_familiarity_value) // 2
+                        liking_value = await person_info_manager.get_value(person_id, "liking_value") or 50
-                    final_liking_value = (old_liking_value + new_liking_value) // 2
+                        liking_value += new_liking_value - 50 / 50
+                    if new_liking_value < 50:
+                        liking_value = await person_info_manager.get_value(person_id, "liking_value") or 50
+                        liking_value -= (50 - new_liking_value / 50) * 1.5
 
-                    await person_info_manager.update_one_field(person_id, "familiarity_value", final_familiarity_value)
+                    await person_info_manager.update_one_field(person_id, "familiarity_value", liking_value)
-                    await person_info_manager.update_one_field(person_id, "liking_value", final_liking_value)
+                    await person_info_manager.update_one_field(person_id, "liking_value", liking_value)
-                    logger.info(
+                    logger.info(f"更新了与 {person_name} 的关系值: 熟悉度={liking_value}, 好感度={liking_value}")
-                        f"更新了与 {person_name} 的关系值: 熟悉度={final_familiarity_value}, 好感度={final_liking_value}"
-                    )
                 except (json.JSONDecodeError, ValueError, TypeError) as e:
                     logger.error(f"解析relation_value JSON失败或值无效: {e}, 响应: {relation_value_response}")
 

src/plugins/built_in/core_actions/_manifest.json

@@ -39,16 +39,6 @@
         "type": "action",
         "name": "emoji", 
         "description": "发送表情包辅助表达情绪"
-      },
-      {
-        "type": "action",
-        "name": "change_to_focus_chat",
-        "description": "切换到专注聊天，从普通模式切换到专注模式"
-      },
-      {
-        "type": "action",
-        "name": "exit_focus_chat", 
-        "description": "退出专注聊天，从专注模式切换到普通模式"
       }
     ]
   }

src/plugins/built_in/core_actions/no_reply.py

@@ -0,0 +1,569 @@
+import random
+import time
+import json
+from typing import Tuple
+
+# 导入新插件系统
+from src.plugin_system import BaseAction, ActionActivationType, ChatMode
+
+# 导入依赖的系统组件
+from src.common.logger import get_logger
+
+# 导入API模块 - 标准Python包方式
+from src.plugin_system.apis import message_api, llm_api
+from src.config.config import global_config
+from json_repair import repair_json
+
+logger = get_logger("core_actions")
+
+
+class NoReplyAction(BaseAction):
+    """不回复动作，使用智能判断机制决定何时结束等待
+
+    新的等待逻辑：
+    - 每0.2秒检查是否有新消息（提高响应性）
+    - 如果累计消息数量达到阈值（默认20条），直接结束等待
+    - 有新消息时进行LLM判断，但最快1秒一次（防止过于频繁）
+    - 如果判断需要回复，则结束等待；否则继续等待
+    - 达到最大超时时间后强制结束
+    """
+
+    focus_activation_type = ActionActivationType.ALWAYS
+    # focus_activation_type = ActionActivationType.RANDOM
+    normal_activation_type = ActionActivationType.NEVER
+    mode_enable = ChatMode.FOCUS
+    parallel_action = False
+
+    # 动作基本信息
+    action_name = "no_reply"
+    action_description = "暂时不回复消息"
+
+    # 连续no_reply计数器
+    _consecutive_count = 0
+
+    # LLM判断的最小间隔时间
+    _min_judge_interval = 1.0  # 最快1秒一次LLM判断
+
+    # 自动结束的消息数量阈值
+    _auto_exit_message_count = 20  # 累计20条消息自动结束
+
+    # 最大等待超时时间
+    _max_timeout = 600  # 1200秒
+
+    # 跳过LLM判断的配置
+    _skip_judge_when_tired = True
+    _skip_probability = 0.5
+
+    # 新增：回复频率退出专注模式的配置
+    _frequency_check_window = 600  # 频率检查窗口时间（秒）
+
+    # 动作参数定义
+    action_parameters = {"reason": "不回复的原因"}
+
+    # 动作使用场景
+    action_require = ["你发送了消息，目前无人回复"]
+
+    # 关联类型
+    associated_types = []
+
+    async def execute(self) -> Tuple[bool, str]:
+        """执行不回复动作，有新消息时进行判断，但最快1秒一次"""
+        import asyncio
+
+        try:
+            # 增加连续计数
+            NoReplyAction._consecutive_count += 1
+            count = NoReplyAction._consecutive_count
+
+            reason = self.action_data.get("reason", "")
+            start_time = time.time()
+            last_judge_time = 0  # 上次进行LLM判断的时间
+            min_judge_interval = self._min_judge_interval  # 最小判断间隔，从配置获取
+            check_interval = 0.2  # 检查新消息的间隔，设为0.2秒提高响应性
+
+            # 累积判断历史
+            judge_history = []  # 存储每次判断的结果和理由
+
+            # 获取no_reply开始时的上下文消息（10条），用于后续记录
+            context_messages = message_api.get_messages_by_time_in_chat(
+                chat_id=self.chat_id,
+                start_time=start_time - 600,  # 获取开始前10分钟内的消息
+                end_time=start_time,
+                limit=10,
+                limit_mode="latest",
+            )
+
+            # 构建上下文字符串
+            context_str = ""
+            if context_messages:
+                context_str = message_api.build_readable_messages(
+                    messages=context_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=True
+                )
+                context_str = f"当时选择no_reply前的聊天上下文：\n{context_str}\n"
+
+            logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始智能等待，原因: {reason}")
+
+            while True:
+                current_time = time.time()
+                elapsed_time = current_time - start_time
+
+                # 检查是否超时
+                if elapsed_time >= self._max_timeout:
+                    logger.info(f"{self.log_prefix} 达到最大等待时间{self._max_timeout}秒，退出专注模式")
+                    # 标记退出专注模式
+                    self.action_data["_system_command"] = "stop_focus_chat"
+                    exit_reason = f"{global_config.bot.nickname}（你）等待了{self._max_timeout}秒，感觉群里没有新内容，决定退出专注模式，稍作休息"
+                    await self.store_action_info(
+                        action_build_into_prompt=True,
+                        action_prompt_display=exit_reason,
+                        action_done=True,
+                    )
+                    return True, exit_reason
+
+                # **新增**：检查回复频率，决定是否退出专注模式
+                should_exit_focus = await self._check_frequency_and_exit_focus(current_time)
+                if should_exit_focus:
+                    logger.info(f"{self.log_prefix} 检测到回复频率过高，退出专注模式")
+                    # 标记退出专注模式
+                    self.action_data["_system_command"] = "stop_focus_chat"
+                    exit_reason = f"{global_config.bot.nickname}（你）发现自己回复太频繁了，决定退出专注模式，稍作休息"
+                    await self.store_action_info(
+                        action_build_into_prompt=True,
+                        action_prompt_display=exit_reason,
+                        action_done=True,
+                    )
+                    return True, exit_reason
+
+                # **新增**：检查过去10分钟是否完全没有发言，如果是则退出专注模式
+                should_exit_no_activity = await self._check_no_activity_and_exit_focus(current_time)
+                if should_exit_no_activity:
+                    logger.info(f"{self.log_prefix} 检测到过去10分钟完全没有发言，退出专注模式")
+                    # 标记退出专注模式
+                    self.action_data["_system_command"] = "stop_focus_chat"
+                    exit_reason = f"{global_config.bot.nickname}（你）发现自己过去10分钟完全没有说话，感觉可能不太活跃，决定退出专注模式"
+                    await self.store_action_info(
+                        action_build_into_prompt=True,
+                        action_prompt_display=exit_reason,
+                        action_done=True,
+                    )
+                    return True, exit_reason
+
+                # 检查是否有新消息
+                new_message_count = message_api.count_new_messages(
+                    chat_id=self.chat_id, start_time=start_time, end_time=current_time
+                )
+
+                # 如果累计消息数量达到阈值，直接结束等待
+                if new_message_count >= self._auto_exit_message_count:
+                    logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
+                    exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
+                    await self.store_action_info(
+                        action_build_into_prompt=True,
+                        action_prompt_display=exit_reason,
+                        action_done=True,
+                    )
+                    return True, f"累计消息数量达到{new_message_count}条，直接结束等待 (等待时间: {elapsed_time:.1f}秒)"
+
+                # 判定条件：累计3条消息或等待超过5秒且有新消息
+                time_since_last_judge = current_time - last_judge_time
+                should_judge = (
+                    new_message_count >= 3  # 累计3条消息
+                    or (new_message_count > 0 and time_since_last_judge >= 5.0)  # 等待超过5秒且有新消息
+                )
+
+                if should_judge and time_since_last_judge >= min_judge_interval:
+                    # 判断触发原因
+                    trigger_reason = ""
+                    if new_message_count >= 3:
+                        trigger_reason = f"累计{new_message_count}条消息"
+                    elif time_since_last_judge >= 5.0:
+                        trigger_reason = f"等待{time_since_last_judge:.1f}秒且有{new_message_count}条新消息"
+
+                    logger.info(f"{self.log_prefix} 触发判定({trigger_reason})，进行智能判断...")
+
+                    # 获取最近的消息内容用于判断
+                    recent_messages = message_api.get_messages_by_time_in_chat(
+                        chat_id=self.chat_id,
+                        start_time=start_time,
+                        end_time=current_time,
+                    )
+
+                    if recent_messages:
+                        # 使用message_api构建可读的消息字符串
+                        messages_text = message_api.build_readable_messages(
+                            messages=recent_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
+                        )
+
+                        # 获取身份信息
+                        bot_name = global_config.bot.nickname
+                        bot_nickname = ""
+                        if global_config.bot.alias_names:
+                            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
+                        bot_core_personality = global_config.personality.personality_core
+                        identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}"
+
+                        # 构建判断历史字符串（最多显示3条）
+                        history_block = ""
+                        if judge_history:
+                            history_block = "之前的判断历史：\n"
+                            # 只取最近的3条历史记录
+                            recent_history = judge_history[-3:] if len(judge_history) > 3 else judge_history
+                            for i, (timestamp, judge_result, reason) in enumerate(recent_history, 1):
+                                elapsed_seconds = int(timestamp - start_time)
+                                history_block += f"{i}. 等待{elapsed_seconds}秒时判断：{judge_result}，理由：{reason}\n"
+                            history_block += "\n"
+
+                        # 检查过去10分钟的发言频率
+                        frequency_block = ""
+                        should_skip_llm_judge = False  # 是否跳过LLM判断
+
+                        try:
+                            # 获取过去10分钟的所有消息
+                            past_10min_time = current_time - 600  # 10分钟前
+                            all_messages_10min = message_api.get_messages_by_time_in_chat(
+                                chat_id=self.chat_id,
+                                start_time=past_10min_time,
+                                end_time=current_time,
+                            )
+
+                            # 手动过滤bot自己的消息
+                            bot_message_count = 0
+                            if all_messages_10min:
+                                user_id = global_config.bot.qq_account
+
+                                for message in all_messages_10min:
+                                    # 检查消息发送者是否是bot
+                                    sender_id = message.get("user_id", "")
+
+                                    if sender_id == user_id:
+                                        bot_message_count += 1
+
+                            talk_frequency_threshold = global_config.chat.talk_frequency * 10
+
+                            if bot_message_count > talk_frequency_threshold:
+                                over_count = bot_message_count - talk_frequency_threshold
+
+                                # 根据超过的数量设置不同的提示词和跳过概率
+                                skip_probability = 0
+                                if over_count <= 3:
+                                    frequency_block = "你感觉稍微有些累，回复的有点多了。\n"
+                                elif over_count <= 5:
+                                    frequency_block = "你今天说话比较多，感觉有点疲惫，想要稍微休息一下。\n"
+                                else:
+                                    frequency_block = "你发现自己说话太多了，感觉很累，想要安静一会儿，除非有重要的事情否则不想回复。\n"
+                                    skip_probability = self._skip_probability
+
+                                # 根据配置和概率决定是否跳过LLM判断
+                                if self._skip_judge_when_tired and random.random() < skip_probability:
+                                    should_skip_llm_judge = True
+                                    logger.info(
+                                        f"{self.log_prefix} 发言过多(超过{over_count}条)，随机决定跳过此次LLM判断(概率{skip_probability * 100:.0f}%)"
+                                    )
+
+                                logger.info(
+                                    f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，超过阈值{talk_frequency_threshold}，添加疲惫提示"
+                                )
+                            else:
+                                # 回复次数少时的正向提示
+                                under_count = talk_frequency_threshold - bot_message_count
+
+                                if under_count >= talk_frequency_threshold * 0.8:  # 回复很少（少于20%）
+                                    frequency_block = "你感觉精力充沛，状态很好。\n"
+                                elif under_count >= talk_frequency_threshold * 0.5:  # 回复较少（少于50%）
+                                    frequency_block = "你感觉状态不错。\n"
+                                else:  # 刚好达到阈值
+                                    frequency_block = ""
+
+                                logger.info(
+                                    f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，未超过阈值{talk_frequency_threshold}，添加正向提示"
+                                )
+
+                        except Exception as e:
+                            logger.warning(f"{self.log_prefix} 检查发言频率时出错: {e}")
+                            frequency_block = ""
+
+                        # 如果决定跳过LLM判断，直接更新时间并继续等待
+                        if should_skip_llm_judge:
+                            last_judge_time = time.time()  # 更新判断时间，避免立即重新判断
+                            continue  # 跳过本次LLM判断，继续循环等待
+
+                        # 构建判断上下文
+                        judge_prompt = f"""
+{identity_block}
+
+你现在正在QQ群参与聊天，以下是聊天内容：
+{context_str}
+在以上的聊天中，你选择了暂时不回复，现在，你看到了新的聊天消息如下：
+{messages_text}
+
+{history_block}
+请注意：{frequency_block}
+请你判断，是否要结束不回复的状态，重新加入聊天讨论。
+
+判断标准：
+1. 如果有人直接@你、提到你的名字或明确向你询问，应该回复
+2. 如果话题发生重要变化，需要你参与讨论，应该回复
+3. 如果只是普通闲聊、重复内容或与你无关的讨论，不需要回复
+4. 如果消息内容过于简单（如单纯的表情、"哈哈"等），不需要回复
+5. 参考之前的判断历史，如果情况有明显变化或持续等待时间过长，考虑调整判断
+
+请用JSON格式回复你的判断，严格按照以下格式：
+{{
+    "should_reply": true/false,
+    "reason": "详细说明你的判断理由"
+}}
+"""
+
+                        try:
+                            # 获取可用的模型配置
+                            available_models = llm_api.get_available_models()
+
+                            # 使用 utils_small 模型
+                            small_model = getattr(available_models, "utils_small", None)
+
+                            print(judge_prompt)
+
+                            if small_model:
+                                # 使用小模型进行判断
+                                success, response, reasoning, model_name = await llm_api.generate_with_model(
+                                    prompt=judge_prompt,
+                                    model_config=small_model,
+                                    request_type="plugin.no_reply_judge",
+                                    temperature=0.7,  # 进一步降低温度，提高JSON输出的一致性和准确性
+                                )
+
+                                # 更新上次判断时间
+                                last_judge_time = time.time()
+
+                                if success and response:
+                                    response = response.strip()
+                                    logger.info(f"{self.log_prefix} 模型({model_name})原始JSON响应: {response}")
+
+                                    # 解析LLM的JSON响应，提取判断结果和理由
+                                    judge_result, reason = self._parse_llm_judge_response(response)
+
+                                    logger.info(
+                                        f"{self.log_prefix} JSON解析结果 - 判断: {judge_result}, 理由: {reason}"
+                                    )
+
+                                    # 将判断结果保存到历史中
+                                    judge_history.append((current_time, judge_result, reason))
+
+                                    if judge_result == "需要回复":
+                                        logger.info(f"{self.log_prefix} 模型判断需要回复，结束等待")
+
+                                        full_prompt = f"{global_config.bot.nickname}（你）的想法是：{reason}"
+                                        await self.store_action_info(
+                                            action_build_into_prompt=True,
+                                            action_prompt_display=full_prompt,
+                                            action_done=True,
+                                        )
+                                        return True, f"检测到需要回复的消息，结束等待 (等待时间: {elapsed_time:.1f}秒)"
+                                    else:
+                                        logger.info(f"{self.log_prefix} 模型判断不需要回复，理由: {reason}，继续等待")
+                                        # 更新开始时间，避免重复判断同样的消息
+                                        start_time = current_time
+                                else:
+                                    logger.warning(f"{self.log_prefix} 模型判断失败，继续等待")
+                            else:
+                                logger.warning(f"{self.log_prefix} 未找到可用的模型配置，继续等待")
+                                last_judge_time = time.time()  # 即使失败也更新时间，避免频繁重试
+
+                        except Exception as e:
+                            logger.error(f"{self.log_prefix} 模型判断异常: {e}，继续等待")
+                            last_judge_time = time.time()  # 异常时也更新时间，避免频繁重试
+
+                # 每10秒输出一次等待状态
+                if elapsed_time < 60:
+                    if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
+                        logger.info(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，等待新消息...")
+                        await asyncio.sleep(1)
+                else:
+                    if int(elapsed_time) % 60 == 0 and int(elapsed_time) > 0:
+                        logger.info(f"{self.log_prefix} 已等待{elapsed_time / 60:.0f}分钟，等待新消息...")
+                        await asyncio.sleep(1)
+
+                # 短暂等待后继续检查
+                await asyncio.sleep(check_interval)
+
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 不回复动作执行失败: {e}")
+            # 即使执行失败也要记录
+            exit_reason = f"执行异常: {str(e)}"
+            full_prompt = f"{context_str}{exit_reason}，你思考是否要进行回复"
+            await self.store_action_info(
+                action_build_into_prompt=True,
+                action_prompt_display=full_prompt,
+                action_done=True,
+            )
+            return False, f"不回复动作执行失败: {e}"
+
+    async def _check_frequency_and_exit_focus(self, current_time: float) -> bool:
+        """检查回复频率，决定是否退出专注模式
+
+        Args:
+            current_time: 当前时间戳
+
+        Returns:
+            bool: 是否应该退出专注模式
+        """
+        try:
+            # 只在auto模式下进行频率检查
+            if global_config.chat.chat_mode != "auto":
+                return False
+
+            # 获取检查窗口内的所有消息
+            window_start_time = current_time - self._frequency_check_window
+            all_messages = message_api.get_messages_by_time_in_chat(
+                chat_id=self.chat_id,
+                start_time=window_start_time,
+                end_time=current_time,
+            )
+
+            if not all_messages:
+                return False
+
+            # 统计bot自己的回复数量
+            bot_message_count = 0
+            user_id = global_config.bot.qq_account
+
+            for message in all_messages:
+                sender_id = message.get("user_id", "")
+                if sender_id == user_id:
+                    bot_message_count += 1
+
+            # 计算当前回复频率（每分钟回复数）
+            window_minutes = self._frequency_check_window / 60
+            current_frequency = bot_message_count / window_minutes
+
+            # 计算阈值频率：使用 exit_focus_threshold * 1.5
+            threshold_multiplier = global_config.chat.exit_focus_threshold * 1.5
+            threshold_frequency = global_config.chat.talk_frequency * threshold_multiplier
+
+            # 判断是否超过阈值
+            if current_frequency > threshold_frequency:
+                logger.info(
+                    f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)，准备退出专注模式"
+                )
+                return True
+            else:
+                logger.debug(
+                    f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，未超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)"
+                )
+                return False
+
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 检查回复频率时出错: {e}")
+            return False
+
+    async def _check_no_activity_and_exit_focus(self, current_time: float) -> bool:
+        """检查过去10分钟是否完全没有发言，决定是否退出专注模式
+
+        Args:
+            current_time: 当前时间戳
+
+        Returns:
+            bool: 是否应该退出专注模式
+        """
+        try:
+            # 只在auto模式下进行检查
+            if global_config.chat.chat_mode != "auto":
+                return False
+
+            # 获取过去10分钟的所有消息
+            past_10min_time = current_time - 600  # 10分钟前
+            all_messages = message_api.get_messages_by_time_in_chat(
+                chat_id=self.chat_id,
+                start_time=past_10min_time,
+                end_time=current_time,
+            )
+
+            if not all_messages:
+                # 如果完全没有消息，也不需要退出专注模式
+                return False
+
+            # 统计bot自己的回复数量
+            bot_message_count = 0
+            user_id = global_config.bot.qq_account
+
+            for message in all_messages:
+                sender_id = message.get("user_id", "")
+                if sender_id == user_id:
+                    bot_message_count += 1
+
+            # 如果过去10分钟bot一条消息也没有发送，退出专注模式
+            if bot_message_count == 0:
+                logger.info(f"{self.log_prefix} 过去10分钟bot完全没有发言，准备退出专注模式")
+                return True
+            else:
+                logger.debug(f"{self.log_prefix} 过去10分钟bot发言{bot_message_count}条，继续保持专注模式")
+                return False
+
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 检查无活动状态时出错: {e}")
+            return False
+
+    def _parse_llm_judge_response(self, response: str) -> tuple[str, str]:
+        """解析LLM判断响应，使用JSON格式提取判断结果和理由
+
+        Args:
+            response: LLM的原始JSON响应
+
+        Returns:
+            tuple: (判断结果, 理由)
+        """
+        try:
+            # 使用repair_json修复可能有问题的JSON格式
+            fixed_json_string = repair_json(response)
+            logger.debug(f"{self.log_prefix} repair_json修复后的响应: {fixed_json_string}")
+
+            # 如果repair_json返回的是字符串，需要解析为Python对象
+            if isinstance(fixed_json_string, str):
+                result_json = json.loads(fixed_json_string)
+            else:
+                # 如果repair_json直接返回了字典对象，直接使用
+                result_json = fixed_json_string
+
+            # 从JSON中提取判断结果和理由
+            should_reply = result_json.get("should_reply", False)
+            reason = result_json.get("reason", "无法获取判断理由")
+
+            # 转换布尔值为中文字符串
+            judge_result = "需要回复" if should_reply else "不需要回复"
+
+            logger.debug(f"{self.log_prefix} JSON解析成功 - 判断: {judge_result}, 理由: {reason}")
+            return judge_result, reason
+
+        except (json.JSONDecodeError, KeyError, TypeError) as e:
+            logger.warning(f"{self.log_prefix} JSON解析失败，尝试文本解析: {e}")
+
+            # 如果JSON解析失败，回退到简单的关键词匹配
+            try:
+                response_lower = response.lower()
+
+                if "true" in response_lower or "需要回复" in response:
+                    judge_result = "需要回复"
+                    reason = "从响应文本中检测到需要回复的指示"
+                elif "false" in response_lower or "不需要回复" in response:
+                    judge_result = "不需要回复"
+                    reason = "从响应文本中检测到不需要回复的指示"
+                else:
+                    judge_result = "不需要回复"  # 默认值
+                    reason = f"无法解析响应格式，使用默认判断。原始响应: {response[:100]}..."
+
+                logger.debug(f"{self.log_prefix} 文本解析结果 - 判断: {judge_result}, 理由: {reason}")
+                return judge_result, reason
+
+            except Exception as fallback_e:
+                logger.error(f"{self.log_prefix} 文本解析也失败: {fallback_e}")
+                return "不需要回复", f"解析异常: {str(e)}, 回退解析也失败: {str(fallback_e)}"
+
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
+            return "不需要回复", f"解析异常: {str(e)}"
+
+    @classmethod
+    def reset_consecutive_count(cls):
+        """重置连续计数器"""
+        cls._consecutive_count = 0
+        logger.debug("NoReplyAction连续计数器已重置")

src/plugins/built_in/core_actions/plugin.py

@@ -7,7 +7,6 @@
 
 import random
 import time
-import json
 from typing import List, Tuple, Type
 
 # 导入新插件系统
@@ -18,10 +17,8 @@ from src.plugin_system.base.config_types import ConfigField
 from src.common.logger import get_logger
 
 # 导入API模块 - 标准Python包方式
-from src.plugin_system.apis import emoji_api, generator_api, message_api, llm_api
+from src.plugin_system.apis import emoji_api, generator_api, message_api
-from src.config.config import global_config
+from src.plugins.built_in.core_actions.no_reply import NoReplyAction
-from datetime import datetime
-from json_repair import repair_json
 
 logger = get_logger("core_actions")
 
@@ -112,424 +109,6 @@ class ReplyAction(BaseAction):
             return False, f"回复失败: {str(e)}"
 
 
-class NoReplyAction(BaseAction):
-    """不回复动作，使用智能判断机制决定何时结束等待
-
-    新的等待逻辑：
-    - 每0.2秒检查是否有新消息（提高响应性）
-    - 如果累计消息数量达到阈值（默认20条），直接结束等待
-    - 有新消息时进行LLM判断，但最快1秒一次（防止过于频繁）
-    - 如果判断需要回复，则结束等待；否则继续等待
-    - 达到最大超时时间后强制结束
-    """
-
-    focus_activation_type = ActionActivationType.ALWAYS
-    # focus_activation_type = ActionActivationType.RANDOM
-    normal_activation_type = ActionActivationType.NEVER
-    mode_enable = ChatMode.FOCUS
-    parallel_action = False
-
-    # 动作基本信息
-    action_name = "no_reply"
-    action_description = "暂时不回复消息"
-
-    # 连续no_reply计数器
-    _consecutive_count = 0
-
-    # LLM判断的最小间隔时间
-    _min_judge_interval = 1.0  # 最快1秒一次LLM判断
-
-    # 自动结束的消息数量阈值
-    _auto_exit_message_count = 20  # 累计20条消息自动结束
-
-    # 最大等待超时时间
-    _max_timeout = 1200  # 1200秒
-
-    # 跳过LLM判断的配置
-    _skip_judge_when_tired = True
-    _skip_probability_light = 0.2  # 轻度疲惫跳过概率
-    _skip_probability_medium = 0.4  # 中度疲惫跳过概率
-    _skip_probability_heavy = 0.6  # 重度疲惫跳过概率
-
-    # 动作参数定义
-    action_parameters = {"reason": "不回复的原因"}
-
-    # 动作使用场景
-    action_require = ["你发送了消息，目前无人回复"]
-
-    # 关联类型
-    associated_types = []
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行不回复动作，有新消息时进行判断，但最快1秒一次"""
-        import asyncio
-
-        try:
-            # 增加连续计数
-            NoReplyAction._consecutive_count += 1
-            count = NoReplyAction._consecutive_count
-
-            reason = self.action_data.get("reason", "")
-            start_time = time.time()
-            last_judge_time = 0  # 上次进行LLM判断的时间
-            min_judge_interval = self._min_judge_interval  # 最小判断间隔，从配置获取
-            check_interval = 0.2  # 检查新消息的间隔，设为0.2秒提高响应性
-
-            # 累积判断历史
-            judge_history = []  # 存储每次判断的结果和理由
-
-            # 获取no_reply开始时的上下文消息（10条），用于后续记录
-            context_messages = message_api.get_messages_by_time_in_chat(
-                chat_id=self.chat_id,
-                start_time=start_time - 600,  # 获取开始前10分钟内的消息
-                end_time=start_time,
-                limit=10,
-                limit_mode="latest",
-            )
-
-            # 构建上下文字符串
-            context_str = ""
-            if context_messages:
-                context_str = message_api.build_readable_messages(
-                    messages=context_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=True
-                )
-                context_str = f"当时选择no_reply前的聊天上下文：\n{context_str}\n"
-
-            logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始智能等待，原因: {reason}")
-
-            while True:
-                current_time = time.time()
-                elapsed_time = current_time - start_time
-
-                # 检查是否超时
-                if elapsed_time >= self._max_timeout:
-                    logger.info(f"{self.log_prefix} 达到最大等待时间{self._max_timeout}秒，结束等待")
-                    exit_reason = (
-                        f"{global_config.bot.nickname}（你）等待了{self._max_timeout}秒，可以考虑一下是否要进行回复"
-                    )
-                    await self.store_action_info(
-                        action_build_into_prompt=True,
-                        action_prompt_display=exit_reason,
-                        action_done=True,
-                    )
-                    return True, exit_reason
-
-                # 检查是否有新消息
-                new_message_count = message_api.count_new_messages(
-                    chat_id=self.chat_id, start_time=start_time, end_time=current_time
-                )
-
-                # 如果累计消息数量达到阈值，直接结束等待
-                if new_message_count >= self._auto_exit_message_count:
-                    logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
-                    exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
-                    await self.store_action_info(
-                        action_build_into_prompt=True,
-                        action_prompt_display=exit_reason,
-                        action_done=True,
-                    )
-                    return True, f"累计消息数量达到{new_message_count}条，直接结束等待 (等待时间: {elapsed_time:.1f}秒)"
-
-                # 判定条件：累计3条消息或等待超过5秒且有新消息
-                time_since_last_judge = current_time - last_judge_time
-                should_judge = (
-                    new_message_count >= 3  # 累计3条消息
-                    or (new_message_count > 0 and time_since_last_judge >= 5.0)  # 等待超过5秒且有新消息
-                )
-
-                if should_judge and time_since_last_judge >= min_judge_interval:
-                    # 判断触发原因
-                    trigger_reason = ""
-                    if new_message_count >= 3:
-                        trigger_reason = f"累计{new_message_count}条消息"
-                    elif time_since_last_judge >= 5.0:
-                        trigger_reason = f"等待{time_since_last_judge:.1f}秒且有{new_message_count}条新消息"
-
-                    logger.info(f"{self.log_prefix} 触发判定({trigger_reason})，进行智能判断...")
-
-                    # 获取最近的消息内容用于判断
-                    recent_messages = message_api.get_messages_by_time_in_chat(
-                        chat_id=self.chat_id,
-                        start_time=start_time,
-                        end_time=current_time,
-                    )
-
-                    if recent_messages:
-                        # 使用message_api构建可读的消息字符串
-                        messages_text = message_api.build_readable_messages(
-                            messages=recent_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
-                        )
-
-                        # 参考simple_planner构建更完整的判断信息
-                        # 获取时间信息
-                        time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
-
-                        # 获取身份信息
-                        bot_name = global_config.bot.nickname
-                        bot_nickname = ""
-                        if global_config.bot.alias_names:
-                            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
-                        bot_core_personality = global_config.personality.personality_core
-                        identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}"
-
-                        # 构建判断历史字符串（最多显示3条）
-                        history_block = ""
-                        if judge_history:
-                            history_block = "之前的判断历史：\n"
-                            # 只取最近的3条历史记录
-                            recent_history = judge_history[-3:] if len(judge_history) > 3 else judge_history
-                            for i, (timestamp, judge_result, reason) in enumerate(recent_history, 1):
-                                elapsed_seconds = int(timestamp - start_time)
-                                history_block += f"{i}. 等待{elapsed_seconds}秒时判断：{judge_result}，理由：{reason}\n"
-                            history_block += "\n"
-
-                        # 检查过去10分钟的发言频率
-                        frequency_block = ""
-                        should_skip_llm_judge = False  # 是否跳过LLM判断
-
-                        try:
-                            # 获取过去10分钟的所有消息
-                            past_10min_time = current_time - 600  # 10分钟前
-                            all_messages_10min = message_api.get_messages_by_time_in_chat(
-                                chat_id=self.chat_id,
-                                start_time=past_10min_time,
-                                end_time=current_time,
-                            )
-
-                            # 手动过滤bot自己的消息
-                            bot_message_count = 0
-                            if all_messages_10min:
-                                user_id = global_config.bot.qq_account
-
-                                for message in all_messages_10min:
-                                    # 检查消息发送者是否是bot
-                                    sender_id = message.get("user_id", "")
-
-                                    if sender_id == user_id:
-                                        bot_message_count += 1
-
-                            talk_frequency_threshold = global_config.chat.talk_frequency * 10
-
-                            if bot_message_count > talk_frequency_threshold:
-                                over_count = bot_message_count - talk_frequency_threshold
-
-                                # 根据超过的数量设置不同的提示词和跳过概率
-                                if over_count <= 3:
-                                    frequency_block = "你感觉稍微有些累，回复的有点多了。\n"
-                                elif over_count <= 5:
-                                    frequency_block = "你今天说话比较多，感觉有点疲惫，想要稍微休息一下。\n"
-                                else:
-                                    frequency_block = "你发现自己说话太多了，感觉很累，想要安静一会儿，除非有重要的事情否则不想回复。\n"
-                                    skip_probability = self._skip_probability_heavy
-
-                                # 根据配置和概率决定是否跳过LLM判断
-                                if self._skip_judge_when_tired and random.random() < skip_probability:
-                                    should_skip_llm_judge = True
-                                    logger.info(
-                                        f"{self.log_prefix} 发言过多(超过{over_count}条)，随机决定跳过此次LLM判断(概率{skip_probability * 100:.0f}%)"
-                                    )
-
-                                logger.info(
-                                    f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，超过阈值{talk_frequency_threshold}，添加疲惫提示"
-                                )
-                            else:
-                                # 回复次数少时的正向提示
-                                under_count = talk_frequency_threshold - bot_message_count
-
-                                if under_count >= talk_frequency_threshold * 0.8:  # 回复很少（少于20%）
-                                    frequency_block = "你感觉精力充沛，状态很好。\n"
-                                elif under_count >= talk_frequency_threshold * 0.5:  # 回复较少（少于50%）
-                                    frequency_block = "你感觉状态不错。\n"
-                                else:  # 刚好达到阈值
-                                    frequency_block = ""
-
-                                logger.info(
-                                    f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，未超过阈值{talk_frequency_threshold}，添加正向提示"
-                                )
-
-                        except Exception as e:
-                            logger.warning(f"{self.log_prefix} 检查发言频率时出错: {e}")
-                            frequency_block = ""
-
-                        # 如果决定跳过LLM判断，直接更新时间并继续等待
-                        if should_skip_llm_judge:
-                            last_judge_time = time.time()  # 更新判断时间，避免立即重新判断
-                            start_time = current_time  # 更新开始时间，避免重复计算同样的消息
-                            continue  # 跳过本次LLM判断，继续循环等待
-
-                        # 构建判断上下文
-                        judge_prompt = f"""
-{time_block}
-{identity_block}
-
-你现在正在QQ群参与聊天，以下是聊天内容：
-{context_str}
-在以上的聊天中，你选择了暂时不回复，现在，你看到了新的聊天消息如下：
-{messages_text}
-
-{history_block}
-请注意：{frequency_block}
-请你判断，是否要结束不回复的状态，重新加入聊天讨论。
-
-判断标准：
-1. 如果有人直接@你、提到你的名字或明确向你询问，应该回复
-2. 如果话题发生重要变化，需要你参与讨论，应该回复
-3. 如果只是普通闲聊、重复内容或与你无关的讨论，不需要回复
-4. 如果消息内容过于简单（如单纯的表情、"哈哈"等），不需要回复
-5. 参考之前的判断历史，如果情况有明显变化或持续等待时间过长，考虑调整判断
-
-请用JSON格式回复你的判断，严格按照以下格式：
-{{
-    "should_reply": true/false,
-    "reason": "详细说明你的判断理由"
-}}
-"""
-
-                        try:
-                            # 获取可用的模型配置
-                            available_models = llm_api.get_available_models()
-
-                            # 使用 utils_small 模型
-                            small_model = getattr(available_models, "utils_small", None)
-
-                            print(judge_prompt)
-
-                            if small_model:
-                                # 使用小模型进行判断
-                                success, response, reasoning, model_name = await llm_api.generate_with_model(
-                                    prompt=judge_prompt,
-                                    model_config=small_model,
-                                    request_type="plugin.no_reply_judge",
-                                    temperature=0.7,  # 进一步降低温度，提高JSON输出的一致性和准确性
-                                )
-
-                                # 更新上次判断时间
-                                last_judge_time = time.time()
-
-                                if success and response:
-                                    response = response.strip()
-                                    logger.info(f"{self.log_prefix} 模型({model_name})原始JSON响应: {response}")
-
-                                    # 解析LLM的JSON响应，提取判断结果和理由
-                                    judge_result, reason = self._parse_llm_judge_response(response)
-
-                                    logger.info(
-                                        f"{self.log_prefix} JSON解析结果 - 判断: {judge_result}, 理由: {reason}"
-                                    )
-
-                                    # 将判断结果保存到历史中
-                                    judge_history.append((current_time, judge_result, reason))
-
-                                    if judge_result == "需要回复":
-                                        logger.info(f"{self.log_prefix} 模型判断需要回复，结束等待")
-
-                                        full_prompt = f"{global_config.bot.nickname}（你）的想法是：{reason}"
-                                        await self.store_action_info(
-                                            action_build_into_prompt=True,
-                                            action_prompt_display=full_prompt,
-                                            action_done=True,
-                                        )
-                                        return True, f"检测到需要回复的消息，结束等待 (等待时间: {elapsed_time:.1f}秒)"
-                                    else:
-                                        logger.info(f"{self.log_prefix} 模型判断不需要回复，理由: {reason}，继续等待")
-                                        # 更新开始时间，避免重复判断同样的消息
-                                        start_time = current_time
-                                else:
-                                    logger.warning(f"{self.log_prefix} 模型判断失败，继续等待")
-                            else:
-                                logger.warning(f"{self.log_prefix} 未找到可用的模型配置，继续等待")
-                                last_judge_time = time.time()  # 即使失败也更新时间，避免频繁重试
-
-                        except Exception as e:
-                            logger.error(f"{self.log_prefix} 模型判断异常: {e}，继续等待")
-                            last_judge_time = time.time()  # 异常时也更新时间，避免频繁重试
-
-                # 每10秒输出一次等待状态
-                if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
-                    logger.info(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，等待新消息...")
-                    await asyncio.sleep(1)
-
-                # 短暂等待后继续检查
-                await asyncio.sleep(check_interval)
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 不回复动作执行失败: {e}")
-            # 即使执行失败也要记录
-            exit_reason = f"执行异常: {str(e)}"
-            full_prompt = f"{context_str}{exit_reason}，你思考是否要进行回复"
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=full_prompt,
-                action_done=True,
-            )
-            return False, f"不回复动作执行失败: {e}"
-
-    def _parse_llm_judge_response(self, response: str) -> tuple[str, str]:
-        """解析LLM判断响应，使用JSON格式提取判断结果和理由
-
-        Args:
-            response: LLM的原始JSON响应
-
-        Returns:
-            tuple: (判断结果, 理由)
-        """
-        try:
-            # 使用repair_json修复可能有问题的JSON格式
-            fixed_json_string = repair_json(response)
-            logger.debug(f"{self.log_prefix} repair_json修复后的响应: {fixed_json_string}")
-
-            # 如果repair_json返回的是字符串，需要解析为Python对象
-            if isinstance(fixed_json_string, str):
-                result_json = json.loads(fixed_json_string)
-            else:
-                # 如果repair_json直接返回了字典对象，直接使用
-                result_json = fixed_json_string
-
-            # 从JSON中提取判断结果和理由
-            should_reply = result_json.get("should_reply", False)
-            reason = result_json.get("reason", "无法获取判断理由")
-
-            # 转换布尔值为中文字符串
-            judge_result = "需要回复" if should_reply else "不需要回复"
-
-            logger.debug(f"{self.log_prefix} JSON解析成功 - 判断: {judge_result}, 理由: {reason}")
-            return judge_result, reason
-
-        except (json.JSONDecodeError, KeyError, TypeError) as e:
-            logger.warning(f"{self.log_prefix} JSON解析失败，尝试文本解析: {e}")
-
-            # 如果JSON解析失败，回退到简单的关键词匹配
-            try:
-                response_lower = response.lower()
-
-                if "true" in response_lower or "需要回复" in response:
-                    judge_result = "需要回复"
-                    reason = "从响应文本中检测到需要回复的指示"
-                elif "false" in response_lower or "不需要回复" in response:
-                    judge_result = "不需要回复"
-                    reason = "从响应文本中检测到不需要回复的指示"
-                else:
-                    judge_result = "不需要回复"  # 默认值
-                    reason = f"无法解析响应格式，使用默认判断。原始响应: {response[:100]}..."
-
-                logger.debug(f"{self.log_prefix} 文本解析结果 - 判断: {judge_result}, 理由: {reason}")
-                return judge_result, reason
-
-            except Exception as fallback_e:
-                logger.error(f"{self.log_prefix} 文本解析也失败: {fallback_e}")
-                return "不需要回复", f"解析异常: {str(e)}, 回退解析也失败: {str(fallback_e)}"
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
-            return "不需要回复", f"解析异常: {str(e)}"
-
-    @classmethod
-    def reset_consecutive_count(cls):
-        """重置连续计数器"""
-        cls._consecutive_count = 0
-        logger.debug("NoReplyAction连续计数器已重置")
-
-
 class EmojiAction(BaseAction):
     """表情动作 - 发送表情包"""
 
@@ -596,67 +175,6 @@ class EmojiAction(BaseAction):
             return False, f"表情发送失败: {str(e)}"
 
 
-class ExitFocusChatAction(BaseAction):
-    """退出专注聊天动作 - 从专注模式切换到普通模式"""
-
-    # 激活设置
-    focus_activation_type = ActionActivationType.NEVER
-    normal_activation_type = ActionActivationType.NEVER
-    mode_enable = ChatMode.FOCUS
-    parallel_action = False
-
-    # 动作基本信息
-    action_name = "exit_focus_chat"
-    action_description = "退出专注聊天，从专注模式切换到普通模式"
-
-    # LLM判断提示词
-    llm_judge_prompt = """
-    判定是否需要退出专注聊天的条件：
-    1. 很长时间没有回复，应该退出专注聊天
-    2. 当前内容不需要持续专注关注
-    3. 聊天内容已经完成，话题结束
-    
-    请回答"是"或"否"。
-    """
-
-    # 动作参数定义
-    action_parameters = {}
-
-    # 动作使用场景
-    action_require = [
-        "很长时间没有回复，你决定退出专注聊天",
-        "当前内容不需要持续专注关注，你决定退出专注聊天",
-        "聊天内容已经完成，你决定退出专注聊天",
-    ]
-
-    # 关联类型
-    associated_types = []
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行退出专注聊天动作"""
-        logger.info(f"{self.log_prefix} 决定退出专注聊天: {self.reasoning}")
-
-        try:
-            # 标记状态切换请求
-            self._mark_state_change()
-
-            # 重置NoReplyAction的连续计数器
-            NoReplyAction.reset_consecutive_count()
-
-            status_message = "决定退出专注聊天模式"
-            return True, status_message
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 退出专注聊天动作执行失败: {e}")
-            return False, f"退出专注聊天失败: {str(e)}"
-
-    def _mark_state_change(self):
-        """标记状态切换请求"""
-        # 通过action_data传递状态切换命令
-        self.action_data["_system_command"] = "stop_focus_chat"
-        logger.info(f"{self.log_prefix} 已标记状态切换命令: stop_focus_chat")
-
-
 @register_plugin
 class CoreActionsPlugin(BasePlugin):
     """核心动作插件
@@ -686,7 +204,7 @@ class CoreActionsPlugin(BasePlugin):
     config_schema = {
         "plugin": {
             "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
-            "config_version": ConfigField(type=str, default="0.0.9", description="配置文件版本"),
+            "config_version": ConfigField(type=str, default="0.1.0", description="配置文件版本"),
         },
         "components": {
             "enable_reply": ConfigField(type=bool, default=True, description="是否启用'回复'动作"),
@@ -742,7 +260,7 @@ class CoreActionsPlugin(BasePlugin):
         auto_exit_message_count = self.get_config("no_reply.auto_exit_message_count", 20)
         NoReplyAction._auto_exit_message_count = auto_exit_message_count
 
-        max_timeout = self.get_config("no_reply.max_timeout", 1200)
+        max_timeout = self.get_config("no_reply.max_timeout", 600)
         NoReplyAction._max_timeout = max_timeout
 
         skip_judge_when_tired = self.get_config("no_reply.skip_judge_when_tired", True)
@@ -757,6 +275,10 @@ class CoreActionsPlugin(BasePlugin):
         skip_probability_heavy = self.get_config("no_reply.skip_probability_heavy", 0.6)
         NoReplyAction._skip_probability_heavy = skip_probability_heavy
 
+        # 新增：频率检测相关配置
+        frequency_check_window = self.get_config("no_reply.frequency_check_window", 600)
+        NoReplyAction._frequency_check_window = frequency_check_window
+
         # --- 根据配置注册组件 ---
         components = []
         if self.get_config("components.enable_reply", True):
@@ -765,8 +287,6 @@ class CoreActionsPlugin(BasePlugin):
             components.append((NoReplyAction.get_action_info(), NoReplyAction))
         if self.get_config("components.enable_emoji", True):
             components.append((EmojiAction.get_action_info(), EmojiAction))
-        if self.get_config("components.enable_exit_focus", True):
-            components.append((ExitFocusChatAction.get_action_info(), ExitFocusChatAction))
 
         # components.append((DeepReplyAction.get_action_info(), DeepReplyAction))