Update core scripts and tools schema

agent_loop.py

@@ -45,6 +45,7 @@ def agent_runner_loop(client, system_prompt, user_input, handler, tools_schema,
     ]
     for turn in range(max_turns):
         yield f"**LLM Running (Turn {turn+1}) ...**\n\n"
+        if (turn+1) % 10 == 0: client.last_tools = ''  # 每10轮重置一次工具描述，避免上下文过大导致的模型性能下降
         response_gen = client.chat(messages=messages, tools=tools_schema)
         response = yield from response_gen
         yield '\n\n'

assets/tools_schema.json

@@ -61,7 +61,7 @@
       "candidates": {"type": "array", "items": {"type": "string"}, "description": "提供给用户的可选快捷选项列表。"}}, "required": ["question"]}
   }},
   {"type": "function", "function": {
-    "name": "conclude_and_reflect",
+    "name": "trigger_memory_update",
     "description": "用户的任务完成后，若发现值得长期记忆的信息（环境事实/用户偏好/避坑经验），调用此工具触发记忆提炼。不允许在记忆更新或自主流程内调用。",
     "parameters": {"type": "object", "properties": {}}}
   }

ga.py

@@ -438,7 +438,7 @@ class GenericAgentHandler(BaseHandler):
         yield "[Info] Final response to user.\n"
         return StepOutcome(response, next_prompt=None, should_exit=True)
     
-    def do_conclude_and_reflect(self, args, response):
+    def do_trigger_memory_update(self, args, response):
         '''Agent觉得当前任务完成后有重要信息需要记忆时调用此工具。
         '''
         prompt = '''### [总结提炼经验] 既然你觉得当前任务有重要信息需要记忆，请提取最近一次任务中【事实验证成功且长期有效】的环境事实、用户偏好、重要步骤，更新记忆。

sidercall.py

@@ -226,7 +226,7 @@ class ToolClient:
             prompt += f"=== {role} ===\n{m['content']}\n\n"
             self.total_cd_tokens += len(m['content'])
             
-        if self.total_cd_tokens > 9000: self.last_tools = ''
+        if self.total_cd_tokens > 6000: self.last_tools = ''
 
         prompt += "=== ASSISTANT ===\n" 
         return prompt