devel
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@ -5,6 +5,7 @@ LLM 引擎：负责意图理解、工具选择决策、最终回复生成
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生产环境可替换 _call_llm_api() 为真实 API 调用（OpenAI / Anthropic 等）
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"""
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import json
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import re
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from dataclasses import dataclass
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@ -12,7 +13,9 @@ from dataclasses import dataclass
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from mcp.mcp_protocol import MCPRequest, MCPMethod, ToolSchema
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from utils.logger import get_logger
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from openai import OpenAI
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import os
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os.environ["PYTHONIOENCODING"] = "utf-8"
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# ── 工具调用决策结果 ───────────────────────────────────────────
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@dataclass
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@ -74,6 +77,24 @@ class MonicaClient:
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arguments=response['arguments'],
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reasoning=response['reasoning'])
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def chat(self, model_name: str, user_input: str, context: str = '') -> str:
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message = f"""##用户输入\n{user_input}\n\n"""\
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f"""##历史消息\n{context}\n\n"""
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messages = [{
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"role": "user",
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"content": [{
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"type": "text",
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"text": message
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}]
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}]
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completion = self.client.chat.completions.create(
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model=model_name,
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messages=messages
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)
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self.logger.info(completion.choices[0].message.content)
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return completion.choices[0].message.content
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# ── LLM 引擎 ──────────────────────────────────────────────────
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class LLMEngine:
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@ -164,7 +185,7 @@ class LLMEngine:
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def generate_direct_reply(self, user_input: str, context: str = "") -> str:
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"""无需工具时直接生成回复"""
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self.logger.info("💬 直接生成回复（无需工具）")
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return f"[{self.model_name}] 您好！关于「{user_input}」，这是一个直接回复示例。\n（生产环境此处调用真实 LLM API）"
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return self.client.chat(self.model_name, user_input, context=context)
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# ── Prompt 构造 ─────────────────────────────────────────────
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639
logs/agent.log
639
logs/agent.log
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@ -523,3 +523,642 @@
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你好
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...
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[2026-02-28 23:12:57,256] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回
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[2026-03-02 10:49:15,081] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
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[2026-03-02 10:49:15,081] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
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[2026-03-02 10:49:15,081] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
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[2026-03-02 10:49:15,119] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
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[2026-03-02 10:49:15,119] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
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[2026-03-02 10:49:15,119] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
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[2026-03-02 10:59:16,161] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
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[2026-03-02 10:59:16,162] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
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[2026-03-02 10:59:16,162] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
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[2026-03-02 10:59:16,200] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
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[2026-03-02 10:59:16,201] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
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[2026-03-02 10:59:16,201] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
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[2026-03-02 11:00:03,211] [agent.CLIENT] INFO: =======================================================
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[2026-03-02 11:00:03,211] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：
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[2026-03-02 11:00:03,211] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：...
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[2026-03-02 11:00:03,211] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
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[2026-03-02 11:00:03,211] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：...
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[2026-03-02 11:00:03,212] [agent.LLM] DEBUG: 📝 Prompt 已构造 (456 chars)
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[2026-03-02 11:02:33,970] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
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[2026-03-02 11:02:33,970] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
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[2026-03-02 11:02:33,970] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
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[2026-03-02 11:02:33,970] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
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[2026-03-02 11:02:33,970] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
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[2026-03-02 11:02:33,971] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
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[2026-03-02 11:02:33,971] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
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[2026-03-02 11:02:33,971] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
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[2026-03-02 11:02:34,008] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
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[2026-03-02 11:02:34,008] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
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[2026-03-02 11:02:34,008] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
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[2026-03-02 11:02:39,526] [agent.CLIENT] INFO: =======================================================
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[2026-03-02 11:02:39,526] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(BaseTool):\n name = \"my demo tool\"\n description = \"this demo is used to show how to write a agent tool \"\n parameters = {\n \"parameter1\": {\n \"type\": \"string\",\n \"description\": \"the first parameter\"\n },\n \"parameter2\": {\n \"type\": \"integer\",\n \"description\": \"the second parameter\"\n }\n }\n def execute(parameter1: str, parameter2: int):\n ...\n"
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[2026-03-02 11:02:39,526] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(Base...
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[2026-03-02 11:02:39,526] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
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[2026-03-02 11:02:39,526] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDem...
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[2026-03-02 11:02:39,526] [agent.LLM] DEBUG: 📝 Prompt 已构造 (985 chars)
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[2026-03-02 11:03:09,470] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
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[2026-03-02 11:03:09,470] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
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[2026-03-02 11:03:09,470] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
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[2026-03-02 11:03:09,471] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
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[2026-03-02 11:03:09,471] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
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[2026-03-02 11:03:09,471] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
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[2026-03-02 11:03:09,471] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
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[2026-03-02 11:03:09,471] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
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[2026-03-02 11:03:09,509] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
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[2026-03-02 11:03:09,509] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
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[2026-03-02 11:03:09,510] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
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[2026-03-02 11:03:12,558] [agent.CLIENT] INFO: =======================================================
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[2026-03-02 11:03:12,558] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(BaseTool):\n name = \"my demo tool\"\n description = \"this demo is used to show how to write a agent tool \"\n parameters = {\n \"parameter1\": {\n \"type\": \"string\",\n \"description\": \"the first parameter\"\n },\n \"parameter2\": {\n \"type\": \"integer\",\n \"description\": \"the second parameter\"\n }\n }\n def execute(parameter1: str, parameter2: int):\n ...\n"
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[2026-03-02 11:03:12,558] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(Base...
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[2026-03-02 11:03:12,558] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
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[2026-03-02 11:03:12,558] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDem...
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[2026-03-02 11:03:12,558] [agent.LLM] DEBUG: 📝 Prompt 已构造 (985 chars)
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[2026-03-02 11:06:15,110] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
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[2026-03-02 11:06:15,110] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
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[2026-03-02 11:06:15,110] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
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[2026-03-02 11:06:15,110] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
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[2026-03-02 11:06:15,111] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
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[2026-03-02 11:06:15,111] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
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[2026-03-02 11:06:15,111] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
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[2026-03-02 11:06:15,111] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
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[2026-03-02 11:06:15,148] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
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[2026-03-02 11:06:15,148] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
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[2026-03-02 11:06:15,148] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
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[2026-03-02 11:06:18,486] [agent.CLIENT] INFO: =======================================================
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[2026-03-02 11:06:18,486] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(BaseTool):\n name = \"my demo tool\"\n description = \"this demo is used to show how to write a agent tool \"\n parameters = {\n \"parameter1\": {\n \"type\": \"string\",\n \"description\": \"the first parameter\"\n },\n \"parameter2\": {\n \"type\": \"integer\",\n \"description\": \"the second parameter\"\n }\n }\n def execute(parameter1: str, parameter2: int):\n ...\n"
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[2026-03-02 11:06:18,486] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(Base...
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[2026-03-02 11:06:18,486] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
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[2026-03-02 11:06:18,486] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDem...
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[2026-03-02 11:06:18,486] [agent.LLM] DEBUG: 📝 Prompt 已构造 (985 chars)
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[2026-03-02 11:06:21,669] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"name": "process_status_query_tool", "description": "Tool for querying current system process status", "parameters": {"process_id": {"type": "integer", "description": "The ID of the process to query"}}, "execution_code": "def execute(process_id: int):\n import psutil\n process = psutil.Process(process_id)\n return {\n 'name': process.name(),\n 'status': process.status(),\n 'cpu_usage': process.cpu_percent(interval=1),\n 'memory_info': process.memory_info()._asdict()\n }"}, "reasoning": "The user requested code for an agent skill tool that queries system process status. The agent_skill_generator tool is necessary to generate the required skill code based on the provided input."}
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[2026-03-02 11:06:21,669] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
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[2026-03-02 11:06:21,669] [agent.LLM] DEBUG: 💡 推理: The user requested code for an agent skill tool that queries system process status. The agent_skill_generator tool is necessary to generate the required skill code based on the provided input.
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[2026-03-02 11:06:21,669] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
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方法: tools/call
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工具: agent_skill_generator
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参数: {'name': 'process_status_query_tool', 'description': 'Tool for querying current system process status', 'parameters': {'process_id': {'type': 'integer', 'description': 'The ID of the process to query'}}, 'execution_code': "def execute(process_id: int):\n import psutil\n process = psutil.Process(process_id)\n return {\n 'name': process.name(),\n 'status': process.status(),\n 'cpu_usage': process.cpu_percent(interval=1),\n 'memory_info': process.memory_info()._asdict()\n }"}
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请求体: {'jsonrpc': '2.0', 'id': '11f24daa', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'name': 'process_status_query_tool', 'description': 'Tool for querying current system process status', 'parameters': {'process_id': {'type': 'integer', 'description': 'The ID of the process to query'}}, 'execution_code': "def execute(process_id: int):\n import psutil\n process = psutil.Process(process_id)\n return {\n 'name': process.name(),\n 'status': process.status(),\n 'cpu_usage': process.cpu_percent(interval=1),\n 'memory_info': process.memory_info()._asdict()\n }"}}}
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[2026-03-02 11:06:21,669] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
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[2026-03-02 11:06:21,669] [agent.MCP] INFO: 📨 收到请求 id=11f24daa method=tools/call
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[2026-03-02 11:06:21,669] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'name': 'process_status_query_tool', 'description': 'Tool for querying current system process status', 'parameters': {'process_id': {'type': 'integer', 'description': 'The ID of the process to query'}}, 'execution_code': "def execute(process_id: int):\n import psutil\n process = psutil.Process(process_id)\n return {\n 'name': process.name(),\n 'status': process.status(),\n 'cpu_usage': process.cpu_percent(interval=1),\n 'memory_info': process.memory_info()._asdict()\n }"}
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[2026-03-02 11:06:21,669] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'name'
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[2026-03-02 11:06:21,669] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'name'"}
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[2026-03-02 11:13:52,278] [agent.CLIENT] INFO: =======================================================
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[2026-03-02 11:13:52,278] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(BaseTool):\n name = \"my demo tool\"\n description = \"this demo is used to show how to write a agent tool \"\n parameters = {\n \"type\": \"string\",\n \"description\": \"the first parameter\"\n },\n \"parameter2\": {\n \"type\": \"integer\",\n \"description\": \"the second parameter\"\n }\n }\n def execute(self, parameter1: str, parameter2: int):\n ...\n"
|
||||
[2026-03-02 11:13:52,278] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDemoTool(Base...
|
||||
[2026-03-02 11:13:52,278] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:13:52,278] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下：\nclass MyDem...
|
||||
[2026-03-02 11:13:52,278] [agent.LLM] DEBUG: 📝 Prompt 已构造 (1047 chars)
|
||||
[2026-03-02 11:13:55,354] [agent.Monica] INFO: {
|
||||
"need_tool": true,
|
||||
"tool_name": "agent_skill_generator",
|
||||
"arguments": {
|
||||
"tool_name": "System Process State Query",
|
||||
"description": "A tool designed to retrieve the current state of system processes.",
|
||||
"parameters": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "A keyword or query to filter specific system processes."
|
||||
}
|
||||
}
|
||||
},
|
||||
"reasoning": "The user has requested to generate an agent skill tool that can query the current system process states with specific parameters. The `agent_skill_generator` is the appropriate tool to generate the required agent tool code."
|
||||
}
|
||||
[2026-03-02 11:13:55,354] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:13:55,354] [agent.LLM] DEBUG: 💡 推理: The user has requested to generate an agent skill tool that can query the current system process states with specific parameters. The `agent_skill_generator` is the appropriate tool to generate the required agent tool code.
|
||||
[2026-03-02 11:13:55,354] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'tool_name': 'System Process State Query', 'description': 'A tool designed to retrieve the current state of system processes.', 'parameters': {'query': {'type': 'string', 'description': 'A keyword or query to filter specific system processes.'}}}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'a37c6fb6', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'tool_name': 'System Process State Query', 'description': 'A tool designed to retrieve the current state of system processes.', 'parameters': {'query': {'type': 'string', 'description': 'A keyword or query to filter specific system processes.'}}}}}
|
||||
[2026-03-02 11:13:55,354] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:13:55,354] [agent.MCP] INFO: 📨 收到请求 id=a37c6fb6 method=tools/call
|
||||
[2026-03-02 11:13:55,354] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'tool_name': 'System Process State Query', 'description': 'A tool designed to retrieve the current state of system processes.', 'parameters': {'query': {'type': 'string', 'description': 'A keyword or query to filter specific system processes.'}}}
|
||||
[2026-03-02 11:13:55,354] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'tool_name'
|
||||
[2026-03-02 11:13:55,354] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'tool_name'"}
|
||||
[2026-03-02 11:24:14,691] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:24:14,691] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:24:14,691] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:24:14,691] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:24:14,691] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:24:14,692] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:24:14,692] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:24:14,692] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:24:14,729] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:24:14,730] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:24:14,730] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:24:35,048] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:24:35,048] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询
|
||||
[2026-03-02 11:24:35,048] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询...
|
||||
[2026-03-02 11:24:35,048] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:24:35,048] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询...
|
||||
[2026-03-02 11:24:35,048] [agent.LLM] DEBUG: 📝 Prompt 已构造 (432 chars)
|
||||
[2026-03-02 11:24:37,629] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "实现当前系统进程状态查询"}, "reasoning": "用户请求生成一个agent技能工具用于查询当前系统进程状态，这要求创建一个相关功能的工具，而agent_skill_generator正是为了生成这样的工具。"}
|
||||
[2026-03-02 11:24:37,630] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:24:37,630] [agent.LLM] DEBUG: 💡 推理: 用户请求生成一个agent技能工具用于查询当前系统进程状态，这要求创建一个相关功能的工具，而agent_skill_generator正是为了生成这样的工具。
|
||||
[2026-03-02 11:24:37,630] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': '实现当前系统进程状态查询'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '7aa01ed6', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': '实现当前系统进程状态查询'}}}
|
||||
[2026-03-02 11:24:37,630] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:24:37,630] [agent.MCP] INFO: 📨 收到请求 id=7aa01ed6 method=tools/call
|
||||
[2026-03-02 11:24:37,630] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': '实现当前系统进程状态查询'}
|
||||
[2026-03-02 11:24:37,630] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'description'
|
||||
[2026-03-02 11:24:37,630] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'description'"}
|
||||
[2026-03-02 11:30:16,159] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:30:16,159] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:30:16,160] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:30:16,160] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:30:16,160] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:30:16,160] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:30:16,160] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:30:16,160] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:30:16,199] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:30:16,200] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:30:16,200] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:30:31,049] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:30:31,049] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 生成agent技能工具用于实现当前系统进程状态查询
|
||||
[2026-03-02 11:30:31,049] [agent.MEMORY] DEBUG: 💬 [USER] 生成agent技能工具用于实现当前系统进程状态查询...
|
||||
[2026-03-02 11:30:31,049] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:30:31,049] [agent.LLM] INFO: 💭 分析意图: 生成agent技能工具用于实现当前系统进程状态查询...
|
||||
[2026-03-02 11:30:31,049] [agent.LLM] DEBUG: 📝 Prompt 已构造 (432 chars)
|
||||
[2026-03-02 11:30:33,326] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "实现当前系统进程状态查询的工具"}, "reasoning": "用户请求生成一个agent技能工具用于查询当前系统的进程状态，符合agent_skill_generator工具的功能需求。"}
|
||||
[2026-03-02 11:30:33,327] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:30:33,327] [agent.LLM] DEBUG: 💡 推理: 用户请求生成一个agent技能工具用于查询当前系统的进程状态，符合agent_skill_generator工具的功能需求。
|
||||
[2026-03-02 11:30:33,327] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': '实现当前系统进程状态查询的工具'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '2f435cb4', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': '实现当前系统进程状态查询的工具'}}}
|
||||
[2026-03-02 11:30:33,327] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:30:33,327] [agent.MCP] INFO: 📨 收到请求 id=2f435cb4 method=tools/call
|
||||
[2026-03-02 11:30:33,327] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': '实现当前系统进程状态查询的工具'}
|
||||
[2026-03-02 11:30:33,327] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'description'
|
||||
[2026-03-02 11:30:33,327] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'description'"}
|
||||
[2026-03-02 11:30:54,686] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:30:54,686] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:30:54,686] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:30:54,686] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:30:54,686] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:30:54,687] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:30:54,687] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:30:54,687] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:30:54,725] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:30:54,725] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:30:54,725] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:31:54,249] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:31:54,249] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:31:54,250] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:31:54,291] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:31:54,291] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:31:54,292] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:32:03,024] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:32:03,024] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: create tool to get system name
|
||||
[2026-03-02 11:32:03,025] [agent.MEMORY] DEBUG: 💬 [USER] create tool to get system name...
|
||||
[2026-03-02 11:32:03,025] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:32:03,025] [agent.LLM] INFO: 💭 分析意图: create tool to get system name...
|
||||
[2026-03-02 11:32:03,025] [agent.LLM] DEBUG: 📝 Prompt 已构造 (442 chars)
|
||||
[2026-03-02 11:32:05,556] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"task_description": "Create a tool to retrieve the name of the system or operating system."}, "reasoning": "The user wants to create a tool capable of retrieving the system name. This requires generating code for the tool, which falls under the capabilities of the 'agent_skill_generator' tool."}
|
||||
[2026-03-02 11:32:05,556] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:32:05,557] [agent.LLM] DEBUG: 💡 推理: The user wants to create a tool capable of retrieving the system name. This requires generating code for the tool, which falls under the capabilities of the 'agent_skill_generator' tool.
|
||||
[2026-03-02 11:32:05,557] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'task_description': 'Create a tool to retrieve the name of the system or operating system.'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '40b58637', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'task_description': 'Create a tool to retrieve the name of the system or operating system.'}}}
|
||||
[2026-03-02 11:32:05,557] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:32:05,557] [agent.MCP] INFO: 📨 收到请求 id=40b58637 method=tools/call
|
||||
[2026-03-02 11:32:05,557] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'task_description': 'Create a tool to retrieve the name of the system or operating system.'}
|
||||
[2026-03-02 11:32:05,557] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'task_description'
|
||||
[2026-03-02 11:32:05,557] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'task_description'"}
|
||||
[2026-03-02 11:34:15,429] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:34:15,430] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:34:15,430] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:34:15,468] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:34:15,468] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:34:15,468] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:34:22,464] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:34:22,465] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: create tool to get system name
|
||||
[2026-03-02 11:34:22,465] [agent.MEMORY] DEBUG: 💬 [USER] create tool to get system name...
|
||||
[2026-03-02 11:34:22,465] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:34:22,465] [agent.LLM] INFO: 💭 分析意图: create tool to get system name...
|
||||
[2026-03-02 11:34:22,465] [agent.LLM] DEBUG: 📝 Prompt 已构造 (442 chars)
|
||||
[2026-03-02 11:34:24,754] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "A tool to retrieve the name of the operating system or system information."}, "reasoning": "The task involves creating a specialized tool, which aligns with the purpose of the 'agent_skill_generator' to generate agent tools based on user requirements."}
|
||||
[2026-03-02 11:34:24,754] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:34:24,754] [agent.LLM] DEBUG: 💡 推理: The task involves creating a specialized tool, which aligns with the purpose of the 'agent_skill_generator' to generate agent tools based on user requirements.
|
||||
[2026-03-02 11:34:24,755] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': 'A tool to retrieve the name of the operating system or system information.'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '29799203', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': 'A tool to retrieve the name of the operating system or system information.'}}}
|
||||
[2026-03-02 11:34:24,755] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:34:24,755] [agent.MCP] INFO: 📨 收到请求 id=29799203 method=tools/call
|
||||
[2026-03-02 11:34:24,755] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': 'A tool to retrieve the name of the operating system or system information.'}
|
||||
[2026-03-02 11:34:24,755] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'execution_code'
|
||||
[2026-03-02 11:34:24,755] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'execution_code'"}
|
||||
[2026-03-02 11:35:56,812] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:35:56,812] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:35:56,813] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:35:56,813] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:35:56,813] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:35:56,813] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:35:56,813] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:35:56,813] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:35:56,850] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:35:56,850] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:35:56,851] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:36:13,470] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:36:13,470] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: create tool to get system name
|
||||
[2026-03-02 11:36:13,470] [agent.MEMORY] DEBUG: 💬 [USER] create tool to get system name...
|
||||
[2026-03-02 11:36:13,470] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:36:13,470] [agent.LLM] INFO: 💭 分析意图: create tool to get system name...
|
||||
[2026-03-02 11:36:13,471] [agent.LLM] DEBUG: 📝 Prompt 已构造 (442 chars)
|
||||
[2026-03-02 11:36:15,863] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "Generate a tool to fetch the system name of the machine it is run on.", "input_format": "", "output_format": "The system name as a string."}, "reasoning": "The user requested to create a tool that retrieves the system name, which requires generating a specific agent skill to achieve this functionality."}
|
||||
[2026-03-02 11:36:15,863] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:36:15,863] [agent.LLM] DEBUG: 💡 推理: The user requested to create a tool that retrieves the system name, which requires generating a specific agent skill to achieve this functionality.
|
||||
[2026-03-02 11:36:15,863] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': 'Generate a tool to fetch the system name of the machine it is run on.', 'input_format': '', 'output_format': 'The system name as a string.'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'c03b28f1', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': 'Generate a tool to fetch the system name of the machine it is run on.', 'input_format': '', 'output_format': 'The system name as a string.'}}}
|
||||
[2026-03-02 11:36:15,863] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:36:15,863] [agent.MCP] INFO: 📨 收到请求 id=c03b28f1 method=tools/call
|
||||
[2026-03-02 11:36:15,863] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': 'Generate a tool to fetch the system name of the machine it is run on.', 'input_format': '', 'output_format': 'The system name as a string.'}
|
||||
[2026-03-02 11:36:15,864] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'input_format'
|
||||
[2026-03-02 11:36:15,864] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'input_format'"}
|
||||
[2026-03-02 11:36:54,380] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:36:54,380] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate python function to get system name
|
||||
[2026-03-02 11:36:54,380] [agent.MEMORY] DEBUG: 💬 [USER] generate python function to get system name...
|
||||
[2026-03-02 11:36:54,380] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:36:54,380] [agent.LLM] INFO: 💭 分析意图: generate python function to get system name...
|
||||
[2026-03-02 11:36:54,380] [agent.LLM] DEBUG: 📝 Prompt 已构造 (506 chars)
|
||||
[2026-03-02 11:36:56,964] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"request": "generate a Python function to get the system name"}, "reasoning": "The user has requested the generation of a Python function to get the system name, which can be fulfilled using the 'agent_skill_generator' tool that creates agent tools or code snippets."}
|
||||
[2026-03-02 11:36:56,964] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:36:56,964] [agent.LLM] DEBUG: 💡 推理: The user has requested the generation of a Python function to get the system name, which can be fulfilled using the 'agent_skill_generator' tool that creates agent tools or code snippets.
|
||||
[2026-03-02 11:36:56,965] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'request': 'generate a Python function to get the system name'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'e985d57b', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'request': 'generate a Python function to get the system name'}}}
|
||||
[2026-03-02 11:36:56,965] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:36:56,965] [agent.MCP] INFO: 📨 收到请求 id=e985d57b method=tools/call
|
||||
[2026-03-02 11:36:56,965] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'request': 'generate a Python function to get the system name'}
|
||||
[2026-03-02 11:36:56,965] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'request'
|
||||
[2026-03-02 11:36:56,966] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() got an unexpected keyword argument 'request'"}
|
||||
[2026-03-02 11:37:17,360] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:37:17,361] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:37:17,361] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:37:17,398] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:37:17,398] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:37:17,399] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:37:33,225] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:37:33,225] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate python tool to get system name
|
||||
[2026-03-02 11:37:33,226] [agent.MEMORY] DEBUG: 💬 [USER] generate python tool to get system name...
|
||||
[2026-03-02 11:37:33,226] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:37:33,226] [agent.LLM] INFO: 💭 分析意图: generate python tool to get system name...
|
||||
[2026-03-02 11:37:33,226] [agent.LLM] DEBUG: 📝 Prompt 已构造 (460 chars)
|
||||
[2026-03-02 11:37:35,615] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "A python tool to get the system name"}, "reasoning": "The user is requesting a Python tool that retrieves the system name. To fulfill this request, the agent_skill_generator is the appropriate tool to generate this code."}
|
||||
[2026-03-02 11:37:35,615] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:37:35,615] [agent.LLM] DEBUG: 💡 推理: The user is requesting a Python tool that retrieves the system name. To fulfill this request, the agent_skill_generator is the appropriate tool to generate this code.
|
||||
[2026-03-02 11:37:35,615] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': 'A python tool to get the system name'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '1bbc8950', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': 'A python tool to get the system name'}}}
|
||||
[2026-03-02 11:37:35,615] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:37:35,615] [agent.MCP] INFO: 📨 收到请求 id=1bbc8950 method=tools/call
|
||||
[2026-03-02 11:37:35,615] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': 'A python tool to get the system name'}
|
||||
[2026-03-02 11:37:35,615] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: AgentSkillGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'code'
|
||||
[2026-03-02 11:37:35,615] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: AgentSkillGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'code'"}
|
||||
[2026-03-02 11:40:22,719] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:40:22,719] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:40:22,720] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:40:22,720] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:40:22,720] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:40:22,720] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:40:22,720] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:40:22,720] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:40:22,757] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:40:22,758] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:40:22,758] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:41:21,329] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:41:21,330] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:41:21,330] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:41:21,368] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:41:21,369] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:41:21,369] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:41:26,744] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:41:26,745] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: create a tool to get system name
|
||||
[2026-03-02 11:41:26,745] [agent.MEMORY] DEBUG: 💬 [USER] create a tool to get system name...
|
||||
[2026-03-02 11:41:26,745] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:41:26,745] [agent.LLM] INFO: 💭 分析意图: create a tool to get system name...
|
||||
[2026-03-02 11:41:26,745] [agent.LLM] DEBUG: 📝 Prompt 已构造 (446 chars)
|
||||
[2026-03-02 11:41:28,904] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"prompt": "Create a tool to get the operating system name of the current system in Python"}, "reasoning": "To create a tool for retrieving the system name, I should use the 'agent_skill_generator' tool to define and construct this functionality."}
|
||||
[2026-03-02 11:41:28,904] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:41:28,905] [agent.LLM] DEBUG: 💡 推理: To create a tool for retrieving the system name, I should use the 'agent_skill_generator' tool to define and construct this functionality.
|
||||
[2026-03-02 11:41:28,905] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'prompt': 'Create a tool to get the operating system name of the current system in Python'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'b4653973', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'prompt': 'Create a tool to get the operating system name of the current system in Python'}}}
|
||||
[2026-03-02 11:41:28,905] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:41:28,905] [agent.MCP] INFO: 📨 收到请求 id=b4653973 method=tools/call
|
||||
[2026-03-02 11:41:28,905] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'prompt': 'Create a tool to get the operating system name of the current system in Python'}
|
||||
[2026-03-02 11:41:28,905] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: ToolGeneratorTool.execute() got an unexpected keyword argument 'prompt'
|
||||
[2026-03-02 11:41:28,905] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: ToolGeneratorTool.execute() got an unexpected keyword argument 'prompt'"}
|
||||
[2026-03-02 11:41:58,733] [agent.MEMORY] INFO: 🗑 对话历史已清空
|
||||
[2026-03-02 11:41:58,733] [agent.CLIENT] INFO: 🗑 会话已清空
|
||||
[2026-03-02 11:42:03,610] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 11:42:03,611] [agent.MCP] INFO: 📌 注册工具: [agent_skill_generator] — 生成agent工具代码
|
||||
[2026-03-02 11:42:03,611] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 11:42:03,650] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 11:42:03,650] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 11:42:03,650] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'agent_skill_generator']
|
||||
[2026-03-02 11:42:09,009] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 11:42:09,009] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: create tool to get system name
|
||||
[2026-03-02 11:42:09,009] [agent.MEMORY] DEBUG: 💬 [USER] create tool to get system name...
|
||||
[2026-03-02 11:42:09,009] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 11:42:09,009] [agent.LLM] INFO: 💭 分析意图: create tool to get system name...
|
||||
[2026-03-02 11:42:09,009] [agent.LLM] DEBUG: 📝 Prompt 已构造 (442 chars)
|
||||
[2026-03-02 11:42:11,109] [agent.Monica] INFO: {"need_tool": true, "tool_name": "agent_skill_generator", "arguments": {"description": "A tool to retrieve the system's name or hostname"}, "reasoning": "The user wants to create a tool to retrieve the system's name, which requires generating custom code for a new tool. The appropriate action is to use the 'agent_skill_generator' tool to create this functionality."}
|
||||
[2026-03-02 11:42:11,109] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [agent_skill_generator]
|
||||
[2026-03-02 11:42:11,109] [agent.LLM] DEBUG: 💡 推理: The user wants to create a tool to retrieve the system's name, which requires generating custom code for a new tool. The appropriate action is to use the 'agent_skill_generator' tool to create this functionality.
|
||||
[2026-03-02 11:42:11,109] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: agent_skill_generator
|
||||
参数: {'description': "A tool to retrieve the system's name or hostname"}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'da607239', 'method': 'tools/call', 'params': {'name': 'agent_skill_generator', 'arguments': {'description': "A tool to retrieve the system's name or hostname"}}}
|
||||
[2026-03-02 11:42:11,109] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [agent_skill_generator]...
|
||||
[2026-03-02 11:42:11,109] [agent.MCP] INFO: 📨 收到请求 id=da607239 method=tools/call
|
||||
[2026-03-02 11:42:11,110] [agent.TOOL] INFO: ▶ 执行工具 [agent_skill_generator]，参数: {'description': "A tool to retrieve the system's name or hostname"}
|
||||
[2026-03-02 11:42:11,110] [agent.TOOL] ERROR: ❌ 工具 [agent_skill_generator] 执行失败: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'code'
|
||||
[2026-03-02 11:42:11,110] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'parameters', and 'code'"}
|
||||
[2026-03-02 13:46:40,526] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 13:46:40,527] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 13:46:40,527] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 13:46:51,425] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 13:46:51,425] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 13:46:51,425] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 13:46:51,425] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 13:46:51,426] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 13:46:51,426] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 13:46:51,426] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 13:46:51,426] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 13:46:51,468] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 13:46:51,468] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 13:46:51,469] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'tool_generator']
|
||||
[2026-03-02 13:47:26,677] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 13:47:26,678] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 13:47:26,678] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 13:47:26,718] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 13:47:26,718] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 13:47:26,719] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'tool_generator']
|
||||
[2026-03-02 13:47:37,167] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 13:47:37,167] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate a python function to get system's name
|
||||
[2026-03-02 13:47:37,167] [agent.MEMORY] DEBUG: 💬 [USER] generate a python function to get system's name...
|
||||
[2026-03-02 13:47:37,167] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 13:47:37,167] [agent.LLM] INFO: 💭 分析意图: generate a python function to get system's name...
|
||||
[2026-03-02 13:47:37,167] [agent.LLM] DEBUG: 📝 Prompt 已构造 (469 chars)
|
||||
[2026-03-02 13:47:39,191] [agent.Monica] INFO: {"need_tool": false, "tool_name": "", "arguments": {}, "reasoning": "The task involves generating a Python function, which can be done directly without requiring the use of any tools."}
|
||||
[2026-03-02 13:47:39,191] [agent.LLM] INFO: 🎯 决策结果: 直接回复
|
||||
[2026-03-02 13:47:39,191] [agent.LLM] DEBUG: 💡 推理: The task involves generating a Python function, which can be done directly without requiring the use of any tools.
|
||||
[2026-03-02 13:47:39,191] [agent.CLIENT] INFO: 💬 无需工具，直接生成回复
|
||||
[2026-03-02 13:47:39,191] [agent.LLM] INFO: 💬 直接生成回复（无需工具）
|
||||
[2026-03-02 13:47:39,191] [agent.MEMORY] DEBUG: 💬 [ASSISTANT] [gpt-4o] 您好！关于「generate a python function to get system's na...
|
||||
[2026-03-02 14:10:27,448] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 14:10:27,448] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 14:10:27,448] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 14:10:27,448] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 14:10:27,449] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 14:10:27,449] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 14:10:27,449] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 14:10:27,449] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 14:10:27,487] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 14:10:27,487] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 14:10:27,488] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'tool_generator']
|
||||
[2026-03-02 14:10:39,389] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:10:39,389] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate a python function to get system's name
|
||||
[2026-03-02 14:10:39,389] [agent.MEMORY] DEBUG: 💬 [USER] generate a python function to get system's name...
|
||||
[2026-03-02 14:10:39,389] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:10:39,389] [agent.LLM] INFO: 💭 分析意图: generate a python function to get system's name...
|
||||
[2026-03-02 14:10:39,390] [agent.LLM] DEBUG: 📝 Prompt 已构造 (469 chars)
|
||||
[2026-03-02 14:10:43,803] [agent.Monica] INFO: {"need_tool": false, "tool_name": "", "arguments": {}, "reasoning": "The task can be accomplished without any tools by providing a Python code snippet to get the system's name using the platform module."}
|
||||
[2026-03-02 14:10:43,803] [agent.LLM] INFO: 🎯 决策结果: 直接回复
|
||||
[2026-03-02 14:10:43,803] [agent.LLM] DEBUG: 💡 推理: The task can be accomplished without any tools by providing a Python code snippet to get the system's name using the platform module.
|
||||
[2026-03-02 14:10:43,803] [agent.CLIENT] INFO: 💬 无需工具，直接生成回复
|
||||
[2026-03-02 14:10:43,803] [agent.LLM] INFO: 💬 直接生成回复（无需工具）
|
||||
[2026-03-02 14:10:45,852] [agent.Monica] INFO: Here is a Python function to get the system's name using the `platform` module:
|
||||
|
||||
```python
|
||||
import platform
|
||||
|
||||
def get_system_name():
|
||||
"""
|
||||
Returns the name of the system/OS.
|
||||
For example: 'Windows', 'Linux', 'Darwin' (for macOS)
|
||||
"""
|
||||
return platform.system()
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
print("System name:", get_system_name())
|
||||
```
|
||||
|
||||
This function uses the `platform.system()` method, which retrieves the name of the operating system your script is running on. For example, it will return `'Windows'` for Windows systems, `'Linux'` for Linux systems, or `'Darwin'` for macOS.
|
||||
[2026-03-02 14:12:22,700] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 14:12:22,700] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 14:12:22,701] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 14:12:22,701] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 14:12:22,701] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 14:12:22,701] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 14:12:22,701] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 14:12:22,701] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 14:12:22,741] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 14:12:22,741] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 14:12:22,742] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'tool_generator']
|
||||
[2026-03-02 14:12:33,611] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:12:33,611] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate a python function to get system's name
|
||||
[2026-03-02 14:12:33,612] [agent.MEMORY] DEBUG: 💬 [USER] generate a python function to get system's name...
|
||||
[2026-03-02 14:12:33,612] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:12:33,612] [agent.LLM] INFO: 💭 分析意图: generate a python function to get system's name...
|
||||
[2026-03-02 14:12:33,612] [agent.LLM] DEBUG: 📝 Prompt 已构造 (469 chars)
|
||||
[2026-03-02 14:12:35,773] [agent.Monica] INFO: {"need_tool": false, "tool_name": "", "arguments": {}, "reasoning": "To generate a Python function that retrieves a system's name, no tool needs to be invoked; providing the Python code directly is sufficient."}
|
||||
[2026-03-02 14:12:35,773] [agent.LLM] INFO: 🎯 决策结果: 直接回复
|
||||
[2026-03-02 14:12:35,773] [agent.LLM] DEBUG: 💡 推理: To generate a Python function that retrieves a system's name, no tool needs to be invoked; providing the Python code directly is sufficient.
|
||||
[2026-03-02 14:12:35,773] [agent.CLIENT] INFO: 💬 无需工具，直接生成回复
|
||||
[2026-03-02 14:12:35,773] [agent.LLM] INFO: 💬 直接生成回复（无需工具）
|
||||
[2026-03-02 14:12:37,842] [agent.Monica] INFO: Here is a Python function to get the system's name using the `platform` module:
|
||||
|
||||
```python
|
||||
import platform
|
||||
|
||||
def get_system_name():
|
||||
"""
|
||||
This function retrieves the system's name.
|
||||
Returns:
|
||||
str: The name of the operating system (e.g., 'Windows', 'Linux', 'Darwin').
|
||||
"""
|
||||
system_name = platform.system()
|
||||
return system_name
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
print(f"System Name: {get_system_name()}")
|
||||
```
|
||||
|
||||
The function `get_system_name()` uses `platform.system()` to determine the system's name. This will return values such as:
|
||||
- `Windows` for Windows,
|
||||
- `Linux` for Linux-based systems,
|
||||
- `Darwin` for macOS systems.
|
||||
[2026-03-02 14:12:37,842] [agent.MEMORY] DEBUG: 💬 [ASSISTANT] Here is a Python function to get the system's name using the...
|
||||
[2026-03-02 14:13:24,958] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:13:24,959] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 将生成的代码转换成本地工具用于调用
|
||||
[2026-03-02 14:13:24,959] [agent.MEMORY] DEBUG: 💬 [USER] 将生成的代码转换成本地工具用于调用...
|
||||
[2026-03-02 14:13:24,959] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:13:24,959] [agent.LLM] INFO: 💭 分析意图: 将生成的代码转换成本地工具用于调用...
|
||||
[2026-03-02 14:13:24,959] [agent.LLM] DEBUG: 📝 Prompt 已构造 (557 chars)
|
||||
[2026-03-02 14:13:29,677] [agent.Monica] INFO: {
|
||||
"need_tool": true,
|
||||
"tool_name": "tool_generator",
|
||||
"arguments": {
|
||||
"code": "import platform\ndef get_system_name():\n return platform.system()"
|
||||
},
|
||||
"reasoning": "The user wants to convert the previously generated code into a local tool for calling, which can be achieved using the tool_generator by providing the generated code."
|
||||
}
|
||||
[2026-03-02 14:13:29,678] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [tool_generator]
|
||||
[2026-03-02 14:13:29,678] [agent.LLM] DEBUG: 💡 推理: The user wants to convert the previously generated code into a local tool for calling, which can be achieved using the tool_generator by providing the generated code.
|
||||
[2026-03-02 14:13:29,678] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: tool_generator
|
||||
参数: {'code': 'import platform\ndef get_system_name():\n return platform.system()'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': 'a7e1eee2', 'method': 'tools/call', 'params': {'name': 'tool_generator', 'arguments': {'code': 'import platform\ndef get_system_name():\n return platform.system()'}}}
|
||||
[2026-03-02 14:13:29,679] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [tool_generator]...
|
||||
[2026-03-02 14:13:29,679] [agent.MCP] INFO: 📨 收到请求 id=a7e1eee2 method=tools/call
|
||||
[2026-03-02 14:13:29,679] [agent.TOOL] INFO: ▶ 执行工具 [tool_generator]，参数: {'code': 'import platform\ndef get_system_name():\n return platform.system()'}
|
||||
[2026-03-02 14:13:29,679] [agent.TOOL] ERROR: ❌ 工具 [tool_generator] 执行失败: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'description', and 'parameters'
|
||||
[2026-03-02 14:13:29,680] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'description', and 'parameters'"}
|
||||
[2026-03-02 14:16:50,715] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 📌 注册工具: [calculator] — 计算数学表达式，支持加减乘除、幂运算、括号等
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 📌 注册工具: [web_search] — 在互联网上搜索信息，返回相关网页摘要
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 📌 注册工具: [file_reader] — 读取本地文件内容，仅限 workspace/ 目录下的文件
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 📌 注册工具: [code_executor] — 在沙箱环境中执行 Python 代码片段，返回标准输出
|
||||
[2026-03-02 14:16:50,716] [agent.MCP] INFO: 📌 注册工具: [tool_generator] — 生成agent工具代码
|
||||
[2026-03-02 14:16:50,716] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
|
||||
[2026-03-02 14:16:50,754] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
|
||||
[2026-03-02 14:16:50,754] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
|
||||
[2026-03-02 14:16:50,754] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor', 'tool_generator']
|
||||
[2026-03-02 14:17:52,746] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:17:52,746] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate the python code to get system's name and generate a tool with the code
|
||||
[2026-03-02 14:17:52,746] [agent.MEMORY] DEBUG: 💬 [USER] generate the python code to get system's name and generate a...
|
||||
[2026-03-02 14:17:52,746] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:17:52,746] [agent.LLM] INFO: 💭 分析意图: generate the python code to get system's name and ...
|
||||
[2026-03-02 14:17:52,746] [agent.LLM] DEBUG: 📝 Prompt 已构造 (533 chars)
|
||||
[2026-03-02 14:17:55,200] [agent.Monica] INFO: {"need_tool": true, "tool_name": "tool_generator", "arguments": {"code": "import platform\n\ndef get_system_name():\n return platform.system()"}, "reasoning": "The user explicitly requested to generate a Python script that retrieves the system's name, and to create a tool based on that code. The provided tool_generator is appropriate for this task."}
|
||||
[2026-03-02 14:17:55,200] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [tool_generator]
|
||||
[2026-03-02 14:17:55,200] [agent.LLM] DEBUG: 💡 推理: The user explicitly requested to generate a Python script that retrieves the system's name, and to create a tool based on that code. The provided tool_generator is appropriate for this task.
|
||||
[2026-03-02 14:17:55,200] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: tool_generator
|
||||
参数: {'code': 'import platform\n\ndef get_system_name():\n return platform.system()'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '6adf8eba', 'method': 'tools/call', 'params': {'name': 'tool_generator', 'arguments': {'code': 'import platform\n\ndef get_system_name():\n return platform.system()'}}}
|
||||
[2026-03-02 14:17:55,200] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [tool_generator]...
|
||||
[2026-03-02 14:17:55,201] [agent.MCP] INFO: 📨 收到请求 id=6adf8eba method=tools/call
|
||||
[2026-03-02 14:17:55,201] [agent.TOOL] INFO: ▶ 执行工具 [tool_generator]，参数: {'code': 'import platform\n\ndef get_system_name():\n return platform.system()'}
|
||||
[2026-03-02 14:17:55,201] [agent.TOOL] ERROR: ❌ 工具 [tool_generator] 执行失败: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'description', and 'parameters'
|
||||
[2026-03-02 14:17:55,201] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: ToolGeneratorTool.execute() missing 3 required positional arguments: 'name', 'description', and 'parameters'"}
|
||||
[2026-03-02 14:21:07,265] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:21:07,266] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate the python function to get system's name and then generate the a tool with the code that includes tool name, tool description
|
||||
[2026-03-02 14:21:07,266] [agent.MEMORY] DEBUG: 💬 [USER] generate the python function to get system's name and then g...
|
||||
[2026-03-02 14:21:07,266] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:21:07,266] [agent.LLM] INFO: 💭 分析意图: generate the python function to get system's name ...
|
||||
[2026-03-02 14:21:07,266] [agent.LLM] DEBUG: 📝 Prompt 已构造 (676 chars)
|
||||
[2026-03-02 14:21:09,618] [agent.Monica] INFO: {"need_tool": true, "tool_name": "tool_generator", "arguments": {"code": "def get_system_name():\n import platform\n return platform.system()", "tool_name": "get_system_name_tool", "tool_description": "A tool to retrieve the name of the operating system."}, "reasoning": "User wants to generate a tool based on the Python function for retrieving the system's name. The tool_generator is appropriate for creating such a tool with the provided code, name, and description."}
|
||||
[2026-03-02 14:21:09,618] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [tool_generator]
|
||||
[2026-03-02 14:21:09,618] [agent.LLM] DEBUG: 💡 推理: User wants to generate a tool based on the Python function for retrieving the system's name. The tool_generator is appropriate for creating such a tool with the provided code, name, and description.
|
||||
[2026-03-02 14:21:09,618] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: tool_generator
|
||||
参数: {'code': 'def get_system_name():\n import platform\n return platform.system()', 'tool_name': 'get_system_name_tool', 'tool_description': 'A tool to retrieve the name of the operating system.'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '766f288c', 'method': 'tools/call', 'params': {'name': 'tool_generator', 'arguments': {'code': 'def get_system_name():\n import platform\n return platform.system()', 'tool_name': 'get_system_name_tool', 'tool_description': 'A tool to retrieve the name of the operating system.'}}}
|
||||
[2026-03-02 14:21:09,618] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [tool_generator]...
|
||||
[2026-03-02 14:21:09,618] [agent.MCP] INFO: 📨 收到请求 id=766f288c method=tools/call
|
||||
[2026-03-02 14:21:09,618] [agent.TOOL] INFO: ▶ 执行工具 [tool_generator]，参数: {'code': 'def get_system_name():\n import platform\n return platform.system()', 'tool_name': 'get_system_name_tool', 'tool_description': 'A tool to retrieve the name of the operating system.'}
|
||||
[2026-03-02 14:21:09,618] [agent.TOOL] ERROR: ❌ 工具 [tool_generator] 执行失败: ToolGeneratorTool.execute() got an unexpected keyword argument 'tool_name'
|
||||
[2026-03-02 14:21:09,618] [agent.CLIENT] ERROR: ❌ 工具调用失败: {'code': -32000, 'message': "工具执行异常: ToolGeneratorTool.execute() got an unexpected keyword argument 'tool_name'"}
|
||||
[2026-03-02 14:27:41,610] [agent.CLIENT] INFO: =======================================================
|
||||
[2026-03-02 14:27:41,610] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: generate the python function to get system's name and then generate the a tool with the code that includes tool name, tool description, tool parameters and code.
|
||||
[2026-03-02 14:27:41,611] [agent.MEMORY] DEBUG: 💬 [USER] generate the python function to get system's name and then g...
|
||||
[2026-03-02 14:27:41,611] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
|
||||
[2026-03-02 14:27:41,611] [agent.LLM] INFO: 💭 分析意图: generate the python function to get system's name ...
|
||||
[2026-03-02 14:27:41,611] [agent.LLM] DEBUG: 📝 Prompt 已构造 (791 chars)
|
||||
[2026-03-02 14:27:44,132] [agent.Monica] INFO: {
|
||||
"need_tool": true,
|
||||
"tool_name": "tool_generator",
|
||||
"arguments": {
|
||||
"name": "get_system_name",
|
||||
"description": "Returns the name of the operating system of the machine.",
|
||||
"parameters": {},
|
||||
"code": "import platform\ndef get_system_name():\n return platform.system()"
|
||||
},
|
||||
"reasoning": "The user requested to generate a Python function that retrieves the system's name and then encapsulate it into a tool with specified attributes such as tool name, description, parameters, and code. To accomplish this task, the `tool_generator` utility can be used with the specified parameters."
|
||||
}
|
||||
[2026-03-02 14:27:44,133] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [tool_generator]
|
||||
[2026-03-02 14:27:44,133] [agent.LLM] DEBUG: 💡 推理: The user requested to generate a Python function that retrieves the system's name and then encapsulate it into a tool with specified attributes such as tool name, description, parameters, and code. To accomplish this task, the `tool_generator` utility can be used with the specified parameters.
|
||||
[2026-03-02 14:27:44,133] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
|
||||
方法: tools/call
|
||||
工具: tool_generator
|
||||
参数: {'name': 'get_system_name', 'description': 'Returns the name of the operating system of the machine.', 'parameters': {}, 'code': 'import platform\ndef get_system_name():\n return platform.system()'}
|
||||
请求体: {'jsonrpc': '2.0', 'id': '9118bd32', 'method': 'tools/call', 'params': {'name': 'tool_generator', 'arguments': {'name': 'get_system_name', 'description': 'Returns the name of the operating system of the machine.', 'parameters': {}, 'code': 'import platform\ndef get_system_name():\n return platform.system()'}}}
|
||||
[2026-03-02 14:27:44,134] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [tool_generator]...
|
||||
[2026-03-02 14:27:44,134] [agent.MCP] INFO: 📨 收到请求 id=9118bd32 method=tools/call
|
||||
[2026-03-02 14:27:44,134] [agent.TOOL] INFO: ▶ 执行工具 [tool_generator]，参数: {'name': 'get_system_name', 'description': 'Returns the name of the operating system of the machine.', 'parameters': {}, 'code': 'import platform\ndef get_system_name():\n return platform.system()'}
|
||||
[2026-03-02 14:27:44,134] [agent.TOOL] INFO: ✅ 工具 [tool_generator] 执行成功
|
||||
|
|
|
|||
2
main.py
2
main.py
|
|
@ -16,6 +16,7 @@ from tools.calculator import CalculatorTool
|
|||
from tools.code_executor import CodeExecutorTool
|
||||
from tools.file_reader import FileReaderTool
|
||||
from tools.web_search import WebSearchTool
|
||||
from tools.tool_generator import ToolGeneratorTool
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger("SYSTEM")
|
||||
|
|
@ -41,6 +42,7 @@ def build_agent(agent_prompt) -> AgentClient:
|
|||
WebSearchTool,
|
||||
FileReaderTool,
|
||||
CodeExecutorTool,
|
||||
ToolGeneratorTool
|
||||
)
|
||||
|
||||
# 2. LLM 引擎
|
||||
|
|
|
|||
|
|
@ -58,3 +58,4 @@ class CodeExecutorTool(BaseTool):
|
|||
)
|
||||
except Exception as exc:
|
||||
return ToolResult(success=False, output=f"执行错误: {type(exc).__name__}: {exc}")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,16 @@
|
|||
import json
|
||||
|
||||
|
||||
|
||||
|
||||
class MyDemoTool(BaseTool):
|
||||
name = "{{ name }}"
|
||||
description = "{{ description }}"
|
||||
parameters = {{ parameters }}
|
||||
def execute(self, parameter1: str, parameter2: int):
|
||||
...
|
||||
"""
|
||||
|
||||
"""
|
||||
生成agent技能工具用于实现当前系统进程状态查询，技能工具代码示例如下："
|
||||
"""
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
from tools.base_tool import BaseTool, ToolResult
|
||||
|
||||
|
||||
class ToolGeneratorTool(BaseTool):
|
||||
name = "tool_generator"
|
||||
description = "生成agent工具代码"
|
||||
parameters = {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "tool name",
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "tool description"
|
||||
|
||||
},
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"description": """tool parameters descriptions"""
|
||||
},
|
||||
"code": {
|
||||
"type": "string",
|
||||
"description": "the code that the LLM generated"
|
||||
}
|
||||
}
|
||||
|
||||
def execute(self, name: str, description: str, parameters: dict, code: str) -> ToolResult:
|
||||
pass
|
||||
Loading…
Reference in New Issue