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sonto 2026-02-28 22:59:41 +08:00
parent 598d45a39d
commit 488b9fd22e
5 changed files with 335 additions and 105 deletions
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42
client/agent_client.py
View File

@ -17,12 +17,12 @@ from utils.logger import get_logger
@dataclass @dataclass
class AgentResponse: class AgentResponse:
"""一次完整 Agent 调用的结果""" """一次完整 Agent 调用的结果"""
user_input: str user_input: str
final_reply: str final_reply: str
tool_used: str | None = None tool_used: str | None = None
tool_output: str | None = None tool_output: str | None = None
success: bool = True success: bool = True
error: str | None = None error: str | None = None
# ── Agent 客户端 ─────────────────────────────────────────────── # ── Agent 客户端 ───────────────────────────────────────────────
@ -44,15 +44,17 @@ class AgentClient:
""" """
def __init__( def __init__(
self, self,
llm: LLMEngine, llm: LLMEngine,
mcp_server: MCPServer, mcp_server: MCPServer,
memory: MemoryStore, memory: MemoryStore,
prompt: str = ""
): ):
self.llm = llm self.llm = llm
self.mcp_server = mcp_server self.mcp_server = mcp_server
self.memory = memory self.memory = memory
self.logger = get_logger("CLIENT") self.agent_prompt = prompt
self.logger = get_logger("CLIENT")
self.logger.info("💻 Agent Client 初始化完成") self.logger.info("💻 Agent Client 初始化完成")
# ── 主入口 ────────────────────────────────────────────────── # ── 主入口 ──────────────────────────────────────────────────
@ -67,7 +69,7 @@ class AgentClient:
Returns: Returns:
AgentResponse 实例 AgentResponse 实例
""" """
self.logger.info(f"{'='*55}") self.logger.info(f"{'=' * 55}")
self.logger.info(f"📨 Step 1 [CLIENT] 收到用户输入: {user_input}") self.logger.info(f"📨 Step 1 [CLIENT] 收到用户输入: {user_input}")
# ── Step 1: 记录用户消息 ──────────────────────────────── # ── Step 1: 记录用户消息 ────────────────────────────────
@ -77,7 +79,7 @@ class AgentClient:
# ── Step 2: LLM 推理决策 ──────────────────────────────── # ── Step 2: LLM 推理决策 ────────────────────────────────
self.logger.info("🧠 Step 2 [LLM] 开始推理，分析意图...") self.logger.info("🧠 Step 2 [LLM] 开始推理，分析意图...")
tool_schemas = self.mcp_server.get_tool_schemas() tool_schemas = self.mcp_server.get_tool_schemas()
decision = self.llm.think_and_decide(user_input, tool_schemas, context) decision = self.llm.think_and_decide(user_input, tool_schemas, context, self.agent_prompt)
# ── 分支：是否需要工具 ────────────────────────────────── # ── 分支：是否需要工具 ──────────────────────────────────
if not decision.need_tool: if not decision.need_tool:
@ -95,10 +97,10 @@ class AgentClient:
return AgentResponse(user_input=user_input, final_reply=reply) return AgentResponse(user_input=user_input, final_reply=reply)
def _handle_tool_call( def _handle_tool_call(
self, self,
user_input: str, user_input: str,
decision, decision,
context: str, context: str,
) -> AgentResponse: ) -> AgentResponse:
"""执行工具调用的完整流程（Step 3 → 4 → 5）""" """执行工具调用的完整流程（Step 3 → 4 → 5）"""
@ -155,4 +157,4 @@ class AgentClient:
def clear_session(self) -> None: def clear_session(self) -> None:
"""清空当前会话""" """清空当前会话"""
self.memory.clear_history() self.memory.clear_history()
self.logger.info("🗑 会话已清空") self.logger.info("🗑 会话已清空")

171
llm/llm_engine.py
View File

@ -18,10 +18,10 @@ from openai import OpenAI
@dataclass @dataclass
class ToolDecision: class ToolDecision:
"""LLM 决策是否调用工具及调用参数""" """LLM 决策是否调用工具及调用参数"""
need_tool: bool need_tool: bool
tool_name: str = "" tool_name: str = ""
arguments: dict = None arguments: dict = None
reasoning: str = "" # 推理过程说明 reasoning: str = "" # 推理过程说明
def __post_init__(self): def __post_init__(self):
self.arguments = self.arguments or {} self.arguments = self.arguments or {}
@ -35,36 +35,46 @@ class ToolDecision:
params={"name": self.tool_name, "arguments": self.arguments}, params={"name": self.tool_name, "arguments": self.arguments},
) )
class MonicaClient:
class MonicaClient:
BASE_URL = "https://openapi.monica.im/v1" BASE_URL = "https://openapi.monica.im/v1"
def __init__(self, api_key): def __init__(self, api_key):
self.client = OpenAI(base_url=self.BASE_URL, self.client = OpenAI(base_url=self.BASE_URL,
api_key=api_key) api_key=api_key)
def create(self, model_name, tool_schemas, user_input) -> ToolDecision: self.logger = get_logger("Monica")
def create(self, model_name: str, tool_schemas, user_input: str, agent_prompt: str = "") -> ToolDecision:
tools = [{ tools = [{
"name": s.name, "name": s.name,
"description": s.description, "description": s.description,
"parameters": s.parameters} for s in tool_schemas] "parameters": s.parameters} for s in tool_schemas]
messages = []
if agent_prompt:
messages.append({
"role": "system",
"content": agent_prompt,
})
messages.append({
"role": "user",
"content": [{
"type": "text",
"text": user_input
}]
})
completion = self.client.chat.completions.create( completion = self.client.chat.completions.create(
model=model_name, model=model_name,
functions=tools, functions=tools,
messages = [ messages=messages
{
"role": "user",
"content": [{
"type": "text",
"text": user_input
}]
}
]
) )
self.logger.info(completion.choices[0].message.content)
response = json.loads(completion.choices[0].message.content) response = json.loads(completion.choices[0].message.content)
return ToolDecision(need_tool=response['need_tool'], return ToolDecision(need_tool=response['need_tool'],
tool_name=response['tool_name'], tool_name=response['tool_name'],
arguments=response['arguments'], arguments=response['arguments'],
reasoning=response['reasoning']) reasoning=response['reasoning'])
# ── LLM 引擎 ────────────────────────────────────────────────── # ── LLM 引擎 ──────────────────────────────────────────────────
class LLMEngine: class LLMEngine:
""" """
@ -82,19 +92,21 @@ class LLMEngine:
""" """
API_KEY = "sk-AUmOuFI731Ty5Nob38jY26d8lydfDT-QkE2giqb0sCuPCAE2JH6zjLM4lZLpvL5WMYPOocaMe2FwVDmqM_9KimmKACjR" API_KEY = "sk-AUmOuFI731Ty5Nob38jY26d8lydfDT-QkE2giqb0sCuPCAE2JH6zjLM4lZLpvL5WMYPOocaMe2FwVDmqM_9KimmKACjR"
def __init__(self, model_name: str = "claude-sonnet-4-6"): def __init__(self, model_name: str = "claude-sonnet-4-6"):
self.model_name = model_name self.model_name = model_name
self.logger = get_logger("LLM") self.logger = get_logger("LLM")
self.logger.info(f"🧠 LLM 引擎初始化，模型: {model_name}") self.logger.info(f"🧠 LLM 引擎初始化，模型: {model_name}")
self.client = MonicaClient(api_key=self.API_KEY) self.client = MonicaClient(api_key=self.API_KEY)
# ── 核心推理流程 ──────────────────────────────────────────── # ── 核心推理流程 ────────────────────────────────────────────
def think_and_decide( def think_and_decide(
self, self,
user_input: str, user_input: str,
tool_schemas: list[ToolSchema], tool_schemas: list[ToolSchema],
context: str = "", context: str = "",
agent_prompt: str = ""
) -> ToolDecision: ) -> ToolDecision:
""" """
Step 1 & 2: 理解意图，决策工具调用（Think 阶段） Step 1 & 2: 理解意图，决策工具调用（Think 阶段）
@ -103,7 +115,7 @@ class LLMEngine:
user_input: 用户输入文本 user_input: 用户输入文本
tool_schemas: 可用工具的 Schema 列表 tool_schemas: 可用工具的 Schema 列表
context: 对话历史上下文摘要 context: 对话历史上下文摘要
agent_prompt: 智能体提示词
Returns: Returns:
ToolDecision 实例 ToolDecision 实例
""" """
@ -115,7 +127,7 @@ class LLMEngine:
# 调用 LLM（Demo 中使用规则模拟） # 调用 LLM（Demo 中使用规则模拟）
# decision = self._call_llm_api(user_input, tool_schemas) # decision = self._call_llm_api(user_input, tool_schemas)
decision = self._call_llm_api(prompt, tool_schemas) decision = self._call_llm_api(prompt, tool_schemas, agent_prompt=agent_prompt)
self.logger.info( self.logger.info(
f"🎯 决策结果: {'调用工具 [' + decision.tool_name + ']' if decision.need_tool else '直接回复'}" f"🎯 决策结果: {'调用工具 [' + decision.tool_name + ']' if decision.need_tool else '直接回复'}"
@ -124,11 +136,11 @@ class LLMEngine:
return decision return decision
def generate_final_reply( def generate_final_reply(
self, self,
user_input: str, user_input: str,
tool_name: str, tool_name: str,
tool_output: str, tool_output: str,
context: str = "", context: str = "",
) -> str: ) -> str:
""" """
Step 5: 整合工具结果，生成最终自然语言回复（Observe 阶段） Step 5: 整合工具结果，生成最终自然语言回复（Observe 阶段）
@ -157,10 +169,10 @@ class LLMEngine:
# ── Prompt 构造 ───────────────────────────────────────────── # ── Prompt 构造 ─────────────────────────────────────────────
def _build_decision_prompt( def _build_decision_prompt(
self, self,
user_input: str, user_input: str,
tool_schemas: list[ToolSchema], tool_schemas: list[ToolSchema],
context: str, context: str,
) -> str: ) -> str:
"""构造工具决策 Prompt（ReAct 格式）""" """构造工具决策 Prompt（ReAct 格式）"""
tools_desc = "\n".join( tools_desc = "\n".join(
@ -178,7 +190,7 @@ class LLMEngine:
# ── 模拟 LLM API（Demo 用规则引擎替代）──────────────────── # ── 模拟 LLM API（Demo 用规则引擎替代）────────────────────
def _call_llm_api(self, user_input: str, tool_schemas: list[ToolSchema]) -> ToolDecision: def _call_llm_api(self, user_input: str, tool_schemas: list[ToolSchema], agent_prompt: str = "") -> ToolDecision:
""" """
模拟 LLM API 调用（Demo 版本使用关键词规则） 模拟 LLM API 调用（Demo 版本使用关键词规则）
@ -192,57 +204,60 @@ class LLMEngine:
) )
# 解析 response.content 中的 tool_use block # 解析 response.content 中的 tool_use block
""" """
return self.client.create(self.model_name, user_input=user_input, tool_schemas=tool_schemas) if self.client:
return self.client.create(self.model_name,
user_input=user_input,
tool_schemas=tool_schemas,
agent_prompt=agent_prompt)
else:
text = user_input.lower()
# 规则匹配：计算器
calc_pattern = re.search(r"[\d\s\+\-\*\/\(\)\^]+[=？?]?", user_input)
if any(kw in text for kw in ["计算", "等于", "多少", "×", "÷"]) and calc_pattern:
expr = re.sub(r"[^0-9+\-*/().**]", "", user_input.replace("×", "*").replace("÷", "/"))
return ToolDecision(
need_tool=True, tool_name="calculator",
arguments={"expression": expr or "1+1"},
reasoning="用户请求数学计算，调用 calculator 工具",
)
text = user_input.lower() # 规则匹配：搜索
if any(kw in text for kw in ["搜索", "查询", "天气", "新闻", "查一下", "search"]):
return ToolDecision(
need_tool=True, tool_name="web_search",
arguments={"query": user_input, "max_results": 3},
reasoning="用户需要实时信息，调用 web_search 工具",
)
# 规则匹配：计算器 # 规则匹配：文件读取
calc_pattern = re.search(r"[\d\s\+\-\*\/\(\)\^]+[=？?]?", user_input) if any(kw in text for kw in ["文件", "读取", "file", "config", "json", "txt"]):
if any(kw in text for kw in ["计算", "等于", "多少", "×", "÷"]) and calc_pattern: filename = re.search(r"[\w\-\.]+\.\w+", user_input)
expr = re.sub(r"[^0-9+\-*/().**]", "", user_input.replace("×","*").replace("÷","/")) return ToolDecision(
need_tool=True, tool_name="file_reader",
arguments={"path": filename.group() if filename else "config.json"},
reasoning="用户请求读取文件，调用 file_reader 工具",
)
# 规则匹配：代码执行
if any(kw in text for kw in ["执行", "运行", "代码", "python", "print", "code"]):
code_match = re.search(r'[`\'"](.+?)[`\'"]', user_input)
code = code_match.group(1) if code_match else 'print("Hello, Agent!")'
return ToolDecision(
need_tool=True, tool_name="code_executor",
arguments={"code": code, "timeout": 5},
reasoning="用户请求执行代码，调用 code_executor 工具",
)
# 默认：直接回复
return ToolDecision( return ToolDecision(
need_tool=True, tool_name="calculator", need_tool=False,
arguments={"expression": expr or "1+1"}, reasoning="问题可直接回答，无需工具",
reasoning="用户请求数学计算，调用 calculator 工具",
) )
# 规则匹配：搜索
if any(kw in text for kw in ["搜索", "查询", "天气", "新闻", "查一下", "search"]):
return ToolDecision(
need_tool=True, tool_name="web_search",
arguments={"query": user_input, "max_results": 3},
reasoning="用户需要实时信息，调用 web_search 工具",
)
# 规则匹配：文件读取
if any(kw in text for kw in ["文件", "读取", "file", "config", "json", "txt"]):
filename = re.search(r"[\w\-\.]+\.\w+", user_input)
return ToolDecision(
need_tool=True, tool_name="file_reader",
arguments={"path": filename.group() if filename else "config.json"},
reasoning="用户请求读取文件，调用 file_reader 工具",
)
# 规则匹配：代码执行
if any(kw in text for kw in ["执行", "运行", "代码", "python", "print", "code"]):
code_match = re.search(r'[`\'"](.+?)[`\'"]', user_input)
code = code_match.group(1) if code_match else 'print("Hello, Agent!")'
return ToolDecision(
need_tool=True, tool_name="code_executor",
arguments={"code": code, "timeout": 5},
reasoning="用户请求执行代码，调用 code_executor 工具",
)
# 默认：直接回复
return ToolDecision(
need_tool=False,
reasoning="问题可直接回答，无需工具",
)
def _synthesize_reply(self, user_input: str, tool_name: str, tool_output: str) -> str: def _synthesize_reply(self, user_input: str, tool_name: str, tool_output: str) -> str:
"""基于工具输出合成最终回复（Demo 版本）""" """基于工具输出合成最终回复（Demo 版本）"""
return ( return (
f"✅ 已通过 [{tool_name}] 工具处理您的请求。\n\n" f"✅ 已通过 [{tool_name}] 工具处理您的请求。\n\n"
f"**执行结果:**\n{tool_output}\n\n" f"**执行结果:**\n{tool_output}\n\n"
f"---\n*由 {self.model_name} 生成 · 工具: {tool_name}*" f"---\n*由 {self.model_name} 生成 · 工具: {tool_name}*"
) )

210
logs/agent.log
View File

@ -208,3 +208,213 @@
--- ---
*由 gpt-... *由 gpt-...
[2026-02-28 16:06:05,167] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回 [2026-02-28 16:06:05,167] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回
[2026-02-28 22:29:24,671] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:29:24,672] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:29:24,672] [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
[2026-02-28 22:29:24,672] [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
[2026-02-28 22:29:24,672] [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
[2026-02-28 22:29:24,672] [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
[2026-02-28 22:29:24,672] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:29:56,971] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:29:56,971] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:29:56,971] [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
[2026-02-28 22:29:56,971] [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
[2026-02-28 22:29:56,971] [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
[2026-02-28 22:29:56,971] [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
[2026-02-28 22:29:56,972] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:30:46,017] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:30:46,017] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:30:46,017] [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
[2026-02-28 22:30:46,018] [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
[2026-02-28 22:30:46,018] [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
[2026-02-28 22:30:46,018] [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
[2026-02-28 22:30:46,018] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:30:53,901] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:30:53,902] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:30:53,902] [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
[2026-02-28 22:30:53,902] [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
[2026-02-28 22:30:53,902] [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
[2026-02-28 22:30:53,902] [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
[2026-02-28 22:30:53,902] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:47:31,295] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:47:31,296] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:47:31,296] [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
[2026-02-28 22:47:31,296] [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
[2026-02-28 22:47:31,296] [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
[2026-02-28 22:47:31,296] [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
[2026-02-28 22:47:31,296] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:51:08,506] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:51:08,507] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:51:08,507] [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
[2026-02-28 22:51:08,507] [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
[2026-02-28 22:51:08,507] [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
[2026-02-28 22:51:08,507] [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
[2026-02-28 22:51:08,507] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:51:43,854] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:51:43,855] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:51:43,855] [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
[2026-02-28 22:51:43,855] [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
[2026-02-28 22:51:43,855] [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
[2026-02-28 22:51:43,855] [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
[2026-02-28 22:51:43,855] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:51:43,891] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
[2026-02-28 22:51:43,891] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
[2026-02-28 22:51:43,891] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor']
[2026-02-28 22:51:58,206] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:51:58,206] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:51:58,206] [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
[2026-02-28 22:51:58,206] [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
[2026-02-28 22:51:58,207] [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
[2026-02-28 22:51:58,207] [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
[2026-02-28 22:51:58,207] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:51:58,232] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
[2026-02-28 22:51:58,232] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
[2026-02-28 22:51:58,232] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor']
[2026-02-28 22:52:08,077] [agent.CLIENT] INFO: =======================================================
[2026-02-28 22:52:08,077] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 1加1等于多少
[2026-02-28 22:52:08,077] [agent.MEMORY] DEBUG: 💬 [USER] 1加1等于多少...
[2026-02-28 22:52:08,078] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
[2026-02-28 22:52:08,078] [agent.LLM] INFO: 💭 分析意图: 1加1等于多少...
[2026-02-28 22:52:08,078] [agent.LLM] DEBUG: 📝 Prompt 已构造 (344 chars)
[2026-02-28 22:54:48,518] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:54:48,518] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:54:48,518] [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
[2026-02-28 22:54:48,519] [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
[2026-02-28 22:54:48,519] [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
[2026-02-28 22:54:48,519] [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
[2026-02-28 22:54:48,519] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:54:48,543] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
[2026-02-28 22:54:48,544] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
[2026-02-28 22:54:48,544] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor']
[2026-02-28 22:55:14,173] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:55:14,173] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:55:14,173] [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
[2026-02-28 22:55:14,174] [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
[2026-02-28 22:55:14,174] [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
[2026-02-28 22:55:14,174] [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
[2026-02-28 22:55:14,174] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:55:54,637] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:55:54,637] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:55:54,637] [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
[2026-02-28 22:55:54,638] [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
[2026-02-28 22:55:54,638] [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
[2026-02-28 22:55:54,638] [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
[2026-02-28 22:55:54,638] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:55:54,675] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
[2026-02-28 22:55:54,676] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
[2026-02-28 22:55:54,676] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor']
[2026-02-28 22:56:03,477] [agent.CLIENT] INFO: =======================================================
[2026-02-28 22:56:03,478] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 1加1等于多少
[2026-02-28 22:56:03,478] [agent.MEMORY] DEBUG: 💬 [USER] 1加1等于多少...
[2026-02-28 22:56:03,478] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
[2026-02-28 22:56:03,478] [agent.LLM] INFO: 💭 分析意图: 1加1等于多少...
[2026-02-28 22:56:03,478] [agent.LLM] DEBUG: 📝 Prompt 已构造 (344 chars)
[2026-02-28 22:56:17,390] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [calculator]
[2026-02-28 22:56:17,391] [agent.LLM] DEBUG: 💡 推理: 用户提出了一个数学问题，需要计算 1 加 1 的值，用 calculator 工具进行计算是最合适的。
[2026-02-28 22:56:17,391] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
方法: tools/call
工具: calculator
参数: {'expression': '1+1'}
请求体: {'jsonrpc': '2.0', 'id': '2bcb050e', 'method': 'tools/call', 'params': {'name': 'calculator', 'arguments': {'expression': '1+1'}}}
[2026-02-28 22:56:17,392] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [calculator]...
[2026-02-28 22:56:17,392] [agent.MCP] INFO: 📨 收到请求 id=2bcb050e method=tools/call
[2026-02-28 22:56:17,392] [agent.TOOL] INFO: ▶ 执行工具 [calculator]，参数: {'expression': '1+1'}
[2026-02-28 22:56:17,392] [agent.TOOL] INFO: ✅ 工具 [calculator] 执行成功
[2026-02-28 22:56:17,393] [agent.CLIENT] INFO: ✅ 工具执行成功，输出: 1+1 = 2...
[2026-02-28 22:56:17,393] [agent.MEMORY] DEBUG: 💬 [TOOL] 1+1 = 2...
[2026-02-28 22:56:17,393] [agent.CLIENT] INFO: ✍️ Step 5 [LLM] 整合工具结果，生成最终回复...
[2026-02-28 22:56:17,393] [agent.LLM] INFO: ✍️ 整合工具结果，生成最终回复...
[2026-02-28 22:56:17,394] [agent.LLM] INFO: 💬 回复已生成 (83 chars)
[2026-02-28 22:56:17,394] [agent.MEMORY] DEBUG: 💬 [ASSISTANT] ✅ 已通过 [calculator] 工具处理您的请求。
**执行结果:**
1+1 = 2
---
*由 gpt-...
[2026-02-28 22:56:17,394] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回
[2026-02-28 22:56:35,957] [agent.CLIENT] INFO: =======================================================
[2026-02-28 22:56:35,958] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 34*67等于多少
[2026-02-28 22:56:35,958] [agent.MEMORY] DEBUG: 💬 [USER] 34*67等于多少...
[2026-02-28 22:56:35,959] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
[2026-02-28 22:56:35,959] [agent.LLM] INFO: 💭 分析意图: 34*67等于多少...
[2026-02-28 22:56:35,959] [agent.LLM] DEBUG: 📝 Prompt 已构造 (471 chars)
[2026-02-28 22:58:14,926] [agent.SYSTEM] INFO: 🔧 开始组装 Agent 系统...
[2026-02-28 22:58:14,926] [agent.MCP] INFO: 🚀 MCP Server [DemoMCPServer] 启动
[2026-02-28 22:58:14,926] [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
[2026-02-28 22:58:14,926] [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
[2026-02-28 22:58:14,926] [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
[2026-02-28 22:58:14,926] [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
[2026-02-28 22:58:14,927] [agent.LLM] INFO: 🧠 LLM 引擎初始化，模型: gpt-4o
[2026-02-28 22:58:14,964] [agent.MEMORY] INFO: 💾 Memory 初始化，最大历史: 20 条
[2026-02-28 22:58:14,964] [agent.CLIENT] INFO: 💻 Agent Client 初始化完成
[2026-02-28 22:58:14,965] [agent.SYSTEM] INFO: ✅ Agent 组装完成，已注册工具: ['calculator', 'web_search', 'file_reader', 'code_executor']
[2026-02-28 22:58:21,018] [agent.CLIENT] INFO: =======================================================
[2026-02-28 22:58:21,018] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 34*56等于多少
[2026-02-28 22:58:21,018] [agent.MEMORY] DEBUG: 💬 [USER] 34*56等于多少...
[2026-02-28 22:58:21,018] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
[2026-02-28 22:58:21,018] [agent.LLM] INFO: 💭 分析意图: 34*56等于多少...
[2026-02-28 22:58:21,019] [agent.LLM] DEBUG: 📝 Prompt 已构造 (348 chars)
[2026-02-28 22:58:23,409] [agent.Monica] INFO: {
"need_tool": true,
"tool_name": "calculator",
"arguments": {
"expression": "34*56"
},
"reasoning": "用户询问的数学问题需要计算具体结果，这可以通过调用 calculator 工具来完成。"
}
[2026-02-28 22:58:23,409] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [calculator]
[2026-02-28 22:58:23,409] [agent.LLM] DEBUG: 💡 推理: 用户询问的数学问题需要计算具体结果，这可以通过调用 calculator 工具来完成。
[2026-02-28 22:58:23,409] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
方法: tools/call
工具: calculator
参数: {'expression': '34*56'}
请求体: {'jsonrpc': '2.0', 'id': '6f404ceb', 'method': 'tools/call', 'params': {'name': 'calculator', 'arguments': {'expression': '34*56'}}}
[2026-02-28 22:58:23,409] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [calculator]...
[2026-02-28 22:58:23,410] [agent.MCP] INFO: 📨 收到请求 id=6f404ceb method=tools/call
[2026-02-28 22:58:23,410] [agent.TOOL] INFO: ▶ 执行工具 [calculator]，参数: {'expression': '34*56'}
[2026-02-28 22:58:23,410] [agent.TOOL] INFO: ✅ 工具 [calculator] 执行成功
[2026-02-28 22:58:23,410] [agent.CLIENT] INFO: ✅ 工具执行成功，输出: 34*56 = 1904...
[2026-02-28 22:58:23,410] [agent.MEMORY] DEBUG: 💬 [TOOL] 34*56 = 1904...
[2026-02-28 22:58:23,410] [agent.CLIENT] INFO: ✍️ Step 5 [LLM] 整合工具结果，生成最终回复...
[2026-02-28 22:58:23,410] [agent.LLM] INFO: ✍️ 整合工具结果，生成最终回复...
[2026-02-28 22:58:23,410] [agent.LLM] INFO: 💬 回复已生成 (88 chars)
[2026-02-28 22:58:23,410] [agent.MEMORY] DEBUG: 💬 [ASSISTANT] ✅ 已通过 [calculator] 工具处理您的请求。
**执行结果:**
34*56 = 1904
---
*由...
[2026-02-28 22:58:23,410] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回
[2026-02-28 22:58:37,709] [agent.CLIENT] INFO: =======================================================
[2026-02-28 22:58:37,710] [agent.CLIENT] INFO: 📨 Step 1 [CLIENT] 收到用户输入: 23*45等于多少
[2026-02-28 22:58:37,710] [agent.MEMORY] DEBUG: 💬 [USER] 23*45等于多少...
[2026-02-28 22:58:37,710] [agent.CLIENT] INFO: 🧠 Step 2 [LLM] 开始推理，分析意图...
[2026-02-28 22:58:37,711] [agent.LLM] INFO: 💭 分析意图: 23*45等于多少...
[2026-02-28 22:58:37,711] [agent.LLM] DEBUG: 📝 Prompt 已构造 (478 chars)
[2026-02-28 22:58:47,485] [agent.Monica] INFO: {"need_tool": true, "tool_name": "calculator", "arguments": {"expression": "23*45"}, "reasoning": "计算数学表达式需要调用 calculator 工具直接获得结果，以确保准确性。"}
[2026-02-28 22:58:47,486] [agent.LLM] INFO: 🎯 决策结果: 调用工具 [calculator]
[2026-02-28 22:58:47,486] [agent.LLM] DEBUG: 💡 推理: 计算数学表达式需要调用 calculator 工具直接获得结果，以确保准确性。
[2026-02-28 22:58:47,487] [agent.CLIENT] INFO: 📡 Step 3 [MCP] 发送工具调用请求
方法: tools/call
工具: calculator
参数: {'expression': '23*45'}
请求体: {'jsonrpc': '2.0', 'id': '246d9a09', 'method': 'tools/call', 'params': {'name': 'calculator', 'arguments': {'expression': '23*45'}}}
[2026-02-28 22:58:47,487] [agent.CLIENT] INFO: 🔧 Step 4 [TOOL] MCP Server 执行工具 [calculator]...
[2026-02-28 22:58:47,487] [agent.MCP] INFO: 📨 收到请求 id=246d9a09 method=tools/call
[2026-02-28 22:58:47,487] [agent.TOOL] INFO: ▶ 执行工具 [calculator]，参数: {'expression': '23*45'}
[2026-02-28 22:58:47,488] [agent.TOOL] INFO: ✅ 工具 [calculator] 执行成功
[2026-02-28 22:58:47,488] [agent.CLIENT] INFO: ✅ 工具执行成功，输出: 23*45 = 1035...
[2026-02-28 22:58:47,488] [agent.MEMORY] DEBUG: 💬 [TOOL] 23*45 = 1035...
[2026-02-28 22:58:47,489] [agent.CLIENT] INFO: ✍️ Step 5 [LLM] 整合工具结果，生成最终回复...
[2026-02-28 22:58:47,489] [agent.LLM] INFO: ✍️ 整合工具结果，生成最终回复...
[2026-02-28 22:58:47,489] [agent.LLM] INFO: 💬 回复已生成 (88 chars)
[2026-02-28 22:58:47,489] [agent.MEMORY] DEBUG: 💬 [ASSISTANT] ✅ 已通过 [calculator] 工具处理您的请求。
**执行结果:**
23*45 = 1035
---
*由...
[2026-02-28 22:58:47,489] [agent.CLIENT] INFO: 🎉 [CLIENT] 流程完成，回复已返回

16
main.py
View File

@ -6,7 +6,7 @@ main.py
""" """
import sys import sys
import argparse
# ── 导入各模块 ───────────────────────────────────────────────── # ── 导入各模块 ─────────────────────────────────────────────────
from client.agent_client import AgentClient from client.agent_client import AgentClient
from llm.llm_engine import LLMEngine from llm.llm_engine import LLMEngine
@ -22,7 +22,7 @@ logger = get_logger("SYSTEM")
# ── 系统组装 ─────────────────────────────────────────────────── # ── 系统组装 ───────────────────────────────────────────────────
def build_agent() -> AgentClient: def build_agent(agent_prompt) -> AgentClient:
""" """
工厂函数：组装并返回完整的 Agent 实例 工厂函数：组装并返回完整的 Agent 实例
@ -50,7 +50,7 @@ def build_agent() -> AgentClient:
memory = MemoryStore(max_history=20) memory = MemoryStore(max_history=20)
# 4. 组装客户端 # 4. 组装客户端
client = AgentClient(llm=llm, mcp_server=mcp_server, memory=memory) client = AgentClient(llm=llm, mcp_server=mcp_server, memory=memory, prompt=agent_prompt)
logger.info(f"✅ Agent 组装完成，已注册工具: {mcp_server.list_tools()}") logger.info(f"✅ Agent 组装完成，已注册工具: {mcp_server.list_tools()}")
return client return client
@ -146,11 +146,13 @@ def main() -> None:
python main.py → 交互模式（默认） python main.py → 交互模式（默认）
python main.py demo → 演示模式（自动执行预设场景） python main.py demo → 演示模式（自动执行预设场景）
""" """
client = build_agent() parser = argparse.ArgumentParser()
parser.add_argument("-d", "--daemon", help="服务模式", action="store_true")
parser.add_argument("-p", "--prompt", default="你是一个通用智能体，非常擅长将用户指令分解成可以执行的任务进行执行。", help="智能体提示此词, 例如：你是一个XXXXX，非常擅长……")
args = parser.parse_args(sys.argv[1:])
client = build_agent(args.prompt)
mode = sys.argv[1] if len(sys.argv) > 1 else "interactive" if args.daemon:
if mode == "demo":
run_demo(client) run_demo(client)
else: else:
run_interactive(client) run_interactive(client)

1
requirements.txt Normal file
View File

@ -0,0 +1 @@
openai