接近真实 AI Agent 框架的工业级模板

融合了很多现代 Agent 框架思想，例如：

• LangChain
• Microsoft AutoGen
• CrewAI
• MetaGPT

这个模板包含：

完整 Agent 系统组件

User ↓ Router ↓ Planner (LLM) ↓ Task Queue ↓ Executor ↓ Tool Calls ↓ Memory (Vector + Short) ↓ Critic / Reflection ↓ Loop

并支持：

• Tool Calling
• RAG Memory
• Planner + Critic
• Multi-Agent
• Async 执行
• Task Graph

工业级 Agent 框架模板（Python）

""" Industrial AI Agent Framework Template """importasyncioimportuuidimporttimefromdataclassesimportdataclass,fieldfromtypingimportList,Dict,Callable,Any# ===============================# Data Structures# ===============================@dataclassclassMessage:role:strcontent:str@dataclassclassTask:id:strdescription:strstatus:str="pending"result:str=""@dataclassclassAgentState:goal:strtasks:List[Task]=field(default_factory=list)history:List[str]=field(default_factory=list)reflections:List[str]=field(default_factory=list)# ===============================# LLM Interface (Mock)# ===============================classLLM:asyncdefgenerate(self,prompt:str):awaitasyncio.sleep(0.2)returnf"LLM_RESPONSE:{prompt[:50]}"# ===============================# Memory System# ===============================classVectorMemory:def__init__(self):self.data=[]defadd(self,text):embedding=hash(text)%10000self.data.append((embedding,text))defsearch(self,query):q=hash(query)%10000ranked=sorted(self.data,key=lambdax:abs(x[0]-q))return[x[1]forxinranked[:3]]classShortTermMemory:def__init__(self):self.buffer=[]defadd(self,text):self.buffer.append(text)iflen(self.buffer)>10:self.buffer.pop(0)defget(self):returnself.buffer# ===============================# Tool System# ===============================classToolRegistry:def__init__(self):self.tools:Dict[str,Callable]={}defregister(self,name,func):self.tools[name]=funcasyncdefcall(self,name,*args):ifnamenotinself.tools:raiseException("tool not found")result=self.tools[name](*args)ifasyncio.iscoroutine(result):result=awaitresultreturnresult# Example toolsdefsearch_web(query):returnf"Search results for{query}"defwrite_file(text):name=f"output_{int(time.time())}.txt"withopen(name,"w")asf:f.write(text)returnnamedefcalculator(expr):returneval(expr)# ===============================# Planner# ===============================classPlanner:def__init__(self,llm):self.llm=llmasyncdefplan(self,goal):prompt=f"Break goal into steps:{goal}"_=awaitself.llm.generate(prompt)steps=[f"Research{goal}",f"Analyze{goal}",f"Write report about{goal}"]return[Task(id=str(uuid.uuid4()),description=s)forsinsteps]# ===============================# Tool Agent# ===============================classToolAgent:def__init__(self,llm,tools):self.llm=llm self.tools=toolsasyncdefact(self,task):if"Research"intask.description:returnawaitself.tools.call("search",task.description)if"Analyze"intask.description:returnf"Analysis result of{task.description}"if"Write"intask.description:returnawaitself.tools.call("write","Generated report")return"done"# ===============================# Critic Agent# ===============================classCriticAgent:def__init__(self,llm):self.llm=llmasyncdefreview(self,result):prompt=f"Evaluate result:{result}"critique=awaitself.llm.generate(prompt)returncritique# ===============================# Task Manager# ===============================classTaskManager:def__init__(self):self.queue:List[Task]=[]defadd_tasks(self,tasks):self.queue.extend(tasks)defnext_task(self):fortinself.queue:ift.status=="pending":returntreturnNone# ===============================# Agent System# ===============================classAgentSystem:def__init__(self):self.llm=LLM()self.memory=VectorMemory()self.short_memory=ShortTermMemory()self.tools=ToolRegistry()self.tools.register("search",search_web)self.tools.register("write",write_file)self.tools.register("calc",calculator)self.planner=Planner(self.llm)self.executor=ToolAgent(self.llm,self.tools)self.critic=CriticAgent(self.llm)self.tasks=TaskManager()asyncdefrun(self,goal):state=AgentState(goal)print("Goal:",goal)# planningtasks=awaitself.planner.plan(goal)self.tasks.add_tasks(tasks)state.tasks=taskswhileTrue:task=self.tasks.next_task()ifnottask:breakprint("\nExecuting:",task.description)result=awaitself.executor.act(task)task.result=result task.status="done"print("Result:",result)self.short_memory.add(result)self.memory.add(result)critique=awaitself.critic.review(result)print("Critic:",critique)state.history.append(result)state.reflections.append(critique)returnstate# ===============================# Multi-Agent Coordinator# ===============================classMultiAgentCoordinator:def__init__(self,num_agents=2):self.agents=[AgentSystem()for_inrange(num_agents)]asyncdefrun(self,goal):tasks=[agent.run(goal)foragentinself.agents]results=awaitasyncio.gather(*tasks)returnresults# ===============================# Main# ===============================asyncdefmain():system=MultiAgentCoordinator(2)results=awaitsystem.run("Future of AI Agents")print("\nAll agents finished")forrinresults:print("Reflections:",len(r.reflections))if__name__=="__main__":asyncio.run(main())

这个框架的架构能力

1 Multi-Agent

Coordinator │ ┌─┴─────────┐ Agent 1 Agent 2

类似：

• Microsoft AutoGen
• CrewAI

2 RAG Memory

VectorMemory │ similarity search │ relevant memory

真实系统：

• FAISS
• Chroma
• Weaviate

3 Tool Calling

Agent调用外部能力：

Agent │ ToolRegistry │ API / DB / Web

类似：

• LangChain tool system

4 Critic / Reflection

Result ↓ Critic Agent ↓ Reflection

类似论文：

• Reflexion
• Self-Refine

5 Async 并行 Agent

asyncio.gather

真实系统：

Agent swarm parallel reasoning

如果把这个升级到真正生产级 Agent

需要再增加5个模块：

1 Tool Calling JSON Schema

类似

OpenAI function calling

2 Planner Tree Search

Tree-of-Thought MCTS

3 Persistent Memory

Redis VectorDB

4 Agent Router

User request ↓ Router ↓ choose agent

5 Observability

Tracing Token usage Cost monitor

类似：

• LangSmith