使用Deepseek扩展技术达人的微博，生成参考代码

这是OpenResty作者的微博:

一个好用的 AI Agent 程序框架需要满足：1. 小巧轻便和高效，2. 方便自动最大化请求并发度, 3. 透明切换不同的 LLM（厂商 API 或本地部署的），4. 透明混用大小不同的模型，5. 透明切换实时请求方式和批处理离线方式，6. 自如重跑单个处理环节，7. 方便对数据流进行回溯排查问题，8. 自如切换流式输出或批量输出模式，9. 透明的分布式请求和错误重试机制。10. 透明的纯内存或基于硬盘的海量数据的存储与计算。11. 透明的本地和远方 LLM prompt 缓存，等等。兼顾高吞吐量、低延迟、低成本、高开发效率和高可调试性。
所以我前面自己从零写了一个 Chunbot AI 框架，不依赖外部服务组件，经过大半年的特性和优化积累之后，现在也只有几千行代码，极为小巧和高效。其自带自动化测试台，轻松驱动上亿 token 量级的庞大的测试集。

输入给Deepseek，要求根据描述实现一个Python Agent框架，最小化依赖，结果:

以下是一个基于描述的轻量级Python Agent框架设计，尽量保持简洁并减少依赖：

import asyncio
import json
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union

# 基础数据模型
@dataclass
class LLMRequest:
    prompt: str
    model: str
    params: dict = None
    context: dict = None

@dataclass
class LLMResponse:
    content: str
    model: str
    metadata: dict = None

@dataclass
class ProcessingStep:
    name: str
    input_data: Any
    output_data: Any
    context: dict

# 核心框架组件
class AgentCore:
    def __init__(self):
        self.model_providers = {}
        self.pipeline = []
        self.storage_backend = MemoryStorage()
        self.cache = PromptCache()
        self.executor = AsyncExecutor()
        self.error_handler = RetryHandler()
        self.current_context = {}

    def register_model(self, name: str, provider: 'ModelProvider'):
        self.model_providers[name] = provider

    def add_pipeline_step(self, step: callable):
        self.pipeline.append(step)

    async def process(self, request: LLMRequest) -> LLMResponse:
        self.current_context = request.context or {}
        result = None
        
        for step in self.pipeline:
            result = await self._execute_step(step, request, result)
            self._record_step(step.__name__, request, result)
            
        return LLMResponse(content=result, model=request.model)

    async def _execute_step(self, step, request, prev_result):
        return await self.error_handler.execute_with_retry(
            step, request, prev_result, self.current_context
        )

    def _record_step(self, step_name, request, result):
        self.storage_backend.store(
            ProcessingStep(
                name=step_name,
                input_data=request,
                output_data=result,
                context=self.current_context.copy()
            )
        )

# 抽象模型接口
class ModelProvider(ABC):
    @abstractmethod
    async def generate(self, prompt: str, **kwargs) -> str:
        pass

class OpenAIProvider(ModelProvider):
    def __init__(self, api_key: str):
        self.api_key = api_key

    async def generate(self, prompt: str, **kwargs) -> str:
        # 实现实际的API调用逻辑
        pass

class LocalModelProvider(ModelProvider):
    async def generate(self, prompt: str, **kwargs) -> str:
        # 实现本地模型推理逻辑
        pass

# 执行与并发控制
class AsyncExecutor:
    async def execute(self, tasks: List[callable], max_concurrency: int = 10):
        semaphore = asyncio.Semaphore(max_concurrency)
        
        async def wrapper(task):
            async with semaphore:
                return await task
                
        return await asyncio.gather(*[wrapper(t) for t in tasks])

# 存储抽象层
class StorageBackend(ABC):
    @abstractmethod
    def store(self, data: Any):
        pass

class MemoryStorage(StorageBackend):
    def __init__(self):
        self.data = []
        
    def store(self, data: Any):
        self.data.append(data)

class DiskStorage(StorageBackend):
    def store(self, data: Any):
        # 实现磁盘存储逻辑
        pass

# 错误处理与重试
class RetryHandler:
    async def execute_with_retry(self, func: callable, *args, max_retries=3, **kwargs):
        for attempt in range(max_retries):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                if attempt == max_retries - 1:
                    raise
                await asyncio.sleep(2 ** attempt)

# 缓存系统
class PromptCache:
    def __init__(self):
        self.cache = {}
        
    def get(self, prompt: str, model: str) -> Optional[str]:
        return self.cache.get((model, prompt))
    
    def set(self, prompt: str, model: str, response: str):
        self.cache[(model, prompt)] = response

# 测试工具
class TestBench:
    def __init__(self, agent: AgentCore):
        self.agent = agent
        
    async def run_test(self, test_cases: List[LLMRequest]):
        return await self.agent.executor.execute(
            [self.agent.process(case) for case in test_cases]
        )

# 使用示例
async def main():
    agent = AgentCore()
    agent.register_model("gpt-4", OpenAIProvider("sk-..."))
    agent.register_model("local", LocalModelProvider())
    
    @agent.add_pipeline_step
    async def preprocess(request, prev):
        return request.prompt.upper()
        
    @agent.add_pipeline_step
    async def inference(request, prev):
        return await agent.model_providers[request.model].generate(prev)
    
    response = await agent.process(LLMRequest(
        prompt="Hello world",
        model="gpt-4"
    ))
    
    print(response.content)

if __name__ == "__main__":
    asyncio.run(main())