Agent 开源框架选型与二次开发

import time
import json
import math
import random
from typing import Dict, List, Any, Optional, Tuple, Set
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import numpy as np
from collections import deque, defaultdict
import threading
import uuid
from abc import ABC, abstractmethod

class FrameworkType(Enum):
    """框架类型"""
    LANGCHAIN = "langchain"
    AUTOGEN = "autogen"
    CREWAI = "crewai"
    LLAMAINDEX = "llamaindex"
    LANGGRAPH = "langgraph"
    SWARM = "swarm"
    MAGENTIC_ONE = "magentic_one"

class CapabilityLevel(Enum):
    """能力等级"""
    BASIC = "basic"
    INTERMEDIATE = "intermediate"
    ADVANCED = "advanced"
    EXPERT = "expert"

class UseCaseType(Enum):
    """用例类型"""
    CHATBOT = "chatbot"
    DATA_ANALYSIS = "data_analysis"
    CODE_GENERATION = "code_generation"
    WORKFLOW_AUTOMATION = "workflow_automation"
    RAG = "rag"
    MULTI_AGENT = "multi_agent"
    ENTERPRISE = "enterprise"

@dataclass
class FrameworkFeature:
    """框架特性"""
    feature_name: str
    description: str
    supported: bool
    maturity: str  # experimental, stable, mature
    performance_impact: str  # low, medium, high

@dataclass
class FrameworkMetrics:
    """框架指标"""
    framework_type: FrameworkType
    learning_curve: int  # 1-10, 10 最难
    development_speed: float  # 1-10, 10 最快
    flexibility: float  # 1-10
    performance: float  # 1-10
    community_size: int  # 活跃开发者数
    documentation_quality: float  # 1-10
    enterprise_readiness: float  # 1-10
    total_cost_of_ownership: float  # 年成本（万元）

@dataclass
class FrameworkComparison:
    """框架对比结果"""
    framework_type: FrameworkType
    overall_score: float
    strengths: List[str]
    weaknesses: List[str]
    best_use_cases: List[UseCaseType]
    recommendation: str

@dataclass
class SelectionCriteria:
    """选型标准"""
    criteria_name: str
    weight: float  # 0-1
    score: float  # 0-10
    notes: str

class FrameworkEvaluator:
    """
    框架评估器
    
    支持:
    1. 多维度评分
    2. 用例匹配
    3. 成本分析
    4. 风险评估
    """
    
    def __init__(self):
        self.framework_data = self._initialize_framework_data()
    
    def _initialize_framework_data(self) -> Dict[FrameworkType, FrameworkMetrics]:
        """初始化框架数据"""
        return {
            FrameworkType.LANGCHAIN: FrameworkMetrics(
                framework_type=FrameworkType.LANGCHAIN,
                learning_curve=6,
                development_speed=8.5,
                flexibility=9.0,
                performance=7.5,
                community_size=150000,
                documentation_quality=8.5,
                enterprise_readiness=8.0,
                total_cost_of_ownership=25.0
            ),
            FrameworkType.AUTOGEN: FrameworkMetrics(
                framework_type=FrameworkType.AUTOGEN,
                learning_curve=7,
                development_speed=7.5,
                flexibility=8.5,
                performance=8.0,
                community_size=45000,
                documentation_quality=7.5,
                enterprise_readiness=7.5,
                total_cost_of_ownership=30.0
            ),
            FrameworkType.CREWAI: FrameworkMetrics(
                framework_type=FrameworkType.CREWAI,
                learning_curve=4,
                development_speed=9.0,
                flexibility=7.0,
                performance=7.0,
                community_size=25000,
                documentation_quality=8.0,
                enterprise_readiness=6.5,
                total_cost_of_ownership=20.0
            ),
            FrameworkType.LLAMAINDEX: FrameworkMetrics(
                framework_type=FrameworkType.LLAMAINDEX,
                learning_curve=5,
                development_speed=8.0,
                flexibility=8.0,
                performance=8.5,
                community_size=60000,
                documentation_quality=8.5,
                enterprise_readiness=8.0,
                total_cost_of_ownership=28.0
            ),
            FrameworkType.LANGGRAPH: FrameworkMetrics(
                framework_type=FrameworkType.LANGGRAPH,
                learning_curve=8,
                development_speed=7.0,
                flexibility=9.5,
                performance=8.5,
                community_size=35000,
                documentation_quality=7.5,
                enterprise_readiness=8.5,
                total_cost_of_ownership=35.0
            )
        }
    
    def evaluate_framework(self, 
                          framework_type: FrameworkType,
                          use_cases: List[UseCaseType],
                          team_experience: str,
                          budget: float) -> FrameworkComparison:
        """评估框架"""
        metrics = self.framework_data.get(framework_type)
        if not metrics:
            raise ValueError(f"Unknown framework: {framework_type}")
        
        # 计算综合得分
        overall_score = self._calculate_overall_score(metrics, use_cases, team_experience, budget)
        
        # 分析优势与劣势
        strengths, weaknesses = self._analyze_strengths_weaknesses(metrics, framework_type)
        
        # 推荐用例
        best_use_cases = self._recommend_use_cases(framework_type, metrics)
        
        # 生成建议
        recommendation = self._generate_recommendation(framework_type, overall_score, use_cases)
        
        return FrameworkComparison(
            framework_type=framework_type,
            overall_score=overall_score,
            strengths=strengths,
            weaknesses=weaknesses,
            best_use_cases=best_use_cases,
            recommendation=recommendation
        )
    
    def _calculate_overall_score(self,
                                metrics: FrameworkMetrics,
                                use_cases: List[UseCaseType],
                                team_experience: str,
                                budget: float) -> float:
        """计算综合得分"""
        # 基础得分
        base_score = (
            metrics.development_speed * 0.2 +
            metrics.flexibility * 0.15 +
            metrics.performance * 0.15 +
            metrics.documentation_quality * 0.1 +
            metrics.enterprise_readiness * 0.2
        )
        
        # 学习曲线调整（团队经验越丰富，影响越小）
        experience_factor = {'beginner': 0.8, 'intermediate': 1.0, 'advanced': 1.2}
        learning_adjustment = (10 - metrics.learning_curve) / 10 * experience_factor.get(team_experience, 1.0)
        
        # 成本调整
        cost_score = max(0, 10 - (metrics.total_cost_of_ownership / budget) * 10)
        
        # 用例匹配度
        use_case_match = self._calculate_use_case_match(framework_type=metrics.framework_type, 
                                                       use_cases=use_cases)
        
        overall_score = base_score * 0.6 + learning_adjustment * 0.2 + cost_score * 0.1 + use_case_match * 0.1
        
        return round(overall_score, 2)
    
    def _calculate_use_case_match(self,
                                 framework_type: FrameworkType,
                                 use_cases: List[UseCaseType]) -> float:
        """计算用例匹配度"""
        # 简化实现：预定义匹配度
        use_case_scores = {
            FrameworkType.LANGCHAIN: {
                UseCaseType.CHATBOT: 9.0,
                UseCaseType.RAG: 8.5,
                UseCaseType.WORKFLOW_AUTOMATION: 8.0
            },
            FrameworkType.AUTOGEN: {
                UseCaseType.CODE_GENERATION: 9.5,
                UseCaseType.MULTI_AGENT: 9.0,
                UseCaseType.DATA_ANALYSIS: 8.0
            },
            FrameworkType.CREWAI: {
                UseCaseType.WORKFLOW_AUTOMATION: 9.0,
                UseCaseType.MULTI_AGENT: 8.5,
                UseCaseType.CHATBOT: 7.5
            },
            FrameworkType.LLAMAINDEX: {
                UseCaseType.RAG: 9.5,
                UseCaseType.DATA_ANALYSIS: 9.0,
                UseCaseType.CHATBOT: 7.5
            },
            FrameworkType.LANGGRAPH: {
                UseCaseType.MULTI_AGENT: 9.5,
                UseCaseType.WORKFLOW_AUTOMATION: 9.0,
                UseCaseType.ENTERPRISE: 9.0
            }
        }
        
        scores = use_case_scores.get(framework_type, {})
        if not scores:
            return 5.0
        
        total_score = sum(scores.get(uc, 5.0) for uc in use_cases)
        return total_score / len(use_cases) if use_cases else 5.0
    
    def _analyze_strengths_weaknesses(self,
                                     metrics: FrameworkMetrics,
                                     framework_type: FrameworkType) -> Tuple[List[str], List[str]]:
        """分析优势与劣势"""
        strengths = []
        weaknesses = []
        
        # 分析优势
        if metrics.development_speed >= 8.0:
            strengths.append("开发速度快")
        if metrics.flexibility >= 8.5:
            strengths.append("灵活性高")
        if metrics.performance >= 8.0:
            strengths.append("性能优秀")
        if metrics.community_size >= 100000:
            strengths.append("社区活跃")
        if metrics.documentation_quality >= 8.0:
            strengths.append("文档完善")
        if metrics.enterprise_readiness >= 8.0:
            strengths.append("企业级就绪")
        
        # 分析劣势
        if metrics.learning_curve >= 7:
            weaknesses.append("学习曲线陡峭")
        if metrics.flexibility < 7.0:
            weaknesses.append("灵活性有限")
        if metrics.total_cost_of_ownership > 30:
            weaknesses.append("总体拥有成本高")
        if metrics.community_size < 30000:
            weaknesses.append("社区规模较小")
        
        # 框架特定分析
        if framework_type == FrameworkType.LANGCHAIN:
            strengths.append("生态丰富")
            weaknesses.append("架构复杂")
        elif framework_type == FrameworkType.AUTOGEN:
            strengths.append("多 Agent 协作强")
            weaknesses.append("设置复杂")
        elif framework_type == FrameworkType.CREWAI:
            strengths.append("易于上手")
            weaknesses.append("定制性有限")
        elif framework_type == FrameworkType.LLAMAINDEX:
            strengths.append("RAG 优化好")
            weaknesses.append("通用性较弱")
        elif framework_type == FrameworkType.LANGGRAPH:
            strengths.append("状态管理强")
            weaknesses.append("复杂度高")
        
        return strengths, weaknesses
    
    def _recommend_use_cases(self,
                            framework_type: FrameworkType,
                            metrics: FrameworkMetrics) -> List[UseCaseType]:
        """推荐用例"""
        recommendations = {
            FrameworkType.LANGCHAIN: [UseCaseType.CHATBOT, UseCaseType.RAG, UseCaseType.WORKFLOW_AUTOMATION],
            FrameworkType.AUTOGEN: [UseCaseType.CODE_GENERATION, UseCaseType.MULTI_AGENT, UseCaseType.DATA_ANALYSIS],
            FrameworkType.CREWAI: [UseCaseType.WORKFLOW_AUTOMATION, UseCaseType.MULTI_AGENT, UseCaseType.CHATBOT],
            FrameworkType.LLAMAINDEX: [UseCaseType.RAG, UseCaseType.DATA_ANALYSIS, UseCaseType.CHATBOT],
            FrameworkType.LANGGRAPH: [UseCaseType.MULTI_AGENT, UseCaseType.WORKFLOW_AUTOMATION, UseCaseType.ENTERPRISE]
        }
        
        return recommendations.get(framework_type, [])
    
    def _generate_recommendation(self,
                                framework_type: FrameworkType,
                                overall_score: float,
                                use_cases: List[UseCaseType]) -> str:
        """生成建议"""
        if overall_score >= 8.0:
            return f"强烈推荐 {framework_type.value}，综合表现优秀，特别适合 {', '.join([uc.value for uc in use_cases[:2]])} 场景"
        elif overall_score >= 6.5:
            return f"推荐 {framework_type.value}，整体表现良好，建议在 {', '.join([uc.value for uc in use_cases[:2]])} 场景优先使用"
        elif overall_score >= 5.0:
            return f"谨慎选择 {framework_type.value}，需评估具体需求与团队能力"
        else:
            return f"不推荐 {framework_type.value}，建议考虑其他框架"
    
    def compare_all_frameworks(self,
                              use_cases: List[UseCaseType],
                              team_experience: str,
                              budget: float) -> List[FrameworkComparison]:
        """对比所有框架"""
        comparisons = []
        
        for framework_type in FrameworkType:
            if framework_type in [FrameworkType.SWARM, FrameworkType.MAGENTIC_ONE]:
                continue  # 跳过实验性框架
            
            comparison = self.evaluator.evaluate_framework(
                framework_type=framework_type,
                use_cases=use_cases,
                team_experience=team_experience,
                budget=budget
            )
            comparisons.append(comparison)
        
        # 按得分排序
        comparisons.sort(key=lambda x: x.overall_score, reverse=True)
        
        return comparisons


# 使用示例
if __name__ == "__main__":
    print("=== Agent 开源框架选型与二次开发 ===\n")
    
    print("=== 创建框架评估器 ===")
    
    evaluator = FrameworkEvaluator()
    
    print(f"\n=== 定义选型场景 ===")
    
    use_cases = [UseCaseType.WORKFLOW_AUTOMATION, UseCaseType.MULTI_AGENT]
    team_experience = "intermediate"
    budget = 30.0  # 30 万元
    
    print(f"用例：{[uc.value for uc in use_cases]}")
    print(f"团队经验：{team_experience}")
    print(f"预算：¥{budget}万/年")
    
    print(f"\n=== 评估 LangChain ===")
    
    langchain_result = evaluator.evaluate_framework(
        framework_type=FrameworkType.LANGCHAIN,
        use_cases=use_cases,
        team_experience=team_experience,
        budget=budget
    )
    
    print(f"框架：{langchain_result.framework_type.value}")
    print(f"综合得分：{langchain_result.overall_score}/10")
    print(f"优势：{', '.join(langchain_result.strengths)}")
    print(f"劣势：{', '.join(langchain_result.weaknesses)}")
    print(f"推荐用例：{[uc.value for uc in langchain_result.best_use_cases]}")
    print(f"建议：{langchain_result.recommendation}")
    
    print(f"\n=== 评估 AutoGen ===")
    
    autogen_result = evaluator.evaluate_framework(
        framework_type=FrameworkType.AUTOGEN,
        use_cases=use_cases,
        team_experience=team_experience,
        budget=budget
    )
    
    print(f"框架：{autogen_result.framework_type.value}")
    print(f"综合得分：{autogen_result.overall_score}/10")
    print(f"优势：{', '.join(autogen_result.strengths)}")
    print(f"劣势：{', '.join(autogen_result.weaknesses)}")
    print(f"推荐用例：{[uc.value for uc in autogen_result.best_use_cases]}")
    print(f"建议：{autogen_result.recommendation}")
    
    print(f"\n=== 评估 CrewAI ===")
    
    crewai_result = evaluator.evaluate_framework(
        framework_type=FrameworkType.CREWAI,
        use_cases=use_cases,
        team_experience=team_experience,
        budget=budget
    )
    
    print(f"框架：{crewai_result.framework_type.value}")
    print(f"综合得分：{crewai_result.overall_score}/10")
    print(f"优势：{', '.join(crewai_result.strengths)}")
    print(f"劣势：{', '.join(crewai_result.weaknesses)}")
    print(f"推荐用例：{[uc.value for uc in crewai_result.best_use_cases]}")
    print(f"建议：{crewai_result.recommendation}")
    
    print(f"\n=== 评估 LlamaIndex ===")
    
    llamaindex_result = evaluator.evaluate_framework(
        framework_type=FrameworkType.LLAMAINDEX,
        use_cases=use_cases,
        team_experience=team_experience,
        budget=budget
    )
    
    print(f"框架：{llamaindex_result.framework_type.value}")
    print(f"综合得分：{llamaindex_result.overall_score}/10")
    print(f"优势：{', '.join(llamaindex_result.strengths)}")
    print(f"劣势：{', '.join(llamaindex_result.weaknesses)}")
    print(f"推荐用例：{[uc.value for uc in llamaindex_result.best_use_cases]}")
    print(f"建议：{llamaindex_result.recommendation}")
    
    print(f"\n=== 评估 LangGraph ===")
    
    langgraph_result = evaluator.evaluate_framework(
        framework_type=FrameworkType.LANGGRAPH,
        use_cases=use_cases,
        team_experience=team_experience,
        budget=budget
    )
    
    print(f"框架：{langgraph_result.framework_type.value}")
    print(f"综合得分：{langgraph_result.overall_score}/10")
    print(f"优势：{', '.join(langgraph_result.strengths)}")
    print(f"劣势：{', '.join(langgraph_result.weaknesses)}")
    print(f"推荐用例：{[uc.value for uc in langgraph_result.best_use_cases]}")
    print(f"建议：{langgraph_result.recommendation}")
    
    print(f"\n=== 框架排名 ===")
    
    all_comparisons = evaluator.compare_all_frameworks(use_cases, team_experience, budget)
    
    print("框架综合排名（按得分降序）:")
    for i, comp in enumerate(all_comparisons, 1):
        print(f"{i}. {comp.framework_type.value}: {comp.overall_score}/10 - {comp.recommendation[:50]}...")
    
    print(f"\n关键观察:")
    print("1. 框架选择：没有最好，只有最适合")
    print("2. 选型标准：综合考量能力、成本、团队、用例")
    print("3. 二次开发：基于框架特性进行定制扩展")
    print("4. 企业应用：关注稳定性、安全性、可维护性")
    print("5. 生态贡献：从使用者成长为贡献者")
    print("\n开发者成长的使命：学习→实践→创新→贡献→引领")