模块7：智能体架构迁移方案
1. 现状分析
1.1 当前LangGraph智能体架构
LangGraph采用图结构来表示智能体的决策流程，通过节点和边来定义智能体的行为模式。当前架构特点：
核心组件：
- StateGraph: 状态图管理器，负责维护智能体的状态转换
- Node: 功能节点，封装具体的业务逻辑
- Edge: 边定义，控制状态流转规则
- Memory: 记忆管理，维护智能体的历史信息
- 基于图的可视化工作流设计
- 支持复杂的条件分支和循环逻辑
- 内置状态管理和记忆机制
- 支持多智能体协作
- 提供丰富的调试和监控工具
- 学习曲线陡峭，需要理解图论概念
- 性能开销较大，特别是在复杂图结构中
- 扩展性受限，自定义节点开发复杂
- 调试困难，需要专门的图调试工具
- 与现有系统集成复杂度较高
1.2 Agno智能体架构概述
Agno采用更加灵活的模块化架构设计，核心思想是"智能体即服务"。主要特点：
核心组件：
- Agent: 智能体基类，提供统一的智能体接口
- Model: 模型管理，支持多种LLM集成
- Tool: 工具系统，可插拔的工具架构
- Memory: 记忆系统，支持多种存储后端
- Workflow: 工作流引擎，支持复杂业务流程
- 模块化设计，易于扩展和维护
- 统一的API接口，降低集成复杂度
- 高性能异步执行
- 丰富的内置工具和模型支持
- 完善的错误处理和监控机制
2. Agno智能体架构设计
2.1 核心智能体模型
from dataclasses import dataclass
from typing import Dict, List, Optional, Any, Callable
from enum import Enum
from datetime import datetime
import asyncio
import uuid
class AgentStatus(Enum):
"""智能体状态枚举"""
IDLE = "idle"
RUNNING = "running"
PAUSED = "paused"
ERROR = "error"
COMPLETED = "completed"
class MessageType(Enum):
"""消息类型枚举"""
SYSTEM = "system"
USER = "user"
ASSISTANT = "assistant"
TOOL = "tool"
ERROR = "error"
@dataclass
class Message:
"""消息数据类"""
id: str
type: MessageType
content: str
sender: str
timestamp: datetime
metadata: Optional[Dict[str, Any]] = None
@dataclass
class AgentConfig:
"""智能体配置类"""
agent_id: str
name: str
description: str
model_config: Dict[str, Any]
tool_configs: List[Dict[str, Any]]
memory_config: Dict[str, Any]
workflow_config: Optional[Dict[str, Any]] = None
max_iterations: int = 100
timeout: int = 300
enable_monitoring: bool = True
@dataclass
class AgentState:
"""智能体状态类"""
agent_id: str
status: AgentStatus
current_task: Optional[str]
context: Dict[str, Any]
message_history: List[Message]
execution_stats: Dict[str, Any]
created_at: datetime
updated_at: datetime
class BaseAgent:
"""Agno智能体基类"""
def __init__(self, config: AgentConfig):
self.config = config
self.agent_id = config.agent_id
self.name = config.name
self.status = AgentStatus.IDLE
self.current_task = None
self.context = {}
self.message_history = []
self.execution_stats = {
"total_tasks": 0,
"successful_tasks": 0,
"failed_tasks": 0,
"average_execution_time": 0.0
}
self.created_at = datetime.now()
self.updated_at = datetime.now()
self.tools = {}
self.memory = None
self.model = None
self.workflow_engine = None
self.monitoring_enabled = config.enable_monitoring
self.logger = self._setup_logging()
def _setup_logging(self):
"""设置日志"""
import logging
logger = logging.getLogger(f"Agent.{self.agent_id}")
logger.setLevel(logging.INFO)
return logger
async def initialize(self):
"""初始化智能体"""
try:
self.logger.info(f"正在初始化智能体 {self.name}")
# 初始化模型
await self._initialize_model()
# 初始化工具
await self._initialize_tools()
# 初始化记忆
await self._initialize_memory()
# 初始化工作流引擎
await self._initialize_workflow()
self.logger.info(f"智能体 {self.name} 初始化成功")
except Exception as e:
self.logger.error(f"智能体初始化失败: {str(e)}")
self.status = AgentStatus.ERROR
raise
async def _initialize_model(self):
"""初始化模型"""
# 根据配置初始化相应的LLM模型
model_type = self.config.model_config.get("type", "openai")
model_params = self.config.model_config.get("params", {})
# 这里可以集成不同的模型提供商
if model_type == "openai":
from openai import AsyncOpenAI
self.model = AsyncOpenAI(**model_params)
elif model_type == "anthropic":
from anthropic import AsyncAnthropic
self.model = AsyncAnthropic(**model_params)
else:
raise ValueError(f"不支持的模型类型: {model_type}")
async def _initialize_tools(self):
"""初始化工具"""
for tool_config in self.config.tool_configs:
tool_name = tool_config["name"]
tool_class = tool_config["class"]
tool_params = tool_config.get("params", {})
# 动态导入工具类
tool_instance = self._create_tool_instance(tool_class, tool_params)
self.tools[tool_name] = tool_instance
def _create_tool_instance(self, tool_class: str, params: Dict[str, Any]):
"""创建工具实例"""
# 这里可以实现工具类的动态导入和实例化
# 简化实现，实际应该使用更安全的导入机制
if tool_class == "CodeExecutorTool":
return CodeExecutorTool(**params)
elif tool_class == "WebSearchTool":
return WebSearchTool(**params)
else:
raise ValueError(f"不支持的工具类: {tool_class}")
async def _initialize_memory(self):
"""初始化记忆"""
memory_type = self.config.memory_config.get("type", "local")
memory_params = self.config.memory_config.get("params", {})
if memory_type == "local":
self.memory = LocalMemoryStorage(**memory_params)
elif memory_type == "vector":
self.memory = VectorMemoryStorage(**memory_params)
else:
raise ValueError(f"不支持的记忆类型: {memory_type}")
await self.memory.initialize()
async def _initialize_workflow(self):
"""初始化工作流引擎"""
if self.config.workflow_config:
self.workflow_engine = WorkflowEngine(self.config.workflow_config)
await self.workflow_engine.initialize()
async def process_message(self, message: Message) -> Message:
"""处理消息"""
start_time = datetime.now()
try:
self.status = AgentStatus.RUNNING
self.current_task = f"处理消息: {message.content[:50]}..."
# 添加到消息历史
self.message_history.append(message)
# 处理消息
response = await self._generate_response(message)
# 创建响应消息
response_message = Message(
id=str(uuid.uuid4()),
type=MessageType.ASSISTANT,
content=response,
sender=self.name,
timestamp=datetime.now(),
metadata={"processing_time": (datetime.now() - start_time).total_seconds()}
)
# 添加到消息历史
self.message_history.append(response_message)
# 更新执行统计
self._update_execution_stats(True, (datetime.now() - start_time).total_seconds())
self.status = AgentStatus.IDLE
self.current_task = None
return response_message
except Exception as e:
self.logger.error(f"消息处理失败: {str(e)}")
self.status = AgentStatus.ERROR
# 创建错误消息
error_message = Message(
id=str(uuid.uuid4()),
type=MessageType.ERROR,
content=f"处理消息时发生错误: {str(e)}",
sender=self.name,
timestamp=datetime.now()
)
# 更新执行统计
self._update_execution_stats(False, (datetime.now() - start_time).total_seconds())
return error_message
async def _generate_response(self, message: Message) -> str:
"""生成响应"""
# 获取相关记忆
relevant_memories = await self.memory.search(
query=message.content,
limit=5,
agent_id=self.agent_id
)
# 构建上下文
context = {
"message_history": self.message_history[-10:], # 最近10条消息
"relevant_memories": relevant_memories,
"current_task": self.current_task,
"agent_context": self.context
}
# 调用模型生成响应
if self.model:
# 这里简化实现，实际需要根据具体模型API调整
prompt = self._build_prompt(message.content, context)
response = await self._call_model(prompt)
return response
else:
return "模型未初始化"
def _build_prompt(self, user_input: str, context: Dict[str, Any]) -> str:
"""构建提示词"""
prompt_parts = []
# 系统提示
prompt_parts.append(f"你是一个名为 {self.name} 的智能体助手。")
prompt_parts.append(f"描述: {self.config.description}")
# 历史消息
if context["message_history"]:
prompt_parts.append("\n历史对话:")
for msg in context["message_history"][-5:]: # 最近5条
prompt_parts.append(f"{msg.sender}: {msg.content}")
# 相关记忆
if context["relevant_memories"]:
prompt_parts.append("\n相关记忆:")
for memory in context["relevant_memories"]:
prompt_parts.append(f"- {memory.get('content', '')}")
# 用户输入
prompt_parts.append(f"\n用户: {user_input}")
prompt_parts.append("助手:")
return "\n".join(prompt_parts)
async def _call_model(self, prompt: str) -> str:
"""调用模型"""
# 这里简化实现，实际需要根据具体模型API调整
try:
# 模拟模型调用
await asyncio.sleep(0.1) # 模拟延迟
return f"基于您的输入 '{prompt[:50]}...' 的响应"
except Exception as e:
return f"模型调用失败: {str(e)}"
def _update_execution_stats(self, success: bool, execution_time: float):
"""更新执行统计"""
self.execution_stats["total_tasks"] += 1
if success:
self.execution_stats["successful_tasks"] += 1
else:
self.execution_stats["failed_tasks"] += 1
# 更新平均执行时间
total_tasks = self.execution_stats["total_tasks"]
current_avg = self.execution_stats["average_execution_time"]
self.execution_stats["average_execution_time"] = (
(current_avg * (total_tasks - 1) + execution_time) / total_tasks
)
async def execute_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""执行任务"""
start_time = datetime.now()
task_id = task.get("task_id", str(uuid.uuid4()))
try:
self.status = AgentStatus.RUNNING
self.current_task = task.get("description", "未知任务")
self.logger.info(f"开始执行任务 {task_id}: {self.current_task}")
# 根据任务类型执行
task_type = task.get("type", "default")
if task_type == "code_generation":
result = await self._execute_code_generation_task(task)
elif task_type == "data_analysis":
result = await self._execute_data_analysis_task(task)
elif task_type == "web_search":
result = await self._execute_web_search_task(task)
else:
result = await self._execute_default_task(task)
# 保存任务结果到记忆
await self.memory.add({
"type": "task_result",
"task_id": task_id,
"task_type": task_type,
"description": self.current_task,
"result": result,
"timestamp": datetime.now().isoformat(),
"execution_time": (datetime.now() - start_time).total_seconds()
})
self.status = AgentStatus.COMPLETED
self.current_task = None
return {
"task_id": task_id,
"success": True,
"result": result,
"execution_time": (datetime.now() - start_time).total_seconds()
}
except Exception as e:
self.logger.error(f"任务 {task_id} 执行失败: {str(e)}")
self.status = AgentStatus.ERROR
return {
"task_id": task_id,
"success": False,
"error": str(e),
"execution_time": (datetime.now() - start_time).total_seconds()
}
async def _execute_code_generation_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""执行代码生成任务"""
requirements = task.get("requirements", "")
language = task.get("language", "python")
# 使用代码执行工具
if "code_executor" in self.tools:
result = await self.tools["code_executor"].execute({
"action": "generate",
"requirements": requirements,
"language": language
})
return result
else:
return {"error": "代码执行工具未配置"}
async def _execute_data_analysis_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""执行数据分析任务"""
data = task.get("data", [])
analysis_type = task.get("analysis_type", "summary")
# 这里可以实现数据分析逻辑
return {
"analysis_type": analysis_type,
"data_size": len(data),
"summary": f"分析了 {len(data)} 条数据"
}
async def _execute_web_search_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""执行网络搜索任务"""
query = task.get("query", "")
max_results = task.get("max_results", 5)
# 使用网络搜索工具
if "web_search" in self.tools:
result = await self.tools["web_search"].search({
"query": query,
"max_results": max_results
})
return result
else:
return {"error": "网络搜索工具未配置"}
async def _execute_default_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""执行默认任务"""
description = task.get("description", "")
# 使用模型处理任务
if self.model:
prompt = f"请处理以下任务: {description}"
response = await self._call_model(prompt)
return {"response": response}
else:
return {"error": "模型未初始化"}
def get_state(self) -> AgentState:
"""获取智能体状态"""
return AgentState(
agent_id=self.agent_id,
status=self.status,
current_task=self.current_task,
context=self.context.copy(),
message_history=self.message_history.copy(),
execution_stats=self.execution_stats.copy(),
created_at=self.created_at,
updated_at=datetime.now()
)
async def pause(self):
"""暂停智能体"""
if self.status == AgentStatus.RUNNING:
self.status = AgentStatus.PAUSED
self.logger.info(f"智能体 {self.name} 已暂停")
async def resume(self):
"""恢复智能体"""
if self.status == AgentStatus.PAUSED:
self.status = AgentStatus.IDLE
self.logger.info(f"智能体 {self.name} 已恢复")
async def shutdown(self):
"""关闭智能体"""
self.logger.info(f"正在关闭智能体 {self.name}")
self.status = AgentStatus.IDLE
# 清理资源
if self.memory:
await self.memory.close()
if self.workflow_engine:
await self.workflow_engine.shutdown()
self.logger.info(f"智能体 {self.name} 已关闭")
class CodeExecutorTool:
"""代码执行工具"""
def __init__(self, **kwargs):
self.config = kwargs
self.supported_languages = ["python", "javascript", "bash"]
async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
"""执行代码"""
action = params.get("action", "execute")
code = params.get("code", "")
language = params.get("language", "python")
if language not in self.supported_languages:
return {"error": f"不支持的语言: {language}"}
if action == "execute":
return await self._execute_code(code, language)
elif action == "generate":
return await self._generate_code(params.get("requirements", ""), language)
else:
return {"error": f"不支持的操作: {action}"}
async def _execute_code(self, code: str, language: str) -> Dict[str, Any]:
"""执行代码"""
# 这里应该实现安全的代码执行环境
# 简化实现
return {
"language": language,
"code": code,
"output": "代码执行成功（模拟输出）",
"status": "success"
}
async def _generate_code(self, requirements: str, language: str) -> Dict[str, Any]:
"""生成代码"""
# 这里应该实现代码生成逻辑
return {
"language": language,
"requirements": requirements,
"generated_code": f"# 根据需求 '{requirements}' 生成的 {language} 代码",
"status": "success"
}
class WebSearchTool:
"""网络搜索工具"""
def __init__(self, **kwargs):
self.config = kwargs
self.max_results = self.config.get("max_results", 10)
async def search(self, params: Dict[str, Any]) -> Dict[str, Any]:
"""搜索"""
query = params.get("query", "")
max_results = params.get("max_results", self.max_results)
# 这里应该实现真实的网络搜索
# 简化实现，返回模拟结果
results = []
for i in range(min(max_results, 5)):
results.append({
"title": f"搜索结果 {i+1} for '{query}'",
"url": f"https://example.com/result{i+1}",
"snippet": f"这是关于 '{query}' 的第 {i+1} 个搜索结果的摘要...",
"score": 0.9 - (i * 0.1)
})
return {
"query": query,
"results": results,
"total_results": len(results),
"search_time": 0.5
}
class WorkflowEngine:
"""工作流引擎"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.workflows = {}
self.active_executions = {}
async def initialize(self):
"""初始化工作流引擎"""
# 加载工作流定义
for workflow_name, workflow_config in self.config.get("workflows", {}).items():
self.workflows[workflow_name] = Workflow(workflow_config)
async def execute_workflow(self, workflow_name: str, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""执行工作流"""
if workflow_name not in self.workflows:
return {"error": f"工作流 {workflow_name} 不存在"}
workflow = self.workflows[workflow_name]
execution_id = str(uuid.uuid4())
try:
result = await workflow.execute(inputs)
return {
"execution_id": execution_id,
"workflow_name": workflow_name,
"success": True,
"result": result
}
except Exception as e:
return {
"execution_id": execution_id,
"workflow_name": workflow_name,
"success": False,
"error": str(e)
}
async def shutdown(self):
"""关闭工作流引擎"""
# 清理活跃的执行
self.active_executions.clear()
class Workflow:
"""工作流定义"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.steps = config.get("steps", [])
self.name = config.get("name", "unnamed")
async def execute(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""执行工作流"""
context = inputs.copy()
for step in self.steps:
step_name = step.get("name", "unnamed_step")
step_type = step.get("type", "process")
try:
if step_type == "process":
# 处理步骤
result = await self._execute_process_step(step, context)
elif step_type == "condition":
# 条件步骤
result = await self._execute_condition_step(step, context)
elif step_type == "loop":
# 循环步骤
result = await self._execute_loop_step(step, context)
else:
result = {"error": f"不支持的步骤类型: {step_type}"}
# 更新上下文
context.update(result)
except Exception as e:
return {"error": f"步骤 {step_name} 执行失败: {str(e)}"}
return context
async def _execute_process_step(self, step: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
"""执行处理步骤"""
# 这里应该实现具体的处理逻辑
return {"step_result": f"执行了步骤: {step.get('name', 'unnamed')}"}
async def _execute_condition_step(self, step: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
"""执行条件步骤"""
condition = step.get("condition", "")
true_branch = step.get("true_branch", {})
false_branch = step.get("false_branch", {})
# 这里应该实现条件判断逻辑
condition_result = True # 模拟条件结果
if condition_result:
return await self._execute_process_step(true_branch, context)
else:
return await self._execute_process_step(false_branch, context)
async def _execute_loop_step(self, step: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
"""执行循环步骤"""
iterations = step.get("iterations", 1)
loop_body = step.get("loop_body", {})
results = []
for i in range(iterations):
result = await self._execute_process_step(loop_body, context)
results.append(result)
return {"loop_results": results}
class LocalMemoryStorage:
"""本地记忆存储"""
def __init__(self, **kwargs):
self.config = kwargs
self.memories = {}
self.max_size = self.config.get("max_size", 1000)
async def initialize(self):
"""初始化存储"""
# 这里可以实现存储初始化逻辑
pass
async def add(self, memory: Dict[str, Any]) -> str:
"""添加记忆"""
memory_id = str(uuid.uuid4())
memory["id"] = memory_id
memory["created_at"] = datetime.now().isoformat()
self.memories[memory_id] = memory
# 检查存储大小限制
if len(self.memories) > self.max_size:
# 删除最旧的记忆
oldest_id = min(self.memories.keys(),
key=lambda x: self.memories[x]["created_at"])
del self.memories[oldest_id]
return memory_id
async def search(self, query: str, limit: int = 5, **kwargs) -> List[Dict[str, Any]]:
"""搜索记忆"""
# 简单的文本匹配搜索
results = []
for memory_id, memory in self.memories.items():
# 这里应该实现更复杂的搜索逻辑
if query.lower() in str(memory).lower():
results.append(memory)
# 按相关性排序（简化实现）
return results[:limit]
async def close(self):
"""关闭存储"""
# 清理资源
self.memories.clear()
class VectorMemoryStorage:
"""向量记忆存储"""
def __init__(self, **kwargs):
self.config = kwargs
self.memories = {}
self.vectors = {}
self.dimension = self.config.get("dimension", 384)
async def initialize(self):
"""初始化存储"""
# 这里可以实现向量存储初始化逻辑
pass
async def add(self, memory: Dict[str, Any]) -> str:
"""添加记忆"""
memory_id = str(uuid.uuid4())
memory["id"] = memory_id
memory["created_at"] = datetime.now().isoformat()
# 生成向量表示（简化实现）
vector = self._generate_vector(memory.get("content", ""))
self.memories[memory_id] = memory
self.vectors[memory_id] = vector
return memory_id
def _generate_vector(self, content: str) -> List[float]:
"""生成向量表示"""
# 这里应该使用真实的向量化模型
# 简化实现：返回随机向量
import random
return [random.random() for _ in range(self.dimension)]
async def search(self, query: str, limit: int = 5, **kwargs) -> List[Dict[str, Any]]:
"""搜索记忆"""
# 生成查询向量
query_vector = self._generate_vector(query)
# 计算相似度
similarities = []
for memory_id, vector in self.vectors.items():
similarity = self._cosine_similarity(query_vector, vector)
similarities.append((memory_id, similarity))
# 按相似度排序
similarities.sort(key=lambda x: x[1], reverse=True)
# 返回最相似的结果
results = []
for memory_id, similarity in similarities[:limit]:
memory = self.memories[memory_id]
memory["similarity"] = similarity
results.append(memory)
return results
def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
"""计算余弦相似度"""
# 简化实现
import math
dot_product = sum(a * b for a, b in zip(vec1, vec2))
magnitude1 = math.sqrt(sum(a * a for a in vec1))
magnitude2 = math.sqrt(sum(a * a for a in vec2))
if magnitude1 == 0 or magnitude2 == 0:
return 0.0
return dot_product / (magnitude1 * magnitude2)
async def close(self):
"""关闭存储"""
# 清理资源
self.memories.clear()
self.vectors.clear()
## 3. 迁移挑战与解决方案
### 3.1 架构模式转换挑战
**挑战描述：**
LangGraph采用图结构架构，而Agno采用模块化架构，两种架构模式存在根本性差异，直接迁移会导致大量代码重构。
**解决方案：**
class NodeToAgentConverter: """节点到智能体转换器""" def __init__(self): self.conversion_rules = { "data_processor": "DataAnalysisAgent", "code_generator": "CodeGenerationAgent", "decision_maker": "DecisionAgent", "validator": "ValidationAgent", "executor": "ExecutionAgent" } def convert_node(self, node_config: Dict[str, Any]) -> Dict[str, Any]: """转换节点配置""" node_type = node_config.get("type", "unknown") # 获取对应的智能体类型 agent_type = self.conversion_rules.get(node_type, "GenericAgent") # 转换配置 agent_config = { "agent_id": f"agent_{node_config.get('id', 'unknown')}", "name": node_config.get("name", f"Agent_{node_type}"), "type": agent_type, "description": node_config.get("description", f"Converted from {node_type} node"), "capabilities": self._extract_capabilities(node_config), "tools": self._extract_tools(node_config), "parameters": self._extract_parameters(node_config) } return agent_config def _extract_capabilities(self, node_config: Dict[str, Any]) -> List[str]: """提取能力""" capabilities = [] # 基于节点类型推断能力 node_type = node_config.get("type", "") if "data" in node_type: capabilities.extend(["data_processing", "analysis", "transformation"]) if "code" in node_type: capabilities.extend(["code_generation", "execution", "debugging"]) if "decision" in node_type: capabilities.extend(["decision_making", "reasoning", "evaluation"]) # 从配置中提取显式定义的能力 if "capabilities" in node_config: capabilities.extend(node_config["capabilities"]) return list(set(capabilities)) # 去重 def _extract_tools(self, node_config: Dict[str, Any]) -> List[str]: """提取工具""" tools = [] # 从配置中提取工具 if "tools" in node_config: tools.extend(node_config["tools"]) if "external_services" in node_config: tools.extend(node_config["external_services"]) return tools def _extract_parameters(self, node_config: Dict[str, Any]) -> Dict[str, Any]: """提取参数""" parameters = {} # 复制相关参数 parameter_keys = [ "timeout", "retry_count", "max_iterations", "accuracy_threshold", "output_format" ] for key in parameter_keys: if key in node_config: parameters[key] = node_config[key] return parameters
3.2 智能体行为一致性挑战
挑战描述： LangGraph中的节点行为与Agno智能体的行为模型存在差异，需要确保迁移后的行为一致性。
解决方案：
```python class BehaviorConsistencyValidator: """行为一致性验证器""" def __init__(self): self.test_cases = [] self.validation_metrics = {} self.behavior_mappings = {} def create_test_suite(self, langgraph_behavior: Dict[str, Any]) -> Dict[str, Any]: """创建测试套件""" test_suite = { "input_validation": [], "processing_logic": [], "output_validation": [], "error_handling": [], "performance_benchmarks": [] } # 输入验证测试 test_suite["input_validation"] = self._generate_input_tests(langgraph_behavior) # 处理逻辑测试 test_suite["processing_logic"] = self._generate_processing_tests(langgraph_behavior) # 输出验证测试 test_suite["output_validation"] = self._generate_output_tests(langgraph_behavior) # 错误处理测试 test_suite["error_handling"] = self._generate_error_tests(langgraph_behavior) # 性能基准测试 test_suite["performance_benchmarks"] = self._generate_performance_tests(langgraph_behavior) return test_suite def _generate_input_tests(self, behavior: Dict[str, Any]) -> List[Dict[str, Any]]: """生成输入验证测试""" tests = [] # 基于输入规范生成测试 input_spec = behavior.get("input_specification", {}) # 必填字段测试 required_fields = input_spec.get("required_fields", []) for field in required_fields: tests.append({ "name": f"测试必填字段: {field}", "input": {f: "test_value" for f in required_fields if f != field}, # 缺少必填字段 "expected_behavior": "should_reject", "expected_error": f"Missing required field: {field}" }) # 数据类型测试 field_types = input_spec.get("field_types", {}) for field, expected_type in field_types.items(): tests.append({ "name": f"测试字段类型: {field} 应该是 {expected_type}", "input": {field: self._generate_wrong_type_value(expected_type)}, "expected_behavior": "should_reject", "expected_error": f"Invalid type for field: {field}" }) # 边界值测试 constraints = input_spec.get("constraints", {}) for field, constraint in constraints.items(): if "min" in constraint: tests.append({ "name": f"测试最小值约束: {field}", "input": {field: constraint["min"] - 1}, "expected_behavior": "should_reject", "expected_error": f"Value below minimum for field: {field}" }) if "max" in constraint: tests.append({ "name": f"测试最大值约束: {field}", "input": {field: constraint["max"] + 1}, "expected_behavior": "should_reject", "expected_error": f"Value above maximum for field: {field}" }) return tests def _generate_wrong_type_value(self, expected_type: str) -> Any: """生成错误类型的测试值""" type_mappings = { "string": 123, "integer": "not_a_number", "boolean": "not_a_boolean", "array": "not_an_array", "object": "not_an_object" } return type_mappings.get(expected_type, "wrong_type") def _generate_processing_tests(self, behavior: Dict[str, Any]) -> List[Dict[str, Any]]: """生成处理逻辑测试""" tests = [] # 基于处理逻辑生成测试 processing_steps = behavior.get("processing_steps", []) for i, step in enumerate(processing_steps): step_name = step.get("name", f"step_{i}") # 正常处理测试 tests.append({ "name": f"测试处理步骤: {step_name}", "input": self._generate_valid_input(behavior), "expected_behavior": "should_process", "expected_output": f"should_contain_step_{i}_result" }) # 条件逻辑测试 if step.get("conditional"): tests.append({ "name": f"测试条件逻辑: {step_name}", "input": self._generate_conditional_input(step), "expected_behavior": "should_branch_correctly", "expected_output": f"should_follow_condition_{step.get('condition_id')}" }) return tests def _generate_output_tests(self, behavior: Dict[str, Any]) -> List[Dict[str, Any]]: """生成输出验证测试""" tests = [] # 基于输出规范生成测试 output_spec = behavior.get("output_specification", {}) # 输出格式测试 tests.append({ "name": "测试输出格式", "input": self._generate_valid_input(behavior), "expected_behavior": "should_produce_valid_output", "expected_output": self._generate_expected_output(output_spec) }) # 输出字段测试 required_output_fields = output_spec.get("required_fields", []) for field in required_output_fields: tests.append({ "name": f"测试输出字段: {field}", "input": self._generate_valid_input(behavior), "expected_behavior": "should_include_field", "expected_output": f"should_contain_field_{field}" }) return tests def _generate_error_tests(self, behavior: Dict[str, Any]) -> List[Dict[str, Any]]: """生成错误处理测试""" tests = [] # 基于错误处理规范生成测试 error_handling = behavior.get("error_handling", {}) # 已知错误类型测试 known_errors = error_handling.get("known_errors", []) for error_type in known_errors: tests.append({ "name": f"测试错误处理: {error_type}", "input": self._generate_error_input(error_type), "expected_behavior": "should_handle_error", "expected_error": f"should_handle_{error_type}_gracefully" }) # 未知错误测试 tests.append({ "name": "测试未知错误处理", "input": self._generate_unknown_error_input(), "expected_behavior": "should_handle_unknown_error", "expected_error": "should_not_crash" }) return tests def _generate_performance_tests(self, behavior: Dict[str, Any]) -> List[Dict[str, Any]]: """生成性能基准测试""" tests = [] # 基于性能要求生成测试 performance_requirements = behavior.get("performance_requirements", {}) # 响应时间测试 max_response_time = performance_requirements.get("max_response_time", 1000) tests.append({ "name": "测试响应时间", "input": self._generate_valid_input(behavior), "expected_behavior": "should_meet_response_time", "expected_performance": f"response_time_should_be_below_{max_response_time}ms" }) # 吞吐量测试 min_throughput = performance_requirements.get("min_throughput", 10) tests.append({ "name": "测试吞吐量", "input": self._generate_batch_input(), "expected_behavior": "should_meet_throughput", "expected_performance": f"throughput_should_be_above_{min_throughput}_per_second" }) return tests def _generate_valid_input(self, behavior: Dict[str, Any]) -> Dict[str, Any]: """生成有效输入""" input_spec = behavior.get("input_specification", {}) valid_input = {} # 基于输入规范生成有效值 required_fields = input_spec.get("required_fields", []) field_types = input_spec.get("field_types", {}) for field in required_fields: field_type = field_types.get(field, "string") valid_input[field] = self._generate_valid_value(field_type) return valid_input def _generate_valid_value(self, field_type: str) -> Any: """生成有效值""" type_mappings = { "string": "test_string", "integer": 42, "boolean": True, "array": [1, 2, 3], "object": {"key": "value"} } return type_mappings.get(field_type, "default_value") def _generate_conditional_input(self, step: Dict[str, Any]) -> Dict[str, Any]: """生成条件输入""" # 根据条件逻辑生成测试输入 condition = step.get("condition", {}) field = condition.get("field", "test_field") value = condition.get("value", "condition_value") return {field: value} def _generate_expected_output(self, output_spec: Dict[str, Any]) -> Dict[str, Any]: """生成期望输出""" expected_output = {} # 基于输出规范生成期望输出 required_fields = output_spec.get("required_fields", []) field_types = output_spec.get("field_types", {}) for field in required_fields: field_type = field_types.get(field, "string") expected_output[field] = self._generate_valid_value(field_type) return expected_output def _generate_error_input(self, error_type: str) -> Dict[str, Any]: """生成错误输入""" # 根据错误类型生成会触发错误的输入 error_inputs = { "validation_error": {"invalid_field": "invalid_value"}, "timeout_error": {"long_running_task": True}, "resource_error": {"resource_intensive": True} } return error_inputs.get(error_type, {"error_trigger": True}) def _generate_unknown_error_input(self) -> Dict[str, Any]: """生成未知错误输入""" return {"unexpected": "input_that_might_cause_unknown_errors"} def _generate_batch_input(self) -> List[Dict[str, Any]]: """生成批量输入""" return [ {"batch_item": i, "data": f"test_data_{i}"} for i in range(10) ] async def validate_behavior_consistency(self, langgraph_implementation: Any, agno_implementation: Any, test_suite: Dict[str, Any]) -> Dict[str, Any]: """验证行为一致性""" validation_results = { "overall_consistency": 0.0, "test_results": {}, "inconsistencies": [], "recommendations": [] } # 执行所有测试用例 all_test_results = [] for test_category, tests in test_suite.items(): category_results = [] for test in tests: # 在两个实现上执行测试 langgraph_result = await self._execute_test(langgraph_implementation, test) agno_result = await self._execute_test(agno_implementation, test) # 比较结果 consistency_score = self._compare_results(langgraph_result, agno_result, test) test_result = { "test_name": test["name"], "category": test_category, "consistency_score": consistency_score, "langgraph_result": langgraph_result, "agno_result": agno_result, "passed": consistency_score >= 0.9 # 90%一致性阈值 } category_results.append(test_result) all_test_results.append(test_result) # 记录不一致 if consistency_score < 0.9: validation_results["inconsistencies"].append({ "test": test["name"], "expected": test.get("expected_behavior", ""), "langgraph_behavior": langgraph_result.get("behavior", ""), "agno_behavior": agno_result.get("behavior", ""), "consistency_score": consistency_score }) validation_results["test_results"][test_category] = category_results # 计算总体一致性 if all_test_results: total_score = sum(r["consistency_score"] for r in all_test_results) validation_results["overall_consistency"] = total_score / len(all_test_results) # 生成建议 validation_results["recommendations"] = self._generate_validation_recommendations( validation_results["inconsistencies"] ) return validation_results async def _execute_test(self, implementation: Any, test: Dict[str, Any]) -> Dict[str, Any]: """执行测试""" try: # 这里应该根据测试类型调用相应的实现 test_input = test.get("input", {}) # 模拟执行结果 return { "behavior": "executed", "output": f"result_for_{test['name']}", "success": True } except Exception as e: return { "behavior": "failed", "error": str(e), "success": False } def _compare_results(self, langgraph_result: Dict[str, Any], agno_result: Dict[str, Any], test: Dict[str, Any]) -> float: """比较结果一致性""" # 这里应该实现更复杂的比较逻辑 # 简化实现：基于成功状态和输出相似度 if langgraph_result.get("success") == agno_result.get("success"): # 成功状态一致，检查输出相似度 langgraph_output = str(langgraph_result.get("output", "")) agno_output = str(agno_result.get("output", "")) # 简单的相似度计算 if langgraph_output == agno_output: return 1.0 elif langgraph_output in agno_output or agno_output in langgraph_output: return 0.8 else: return 0.5 else: # 成功状态不一致 return 0.0 def _generate_validation_recommendations(self, inconsistencies: List[Dict[str, Any]]) -> List[str]: """生成验证建议""" recommendations = [] if not inconsistencies: recommendations.append("行为一致性验证通过，无需修改") return recommendations