架构流程图解
graph TD
A[用户请求] --> B[规划阶段]
B --> C[任务分解]
C --> D[依赖分析]
D --> E[策略制定]
E --> F[执行阶段]
F --> G[工具调用]
G --> H[状态更新]
H --> I{是否完成所有任务?}
I -->|否| G
I -->|是| J[评估阶段]
J --> K[质量评估]
K --> L[一致性检查]
L --> M[完整性验证]
M --> N{是否通过评估?}
N -->|否| O[错误诊断]
O --> P[重新规划]
P --> F
N -->|是| Q[返回结果]核心组件与接口定义
1. 任务抽象
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Dict, Any, Optional
import uuid
@dataclass
class Task:
id: str
description: str
required_tool: str
parameters: Dict[str, Any]
dependencies: List[str]
priority: int
estimated_duration: Optional[int] = None
def __post_init__(self):
if not self.id:
self.id = str(uuid.uuid4())
@dataclass
class ExecutionResult:
task_id: str
status: str # 'success', 'failed', 'partial'
output: Any
execution_time: float
tool_calls: List[Dict]
errors: Optional[List[str]] = None
warnings: Optional[List[str]] = None2. 工具接口标准化
class BaseTool(ABC):
def __init__(self, name: str, schema: Dict[str, Any]):
self.name = name
self.schema = schema
@abstractmethod
def execute(self, parameters: Dict[str, Any]) -> Any:
pass
def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
# 实现参数验证逻辑
return True
def get_schema(self) -> Dict[str, Any]:
return self.schema
class ToolRegistry:
def __init__(self):
self._tools = {}
def register_tool(self, tool: BaseTool):
self._tools[tool.name] = tool
def get_tool(self, name: str) -> Optional[BaseTool]:
return self._tools.get(name)
def list_tools(self) -> List[str]:
return list(self._tools.keys())优缺点分析
| 优势 | 劣势 |
|---|---|
| 专业化分工，提升复杂任务质量 | 协调与通信开销增加，延迟上升 |
| 组件化扩展，新增能力更平滑 | 调试链路更长，问题定位更困难 |
| 故障隔离更好，单点失败可降级 | 一致性维护复杂，需共享记忆/协议 |
| 可引入 Critic 闭环保障输出质量 | 资源成本更高，治理与监控更重 |
进阶模式分析
在基础模式的基础上，进阶架构模式通过引入更复杂的协作机制、增强的知识处理能力和智能的自我改进机制，为处理复杂任务提供了更强的能力。本节将深入分析三种主要的进阶模式：多Agent协作模式、RAG增强Agent模式和反思/自监督模式。
多Agent协作模式（Router + Worker + Critic）
多Agent协作模式是处理复杂任务的高级架构，它通过分工合作实现专业化处理，每种Agent专注于特定的领域和能力。
架构模式分析
graph TB
A[用户输入] --> B[Router Agent]
B --> C[Worker Agent 1]
B --> D[Worker Agent 2]
B --> E[Worker Agent 3]
C --> F[Critic Agent]
D --> F
E --> F
F --> G{结果评估}
G -->|通过| H[最终输出]
G -->|需要改进| I[反馈循环]
I --> C
I --> D
I --> E
F --> J[执行结果记录]核心组件职责
Router Agent（路由代理）
负责任务理解、分发和协调：
class RouterAgent:
def __init__(self, llm_client, worker_registry):
self.llm = llm_client
self.workers = worker_registry
self.task_analyzer = TaskAnalyzer()
async def route_task(self, user_input):
# 任务分析
task_analysis = await self.analyze_task(user_input)
# 选择合适的Worker
selected_workers = await self.select_workers(task_analysis)
# 创建执行计划
execution_plan = await self.create_execution_plan(task_analysis, selected_workers)
return execution_plan
async def analyze_task(self, user_input):
prompt = f"""
分析以下任务，识别所需的技能和能力：
任务：{user_input}
请返回：
1. 任务类型（开发/分析/客服/运维）
2. 复杂度评估（简单/中等/复杂）
3. 所需工具和能力
4. 预期输出格式
"""
return await self.llm.complete(prompt)Worker Agent（工作代理）
专注特定领域任务执行：
class WorkerAgent:
def __init__(self, role, capabilities, tools):
self.role = role
self.capabilities = capabilities
self.tools = tools
self.llm = create_agent_llm(role)
async def execute_task(self, task_spec):
try:
# 解析任务规范
task_details = self.parse_task(task_spec)
# 检查能力匹配
if not self.can_handle(task_details):
return TaskResult(status="unhandled", reason="capability_mismatch")
# 执行任务
result = await self.perform_task(task_details)
# 质量检查
quality_score = await self.validate_result(result)
return TaskResult(
status="completed",
result=result,
quality_score=quality_score,
worker_id=self.role
)
except Exception as e:
return TaskResult(status="failed", error=str(e))Critic Agent（评审代理）
负责质量评估和改进建议：
class CriticAgent:
def __init__(self, llm_client, quality_criteria):
self.llm = llm_client
self.criteria = quality_criteria
self.evaluator = QualityEvaluator()
async def evaluate_results(self, worker_results):
evaluations = []
for result in worker_results:
evaluation = await self.evaluate_single_result(result)
evaluations.append(evaluation)
# 整体质量评估
overall_quality = await self.assess_overall_quality(evaluations)
# 生成改进建议
improvements = await self.generate_improvements(evaluations)
return EvaluationResult(
overall_quality=overall_quality,
individual_evaluations=evaluations,
improvements=improvements
)协作模式配置
# multi_agent_config.yaml
agents:
router:
model: "gpt-4-turbo"
system_prompt: "You are a task router that analyzes user requests..."
capabilities: ["task_classification", "worker_selection", "planning"]
workers:
- role: "developer"
model: "gpt-4"
tools: ["git", "docker", "python", "sql"]
capabilities: ["code_generation", "debugging", "testing"]
- role: "analyst"
model: "gpt-4"
tools: ["pandas", "numpy", "matplotlib"]
capabilities: ["data_analysis", "visualization", "reporting"]
- role: "support"
model: "gpt-4-turbo"
tools: ["knowledge_base", "ticket_system"]
capabilities: ["customer_service", "issue_resolution"]
critic:
model: "gpt-4"
evaluation_criteria:
- "accuracy"
- "completeness"
- "relevance"
- "quality"优势与局限分析
| 优势 | 局限 |
|---|---|
| 专业化和深度处理 | 协调复杂性增加 |
| 可扩展性和模块化 | 通信开销较高 |
| 容错性更强 | 整体延迟增加 |
| 易于维护和升级 | 资源消耗较大 |
RAG增强Agent模式
RAG（检索增强生成）Agent通过结合外部知识源和LLM的能力，显著提升了知识准确性和时效性。
架构模式分析
graph LR
A[用户查询] --> B[查询理解]
B --> C[检索策略选择]
C --> D[向量检索]
C --> E[关键词检索]
C --> F[混合检索]
D --> G[知识融合]
E --> G
F --> G
G --> H[生成引擎]
H --> I[引用生成]
I --> J[最终响应]检索策略实现
混合检索策略
class HybridRAGAgent:
def __init__(self, vector_store, bm25_index, llm_client):
self.vector_store = vector_store
self.bm25_index = bm25_index
self.llm = llm_client
self.fusion_engine = RetrievalFusion()
async def hybrid_retrieve(self, query, top_k=10):
# 并行执行多种检索策略
vector_results = await self.vector_search(query, top_k)
keyword_results = await self.keyword_search(query, top_k)
# 语义重排序
reranked_results = await self.semantic_rerank(
query, vector_results, keyword_results
)
# 融合策略
fused_results = self.fusion_engine.fuse(
vector_results, keyword_results, reranked_results
)
return fused_results
async def semantic_rerank(self, query, vector_results, keyword_results):
prompt = f"""
对以下检索结果进行语义相关性重排序：
查询：{query}
向量检索结果：
{vector_results}
关键词检索结果：
{keyword_results}
请根据与查询的语义相关性，重新排序结果。
"""
rerank_result = await self.llm.complete(prompt)
return self.parse_rerank_results(rerank_result)增量索引更新
class IncrementalIndexManager:
def __init__(self, vector_store, document_processor):
self.vector_store = vector_store
self.processor = document_processor
self.update_queue = asyncio.Queue()
self.version_tracker = DocumentVersionTracker()
async def add_document(self, doc_id, content, metadata):
# 文档预处理
chunks = await self.processor.chunk_document(content)
# 生成向量嵌入
embeddings = await self.generate_embeddings(chunks)
# 增量更新索引
await self.vector_store.add_documents(
documents=chunks,
embeddings=embeddings,
metadatas=[metadata] * len(chunks),
ids=[f"{doc_id}_{i}" for i in range(len(chunks))]
)
# 更新版本追踪
await self.version_tracker.update_document(doc_id, metadata)
async def update_document(self, doc_id, content, metadata):
await self.add_document(doc_id, content, metadata)
return True多Agent协作模式（Router + Worker + Critic）
多Agent协作模式是复杂业务场景下的核心架构设计，通过Router的智能调度、Worker的专业执行和Critic的质量保证，构建了可扩展、高可靠性的Agent系统。本节深入分析该模式的设计原理、实现细节和最佳实践。
1. 架构概述与设计原理
1.1 核心设计理念
多Agent协作模式基于"专业分工、质量闭环"的理念，通过将复杂任务分解给专门的Agent执行，实现了：
- 专业化执行：每个Worker Agent专注于特定领域
- 智能调度：Router根据任务特征动态分配资源
- 质量保证：Critic Agent提供闭环质量控制
graph TB
User[用户请求] --> Router[Router Agent]
Router -->|任务解析| Router
Router -->|调度决策| W1[Worker Agent 1
开发领域]
Router -->|调度决策| W2[Worker Agent 2
运维领域]
Router -->|调度决策| W3[Worker Agent 3
客服领域]
W1 -->|执行结果| Critic[Critic Agent]
W2 -->|执行结果| Critic
W3 -->|执行结果| Critic
Critic -->|质量评估| Router
Critic -->|修订建议| W1
Critic -->|修订建议| W2
Critic -->|修订建议| W3
Router -->|最终结果| User
subgraph "协作机制"
Direction[任务方向性]
Load[负载均衡]
Fallback[故障回退]
end1.2 模式优势与适用场景
优势分析：
- 可扩展性：新增领域只需增加对应Worker Agent
- 容错性：单点故障不影响整体系统
- 专业性：每个Agent深度优化特定领域能力
- 可维护性：组件职责清晰，便于调试和升级
适用场景：
- 企业级复杂业务流程
- 多领域专业知识融合任务
- 需要质量保证的敏感业务
- 高并发、高可靠性要求场景
2. 核心组件深度分析
2.1 Router Agent：智能调度中心
Router Agent是多Agent系统的核心调度器，负责任务理解、决策制定和资源协调。
2.1.1 任务理解与分类
class RouterAgent:
def __init__(self, config):
self.task_classifier = TaskClassifier()
self.skill_graph = SkillGraph()
self.load_balancer = LoadBalancer()
self.quality_predictor = QualityPredictor()
async def route_task(self, user_request: TaskRequest) -> RoutingDecision:
"""核心路由决策逻辑"""
# 1. 任务理解与分析
task_analysis = await self.analyze_task(user_request)
# 2. 技能匹配与选择
available_agents = self.skill_graph.get_agents_by_capability(
required_skills=task_analysis.skills,
current_load=self.load_balancer.get_current_load()
)
# 3. 质量预测与选择
quality_scores = await self.quality_predictor.predict_quality(
agents=available_agents,
task_type=task_analysis.type,
complexity=task_analysis.complexity
)
# 4. 路由决策
routing_decision = self.make_routing_decision(
task_analysis=task_analysis,
agents=available_agents,
quality_scores=quality_scores,
constraints=user_request.constraints
)
return routing_decision
async def analyze_task(self, request: TaskRequest) -> TaskAnalysis:
"""任务深度分析"""
analysis = TaskAnalysis(
type=self.task_classifier.classify(request.content),
complexity=self.calculate_complexity(request),
required_skills=self.extract_required_skills(request),
dependencies=self.analyze_dependencies(request),
priority=self.calculate_priority(request)
)
return analysis2.1.2 动态负载均衡
class LoadBalancer:
def __init__(self):
self.agent_metrics = {}
self.performance_tracker = PerformanceTracker()
def select_optimal_agent(self, candidates: List[Agent]) -> Agent:
"""基于多维度指标的智能选择"""
scored_agents = []
for agent in candidates:
score = self.calculate_agent_score(agent)
scored_agents.append((agent, score))
# 选择得分最高的Agent
optimal_agent = max(scored_agents, key=lambda x: x[1])[0]
# 更新负载状态
self.update_agent_load(optimal_agent.id)
return optimal_agent
def calculate_agent_score(self, agent: Agent) -> float:
"""多维度Agent评分算法"""
# 性能指标 (40%)
performance_score = self.performance_tracker.get_score(agent.id)
# 当前负载 (30%)
current_load = self.agent_metrics.get(agent.id, {}).get('load', 0)
load_score = 1.0 - current_load # 负载越低得分越高
# 专业匹配度 (20%)
expertise_score = agent.expertise_score
# 历史成功率 (10%)
success_rate = self.agent_metrics.get(agent.id, {}).get('success_rate', 0.8)
# 综合得分计算
total_score = (
performance_score * 0.4 +
load_score * 0.3 +
expertise_score * 0.2 +
success_rate * 0.1
)
return total_score2.2 Worker Agent：专业执行单元
Worker Agent是具体任务的执行者，每个Agent专注于特定领域，通过深度优化实现专业能力。
2.2.1 Worker Agent基类设计
class BaseWorkerAgent:
def __init__(self, agent_id: str, domain: str, capabilities: List[str]):
self.agent_id = agent_id
self.domain = domain
self.capabilities = capabilities
self.state_manager = StateManager()
self.tool_registry = ToolRegistry()
self.performance_monitor = PerformanceMonitor()
async def execute_task(self, task: Task) -> TaskResult:
"""Worker Agent核心执行逻辑"""
# 1. 任务验证与准备
validation_result = await self.validate_task(task)
if not validation_result.is_valid:
return TaskResult(
status=TaskStatus.FAILED,
error=f"Task validation failed: {validation_result.error}"
)
# 2. 上下文准备
context = await self.prepare_context(task)
# 3. 任务执行
execution_result = await self.execute_with_monitoring(task, context)
# 4. 结果后处理
processed_result = await self.post_process(execution_result)
return processed_result
async def execute_with_monitoring(self, task: Task, context: dict) -> ExecutionResult:
"""带监控的执行流程"""
start_time = time.time()
execution_context = ExecutionContext(
task_id=task.id,
agent_id=self.agent_id,
start_time=start_time
)
try:
# 执行具体任务逻辑
result = await self.execute_task_logic(task, context)
# 记录执行指标
execution_time = time.time() - start_time
self.performance_monitor.record_execution(
agent_id=self.agent_id,
task_type=task.type,
execution_time=execution_time,
success=True
)
return ExecutionResult(
task_id=task.id,
result=result,
execution_time=execution_time,
status=ExecutionStatus.SUCCESS
)
except Exception as e:
execution_time = time.time() - start_time
self.performance_monitor.record_execution(
agent_id=self.agent_id,
task_type=task.type,
execution_time=execution_time,
success=False,
error=str(e)
)
return ExecutionResult(
task_id=task.id,
result=None,
execution_time=execution_time,
status=ExecutionStatus.FAILED,
error=str(e)
)2.2.2 领域专业化实现
class DevelopmentWorkerAgent(BaseWorkerAgent):
"""开发领域Worker Agent"""
def __init__(self):
super().__init__(
agent_id="dev_worker_001",
domain="development",
capabilities=[
"code_generation",
"code_review",
"debugging",
"testing"
]
)
async def execute_task_logic(self, task: Task, context: dict):
return {"status": "ok", "task_id": task.id}