多Agent拓扑自愈六栈：从静态接线到运行时动态重构实测

graph TD
    subgraph 静态拓扑坍缩场景
        A[Star中心过载] --> A1[调度延迟 > 30s]
        A1 --> A2[Agent空闲等待]
        A2 --> A3[Token浪费 > 40%]

        B[Sequential链路衰减] --> B1[上下文第4次压缩]
        B1 --> B2[关键信息丢失]
        B2 --> B3[最终输出偏离 > 60%]

        C[Hierarchical单点故障] --> C1[Manager崩溃]
        C1 --> C2[子树3个Agent失联]
        C2 --> C3[任务部分完成无法回滚]
    end

    style A fill:#ff6b6b,color:#fff
    style B fill:#ffa502,color:#fff
    style C fill:#ff4757,color:#fff

from langgraph.graph import StateGraph, END
from langgraph.channels import LastValue

def router(state):
    """根据任务复杂度和Agent健康度决定拓扑路径"""
    if state.get("agent_failed"):
        return "degraded_mesh"  # 降级为Mesh
    if state.get("complexity") > 0.8:
        return "parallel_review"  # 高复杂度走并行评审
    return "sequential_chain"  # 默认顺序链

graph = StateGraph(AgentState)
graph.add_node("orchestrator", orchestrator_fn)
graph.add_node("researcher", researcher_fn)
graph.add_node("writer", writer_fn)
graph.add_node("reviewer", reviewer_fn)

# 拓扑分支点
graph.add_conditional_edges(
    "orchestrator",
    router,
    {
        "sequential_chain": "researcher",
        "parallel_review": ["researcher", "reviewer"],  # 并行扇出
        "degraded_mesh": END,  # 降级退出
    }
)

from autogen import AssistantAgent, GroupChat, GroupChatManager

# AutoGen的"拓扑"完全由speaker_selection_method控制
agents = [researcher, writer, critic, fact_checker, editor]

groupchat = GroupChat(
    agents=agents,
    messages=[],
    max_round=20,
    speaker_selection_method="auto",  # "round_robin"|"auto"|"random"
)

# auto模式的隐式问题：LLM可能反复选择同一个Agent
# 形成"researcher -> critic -> researcher -> critic"的死循环
# 而跳过writer和fact_checker
manager = GroupChatManager(groupchat=groupchat)

from crewai import Crew, Process, Agent, Task

researcher = Agent(role="Researcher", ...)
writer = Agent(role="Writer", ...)
editor = Agent(role="Editor", ...)

# 拓扑在此锁定为Sequential
crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, write_task, edit_task],
    process=Process.sequential,  # 运行时不可变
)
# 如果writer失败：editor永远拿不到输入
# 没有API可以动态插入替代Agent或切换Process

from openai import agents

researcher = agents.Agent(
    name="researcher",
    instructions="Research the topic, then hand off to writer",
    handoffs=["writer"],  # 硬编码handoff目标
)

writer = agents.Agent(
    name="writer",
    instructions="Write based on research, then hand off to reviewer",
    handoffs=["reviewer"],
)

{
  "jsonrpc": "2.0",
  "method": "agent/discover",
  "params": {
    "capabilities": ["code-review", "security-scan"],
    "preferred_topology": "mesh",
    "max_latency_ms": 5000
  }
}

graph TB
    subgraph Qualixar OS拓扑引擎
        Q[Query输入] --> TA[Topology Analyzer]
        TA -->|任务特征| TD[Topology Decision]
        TD --> T1[Sequential]
        TD --> T2[Hierarchical]
        TD --> T3[Mesh]
        TD --> T4[DAG]
        TD --> T5[Debate]

        T1 --> HE[Health Evaluator]
        T2 --> HE
        T3 --> HE
        T4 --> HE
        T5 --> HE

        HE -->|退化检测| TE[Topology Evolver]
        TE -->|拓扑迁移| T1
        TE -->|拓扑迁移| T2
        TE -->|拓扑迁移| T3
        TE -->|拓扑迁移| T4
        TE -->|拓扑迁移| T5
    end

    style TA fill:#4ecdc4,color:#fff
    style HE fill:#ff6b6b,color:#fff
    style TE fill:#45b7d1,color:#fff

from qualixar import TopologyManager, TopologyType

tm = TopologyManager()

# 查询当前拓扑健康度
health = tm.evaluate_health()
# health = {
#   "topology": "star",
#   "bottleneck_score": 0.87,  # 中心节点负载87%
#   "degradation_risk": "high",
#   "recommended_migration": "mesh"
# }

# 触发拓扑迁移
if health["degradation_risk"] == "high":
    tm.migrate_topology(
        target=TopologyType.MESH,
        strategy="gradual",  # "immediate"|"gradual"|"snapshot-restore"
        preserve_state=True,
    )

输入: "实现一个代码审查系统，需要安全扫描、风格检查、性能分析"
输出: 自动生成拓扑图:
  - Agent: code_reviewer (中心节点)
  - Agent: security_scanner (叶子节点)
  - Agent: style_checker (叶子节点)
  - Agent: perf_analyzer (叶子节点)
  - 边: code_reviewer -> security_scanner
  - 边: code_reviewer -> style_checker
  - 边: code_reviewer -> perf_analyzer
  - 边: security_scanner -> code_reviewer (反馈)
  → 自动生成为Star拓扑 + 反馈边

graph TD
    subgraph 六栈自愈能力评级
        L["LangGraph: C+<br/>条件边预编程<br/>预见故障可自愈"]
        A["AutoGen: D<br/>隐式拓扑退化<br/>需外挂监控"]
        C["CrewAI: D-<br/>Process锁定<br/>编译时决策"]
        O["OpenAI SDK: F<br/>Handoff硬链<br/>无fallback"]
        G["A2A: B-<br/>协议层赋能<br/>上层需实现"]
        Q["Qualixar: A<br/>13拓扑形式化<br/>原生迁移"]
    end

from langgraph.graph import StateGraph
from dataclasses import dataclass
from typing import Literal
import time

@dataclass
class TopologyHealth:
    star_bottleneck: float  # 0-1, 中心节点负载
    seq_latency_ms: float   # 累积延迟
    mesh_overhead: float    # 通信开销
    current_topology: str
    recommended: str

class TopologySelfHealer:
    """LangGraph拓扑自愈中间件"""

    TOPOLOGY_THRESHOLDS = {
        "star_bottleneck_max": 0.75,
        "seq_latency_max_ms": 30000,
        "mesh_agent_max": 8,
    }

    def evaluate(self, state) -> TopologyHealth:
        """评估当前拓扑健康度"""
        active_agents = state.get("active_agents", [])
        pending_tasks = state.get("pending_tasks", [])
        avg_latency = state.get("avg_step_latency_ms", 0)

        # 规则引擎 + 轻量LLM判断
        if len(active_agents) > self.TOPOLOGY_THRESHOLDS["mesh_agent_max"]:
            recommended = "hierarchical"  # Agent过多，分层管理
        elif avg_latency > self.TOPOLOGY_THRESHOLDS["seq_latency_max_ms"]:
            recommended = "parallel"  # 延迟过高，并行化
        elif state.get("orchestrator_queue_depth", 0) > 10:
            recommended = "mesh"  # 中心节点过载，去中心化
        else:
            recommended = state.get("current_topology", "star")

        return TopologyHealth(
            star_bottleneck=state.get("orchestrator_load", 0),
            seq_latency_ms=avg_latency * len(pending_tasks),
            mesh_overhead=len(active_agents) ** 2 * 0.1,
            current_topology=state.get("current_topology", "star"),
            recommended=recommended,
        )

    def route(self, state) -> Literal["migrate", "continue"]:
        health = self.evaluate(state)
        if health.recommended != health.current_topology:
            state["target_topology"] = health.recommended
            return "migrate"
        return "continue"

# 集成到StateGraph
builder = StateGraph(dict)
builder.add_node("health_check", health_check_fn)
builder.add_node("topology_migrator", migrator_fn)
builder.add_node("task_executor", executor_fn)

builder.add_conditional_edges(
    "health_check",
    TopologySelfHealer().route,
    {"migrate": "topology_migrator", "continue": "task_executor"}
)