破茧成蝶：使用 React 19、Elysia 与多 Agent 协作构建 AI 驱动的 DevOps 智能体

bun create elysia phx-backend
cd phx-backend
bun add @elysia/eden @redis/client openai zod

// src/agents/supervisor.ts
import Elysia, { t } from 'elysia';
import { OpenAI } from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

export const supervisorAgent = new Elysia()
  .post('/supervise', async ({ body }) => {
    const { userInput } = body;
    
    // 1. 调用 LLM 进行意图分类 (Function Calling)
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: userInput }],
      tools: [{
        type: "function",
        function: {
          name: "route_agent",
          description: "路由到对应的专业Agent",
          parameters: {
            type: "object",
            properties: {
              target: { 
                type: "string", 
                enum: ["LogAnalyzer", "AlertJudge", "AutoHealer"] 
              }
            }
          }
        }
      }]
    });

    // 2. 解析并返回路由指令
    const toolCall = response.choices[0].message.tool_calls[0];
    return { 
      agent: toolCall.function.arguments.target,
      instruction: userInput 
    };
  }, {
    body: t.Object({ userInput: t.String() })
  });

// src/components/ChatInterface.tsx
import { useActionState, useOptimistic, startTransition } from 'react';

// 模拟调用后端 Elysia 服务 (使用 Eden Treaty)
import { eden } from '../api/client';

function ChatInterface() {
  const [messages, setMessages] = useState([]);
  
  // React 19 Action: 异步提交
  const submitMessage = async (prevState: any, formData: FormData) => {
    const input = formData.get('input') as string;
    
    // 乐观更新 UI (Optimistic Update)
    setMessages(prev => [...prev, { role: 'user', content: input }]);
    
    // 调用 Elysia 后端的 Agent 路由
    const { data } = await eden.supervise.post({ userInput: input });
    
    // 根据路由结果，进一步请求特定 Agent (这里简化逻辑)
    const result = await eden.agents[data.agent].post({ instruction: data.instruction });
    
    setMessages(prev => [...prev, { role: 'assistant', content: result.analysis }]);
    return { success: true };
  };

  const [state, formAction] = useActionState(submitMessage, { success: false });

  return (
    <form action={formAction}>
      <input name="input" placeholder="输入您的运维指令..." />
      <button type="submit">发送</button>
    </form>
  );
}

FROM oven/bun:1.0 AS base
WORKDIR /app
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
COPY . .
EXPOSE 3000
CMD ["bun", "run", "src/index.ts"]

# .github/workflows/deploy.yaml
name: AI DevOps Pipeline

on:
  push:
    branches: [ main ]

jobs:
  ai-code-review:
    runs-on: ubuntu-latest
    steps:
      - name: 调用内部 Agent 进行代码审查
        run: |
          curl -X POST https://ai-api.internal/review \
            -H "Authorization: Bearer ${{ secrets.AI_TOKEN }}" \
            -d '{"commit": "${{ github.sha }}"}' \
            --output review_report.json
      - name: 如果风险等级 > 8，阻断流水线
        run: |
          risk=$(jq '.risk_score' review_report.json)
          if [ $risk -gt 8 ]; then exit 1; fi

  deploy-canary:
    needs: ai-code-review
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to K8s Canary
        run: kubectl set image deployment/phx-server phx-server=${{ github.sha }} -n canary
      - name: 等待 2 分钟，让 Agent 观察监控
        run: sleep 120
      - name: 询问 AutoHealer Agent 是否继续全量发布
        run: |
          DECISION=$(curl -s -X POST https://api.phx/agent/decision)
          if [ "$DECISION" != "GO" ]; then exit 1; fi

// 在 Elysia 中集成 OpenTelemetry
import { otel } from '@elysiajs/otel';

app.use(otel({
  serviceName: 'phx-supervisor',
  attributes: { version: '1.0.0' }
}));