054d0ae422
## 新增
- ADR-093: Telegram 告警全面遷移至 SRE 戰情室群組
- 混合策略 allowlist 模式（TYPE-3/4/4D/8M → 群組 + user_id binding）
- nonce 新格式 apr:{short_id}:{action}:{user_id_hash} + Redis 後端映射
- Feature flag TG_GROUP_CUTOVER 灰階切流
- ADR-094: Hermes 自然語言介面（@mention 對話）
- Option C：單 bot + Claude Agent SDK 虛擬分派
- Webhook secret_token + allowed_updates = [message, callback_query, chat_member]
- Prompt Injection 防護：query/describe/summarize only，mutate 走 ApprovalRecord
- Redis session TTL=300s + turn>=5 壓縮
- ADR-095: 12-Agent Claude SDK 整合 × Telegram 視覺分派
- 12 位 agent 完整 emoji/hashtag/handle 表格
- ConsensusEngine weights 擴充（security=0.4 鎖定）
- display_names.py 命名隔離（.claude/agents/ vs src/agents/）
## 更新
- ADR-009: 加 v0.3 變更紀錄指向 ADR-095
- ADR-075: 加更新引用表（ADR-093 D4 allowlist 子條款、ADR-094/095）
- docs/design/hermes-telegram-flows/hermes-flows.html: F1-F7 完整流程圖
## Pre-Flight 確認
- approval_records 表尚不存在 → 將用 BIGINT 全新建立
- docker-compose.yml:78 明碼 token 🔴 P0 待 WS1 修復
- awoooi_migrator 角色尚未建立 → WS2 建立
- claude-agent-sdk 升至 0.1.66（最新）
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
600 lines
21 KiB
Markdown
600 lines
21 KiB
Markdown
# ADR-009: OpenClaw Agent Teams 架構
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**狀態**: **已實作** ✅
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**日期**: 2026-03-23
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**實作完成**: 2026-03-24 (Phase 9.1-9.5 全部完成)
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**決策者**: 統帥 + AI 架構師
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**Phase**: 9.1-9.5 (SDK 研究 → 完整實作)
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## 背景
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AWOOOI 的核心價值是 "AI Sees. AI Acts. You Approve."
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目前 OpenClaw 是單一 AI 大腦，面對複雜告警時：
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- 單一視角可能遺漏問題
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- 無法並行分析多個面向
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- 決策品質依賴單一模型
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Claude 推出了 **Claude Agent SDK** (原 Claude Code SDK，2026-03-20 發布 v0.1.50)，支援多 Agent 協調。我們評估將此概念整合進 AWOOOI 產品。
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### SDK 研究結論 (2026-03-23)
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| 項目 | 研究結果 |
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|------|---------|
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| **SDK 名稱** | `claude-agent-sdk` (PyPI) |
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| **最新版本** | v0.1.50 (2026-03-20) |
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| **Python 版本** | ≥ 3.10 |
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| **核心 API** | `query()`, `ClaudeSDKClient` |
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| **Subagent 支援** | ✅ 原生支援 (`AgentDefinition`) |
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| **自訂 Tools** | ✅ `@tool` 裝飾器 + MCP 整合 |
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## 決策
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**採用 Claude Agent SDK 實作 OpenClaw Agent Teams，升級為多專家共識決策架構。**
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### 為何選擇 Claude Agent SDK (而非自建)
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| 考量 | 自建方案 | Claude Agent SDK |
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|------|---------|------------------|
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| 開發時間 | 2-3 週 | 2-3 天 |
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| Tool 執行 | 需自行實作 | 內建 (Read, Edit, Bash...) |
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| Subagent | 需自行設計 | 原生支援 |
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| Session 管理 | 需自行實作 | 內建 (resume, fork) |
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| MCP 整合 | 需橋接 | 原生支援 |
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| 維護成本 | 高 | 低 (跟隨 Anthropic 更新) |
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### 架構設計
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```
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┌─────────────────────────────────────────────────────────────┐
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│ OpenClaw Coordinator │
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│ (Team Lead Agent) │
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├─────────────────────────────────────────────────────────────┤
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│ ↓ │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
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│ │ Security │ │ BlastRadius │ │ Action │ │
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│ │ Agent │ │ Agent │ │ Planner │ │
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│ │ (資安評估) │ │ (影響範圍) │ │ (行動方案) │ │
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│ └─────────────┘ └─────────────┘ └─────────────┘ │
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│ ↓ ↓ ↓ │
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│ ┌─────────────────────────────────────────────────────┐ │
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│ │ Consensus Engine │ │
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│ │ (共識引擎 - 加權投票) │ │
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│ └─────────────────────────────────────────────────────┘ │
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│ ↓ │
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│ ┌─────────────────────────────────────────────────────┐ │
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│ │ Final Proposal │ │
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│ │ (統一提案 → 人類審批) │ │
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│ └─────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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### Agent 職責
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| Agent | 職責 | 輸出 |
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|-------|------|------|
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| **Coordinator** | 分配任務、彙整共識 | Final Proposal |
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| **SecurityAgent** | 評估安全風險、權限影響 | Risk Score (0-10) |
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| **BlastRadiusAgent** | 分析影響範圍、相依服務 | Affected Services List |
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| **ActionPlannerAgent** | 規劃修復步驟、回滾方案 | Action Steps + Rollback |
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### 共識機制
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```python
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class ConsensusEngine:
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weights = {
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"security": 0.4, # 資安權重最高
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"blast_radius": 0.3, # 影響範圍次之
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"action_plan": 0.3, # 行動方案
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}
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def calculate_confidence(self, results: dict) -> float:
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"""加權計算整體信心分數"""
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score = 0
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for agent, weight in self.weights.items():
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score += results[agent].confidence * weight
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return score
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def should_auto_approve(self, confidence: float) -> bool:
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"""信心分數 > 0.9 且無高風險 → 可自動執行"""
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return confidence > 0.9 and not self.has_high_risk()
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```
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## 技術實作
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### 依賴 (Phase 9.2 研究結果)
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```toml
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# apps/api/pyproject.toml
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[project.dependencies]
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# Phase 9: OpenClaw Agent Teams
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claude-agent-sdk = ">=0.1.50" # Claude Agent SDK (原 Claude Code SDK)
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# Note: SDK 自動包含 Claude Code CLI，無需額外安裝
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```
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#### 安裝指令
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```bash
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# 使用 uv (推薦)
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uv add claude-agent-sdk
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# 使用 pip
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pip install claude-agent-sdk
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# 驗證安裝
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python -c "from claude_agent_sdk import query; print('OK')"
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```
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#### 環境變數
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```bash
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# 必須
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export ANTHROPIC_API_KEY=sk-ant-...
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# 可選 (雲端備援，參考 ADR-006)
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export CLAUDE_CODE_USE_BEDROCK=1 # AWS Bedrock
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export CLAUDE_CODE_USE_VERTEX=1 # Google Vertex AI
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```
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### 核心類別 (使用 Claude Agent SDK)
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```python
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# apps/api/src/services/openclaw_team.py
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import asyncio
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from claude_agent_sdk import (
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query,
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ClaudeAgentOptions,
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AgentDefinition,
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ClaudeSDKClient,
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AssistantMessage,
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ResultMessage,
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)
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from dataclasses import dataclass
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from typing import AsyncIterator
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@dataclass
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class AgentResult:
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agent: str
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analysis: str
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confidence: float
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risk_score: float | None = None
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affected_services: list[str] | None = None
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action_steps: list[str] | None = None
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@dataclass
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class Proposal:
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incident_id: str
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summary: str
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agent_results: list[AgentResult]
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consensus_score: float
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recommended_action: str
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auto_approvable: bool
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class OpenClawTeam:
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"""
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使用 Claude Agent SDK 實作多專家協調分析
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符合 leWOOOgo BRAIN 積木介面
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"""
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def __init__(self):
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# 定義專家 Subagents
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self.agents = {
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"security-expert": AgentDefinition(
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description="資安專家，評估安全風險與權限影響",
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prompt="""你是 AWOOOI 的資安專家。
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分析告警的安全風險，評估：
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1. 是否涉及敏感資料
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2. 是否可能被利用
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3. 權限邊界是否被突破
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輸出 JSON: {"risk_score": 0-10, "analysis": "...", "confidence": 0-1}""",
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tools=["Read", "Grep"], # 只讀權限
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),
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"blast-radius": AgentDefinition(
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description="影響範圍分析師，評估相依服務與影響範圍",
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prompt="""你是 AWOOOI 的影響範圍分析師。
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分析告警的影響範圍：
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1. 直接影響的服務
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2. 間接相依的服務
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3. 使用者影響人數估計
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輸出 JSON: {"affected_services": [...], "blast_radius": "low|medium|high", "confidence": 0-1}""",
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tools=["Read", "Glob", "Grep"],
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),
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"action-planner": AgentDefinition(
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description="行動規劃師，制定修復步驟與回滾方案",
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prompt="""你是 AWOOOI 的行動規劃師。
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根據告警制定修復計畫：
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1. 立即修復步驟 (kubectl 指令)
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2. 驗證步驟
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3. 回滾方案
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注意: 所有 kubectl 必須帶 -n awoooi-prod
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輸出 JSON: {"action_steps": [...], "rollback_steps": [...], "confidence": 0-1}""",
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tools=["Read", "Glob"],
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),
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}
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self.options = ClaudeAgentOptions(
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allowed_tools=["Read", "Glob", "Grep", "Agent"], # Agent 用於調用 Subagent
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agents=self.agents,
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system_prompt="""你是 OpenClaw Coordinator，AWOOOI 的 AI 決策引擎。
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你的任務是協調多個專家 Agent 分析告警，彙整共識並產出最終提案。
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呼叫順序: security-expert → blast-radius → action-planner
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最終輸出統一提案供人類審批。""",
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)
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async def analyze_incident(self, incident: dict) -> Proposal:
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"""
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並行呼叫多個 Subagent 分析告警
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"""
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prompt = f"""
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分析以下告警並產出修復提案:
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```json
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{json.dumps(incident, ensure_ascii=False, indent=2)}
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```
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請依序呼叫以下 Agent:
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1. security-expert - 評估安全風險
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2. blast-radius - 分析影響範圍
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3. action-planner - 規劃修復步驟
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收集所有分析結果後，使用 ConsensusEngine 邏輯 (security 40%, blast_radius 30%, action 30%)
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計算整體信心分數，並產出最終提案。
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輸出格式:
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```json
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{{
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"summary": "一句話摘要",
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"agent_results": [...],
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"consensus_score": 0-1,
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"recommended_action": "建議的 kubectl 指令",
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"auto_approvable": true/false (>0.9 且無高風險)
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}}
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```
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"""
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result_json = None
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async for message in query(prompt=prompt, options=self.options):
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if isinstance(message, ResultMessage):
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# 解析最終結果
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result_json = self._extract_json(message.result)
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if not result_json:
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raise ValueError("Agent Team 未能產出有效提案")
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return Proposal(
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incident_id=incident.get("id", "unknown"),
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summary=result_json.get("summary", ""),
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agent_results=self._parse_agent_results(result_json.get("agent_results", [])),
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consensus_score=result_json.get("consensus_score", 0),
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recommended_action=result_json.get("recommended_action", ""),
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auto_approvable=result_json.get("auto_approvable", False),
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)
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def _extract_json(self, text: str) -> dict:
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"""從回應中提取 JSON"""
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import json
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import re
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match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
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if match:
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return json.loads(match.group(1))
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return json.loads(text)
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def _parse_agent_results(self, results: list) -> list[AgentResult]:
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"""解析各 Agent 結果"""
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return [
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AgentResult(
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agent=r.get("agent", "unknown"),
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analysis=r.get("analysis", ""),
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confidence=r.get("confidence", 0),
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risk_score=r.get("risk_score"),
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affected_services=r.get("affected_services"),
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action_steps=r.get("action_steps"),
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)
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for r in results
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]
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```
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### 替代方案: ClaudeSDKClient (互動式)
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```python
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# 適用於需要人機互動的場景
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async def interactive_analysis(incident: dict):
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async with ClaudeSDKClient(options=options) as client:
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# 第一輪: 安全分析
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await client.query(f"使用 security-expert 分析: {json.dumps(incident)}")
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security_result = await collect_response(client)
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# 人類可在此介入調整
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# 第二輪: 影響範圍
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await client.query("繼續使用 blast-radius 分析影響範圍")
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blast_result = await collect_response(client)
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# ...
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```
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### API 端點
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```python
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# apps/api/src/routes/incidents.py
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@router.post("/api/v1/incidents/{incident_id}/analyze")
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async def analyze_with_team(incident_id: str):
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"""使用 Agent Team 分析告警"""
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incident = await get_incident(incident_id)
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team = OpenClawTeam()
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proposal = await team.analyze_incident(incident)
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return {
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"proposal": proposal,
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"agent_results": proposal.agent_results,
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"consensus_score": proposal.consensus_score,
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"auto_approvable": proposal.auto_approvable
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}
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```
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### UI 呈現
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```tsx
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// apps/web/src/components/incident/agent-team-analysis.tsx
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export function AgentTeamAnalysis({ proposal }: Props) {
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return (
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<GlassCard>
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<h3>{t('incident.teamAnalysis')}</h3>
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{/* 各 Agent 分析結果 */}
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<div className="grid grid-cols-3 gap-4">
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{proposal.agentResults.map(result => (
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<AgentResultCard
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key={result.agent}
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agent={result.agent}
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confidence={result.confidence}
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summary={result.summary}
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/>
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))}
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</div>
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{/* 共識分數 */}
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<ConsensusScore score={proposal.consensusScore} />
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{/* 最終提案 */}
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<ProposalCard proposal={proposal} />
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</GlassCard>
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)
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}
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```
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## 對應 leWOOOgo 積木
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| 積木類別 | 新增模組 |
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|---------|---------|
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| **BRAIN** | `SecurityAgent` |
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| **BRAIN** | `BlastRadiusAgent` |
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| **BRAIN** | `ActionPlannerAgent` |
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| **BRAIN** | `CoordinatorAgent` |
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| **BRAIN** | `ConsensusEngine` |
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## 後果
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### 優點
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1. **多視角分析** - 不同專家 Agent 各司其職
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2. **共識決策** - 加權投票提高決策品質
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3. **可解釋性** - 每個 Agent 的分析過程透明
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4. **彈性擴展** - 可新增更多專家 Agent
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5. **差異化** - 競品無此功能
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### 缺點
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1. **成本增加** - 多 Agent 呼叫增加 API 費用
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2. **延遲增加** - 並行分析仍需等待最慢的 Agent
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3. **複雜度** - 共識機制需要調優
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### 風險
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| 風險 | 緩解措施 |
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|------|---------|
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| API 成本爆炸 | 設定 Token 上限、快取策略 |
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| Agent 意見衝突 | 共識引擎加權投票 |
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| SDK 不穩定 | 先用 Anthropic SDK 模擬 |
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## 與 leWOOOgo 整合 (ADR-003)
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|
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OpenClaw Agent Teams 作為 **BRAIN 積木** 整合進 leWOOOgo 架構：
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|
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ leWOOOgo Engine │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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||
│ 🧱 INPUT ──────→ 🧠 BRAIN ──────────────→ 📢 OUTPUT │
|
||
│ (Prometheus) │ (Telegram) │
|
||
│ │ │
|
||
│ ┌──────┴──────┐ │
|
||
│ │ OpenClawTeam │ ← NEW: Agent Teams │
|
||
│ │ (SDK-based) │ │
|
||
│ └──────┬──────┘ │
|
||
│ │ │
|
||
│ ┌──────┴──────┐ │
|
||
│ │ 🔧 ACTION │ │
|
||
│ │ K8sExecutor │ │
|
||
│ └─────────────┘ │
|
||
│ │
|
||
└─────────────────────────────────────────────────────────────────┘
|
||
```
|
||
|
||
### BRAIN 積木介面實作
|
||
|
||
```python
|
||
# packages/lewooogo-brain/src/openclaw_team_plugin.py
|
||
|
||
from lewooogo_core.interfaces import AgentProvider, AgentInput, AgentOutput
|
||
|
||
|
||
class OpenClawTeamPlugin(AgentProvider):
|
||
"""
|
||
leWOOOgo BRAIN 積木: OpenClaw Agent Teams
|
||
符合 ADR-003 定義的 AgentProvider 介面
|
||
"""
|
||
|
||
id = "openclaw-agent-team"
|
||
name = "OpenClaw Agent Team"
|
||
version = "0.1.0"
|
||
category = "BRAIN"
|
||
|
||
def __init__(self):
|
||
self.team = OpenClawTeam()
|
||
|
||
async def initialize(self) -> None:
|
||
# 驗證 API Key
|
||
assert os.environ.get("ANTHROPIC_API_KEY"), "Missing ANTHROPIC_API_KEY"
|
||
|
||
async def process(self, input: AgentInput) -> AgentOutput:
|
||
proposal = await self.team.analyze_incident(input.payload)
|
||
return AgentOutput(
|
||
result=proposal,
|
||
confidence=proposal.consensus_score,
|
||
metadata={"agent_count": 3, "sdk_version": "0.1.50"},
|
||
)
|
||
|
||
def get_capabilities(self) -> list[str]:
|
||
return [
|
||
"security-analysis",
|
||
"blast-radius-analysis",
|
||
"action-planning",
|
||
"consensus-decision",
|
||
]
|
||
|
||
async def health_check(self) -> dict:
|
||
return {"status": "healthy", "sdk": "claude-agent-sdk"}
|
||
|
||
async def shutdown(self) -> None:
|
||
pass
|
||
```
|
||
|
||
## 與 ADR-006 整合 (AI 備援)
|
||
|
||
Agent Teams 整合現有 AI Fallback 策略：
|
||
|
||
```
|
||
優先級 1: Ollama (本地) → 簡單告警走 Ollama
|
||
優先級 2: Claude Agent SDK → 複雜告警走 Agent Teams
|
||
優先級 3: Gemini API → SDK 失敗時備援
|
||
優先級 4: 靜態回應
|
||
```
|
||
|
||
### 路由邏輯
|
||
|
||
```python
|
||
class OpenClawRouter:
|
||
async def route(self, incident: dict) -> Proposal:
|
||
# 根據告警複雜度選擇處理器
|
||
if self._is_simple_alert(incident):
|
||
# 簡單告警: Ollama 足夠
|
||
return await self.ollama_handler.analyze(incident)
|
||
else:
|
||
# 複雜告警: 使用 Agent Teams
|
||
try:
|
||
return await self.agent_team.analyze_incident(incident)
|
||
except ClaudeSDKError:
|
||
# SDK 失敗，降級到 Gemini
|
||
return await self.gemini_fallback.analyze(incident)
|
||
|
||
def _is_simple_alert(self, incident: dict) -> bool:
|
||
# 判斷邏輯: P3/P4 且影響單一服務 → 簡單
|
||
severity = incident.get("severity", "P3")
|
||
affected = incident.get("affected_services", [])
|
||
return severity in ["P3", "P4"] and len(affected) <= 1
|
||
```
|
||
|
||
## 實作驗收 (2026-03-24)
|
||
|
||
| Phase | 內容 | 狀態 | 實際 |
|
||
|-------|------|------|------|
|
||
| 9.1 | ADR 審核 + SDK 研究 | ✅ 完成 | 0.5 天 |
|
||
| 9.2 | SDK 整合 + POC | ✅ 完成 | 1 天 |
|
||
| 9.3 | 3 專家 Agent 實作 | ✅ 完成 | 1.5 天 |
|
||
| 9.4 | ConsensusEngine + leWOOOgo 整合 | ✅ 完成 | 1 天 |
|
||
| 9.5 | API 端點 + UI 呈現 | ✅ 完成 | 1 天 |
|
||
|
||
**總計: 5 天** (比預估提前完成)
|
||
|
||
### 實作檔案
|
||
|
||
```
|
||
apps/api/src/agents/
|
||
├── __init__.py
|
||
├── security_agent.py # SecurityAgent
|
||
├── blast_radius_agent.py # BlastRadiusAgent
|
||
├── action_planner_agent.py # ActionPlannerAgent
|
||
└── coordinator.py # OpenClawCoordinator
|
||
|
||
apps/api/src/services/
|
||
├── consensus_engine.py # ConsensusEngine
|
||
└── openclaw_team.py # OpenClawTeam 整合
|
||
```
|
||
|
||
### Phase 9.2 POC 驗證項目
|
||
|
||
```bash
|
||
# 1. 安裝 SDK
|
||
cd apps/api && uv add claude-agent-sdk
|
||
|
||
# 2. 建立測試腳本
|
||
cat > scripts/test-agent-team.py << 'EOF'
|
||
import asyncio
|
||
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition
|
||
|
||
async def main():
|
||
# 簡單 Subagent 測試
|
||
options = ClaudeAgentOptions(
|
||
allowed_tools=["Agent"],
|
||
agents={
|
||
"test-agent": AgentDefinition(
|
||
description="測試 Agent",
|
||
prompt="回答問題並回傳 JSON",
|
||
tools=[],
|
||
)
|
||
},
|
||
)
|
||
|
||
async for msg in query(
|
||
prompt="使用 test-agent 回答: 2+2=?",
|
||
options=options,
|
||
):
|
||
print(msg)
|
||
|
||
asyncio.run(main())
|
||
EOF
|
||
|
||
# 3. 執行測試
|
||
python scripts/test-agent-team.py
|
||
```
|
||
|
||
## 相關 ADR
|
||
|
||
- ADR-003: leWOOOgo 模組架構 (BRAIN 積木)
|
||
- ADR-006: AI 備援策略 (Fallback 整合)
|
||
- ADR-001: MCP Protocol 採用 (SDK 支援 MCP)
|
||
|
||
## 參考資料
|
||
|
||
- [Claude Agent SDK Overview](https://platform.claude.com/docs/en/agent-sdk/overview)
|
||
- [Claude Agent SDK Quickstart](https://platform.claude.com/docs/en/agent-sdk/quickstart)
|
||
- [Claude Agent SDK Python GitHub](https://github.com/anthropics/claude-agent-sdk-python)
|
||
- [Claude Agent SDK Demos](https://github.com/anthropics/claude-agent-sdk-demos)
|
||
- [LangGraph + Claude Agent SDK 整合](https://www.mager.co/blog/2026-03-07-langgraph-claude-agent-sdk-ultimate-guide/)
|
||
|
||
## 變更記錄
|
||
|
||
| 日期 | 版本 | 變更 | 作者 |
|
||
|------|------|------|------|
|
||
| 2026-03-23 | v0.1 | 初稿提議 | AI 架構師 |
|
||
| 2026-03-23 | v0.2 | SDK 研究完成，加入具體整合方案 | AI 架構師 |
|
||
| 2026-04-24 | v0.3 | 由 [ADR-095](ADR-095-12agent-sdk-integration.md) 擴充 ConsensusEngine weights 為 12-agent（security=0.4 鎖定，其餘 0.6 重分配），原 3 核心 agent 保留 | 12-Agent 全景分析 |
|