POST https://scrapeapi.pangolinfo.com/api/v2/scrape

{
  "url": "目标 Google 搜索 URL（需包含 udm=50 参数以启用 AI Mode）",
  "parserName": "googleAISearch",
  "screenshot": true,       // 可选，是否返回页面截图
  "param": ["追问提示词"]   // 可选，多轮对话（最多5条，超出会降低响应速度）
}

curl --request POST \
  --url https://scrapeapi.pangolinfo.com/api/v2/scrape \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "url": "https://www.google.com/search?num=10&udm=50&q=javascript",
    "parserName": "googleAISearch",
    "screenshot": true,
    "param": ["how to setup"]
  }'

{
  "code": 0,
  "message": "ok",
  "data": {
    "results_num": 1,
    "ai_overview": 1,
    "json": {
      "type": "organic",
      "items": [{
        "type": "ai_overview",
        "items": [{
          "type": "ai_overview_elem",
          "content": [
            "JavaScript 是一种高级轻量级编程语言...",
            "现代浏览器均内置 JavaScript 执行引擎..."
          ]
        }],
        "references": [{
          "type": "ai_overview_reference",
          "title": "JavaScript - Wikipedia",
          "url": "https://en.wikipedia.org/wiki/JavaScript",
          "domain": "Wikipedia"
        }]
      }]
    },
    "screenshot": "https://image.datasea.network/screenshots/xxxxx.png",
    "taskId": "1768988520324-xxxxx",
    "url": "https://www.google.com/search?num=10&udm=50&q=javascript"
  }
}

import requests
import json

class PangolinAISearchSkill:
    """
    OpenClaw AI搜索技能
    基于 Pangolinfo AI Mode API 实现 Google AI Overview 实时采集
    """
    
    BASE_URL = "https://scrapeapi.pangolinfo.com/api/v2/scrape"
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {api_key}"
        }
    
    def search(
        self, 
        query: str, 
        follow_up: list[str] = None,
        screenshot: bool = False
    ) -> dict:
        """
        执行 Google AI Mode 搜索
        
        Args:
            query: 搜索关键词
            follow_up: 多轮对话提示词列表（最多5条）
            screenshot: 是否返回页面截图
            
        Returns:
            包含 ai_overview 和 references 的结构化数据
        """
        # 构建 Google AI Mode URL（udm=50 是关键参数）
        search_url = (
            f"https://www.google.com/search"
            f"?num=10&udm=50&q={requests.utils.quote(query)}"
        )
        
        payload = {
            "url": search_url,
            "parserName": "googleAISearch",  # 必须固定为此值
            "screenshot": screenshot
        }
        
        # 添加多轮对话（注意：超过5条会降低响应速度）
        if follow_up:
            payload["param"] = follow_up[:5]
        
        try:
            response = requests.post(
                self.BASE_URL,
                headers=self.headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result = response.json()
            
            if result.get("code") != 0:
                raise ValueError(f"API 错误: {result.get('message')}")
            
            return self._parse_response(result["data"])
            
        except requests.exceptions.Timeout:
            raise TimeoutError("AI Mode API 请求超时，请稍后重试")
        except requests.exceptions.RequestException as e:
            raise ConnectionError(f"请求失败: {e}")
    
    def _parse_response(self, data: dict) -> dict:
        """解析 API 响应，提取 AI Overview 内容"""
        parsed = {
            "has_ai_overview": bool(data.get("ai_overview")),
            "ai_content": [],
            "references": [],
            "screenshot_url": data.get("screenshot", ""),
            "task_id": data.get("taskId", "")
        }
        
        items = data.get("json", {}).get("items", [])
        for item in items:
            if item.get("type") == "ai_overview":
                # 提取 AI 摘要内容
                for sub_item in item.get("items", []):
                    if sub_item.get("type") == "ai_overview_elem":
                        parsed["ai_content"].extend(
                            sub_item.get("content", [])
                        )
                # 提取参考来源
                for ref in item.get("references", []):
                    parsed["references"].append({
                        "title": ref.get("title"),
                        "url": ref.get("url"),
                        "domain": ref.get("domain")
                    })
        
        return parsed


# 使用示例
if __name__ == "__main__":
    skill = PangolinAISearchSkill(api_key="YOUR_PANGOLINFO_API_KEY")
    
    # 基础搜索
    result = skill.search("OpenClaw Agent 最佳实践")
    
    if result["has_ai_overview"]:
        print("=== Google AI Overview ===")
        for content in result["ai_content"]:
            print(f"• {content}")
        
        print("\n=== 参考来源 ===")
        for ref in result["references"]:
            print(f"[{ref['domain']}] {ref['title']}")
            print(f"  → {ref['url']}")
    
    # 多轮对话模式
    result_followup = skill.search(
        query="python 异步编程",
        follow_up=["如何使用 asyncio", "与多线程有何区别"]
    )
    print(f"\n多轮对话结果包含 {len(result_followup['ai_content'])} 段 AI 内容")