{"id":"ec2160b3-daa4-46a6-81bb-ea7a1fa54f16","entityType":"agent","slug":"nex-zmh-agent-websearch-skill","name":"multi-search","canonicalUrl":"https://www.xpersona.co/agent/nex-zmh-agent-websearch-skill","canonicalPath":"/agent/nex-zmh-agent-websearch-skill","generatedAt":"2026-10-09T13:56:57.132Z","source":"GITHUB_OPENCLEW","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":null},"description":"智能多引擎搜索，自动检测网络环境并按优先级切换：DuckDuckGo -> Tavily -> Bing API -> Bing爬虫。支持自动配额管理和网络缓存。Invoke when user needs web search with automatic engine selection and network adaptation. --- name: multi-search description: 智能多引擎搜索，自动检测网络环境并按优先级切换：DuckDuckGo -> Tavily -> Bing API -> Bing爬虫。支持自动配额管理和网络缓存。Invoke when user needs web search with automatic engine selection and network adaptation. trigger: 当需要进行网络搜索、需要自动切换引擎、需要配额管理或网络环境可能变化时触发。 metadata: {\"openclaw\":{\"emoji\":\"🔍\",\"requires\":{\"python\":[\"requests\",\"tavily\",\"duckduckgo_search\"],\"env\":[\"TAVILY_API_KEY\",\"BING_API_KEY\"]},\"install\":[{\"id\":\"pip\",\"k","descriptionLabel":"Technical summary","evidenceSummary":"Published capability contract available. 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Tavily API (1000次/月) - 质量最高，需 API Key\n2. DuckDuckGo (无限免费) - 无需 API Key\n3. Bing Web Search API (1000次/月) - 需 API Key\n4. Bing 爬虫 (无限免费) - 最终回退\n\n### 平衡模式 (prefer_quality=False, 默认)\n1. DuckDuckGo (无限免费) - 优先免费引擎\n2. Tavily API (1000次/月) - 如果配置了 API Key\n3. Bing Web Search API (1000次/月)\n4. Bing 爬虫 (无限免费)\n\n## 核心能力\n\n- 智能网络检测与引擎切换\n- 自动配额管理（Tavily/Bing API）\n- 支持网页内容抓取\n- 5分钟网络检测缓存\n\n## 使用方式\n\n### 基本搜索\n```python\nfrom multi_search import search\n\n# 平衡模式 - 优先免费引擎\nresults = search(\"Python tutorial\", max_results=5)\n\n# 质量优先模式 - 优先使用 Tavily\nresults = search(\"AI research\", max_results=5, prefer_quality=True)\n\n# 强制重新检测网络（切换 VPN 后使用）\nresults = search(\"OpenClaw skills\", max_results=5, force_network_check=True)\n```\n\n### 搜索技能（自动质量优先）\n```python\nfrom multi_search import search_skills\n\nresults = search_skills(\"OpenClaw AI agent automation\", max_results=10)\n```\n\n### 查看系统状态\n```python\nfrom multi_search import get_status\n\nstatus = get_status()  # 使用缓存\nstatus = get_status(force_network_check=True)  # 强制重新检测\n```\n\n### 抓取网页详细内容\n```python\nfrom multi_search import search, fetch_web_content, fetch_search_results_content\n\n# 搜索并抓取第一个结果的详细内容\nresults = search(\"OpenClaw new features\", max_results=3)\nif results:\n    content = fetch_web_content(results[0]['href'], max_length=3000)\n    # content['title'], content['content'], content['success']\n\n# 批量抓取所有搜索结果的详细内容\nenriched_results = fetch_search_results_content(results, max_length=2000)\nfor r in enriched_results:\n    if r.get('full_content'):\n        # 使用 summarize 技能总结内容\n        pass\n```\n\n### 与 Summarize 技能结合使用\n```\nOpenClaw 工作流：\n1. 使用 multi-search 搜索关键词\n2. 选择感兴趣的搜索结果\n3. 使用 fetch_web_content() 抓取网页内容\n4. 使用 summarize 技能总结网页内容\n5. 将摘要呈现给用户\n```\n\n## 返回结果格式\n\n```python\n[\n    {\n        'title': '结果标题',\n        'href': 'https://example.com',\n        'body': '结果摘要...',\n        'source': 'duckduckgo'  # 或 'tavily', 'bing_api', 'bing_scraper'\n    }\n]\n```\n\n## 参数说明\n\n- `query`: 搜索关键词\n- `max_results`: 最大结果数（默认5）\n- `prefer_quality`: 是否优先质量（默认False）\n- `force_network_check`: 是否强制重新检测网络（默认False）\n\n## 注意事项\n\n- DuckDuckGo: 免费无限，但某些网络环境无法访问\n- Tavily: 质量高，需要 API key，1000次/月\n- Bing API: 官方稳定，需要 Azure 账号，1000次/月\n- Bing 爬虫: 免费无限，但可能受反爬影响\n","readmeExcerpt":"--- name: multi-search description: 智能多引擎搜索，自动检测网络环境并按优先级切换：DuckDuckGo -> Tavily -> Bing API -> Bing爬虫。支持自动配额管理和网络缓存。Invoke when user needs web search with automatic engine selection and network adaptation. trigger: 当需要进行网络搜索、需要自动切换引擎、需要配额管理或网络环境可能变化时触发。 metadata: {\"openclaw\":{\"emoji\":\"🔍\",\"requires\":{\"python\":[\"requests\",\"tavily\",\"duckduckgo_search\"],\"env\":[\"TAVILY_API_KEY\",\"BING_API_KEY\"]},\"install\":[{\"id\":\"pip\",\"k","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"from multi_search import search\n\n# 平衡模式 - 优先免费引擎\nresults = search(\"Python tutorial\", max_results=5)\n\n# 质量优先模式 - 优先使用 Tavily\nresults = search(\"AI research\", max_results=5, prefer_quality=True)\n\n# 强制重新检测网络（切换 VPN 后使用）\nresults = search(\"OpenClaw skills\", max_results=5, force_network_check=True)"},{"language":"python","snippet":"from multi_search import search_skills\n\nresults = search_skills(\"OpenClaw AI agent automation\", max_results=10)"},{"language":"python","snippet":"from multi_search import get_status\n\nstatus = get_status()  # 使用缓存\nstatus = get_status(force_network_check=True)  # 强制重新检测"},{"language":"python","snippet":"from multi_search import search, fetch_web_content, fetch_search_results_content\n\n# 搜索并抓取第一个结果的详细内容\nresults = search(\"OpenClaw new features\", max_results=3)\nif results:\n    content = fetch_web_content(results[0]['href'], max_length=3000)\n    # content['title'], content['content'], content['success']\n\n# 批量抓取所有搜索结果的详细内容\nenriched_results = fetch_search_results_content(results, max_length=2000)\nfor r in enriched_results:\n    if r.get('full_content'):\n        # 使用 summarize 技能总结内容\n        pass"},{"language":"text","snippet":"OpenClaw 工作流：\n1. 使用 multi-search 搜索关键词\n2. 选择感兴趣的搜索结果\n3. 使用 fetch_web_content() 抓取网页内容\n4. 使用 summarize 技能总结网页内容\n5. 将摘要呈现给用户"},{"language":"python","snippet":"[\n    {\n        'title': '结果标题',\n        'href': 'https://example.com',\n        'body': '结果摘要...',\n        'source': 'duckduckgo'  # 或 'tavily', 'bing_api', 'bing_scraper'\n    }\n]"}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"智能多引擎搜索，自动检测网络环境并按优先级切换：DuckDuckGo -> Tavily -> Bing API -> Bing爬虫。支持自动配额管理和网络缓存。Invoke when user needs web search with automatic engine selection and network adaptation. --- name: multi-search description: 智能多引擎搜索，自动检测网络环境并按优先级切换：DuckDuckGo -> Tavily -> Bing API -> Bing爬虫。支持自动配额管理和网络缓存。Invoke when user needs web search with automatic engine selection and network adaptation. trigger: 当需要进行网络搜索、需要自动切换引擎、需要配额管理或网络环境可能变化时触发。 metadata: {\"openclaw\":{\"emoji\":\"🔍\",\"requires\":{\"python\":[\"requests\",\"tavily\",\"duckduckgo_search\"],\"env\":[\"TAVILY_API_KEY\",\"BING_API_KEY\"]},\"install\":[{\"id\":\"pip\",\"k","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":364,"uniquenessScore":66,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-09T13:56:57.132Z","emptyReason":null},"items":[{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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