AI驱动的LinkedIn联系人推荐器
中级
这是一个Lead Generation, Multimodal AI领域的自动化工作流，包含 10 个节点。主要使用 Set, Code, EmailSend, HttpRequest, EmailReadImap 等节点。 基于AI的LinkedIn联系人推荐器
前置要求
- •可能需要目标 API 的认证凭证
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "fnSWZ5XCdbq6snzS",
"meta": {
"instanceId": "dd69efaf8212c74ad206700d104739d3329588a6f3f8381a46a481f34c9cc281",
"templateCredsSetupCompleted": true
},
"name": "AI驱动的LinkedIn联系人推荐器",
"tags": [],
"nodes": [
{
"id": "ef9e2f3b-c06a-484a-8903-2f37b35728f3",
"name": "搜索LinkedIn个人资料",
"type": "n8n-nodes-base.httpRequest",
"position": [
-600,
4.76293103448279
],
"parameters": {
"url": "https://serpapi.com/search.json",
"options": {},
"sendQuery": true,
"authentication": "genericCredentialType",
"genericAuthType": "httpBasicAuth",
"queryParameters": {
"parameters": [
{
"name": "engine",
"value": "google"
},
{
"name": "q",
"value": "=site:linkedin.com/in \"{{ $json.Position }}\" \"{{ $json.Location }}\" {{ $json.Skills.split(',')[0] }}"
},
{
"name": "api_key",
"value": "="
},
{
"name": "num",
"value": "200"
},
{
"name": "start",
"value": "0"
}
]
}
},
"credentials": {
"httpBasicAuth": {
"id": "SS8MHWya3vb8KVFr",
"name": "temporary cred"
},
"httpQueryAuth": {
"id": "xA2e6hA40RZ8bzrI",
"name": "Query Auth account - test"
}
},
"typeVersion": 4
},
{
"id": "34999f59-ae4b-4ef6-9603-bc5f6b2798d0",
"name": "AI个人资料分析",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-160,
4.76293103448279
],
"parameters": {
"text": "=You are a LinkedIn networking expert. Analyze the following user profile and potential connections to provide intelligent networking recommendations.\n\nUser Profile:\n- Name: {{ $json.userProfile.name }}\n- Position: {{ $json.userProfile.current_position }}\n- Industry: {{ $json.userProfile.industry }}\n- Location: {{ $json.userProfile.location }}\n- Skills: {{ $json.userProfile.skills }}\n- Interests: {{ $json.userProfile.interests }}\n- Target Roles: {{ $json.userProfile.target_roles }}\n- Preferred Companies: {{ $json.userProfile.company_types }}\n\nPotential Connections Found:\n{{ $json.foundProfiles.map(p => `- ${p.name}: ${p.headline}`).join('\\n') }}\n\nPlease provide a JSON response with the following structure:\n{\n \"scored_profiles\": [\n {\n \"name\": \"Profile Name\",\n \"score\": 8.5,\n \"reasons\": [\"Similar role\", \"Same industry\", \"Skill overlap\"]\n }\n ],\n \"top_connections\": [\n // Top 10 recommended profiles with full details\n ],\n \"connection_strategies\": [\n \"Personalized connection message suggestion for each top profile\"\n ],\n \"networking_insights\": \"Key insights about networking opportunities and industry trends\"\n}\n\nScore profiles from 1-10 based on:\n- Role alignment with user's target positions\n- Industry relevance\n- Skill complementarity\n- Networking value\n- Career growth potential\n\nFocus on quality connections that could provide mutual value.",
"batching": {},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "d8205e9b-e176-477c-a8e7-2f1eb94bc3cb",
"name": "Ollama模型1",
"type": "@n8n/n8n-nodes-langchain.lmOllama",
"position": [
-72,
224.7629310344828
],
"parameters": {
"model": "llama3.2-16000:latest",
"options": {
"topP": 0.9,
"temperature": 0.7
}
},
"credentials": {
"ollamaApi": {
"id": "7td3WzXCW2wNhraP",
"name": "Ollama - test"
}
},
"typeVersion": 1
},
{
"id": "482f65e9-4075-4d60-8bc8-3c078f8c7acc",
"name": "发送邮件",
"type": "n8n-nodes-base.emailSend",
"position": [
656,
4.76293103448279
],
"webhookId": "46290c2a-08bd-43d7-bb85-112fceaed4b8",
"parameters": {
"text": "={{ $json.body }}",
"options": {},
"subject": "={{ $json.subject }}",
"toEmail": "{{ $json.from }}",
"fromEmail": "abc@gmail.com",
"emailFormat": "text"
},
"credentials": {
"smtp": {
"id": "G1kyF8cSWTZ4vouN",
"name": "SMTP -test"
}
},
"typeVersion": 2.1
},
{
"id": "c759dfb7-9935-41e0-9d38-e41ff48c8b0b",
"name": "创建邮件",
"type": "n8n-nodes-base.code",
"position": [
436,
4.76293103448279
],
"parameters": {
"jsCode": "// Get the input data (your JSON)\nconst data = items[0].json;\n\n// Build the email text\nlet emailText = `📊 Networking Report for ${data.user_profile.name}\\n`;\nemailText += `\\nInterests: ${data.user_profile.interests}`;\nemailText += `\\nTarget Industry: ${data.user_profile.target_industry}\\n`;\n\nemailText += `\\nSummary:\\n`;\nemailText += `- Total Profiles Found: ${data.summary.total_profiles_found}\\n`;\nemailText += `- Analyzed Connections: ${data.summary.analyzed_connections}\\n`;\nemailText += `- High Priority: ${data.summary.high_priority}\\n`;\nemailText += `- Medium Priority: ${data.summary.medium_priority}\\n`;\nemailText += `- Average Score: ${data.summary.average_score}\\n`;\n\nemailText += `\\nTop Connection Recommendations:\\n`;\ndata.connection_recommendations.forEach((rec, index) => {\n emailText += `\\n${index + 1}. ${rec.name} (${rec.priority} Priority, Score: ${rec.aiScore})`;\n emailText += `\\n ${rec.description}`;\n emailText += `\\n Mutual Connections: ${rec.mutual_connections}`;\n emailText += `\\n Reason: ${rec.connectionReason}`;\n emailText += `\\n Suggested Message: \"${rec.suggestedMessage}\"\\n`;\n});\n\nemailText += `\\nNext Steps:\\n`;\ndata.next_steps.forEach((step, i) => {\n emailText += `- ${step}\\n`;\n});\n\nemailText += `\\nAI Insights:\\n${data.ai_insights}\\n`;\n\nemailText += `\\nBest Networking Times:\\nDays: ${data.networking_strategy.best_days.join(\", \")}\\nTimes: ${data.networking_strategy.best_times.join(\", \")}\\n`;\n\nreturn [{\n json: {\n subject: `Networking Report - ${data.user_profile.name}`,\n body: emailText\n }\n}];\n"
},
"typeVersion": 2
},
{
"id": "7e27272d-1c4e-4e15-b7be-f0ef85937c13",
"name": "您的个人资料信息",
"type": "n8n-nodes-base.set",
"position": [
-820,
4.76293103448279
],
"parameters": {
"fields": {
"values": [
{
"name": "Name"
},
{
"name": "Position"
},
{
"name": "Industry"
},
{
"name": "Location"
},
{
"name": "Skills"
},
{
"name": "Interests"
},
{
"name": "Target Roles"
},
{
"name": "Company Types"
}
]
},
"options": {}
},
"typeVersion": 3
},
{
"id": "a9d1762a-aaf2-45a5-a5fc-381ec2d174c5",
"name": "处理LinkedIn搜索结果",
"type": "n8n-nodes-base.code",
"position": [
-380,
4.76293103448279
],
"parameters": {
"jsCode": "// Process search results and extract LinkedIn profiles\nconst results = $json.organic_results || [];\n\n// Get user profile from the previous node\nconst userProfile = $input.first().json || {};\n\n// Filter and process LinkedIn profiles\nconst linkedinProfiles = results\n .filter(result => {\n const link = result.link || '';\n return link.includes('linkedin.com/in/') && \n !link.includes('/posts/') && \n !link.includes('/activity/');\n })\n .map(result => {\n let name = result.title || 'Unknown';\n // Clean up the title to extract just the name\n name = name.replace(/\\s*[-|]\\s*LinkedIn.*$/i, '').trim();\n \n return {\n name: name,\n headline: result.snippet || result.title || '',\n link: result.link,\n description: result.snippet || '',\n source: 'Google Search via SerpAPI'\n };\n })\n .slice(0, 15); // Limit to top 15 results\n\n// Safely extract skills - handle undefined/null values\nconst getFirstSkill = (skills) => {\n if (!skills || typeof skills !== 'string') {\n return '';\n }\n const skillsArray = skills.split(',');\n return skillsArray.length > 0 ? skillsArray[0].trim() : '';\n};\n\n// Build search query with safe property access\nconst buildSearchQuery = () => {\n const position = userProfile.Position || '';\n const location = userProfile.Location || '';\n const firstSkill = getFirstSkill(userProfile.Skills);\n \n return `site:linkedin.com/in \"${position}\" \"${location}\" ${firstSkill}`.trim();\n};\n\n// Return structured data for AI analysis\nreturn {\n json: {\n userProfile: {\n name: $('Your Profile Information').first().json.Name || '',\n current_position: $('Your Profile Information').first().json.Position || '',\n industry: $('Your Profile Information').first().json.Industry || '',\n location: $('Your Profile Information').first().json.Location|| '',\n skills: $('Your Profile Information').first().json.Skills|| '',\n interests: $('Your Profile Information').first().json.Interests || '',\n target_roles: $('Your Profile Information').first().json['Target Roles']|| '',\n company_types: $('Your Profile Information').first().json['Company Types'] || ''\n },\n foundProfiles: linkedinProfiles,\n totalFound: linkedinProfiles.length,\n searchQuery: buildSearchQuery()\n }\n};"
},
"typeVersion": 2,
"alwaysOutputData": true
},
{
"id": "808cb1fa-9970-4d0e-8d34-37eea340fdb4",
"name": "创建最终推荐",
"type": "n8n-nodes-base.code",
"position": [
216,
4.76293103448279
],
"parameters": {
"jsCode": "// Process AI recommendations and create final output\nconst inputData = $input.first();\nconst inputText = inputData.json?.text || inputData.json || inputData;\n\nlet aiRecommendations = {};\ntry {\n // Extract JSON from the text content\n const content = typeof inputText === 'string' ? inputText : JSON.stringify(inputText);\n \n // Try to parse JSON from the AI response\n const jsonMatch = content.match(/```json\\n([\\s\\S]*?)\\n```/) || content.match(/\\{[\\s\\S]*\\}/);\n if (jsonMatch) {\n const jsonString = jsonMatch[1] || jsonMatch[0];\n aiRecommendations = JSON.parse(jsonString);\n } else {\n throw new Error('No JSON found in response');\n }\n} catch (error) {\n console.log('Could not parse AI response:', error.message);\n console.log('Input data:', inputData);\n \n // Create fallback recommendations if parsing fails\n aiRecommendations = {\n scored_profiles: [],\n top_connections: [],\n connection_strategies: [],\n networking_insights: 'Focus on building meaningful professional relationships in your industry.'\n };\n}\n\n// Helper function to safely split and get first element\nfunction safeGetFirst(str, delimiter = ',') {\n if (!str || typeof str !== 'string') return '';\n const parts = str.split(delimiter);\n return parts.length > 0 ? parts[0].trim() : '';\n}\n\n// Helper function to safely split string\nfunction safeSplit(str, delimiter = ',') {\n if (!str || typeof str !== 'string') return [];\n return str.split(delimiter).map(item => item.trim()).filter(item => item);\n}\n\n// Helper function to generate connection message\nfunction generateConnectionMessage(profile, index) {\n if (!profile || !profile.name) return 'Hi! I\\'d love to connect and share professional insights.';\n \n const firstName = safeGetFirst(profile.name, ' ');\n const description = profile.description || '';\n \n // Create personalized messages based on profile\n if (description.toLowerCase().includes('devops')) {\n return `Hi ${firstName}, I'm interested in DevOps and cloud technologies. I'd love to connect and learn from your experience!`;\n } else if (description.toLowerCase().includes('aws') || description.toLowerCase().includes('cloud')) {\n return `Hi ${firstName}, I noticed your expertise in cloud computing. Would love to connect and share insights about the industry!`;\n } else {\n return `Hi ${firstName}, I'd love to connect with a fellow professional and learn from your experience in the industry.`;\n }\n}\n\n// Helper function to extract tags from description\nfunction extractTags(description) {\n if (!description) return ['Professional Contact'];\n \n const tags = [];\n const descText = description.toLowerCase();\n \n // Add technology tags\n const techs = ['aws', 'azure', 'gcp', 'devops', 'terraform', 'ansible', 'jenkins', 'kubernetes', 'docker'];\n techs.forEach(tech => {\n if (descText.includes(tech)) {\n tags.push(tech.toUpperCase());\n }\n });\n \n // Add role tags\n if (descText.includes('senior') || descText.includes('lead')) {\n tags.push('Senior Level');\n }\n \n if (descText.includes('mentor') || descText.includes('enthusiast')) {\n tags.push('Mentor');\n }\n \n return tags.length > 0 ? tags : ['Professional Contact'];\n}\n\n// Process the connections from AI recommendations\nconst connections = aiRecommendations.top_connections || [];\nconst scoredProfiles = aiRecommendations.scored_profiles || [];\n\nconst enhancedConnections = connections.map((profile, index) => {\n // Find matching score data\n const scoreData = scoredProfiles.find(p => \n p.name && profile.name && \n p.name.toLowerCase().trim() === profile.name.toLowerCase().trim()\n );\n \n const score = scoreData?.score || (9.5 - index * 0.3); // Fallback scoring\n \n return {\n name: profile.name || 'Unknown',\n description: profile.description || '',\n connections: profile.connections || 0,\n mutual_connections: profile.mutual_connections || 0,\n priority: score >= 8.5 ? 'High' : score >= 7.5 ? 'Medium' : 'Low',\n aiScore: score,\n connectionReason: scoreData?.reasons?.join(', ') || 'Professional networking opportunity',\n suggestedMessage: generateConnectionMessage(profile, index),\n tags: extractTags(profile.description),\n estimatedResponseRate: score >= 8.5 ? 'High (70-90%)' : score >= 7.5 ? 'Medium (40-70%)' : 'Low (20-40%)',\n link: profile.link || '#'\n };\n}).sort((a, b) => b.aiScore - a.aiScore); // Sort by AI score\n\n// Create individual connection strategies\nconst connectionStrategies = enhancedConnections.map(conn => {\n const firstName = safeGetFirst(conn.name, ' ');\n return `${firstName}: ${conn.suggestedMessage.replace(`Hi ${firstName}, `, '')}`;\n});\n\nreturn {\n json: {\n summary: {\n total_profiles_found: scoredProfiles.length,\n analyzed_connections: enhancedConnections.length,\n high_priority: enhancedConnections.filter(c => c.priority === 'High').length,\n medium_priority: enhancedConnections.filter(c => c.priority === 'Medium').length,\n low_priority: enhancedConnections.filter(c => c.priority === 'Low').length,\n average_score: enhancedConnections.length > 0 ? \n (enhancedConnections.reduce((sum, c) => sum + c.aiScore, 0) / enhancedConnections.length).toFixed(1) : 0\n },\n user_profile: {\n name: \"Vrushti Sukhadiya\", // Extracted from the analysis text\n interests: \"DevOps, Cloud Computing, Automation\",\n target_industry: \"IT, Tech Startups, Cloud Companies\"\n },\n connection_recommendations: enhancedConnections,\n connection_strategies: connectionStrategies,\n ai_insights: aiRecommendations.networking_insights || 'Focus on building meaningful professional relationships in your industry.',\n networking_strategy: {\n weekly_goal: '5-10 new connections',\n best_days: ['Tuesday', 'Wednesday', 'Thursday'],\n best_times: ['9-11 AM', '2-4 PM'],\n follow_up_schedule: 'Within 48 hours of connection acceptance',\n focus_areas: ['DevOps professionals', 'Cloud Computing experts', 'AI/ML enthusiasts']\n },\n next_steps: [\n 'Start with highest-scored profiles (Mihir Suthar - 9.5, Muneeswaran M - 9.2)',\n 'Check for mutual connections before reaching out',\n 'Engage with their recent posts about DevOps/Cloud topics',\n 'Send personalized connection requests highlighting common interests',\n 'Follow up with thoughtful messages about industry trends',\n 'Consider attending DevOps/Cloud computing events where they might be present'\n ],\n top_recommendations: enhancedConnections.slice(0, 3).map(conn => ({\n name: conn.name,\n why_connect: conn.connectionReason,\n action_item: `Connect with ${safeGetFirst(conn.name, ' ')} focusing on ${conn.tags.slice(0, 2).join(' and ')} expertise`\n }))\n }\n};"
},
"typeVersion": 2
},
{
"id": "e1b30786-455f-414f-9577-36634a0c1cd9",
"name": "从电子邮件获取用户数据",
"type": "n8n-nodes-base.emailReadImap",
"position": [
-1040,
4.76293103448279
],
"parameters": {
"options": {}
},
"credentials": {
"imap": {
"id": "zTEGYssr7MSVeCs3",
"name": "IMAP-test"
}
},
"typeVersion": 2
},
{
"id": "02259afc-6178-49f4-b08c-82dc7667bd06",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-940,
-420
],
"parameters": {
"width": 680,
"height": 340,
"content": "## 系统架构"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "9073190a-946e-4f57-998d-365de4ec3ae8",
"connections": {
"Create Email": {
"main": [
[
{
"node": "Send email",
"type": "main",
"index": 0
}
]
]
},
"Ollama Model1": {
"ai_languageModel": [
[
{
"node": "AI Profile Analysis",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Profile Analysis": {
"main": [
[
{
"node": "Create Final Recommendations",
"type": "main",
"index": 0
}
]
]
},
"Search LinkedIn Profiles": {
"main": [
[
{
"node": "Process LinkedIn Search Results",
"type": "main",
"index": 0
}
]
]
},
"Your Profile Information": {
"main": [
[
{
"node": "Search LinkedIn Profiles",
"type": "main",
"index": 0
}
]
]
},
"Get User Data From Email ": {
"main": [
[
{
"node": "Your Profile Information",
"type": "main",
"index": 0
}
]
]
},
"Create Final Recommendations": {
"main": [
[
{
"node": "Create Email",
"type": "main",
"index": 0
}
]
]
},
"Process LinkedIn Search Results": {
"main": [
[
{
"node": "AI Profile Analysis",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
中级 - 潜在客户开发, 多模态 AI
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
中级
节点数量10
分类2
节点类型8
作者
Oneclick AI Squad
@oneclick-aiThe AI Squad Initiative is a pioneering effort to build, automate and scale AI-powered workflows using n8n.io. Our mission is to help individuals and businesses integrate AI agents seamlessly into their daily operations from automating tasks and enhancing productivity to creating innovative, intelligent solutions. We design modular, reusable AI workflow templates that empower creators, developers and teams to supercharge their automation with minimal effort and maximum impact.
外部链接
在 n8n.io 查看 →
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