{ "name": "Agent 2 (Tavily): Contact Enrichment — finds founder & email every 3h", "description": null, "active": false, "nodes": [ { "parameters": {}, "id": "t_manual", "name": "Manual Trigger", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [ -608, -80 ] }, { "parameters": { "operation": "getAll", "tableId": "prospects", "limit": 15, "filters": { "conditions": [ { "keyName": "status", "condition": "eq", "keyValue": "new" } ] } }, "id": "n_fetch", "name": "Fetch New Prospects", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -384, 16 ] }, { "parameters": { "jsCode": "\nconst items = $input.all();\nreturn items.map(item => {\n const f = item.json.fields || item.json;\n const company = f.Company || item.json.company || '';\n const website = f.Website || item.json.website || '';\n const id = item.json.id || item.json.ID || '';\n const prompt = `Search LinkedIn and the web to find the founder, CEO, and top board members or directors of \"${company}\" (website: ${website}).\nFind up to 4 key decision-makers. Prioritize: Founder > CEO > MD > Director.\nFor each person return: full name, job title, and LinkedIn profile URL.\nReturn ONLY valid JSON array: [{\"name\": \"...\", \"title\": \"...\", \"linkedin_url\": \"...\"}, ...]`;\n return { json: { id, company, website,\n city: f.City || item.json.city || '',\n niche: f.Niche || item.json.niche || '',\n country: f.Country || item.json.country || '',\n prompt } };\n});\n" }, "id": "n_people_prompt", "name": "Build People Search Prompt", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -160, 16 ] }, { "parameters": { "method": "POST", "url": "https://moviesbar1-resource.openai.azure.com/openai/v1/responses", "authentication": "genericCredentialType", "genericAuthType": "httpHeaderAuth", "sendBody": true, "specifyBody": "json", "jsonBody": "={{ {\"model\":\"gpt-5.4\",\"input\":$json.prompt,\"max_output_tokens\":3000} }}", "options": { "batching": { "batch": { "batchSize": 4 } }, "timeout": 280000 } }, "id": "n_people", "name": "GPT Extract — Key People", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 512, 16 ], "retryOnFail": true, "maxTries": 5, "waitBetweenTries": 5000, "onError": "continueRegularOutput" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const __ext=(j)=>j.choices?.[0]?.message?.content || ((j.output||[]).flatMap(o=>o.content||[]).filter(c=>c.type==='output_text').map(c=>c.text).join('')) || '';\n\nconst src = $('Build People Search Prompt').item.json;\nlet content = __ext($json) || '';\nconst fence = content.match(/```(?:json)?\\s*([\\s\\S]*?)```/);\nif (fence) content = fence[1].trim();\nconst arr = content.match(/\\[[\\s\\S]*\\]/); if (arr) content = arr[0];\nlet people = []; try { people = JSON.parse(content); } catch(e) {}\nconst p1 = people[0] || {}; const p2 = people[1] || {};\nreturn { json: { id: src.id, company: src.company, website: src.website, city: src.city,\n niche: src.niche, country: src.country, key_people: JSON.stringify(people),\n founder_name: p1.name || '', founder_linkedin: p1.linkedin_url || '',\n person2_name: p2.name || '', person2_linkedin: p2.linkedin_url || '' } };\n" }, "id": "n_parse_people", "name": "Parse Key People", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 736, 16 ] }, { "parameters": { "jsCode": "\nconst items = $input.all();\nreturn items.map(item => {\n const { company, website, founder_name, person2_name } = item.json;\n const prompt = `Find valid business email addresses for decision-makers at \"${company}\" (website: ${website}).\nTarget people in priority order: ${founder_name || 'the founder/CEO'}${person2_name ? ', then ' + person2_name : ''}, then a generic role inbox.\nSearch: the website contact/about page, LinkedIn, press releases, Crunchbase, and public mentions.\nReturn the 2-3 BEST distinct candidate emails, most likely first.\nCommon formats: firstname@domain.com, firstname.lastname@domain.com, info@domain.com, hello@domain.com.\nReturn ONLY valid JSON: {\"emails\":[{\"email\":\"...\",\"person_name\":\"...\",\"confidence\":\"high|medium|low\",\"source\":\"...\"}]}`;\n return { json: { ...item.json, prompt } };\n});\n" }, "id": "n_email_prompt", "name": "Build Email Find Prompt", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 960, 16 ] }, { "parameters": { "method": "POST", "url": "https://moviesbar1-resource.openai.azure.com/openai/v1/responses", "authentication": "genericCredentialType", "genericAuthType": "httpHeaderAuth", "sendBody": true, "specifyBody": "json", "jsonBody": "={{ {\"model\":\"gpt-5.4\",\"input\":$json.prompt,\"max_output_tokens\":3000} }}", "options": { "batching": { "batch": { "batchSize": 4 } }, "timeout": 280000 } }, "id": "n_find_email", "name": "GPT Extract — Emails", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 1632, 16 ], "retryOnFail": true, "maxTries": 5, "waitBetweenTries": 5000, "onError": "continueRegularOutput" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const __ext=(j)=>j.choices?.[0]?.message?.content || ((j.output||[]).flatMap(o=>o.content||[]).filter(c=>c.type==='output_text').map(c=>c.text).join('')) || '';\n\nconst src = $('Build Email Find Prompt').item.json;\nlet content = __ext($json) || '';\nconst fence = content.match(/```(?:json)?\\s*([\\s\\S]*?)```/);\nif (fence) content = fence[1].trim();\nlet parsed = {}; try { parsed = JSON.parse(content); } catch(e) {}\nlet list = Array.isArray(parsed.emails) ? parsed.emails : [];\nif (!list.length && parsed.email) list = [{ email: parsed.email, confidence: parsed.confidence, source: parsed.source }];\nconst seen = new Set(); const emails = [];\nfor (const e of list) {\n const addr = ((e && e.email) || '').toLowerCase().trim();\n if (!addr || !addr.includes('@') || seen.has(addr)) continue;\n seen.add(addr);\n emails.push({ email: addr, person_name: e.person_name || '', confidence: (e.confidence||'medium').toLowerCase(), source: e.source || 'kimi-email-search' });\n}\nreturn { json: { ...src, email_list: emails, found_email: emails[0]?.email || '', email_confidence: emails[0]?.confidence || 'low' } };\n" }, "id": "n_parse_email", "name": "Parse Email Result", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1856, 16 ] }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const __ext=(j)=>j.choices?.[0]?.message?.content || ((j.output||[]).flatMap(o=>o.content||[]).filter(c=>c.type==='output_text').map(c=>c.text).join('')) || '';\n\nconst src = $('Build Website Extract Prompt').item.json;\nlet content = __ext($json) || '{}';\nconst fence = content.match(/```(?:json)?\\s*([\\s\\S]*?)```/);\nif (fence) content = fence[1].trim();\nlet parsed = {}; try { parsed = JSON.parse(content); } catch(e) {}\nconst cand = Array.isArray(src.email_list) ? src.email_list.slice() : [];\nconst seen = new Set(cand.map(c => c.email));\nconst web = (parsed.contact_email || '').toLowerCase().trim();\nif (web && web.includes('@') && !seen.has(web)) { seen.add(web); cand.push({ email: web, person_name: '', confidence: 'medium', source: 'website-contact' }); }\nconst final_email = cand[0]?.email || '';\nconst premium_fit = (parsed.premium_fit || 'medium').toLowerCase();\n\n// After MAX_ATTEMPTS tries with no email found, park as 'enrich_failed' instead of resetting\n// to 'new' — resetting made every failed prospect re-burn 3 web searches on every run.\nconst MAX_ATTEMPTS = 2;\nconst sd = $getWorkflowStaticData('global');\nif (typeof sd.enrichAttempts !== 'object' || !sd.enrichAttempts) sd.enrichAttempts = {};\nlet status_out = 'enriched';\nif (final_email && final_email.includes('@')) {\n delete sd.enrichAttempts[src.id];\n} else {\n const n = (sd.enrichAttempts[src.id] || 0) + 1;\n sd.enrichAttempts[src.id] = n;\n status_out = n >= MAX_ATTEMPTS ? 'enrich_failed' : 'new';\n}\n\nreturn { json: { ...src, raw_website_data: JSON.stringify({\n title: parsed.title, meta_desc: parsed.meta_desc, has_blog: parsed.has_blog,\n social_links: parsed.social_links, has_testimonials: parsed.has_testimonials }),\n email_candidates: cand, final_email, premium_fit, premium_signals: parsed.premium_signals || '', status_out } };\n" }, "id": "n_parse_website", "name": "Parse Website Data", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2752, 16 ] }, { "parameters": { "operation": "update", "tableId": "prospects", "filters": { "conditions": [ { "keyName": "id", "condition": "eq", "keyValue": "={{ $json.id }}" } ] }, "fieldsUi": { "fieldValues": [ { "fieldId": "status", "fieldValue": "={{ $json.status_out }}" }, { "fieldId": "founder_name", "fieldValue": "={{ $json.founder_name }}" }, { "fieldId": "founder_linkedin", "fieldValue": "={{ $json.founder_linkedin }}" }, { "fieldId": "key_people", "fieldValue": "={{ $json.key_people }}" }, { "fieldId": "email", "fieldValue": "={{ $json.final_email }}" }, { "fieldId": "raw_website_data", "fieldValue": "={{ $json.raw_website_data }}" }, { "fieldId": "premium_fit", "fieldValue": "={{ $json.premium_fit }}" }, { "fieldId": "premium_signals", "fieldValue": "={{ $json.premium_signals }}" }, { "fieldId": "person2_name", "fieldValue": "={{ $json.person2_name }}" }, { "fieldId": "person2_linkedin", "fieldValue": "={{ $json.person2_linkedin }}" }, { "fieldId": "email_valid", "fieldValue": "={{ $json.email_valid_out }}" }, { "fieldId": "email_status", "fieldValue": "={{ $json.email_status_out }}" } ] } }, "id": "n_at_update", "name": "Airtable — Update Enriched", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ 3200, -80 ], "retryOnFail": true, "maxTries": 2, "waitBetweenTries": 2000, "onError": "continueRegularOutput" }, { "parameters": { "jsCode": "\nconst allItems = $('Parse Website Data').all();\nconst retried = allItems.filter(i => !i.json.founder_name && !i.json.final_email).length;\nconst items = allItems.filter(i => i.json.founder_name || i.json.final_email);\nconst count = items.length;\nconst withEmail = items.filter(i => i.json.final_email && i.json.final_email.includes('@')).length;\nconst premium = items.filter(i => ['high','medium'].includes((i.json.premium_fit||'').toLowerCase())).length;\nconst td='padding:8px;border:1px solid #e2e8f0;font-size:13px;vertical-align:top';\nconst th='padding:8px;border:1px solid #e2e8f0;text-align:left;font-size:13px;background:#ebf8ff';\nconst badge=(f)=>{const c={high:'#48bb78',medium:'#ecc94b',low:'#fc8181'}[f]||'#a0aec0';return `${f||'?'}`;};\nconst rows=items.map(i=>{const d=i.json;let pc=0;try{pc=JSON.parse(d.key_people||'[]').length;}catch(e){}\n const li=d.founder_linkedin?`Profile`:'—';\n const site=d.website?`${d.website.replace(/^https?:\\/\\//,'').replace(/\\/$/,'')}`:'—';\n const email=(d.final_email&&d.final_email.includes('@'))?d.final_email:'not found';\n return `${d.company||'—'}${badge((d.premium_fit||'').toLowerCase())}${d.city||'—'}${d.founder_name||'—'}${email}${li}${pc} ppl${site}`;\n}).join('');\nconst html=`\n

🔎 Contact Enrichment Report

\n

Enriched ${count} · ${premium} premium fit · ${withEmail} with email.${retried ? ` · ${retried} failed (Kimi unreachable) — left as new, will retry next run.` : ''}

\n\n\n${rows}
CompanyPremiumCityFounderEmailLinkedInKey PeopleWebsite
\n

Meraku Contact Enrichment · premium-qualified · 7x daily

\n`;\nreturn [{ json: { count, withEmail, premium, summary_html: html } }];\n" }, "id": "n_summary", "name": "Build Summary", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 3424, -80 ] }, { "parameters": { "sendTo": "you@yourdomain.com", "subject": "=[FYI] Enrichment — {{ $json.total }} leads enriched (24h)", "message": "={{ $json.summary_html }}", "options": { "appendAttribution": false } }, "id": "n_summary_email", "name": "Gmail — Daily Summary", "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 64, 336 ] }, { "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "30 7 * * *" }, { "field": "cronExpression", "expression": "45 19 * * *" }, { "field": "cronExpression", "expression": "30 11 * * *" }, { "field": "cronExpression", "expression": "30 15 * * *" } ] } }, "id": "t_sched", "name": "2x Daily (off-peak)", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.1, "position": [ -608, 112 ], "retryOnFail": true }, { "parameters": { "tableId": "activity_log", "fieldsUi": { "fieldValues": [ { "fieldId": "summary", "fieldValue": "=Enrichment: {{ $json.premium }} premium / {{ $json.count }} enriched" }, { "fieldId": "workflow", "fieldValue": "Contact Enrichment" }, { "fieldId": "action", "fieldValue": "enriched" }, { "fieldId": "count", "fieldValue": "={{ $json.premium }}" }, { "fieldId": "details", "fieldValue": "={{ $json.count }} enriched · {{ $json.withEmail }} with email · {{ $json.premium }} premium-fit" } ] } }, "id": "log_enrich", "name": "Log → Activity_Log", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ 3648, -80 ], "retryOnFail": true, "maxTries": 2, "waitBetweenTries": 2000, "onError": "continueRegularOutput" }, { "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "45 7 * * *" } ] } }, "id": "enr_daily_trig", "name": "Daily 7:45 AM IST", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.1, "position": [ -608, 336 ] }, { "parameters": { "operation": "getAll", "tableId": "activity_log", "returnAll": true, "matchType": "allFilters", "filters": { "conditions": [ { "keyName": "workflow", "condition": "eq", "keyValue": "Contact Enrichment" }, { "keyName": "created_at", "condition": "gt", "keyValue": "={{ new Date(Date.now() - 86400000).toISOString() }}" } ] } }, "id": "enr_fetch24", "name": "Fetch 24h Activity", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -384, 336 ], "onError": "continueRegularOutput" }, { "parameters": { "jsCode": "\n// ROBUST: keep only Contact Enrichment rows in JS, so it's correct regardless of the\n// Supabase node's \"Must Match\" (Any/All) setting which was silently pulling every workflow.\nconst cutoff = new Date(Date.now() - 86400000).toISOString();\nconst rows = $input.all().filter(r => {\n if ((r.json.workflow || '') !== 'Contact Enrichment') return false;\n const ts = String(r.json.created_at || r.json.inserted_at || '');\n return ts && ts >= cutoff; // enforce 24h in code (the Supabase created_at filter is ignored)\n});\n\nlet enriched = 0, withEmail = 0, premium = 0;\nconst re = /(\\d+)\\s*enriched.*?(\\d+)\\s*with email.*?(\\d+)\\s*premium/i;\nfor (const r of rows) {\n const m = String(r.json.details || '').match(re);\n if (m) { enriched += +m[1]; withEmail += +m[2]; premium += +m[3]; }\n else { enriched += Number(r.json.count || 0); } // fallback for old rows\n}\nconst runs = rows.length;\nconst noEmail = Math.max(0, enriched - withEmail);\nconst emailPct = enriched ? Math.round(withEmail / enriched * 100) : 0;\nconst premPct = enriched ? Math.round(premium / enriched * 100) : 0;\n\nconst card = (val, label, sub) => `\n
${val}
\n
${label}
${sub ? `
${sub}
` : ''}`;\n\nconst html = `\n

Contact Enrichment — last 24h

\n

${runs} enrichment run${runs === 1 ? '' : 's'} · new prospects turned into contactable leads

\n\n${card(enriched, 'prospects enriched', '')}\n${card(withEmail, 'with a valid email', emailPct + '%')}\n${card(premium, 'premium-fit', premPct + '%')}\n
\n${noEmail ? `

${noEmail} enriched without a usable email yet — kept as new and retried next runs.

` : ''}\n

Meraku · daily enrichment summary · 7:45 AM IST
Replies & bounces are handled in the main Digest email.

\n`;\n\nreturn [{ json: { total: enriched, enriched, withEmail, premium, runs, summary_html: html } }];\n" }, "id": "enr_agg24", "name": "Aggregate 24h Enrichment", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -160, 336 ] }, { "parameters": { "jsCode": "\nconst out = [];\nfor (const item of $input.all()) {\n const d = item.json;\n const cands = Array.isArray(d.email_candidates) ? d.email_candidates : [];\n cands.forEach((c, idx) => {\n if (!c.email || !c.email.includes('@')) return;\n const vs = String(c.verify_status || '');\n out.push({ json: {\n prospect_id: String(d.id),\n email: c.email,\n person_name: c.person_name || (idx === 0 ? d.founder_name : d.person2_name) || '',\n person_role: '',\n linkedin_url: idx === 0 ? (d.founder_linkedin || '') : (idx === 1 ? (d.person2_linkedin || '') : ''),\n rank: idx + 1,\n confidence: c.confidence || 'medium',\n source: c.source || 'agent2',\n status: vs === 'undeliverable' ? 'invalid' : ((vs === 'deliverable' || vs === 'unknown') ? 'verified' : 'pending'),\n status_detail: vs || '',\n is_active: !!d.final_email && c.email.toLowerCase() === String(d.final_email).toLowerCase()\n }});\n });\n}\nreturn out;" }, "id": "pe_flatten_cands", "name": "Flatten Email Candidates", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 3200, 112 ] }, { "parameters": { "tableId": "prospect_emails", "fieldsUi": { "fieldValues": [ { "fieldId": "prospect_id", "fieldValue": "={{ $json.prospect_id }}" }, { "fieldId": "email", "fieldValue": "={{ $json.email }}" }, { "fieldId": "person_name", "fieldValue": "={{ $json.person_name }}" }, { "fieldId": "person_role", "fieldValue": "={{ $json.person_role }}" }, { "fieldId": "linkedin_url", "fieldValue": "={{ $json.linkedin_url }}" }, { "fieldId": "rank", "fieldValue": "={{ $json.rank }}" }, { "fieldId": "confidence", "fieldValue": "={{ $json.confidence }}" }, { "fieldId": "source", "fieldValue": "={{ $json.source }}" }, { "fieldId": "is_active", "fieldValue": "={{ $json.is_active }}" }, { "fieldId": "status", "fieldValue": "={{ $json.status }}" }, { "fieldId": "status_detail", "fieldValue": "={{ $json.status_detail }}" } ] } }, "id": "pe_insert_cand", "name": "Insert Email Candidate", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ 3424, 112 ], "continueOnFail": true }, { "parameters": { "url": "={{ $json.website }}", "sendHeaders": true, "headerParameters": { "parameters": [ { "name": "User-Agent", "value": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" } ] }, "options": { "response": { "response": { "responseFormat": "text", "outputPropertyName": "html" } }, "timeout": 30000 } }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 2080, 16 ], "id": "aaaa1111-cost-fix-0000-fetchhtml0001", "name": "Fetch Website HTML", "alwaysOutputData": true, "onError": "continueRegularOutput" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "\nconst src = $('Parse Email Result').item.json;\nconst html = String($json.html || $json.data || '');\nconst title = (html.match(/]*>([\\s\\S]*?)<\\/title>/i) || [])[1] || '';\nconst metaDesc = (html.match(/]+name=[\"']description[\"'][^>]*content=[\"']([^\"']*)[\"']/i) || html.match(/]+content=[\"']([^\"']*)[\"'][^>]*name=[\"']description[\"']/i) || [])[1] || '';\nconst links = [...html.matchAll(/href=[\"']([^\"'\\s>]+)[\"']/gi)].map(m => m[1]);\nconst social = [...new Set(links.filter(h => /facebook\\.com|instagram\\.com|linkedin\\.com|twitter\\.com|x\\.com|youtube\\.com/i.test(h)))].slice(0, 10);\nconst text = html\n .replace(//gi, ' ')\n .replace(//gi, ' ')\n .replace(/<[^>]+>/g, ' ')\n .replace(/&[a-zA-Z#0-9]+;/g, ' ')\n .replace(/\\s+/g, ' ')\n .trim()\n .slice(0, 15000);\n\nconst pageBlock = text\n ? `FETCHED HOMEPAGE DATA for \"${src.company}\" (${src.website}):\nTITLE: ${title}\nMETA DESCRIPTION: ${metaDesc}\nSOCIAL LINKS FOUND IN HTML: ${JSON.stringify(social)}\nPAGE TEXT (truncated):\n${text}`\n : `The website ${src.website} for \"${src.company}\" could not be fetched (unreachable or no website). A missing/broken website is a NOT-premium signal, but mark premium_fit \"medium\" if you cannot tell.`;\n\nconst prompt = `Analyze this business website from the fetched content below. Do NOT search the web.\n\n${pageBlock}\n\nPART A — Basic data:\n1. Page title and meta description (exact text)\n2. Has a blog/news section? (yes/no)\n3. Social media links present (list)\n4. Has testimonials/reviews? (yes/no)\n5. Contact email visible? (copy it)\n\nPART B — PREMIUM FIT (critical): Meraku Media works with premium high-ticket service businesses (elder care, addiction treatment, private clinics, timeshare exit, tax relief, equipment financing, residency/citizenship services) that can pay a $5,000+/month retainer (Rs 1,00,000+/month in India). Judge whether THIS business is a premium prospect in ITS industry.\n- Signs of PREMIUM (high): luxury/concierge positioning, high fees or premium packages, affluent target clientele, polished branding, multiple locations, corporate/investor backing, awards, professional website.\n- Signs of NOT premium (low): free/government/charity/trust/NGO/subsidized, \"affordable\"/budget messaging, welfare-oriented, poor or no real website, serves a welfare/charity segment with no marketing budget.\n- medium = paid private business but unclear if they have real marketing budget.\n\nReturn ONLY valid JSON: {\"title\":\"...\",\"meta_desc\":\"...\",\"has_blog\":true,\"social_links\":[],\"has_testimonials\":true,\"contact_email\":\"...\",\"premium_fit\":\"high|medium|low\",\"premium_signals\":\"1-2 sentences citing specific evidence from the site\"}`;\nreturn { json: { ...src, prompt } };\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2304, 16 ], "id": "aaaa1111-cost-fix-0000-buildprmpt01", "name": "Build Website Extract Prompt" }, { "parameters": { "method": "POST", "url": "https://moviesbar1-resource.openai.azure.com/openai/v1/responses", "authentication": "genericCredentialType", "genericAuthType": "httpHeaderAuth", "sendBody": true, "specifyBody": "json", "jsonBody": "={{ {\"model\":\"gpt-5.4\",\"input\":$json.prompt,\"max_output_tokens\":3000} }}", "options": { "batching": { "batch": { "batchSize": 4 } }, "timeout": 280000 } }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 2528, 16 ], "id": "aaaa1111-cost-fix-0000-gptextract1", "name": "GPT Extract — Website Data" }, { "parameters": { "jsCode": "const TAVILY_KEYS=[\"YOUR_TAVILY_API_KEY_1\",\"YOUR_TAVILY_API_KEY_2\",\"YOUR_TAVILY_API_KEY_3\"];\nconst __sd=$getWorkflowStaticData('global');\nif(typeof __sd.tavilyExhausted!=='object'||!__sd.tavilyExhausted) __sd.tavilyExhausted={};\nconst __sleep=(ms)=>new Promise(r=>setTimeout(r,ms));\nconst tavilySearch=async(query,maxResults)=>{\n for(const k of TAVILY_KEYS){\n const ex=__sd.tavilyExhausted[k];\n if(ex && Date.now()-ex<24*3600*1000) continue;\n let attempts=0;\n while(attempts<3){\n attempts++;\n try{\n const res=await this.helpers.httpRequest({method:'POST',url:'https://api.tavily.com/search',\n headers:{'Authorization':'Bearer '+k,'Content-Type':'application/json'},\n body:{query:String(query||'').slice(0,390),search_depth:'advanced',max_results:maxResults,include_answer:true},\n json:true,returnFullResponse:true,ignoreHttpStatusErrors:true,timeout:90000});\n const code=Number(res.statusCode||0);\n if(code===429){ if(attempts<3){await __sleep(1500*attempts);continue;} break; }\n if(code===432||code===433||code===401||code===403){ __sd.tavilyExhausted[k]=Date.now(); break; }\n if(code<200||code>=300){ break; }\n return res.body||{results:[]};\n }catch(e){ break; }\n }\n }\n try{ await this.helpers.httpRequest({method:'GET',url:'https://your-n8n-instance.com/webhook/yourbrand-tavily-exhausted',timeout:10000}); }catch(e){}\n return {results:[],answer:'',tavily_error:'all_keys_exhausted'};\n};\nconst out=[];\nfor(const it of $input.all()){\n const d=it.json||{};\n out.push({json: await tavilySearch(((d.company || \"\") + \" founder CEO director owner LinkedIn\"), 8)});\n await __sleep(400);\n}\nreturn out;" }, "name": "Tavily — Find Key People", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 64, 16 ], "id": "2c02d33e-6a30-4d1e-8e5b-0ec8832fafcc" }, { "parameters": { "jsCode": "const items = $input.all();\nconst srcs = $('Build People Search Prompt').all();\nreturn items.map((it, i) => {\n const src = srcs[i] ? srcs[i].json : {};\n const t = it.json || {};\n const results = (t.results || []).map((r, n) => `[${n+1}] ${r.title || ''}\\nURL: ${r.url || ''}\\n${(r.content || '').slice(0, 1200)}`).join('\\n\\n');\n const ctx = (t.answer ? `Search answer: ${t.answer}\\n\\n` : '') + (results || 'No web results found.');\n const prompt = (src.prompt || '') + `\\n\\n---\\nWEB SEARCH RESULTS below — use these as your ONLY research data. Extract what is asked from them; do not invent facts:\\n\\n` + ctx;\n return { json: { ...src, prompt } };\n});" }, "name": "Attach Web Context — Find Key People", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 288, 16 ], "id": "484508d8-9ab3-4b0f-86a1-38619a9e6005" }, { "parameters": { "jsCode": "const TAVILY_KEYS=[\"YOUR_TAVILY_API_KEY_1\",\"YOUR_TAVILY_API_KEY_2\",\"YOUR_TAVILY_API_KEY_3\"];\nconst __sd=$getWorkflowStaticData('global');\nif(typeof __sd.tavilyExhausted!=='object'||!__sd.tavilyExhausted) __sd.tavilyExhausted={};\nconst __sleep=(ms)=>new Promise(r=>setTimeout(r,ms));\nconst tavilySearch=async(query,maxResults)=>{\n for(const k of TAVILY_KEYS){\n const ex=__sd.tavilyExhausted[k];\n if(ex && Date.now()-ex<24*3600*1000) continue;\n let attempts=0;\n while(attempts<3){\n attempts++;\n try{\n const res=await this.helpers.httpRequest({method:'POST',url:'https://api.tavily.com/search',\n headers:{'Authorization':'Bearer '+k,'Content-Type':'application/json'},\n body:{query:String(query||'').slice(0,390),search_depth:'advanced',max_results:maxResults,include_answer:true},\n json:true,returnFullResponse:true,ignoreHttpStatusErrors:true,timeout:90000});\n const code=Number(res.statusCode||0);\n if(code===429){ if(attempts<3){await __sleep(1500*attempts);continue;} break; }\n if(code===432||code===433||code===401||code===403){ __sd.tavilyExhausted[k]=Date.now(); break; }\n if(code<200||code>=300){ break; }\n return res.body||{results:[]};\n }catch(e){ break; }\n }\n }\n try{ await this.helpers.httpRequest({method:'GET',url:'https://your-n8n-instance.com/webhook/yourbrand-tavily-exhausted',timeout:10000}); }catch(e){}\n return {results:[],answer:'',tavily_error:'all_keys_exhausted'};\n};\nconst out=[];\nfor(const it of $input.all()){\n const d=it.json||{};\n out.push({json: await tavilySearch((((d.founder_name ? d.founder_name + \" \" : \"\") + (d.company || \"\") + \" email contact \" + (d.website || \"\"))), 8)});\n await __sleep(400);\n}\nreturn out;" }, "name": "Tavily — Find Email", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1184, 16 ], "id": "61d75759-5ad7-4cb8-8d6c-9b6b6ebec8fe" }, { "parameters": { "jsCode": "const items = $input.all();\nconst srcs = $('Build Email Find Prompt').all();\nreturn items.map((it, i) => {\n const src = srcs[i] ? srcs[i].json : {};\n const t = it.json || {};\n const results = (t.results || []).map((r, n) => `[${n+1}] ${r.title || ''}\\nURL: ${r.url || ''}\\n${(r.content || '').slice(0, 1200)}`).join('\\n\\n');\n const ctx = (t.answer ? `Search answer: ${t.answer}\\n\\n` : '') + (results || 'No web results found.');\n const prompt = (src.prompt || '') + `\\n\\n---\\nWEB SEARCH RESULTS below — use these as your ONLY research data. Extract what is asked from them; do not invent facts:\\n\\n` + ctx;\n return { json: { ...src, prompt } };\n});" }, "name": "Attach Web Context — Find Email", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1408, 16 ], "id": "50bdec7d-5b4f-411b-92e9-5dd67e89bb94" }, { "parameters": { "jsCode": "const ABSTRACT_KEYS=[\"YOUR_ABSTRACTAPI_KEY_1\",\"YOUR_ABSTRACTAPI_KEY_2\",\"YOUR_ABSTRACTAPI_KEY\",\"YOUR_ABSTRACTAPI_KEY\"];\nconst __sd=$getWorkflowStaticData('global');\nif(typeof __sd.abstractExhausted!=='object'||!__sd.abstractExhausted) __sd.abstractExhausted={};\nif(typeof __sd.abstractCache!=='object'||!__sd.abstractCache) __sd.abstractCache={};\nconst __sleep=(ms)=>new Promise(r=>setTimeout(r,ms));\nconst verifyEmail=async(email)=>{\n email=String(email||'').toLowerCase().trim();\n if(!email||!email.includes('@')) return 'undeliverable';\n const cached=__sd.abstractCache[email];\n if(cached && Date.now()-cached.ts < 30*24*3600*1000) return cached.status;\n const KEY_COOLDOWN=24*3600*1000;\n for(const key of ABSTRACT_KEYS){\n const ex=__sd.abstractExhausted[key];\n if(ex && Date.now()-ex=300) return 'error';\n const status=String((res.body&&res.body.email_deliverability&&res.body.email_deliverability.status)||'unknown').toLowerCase();\n const final=(status==='deliverable')?'deliverable':(status==='undeliverable'?'undeliverable':'unknown');\n __sd.abstractCache[email]={status:final,ts:Date.now()};\n return final;\n }catch(e){ return 'error'; }\n }\n }\n return 'quota_exhausted';\n};\n\nconst out=[];\nfor(const item of $input.all()){\n const d={...item.json};\n const cands=Array.isArray(d.email_candidates)?d.email_candidates.map(c=>({...c})):[];\n let pick='', pickStatus='', firstUnknown='', quota=false;\n for(const c of cands){\n const em=String(c.email||'').toLowerCase().trim();\n if(!em||!em.includes('@')){c.verify_status='skipped';continue;}\n const st=await verifyEmail(em);\n c.verify_status=st;\n if(st==='quota_exhausted'){quota=true;break;}\n if(st==='deliverable'){pick=em;pickStatus='verified_deliverable';break;}\n if((st==='unknown'||st==='error')&&!firstUnknown) firstUnknown=em;\n }\n if(!pick&&firstUnknown){pick=firstUnknown;pickStatus='verified_unknown';}\n if(quota){\n d.email_status_out='unverified';\n if(pick) d.final_email=pick;\n d.email_valid_out=!!(d.final_email);\n } else if(pick){\n d.final_email=pick; d.email_valid_out=true; d.email_status_out=pickStatus;\n } else if(cands.length){\n d.final_email=''; d.email_valid_out=false; d.email_status_out='no_valid_email';\n } else {\n d.email_valid_out=!!d.final_email; d.email_status_out='unverified';\n }\n d.email_candidates=cands;\n out.push({json:d});\n}\nreturn out;" }, "name": "Verify Emails (AbstractAPI)", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2976, 16 ], "id": "c2341d0a-8923-4337-83e8-bea8771d84b2" } ], "connections": { "Manual Trigger": { "main": [ [ { "node": "Fetch New Prospects", "type": "main", "index": 0 } ] ] }, "Fetch New Prospects": { "main": [ [ { "node": "Build People Search Prompt", "type": "main", "index": 0 } ] ] }, "Build People Search Prompt": { "main": [ [ { "node": "Tavily — Find Key People", "type": "main", "index": 0 } ] ] }, "GPT Extract — Key People": { "main": [ [ { "node": "Parse Key People", "type": "main", "index": 0 } ] ] }, "Parse Key People": { "main": [ [ { "node": "Build Email Find Prompt", "type": "main", "index": 0 } ] ] }, "Build Email Find Prompt": { "main": [ [ { "node": "Tavily — Find Email", "type": "main", "index": 0 } ] ] }, "GPT Extract — Emails": { "main": [ [ { "node": "Parse Email Result", "type": "main", "index": 0 } ] ] }, "Parse Email Result": { "main": [ [ { "node": "Fetch Website HTML", "type": "main", "index": 0 } ] ] }, "Parse Website Data": { "main": [ [ { "node": "Verify Emails (AbstractAPI)", "type": "main", "index": 0 } ] ] }, "Airtable — Update Enriched": { "main": [ [ { "node": "Build Summary", "type": "main", "index": 0 } ] ] }, "Build Summary": { "main": [ [ { "node": "Log → Activity_Log", "type": "main", "index": 0 } ] ] }, "Fetch 24h Activity": { "main": [ [ { "node": "Aggregate 24h Enrichment", "type": "main", "index": 0 } ] ] }, "Aggregate 24h Enrichment": { "main": [ [ { "node": "Gmail — Daily Summary", "type": "main", "index": 0 } ] ] }, "Daily 7:45 AM IST": { "main": [ [ { "node": "Fetch 24h Activity", "type": "main", "index": 0 } ] ] }, "Flatten Email Candidates": { "main": [ [ { "node": "Insert Email Candidate", "type": "main", "index": 0 } ] ] }, "Fetch Website HTML": { "main": [ [ { "node": "Build Website Extract Prompt", "type": "main", "index": 0 } ] ] }, "Build Website Extract Prompt": { "main": [ [ { "node": "GPT Extract — Website Data", "type": "main", "index": 0 } ] ] }, "GPT Extract — Website Data": { "main": [ [ { "node": "Parse Website Data", "type": "main", "index": 0 } ] ] }, "2x Daily (off-peak)": { "main": [ [ { "node": "Fetch New Prospects", "type": "main", "index": 0 } ] ] }, "Tavily — Find Key People": { "main": [ [ { "node": "Attach Web Context — Find Key People", "type": "main", "index": 0 } ] ] }, "Attach Web Context — Find Key People": { "main": [ [ { "node": "GPT Extract — Key People", "type": "main", "index": 0 } ] ] }, "Tavily — Find Email": { "main": [ [ { "node": "Attach Web Context — Find Email", "type": "main", "index": 0 } ] ] }, "Attach Web Context — Find Email": { "main": [ [ { "node": "GPT Extract — Emails", "type": "main", "index": 0 } ] ] }, "Verify Emails (AbstractAPI)": { "main": [ [ { "node": "Airtable — Update Enriched", "type": "main", "index": 0 }, { "node": "Flatten Email Candidates", "type": "main", "index": 0 } ] ] } }, "settings": { "executionOrder": "v1", "callerPolicy": "workflowsFromSameOwner", "availableInMCP": false, "binaryMode": "separate" } }