This workflow follows the Agent → Execute Workflow Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "Medallion",
"nodes": [
{
"parameters": {
"projectId": {
"__rl": true,
"value": "gen-lang-client-0829338696",
"mode": "id"
},
"modelName": "gemini-2.0-flash",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleVertex",
"typeVersion": 1,
"position": [
1584,
208
],
"id": "f2c84554-db96-45c2-b146-ba3bc883dae7",
"name": "Google Vertex Chat Model",
"credentials": {
"googleApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "return [\n{\njson: {\ndata: [\n{ \"id\": \"S1-01\", \"amount\": 120.00, \"date\": \"2026-03-01\", \"description\": \"SUPERMERCADO\", \"institution_name\": \"BANCO A\" },\n{ \"transactionIdentifier\": \"S1-02\", \"value\": \"80.00\", \"bookingDate\": \"2026-03-02T10:00:00Z\", \"narrative\": \"POSTO GASOLINA\", \"institution_name\": \"BANCO B\" },\n{ \"txid\": \"S1-03\", \"amount\": 60.00, \"date\": \"2026-03-03\", \"description\": \"PADARIA\", \"institution_name\": \"BANCO A\" },\n{ \"transactionId\": \"S1-04\", \"value\": \"90.00\", \"bookingDate\": \"2026-03-04T12:00:00Z\", \"narrative\": \"FARMACIA\", \"institution_name\": \"BANCO B\" },\n{ \"id\": \"S1-05\", \"amount\": 150.00, \"date\": \"2026-03-05\", \"description\": \"ALUGUEL\", \"institution_name\": \"BANCO A\" }\n]\n}\n}\n];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
928,
160
],
"id": "38d3cdaf-da27-4cb8-9f8e-f4b269e1abb5",
"name": "S200 - Camada Bronze (Raw Data)2"
},
{
"parameters": {
"jsCode": "// Normaliza\u00e7\u00e3o de Payloads Multi-Banco (Silver Layer)\nconst items = $input.all();\n\n// Extra\u00edmos o array de transa\u00e7\u00f5es do payload bruto\nconst rawTransactions = items[0].json.data;\n\nconst normalizedData = rawTransactions.map(raw => {\n return {\n transaction_id: raw.id || raw.txid || raw.transactionIdentifier || raw.transactionId, // Normaliza IDs\n amount: parseFloat(raw.amount || raw.value).toFixed(2), // Garante o float com 2 casas\n currency: raw.currency || 'BRL',\n timestamp: new Date(raw.bookingDate || raw.date).toISOString(), // Padroniza para ISO 8601\n source_bank: raw.institution_name,\n category_raw: raw.description || raw.narrative // Unifica a descri\u00e7\u00e3o bruta\n };\n});\n\n// O N8N exige que o retorno seja um array de objetos encapsulados na chave 'json'\nreturn normalizedData.map(item => ({ json: item }));"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1168,
0
],
"id": "8533d03a-795e-415e-ac78-bf988650bde6",
"name": "Camada Silver (Clean Data)"
},
{
"parameters": {
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "transaction_id"
},
{
"fieldToAggregate": "amount"
},
{
"fieldToAggregate": "category_raw"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
1376,
0
],
"id": "76781604-bb9a-47b9-851e-a80f93bbbbac",
"name": "Aggregate"
},
{
"parameters": {
"promptType": "define",
"text": "={\n \"system_prompt\": {\n \"role\": \"Senior Financial Analyst\",\n \"expertise\": \"Open Finance Brazil\",\n \"task\": \"Process an array of raw transactions and return a categorized JSON object.\",\n \"business_rules\": {\n \"categorization\": \"Identify and classify each expense strictly as 'Essential', 'Leisure', or 'Investment'.\",\n \"esg_scoring\": \"Calculate an 'ESG Impact' score ranging from 0 to 10, based on the merchant's category.\"\n },\n \"output_constraints\": {\n \"format\": \"JSON\",\n \"schema\": {\n \"type\": \"array\",\n \"items\": {\n \"id\": \"string\",\n \"category\": \"string\",\n \"esg_score\": \"number\"\n }\n }\n },\n \"input\": \"Analyze the following real transactions and return ONLY the required JSON. Transactions: {{ JSON.stringify($json) }}\"\n }\n}",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
1584,
0
],
"id": "4d3770a1-3fac-4c8f-910e-f82601d9f4b0",
"name": "AI Agent"
},
{
"parameters": {
"jsCode": "return [\n{\njson: {\ndata: [\n{ \"id\": \"S2-01\", \"amount\": 2000.00, \"date\": \"2026-03-01\", \"description\": \"SALARIO\", \"institution_name\": \"BANCO A\" },\n{ \"transactionIdentifier\": \"S2-02\", \"value\": \"400.00\", \"bookingDate\": \"2026-03-02T10:00:00Z\", \"narrative\": \"TESOURO DIRETO\", \"institution_name\": \"XP\" },\n{ \"txid\": \"S2-03\", \"amount\": 300.00, \"date\": \"2026-03-03\", \"description\": \"CDB BANCO\", \"institution_name\": \"INTER\" },\n{ \"transactionId\": \"S2-04\", \"value\": \"200.00\", \"bookingDate\": \"2026-03-04T12:00:00Z\", \"narrative\": \"FII LOGISTICA\", \"institution_name\": \"XP\" },\n{ \"id\": \"S2-05\", \"amount\": 100.00, \"date\": \"2026-03-05\", \"description\": \"SUPERMERCADO\", \"institution_name\": \"BANCO A\" }\n]\n}\n}\n];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
928,
0
],
"id": "43ac9187-8d83-4da8-9a41-e151debe28b5",
"name": "S600 - Camada Bronze (Raw Data)"
},
{
"parameters": {
"jsCode": "return [\n{\njson: {\ndata: [\n{ \"id\": \"S3-01\", \"amount\": 3500.00, \"date\": \"2026-03-01\", \"description\": \"SALARIO GLOBAL\", \"institution_name\": \"BANCO A\" },\n{ \"transactionIdentifier\": \"S3-02\", \"value\": \"500.00\", \"bookingDate\": \"2026-03-02T10:00:00Z\", \"narrative\": \"TESOURO SELIC\", \"institution_name\": \"XP\" },\n{ \"txid\": \"S3-03\", \"amount\": 400.00, \"date\": \"2026-03-03\", \"description\": \"CDB POS FIXADO\", \"institution_name\": \"INTER\" },\n{ \"transactionId\": \"S3-04\", \"value\": \"350.00\", \"bookingDate\": \"2026-03-04T12:00:00Z\", \"narrative\": \"FII ESG\", \"institution_name\": \"XP\" },\n{ \"id\": \"S3-05\", \"amount\": 300.00, \"date\": \"2026-03-05\", \"description\": \"ACOES ENERGIA LIMPA\", \"institution_name\": \"RICO\" },\n{ \"transactionIdentifier\": \"S3-06\", \"value\": \"200.00\", \"bookingDate\": \"2026-03-06T18:00:00Z\", \"narrative\": \"PREVIDENCIA PRIVADA\", \"institution_name\": \"BANCO A\" }\n]\n}\n}\n];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
928,
-160
],
"id": "9db8a88e-6549-49fb-909b-93323a6c971b",
"name": "S900 - Camada Bronze (Raw Data)"
},
{
"parameters": {
"jsCode": "// 1. Busca os dados limpos diretamente do n\u00f3 da Camada Silver\nconst silverData = $('Camada Silver (Clean Data)').all();\nconst transactions = silverData.map(item => item.json);\n\n// 2. Captura a resposta do n\u00f3 Vertex AI (Agente)\n// Em n\u00f3s de IA do N8N, a resposta costuma vir em 'output', 'text' ou 'response'\nconst aiRawOutput = $input.first().json.output || $input.first().json.text || $input.first().json.response || \"\";\n\n// 3. Parser Robusto (Preven\u00e7\u00e3o de Erros de LLM)\n// Remove formata\u00e7\u00f5es Markdown indesejadas que o LLM possa ter inclu\u00eddo\nconst cleanJsonString = aiRawOutput.replace(/```json/gi, '').replace(/```/g, '').trim();\n\nlet ai_insights = [];\ntry {\n // Tenta converter a string gerada pela IA em um array de objetos reais\n ai_insights = JSON.parse(cleanJsonString);\n} catch (error) {\n throw new Error(\"Falha Cr\u00edtica: O Vertex AI n\u00e3o retornou um JSON v\u00e1lido. Retorno da IA: \" + aiRawOutput);\n}\n\n// 4. Vari\u00e1veis da F\u00f3rmula\nlet fluxoCaixa = 0;\nlet qtdInvestimentos = 0; \nlet esgTotal = 0;\n\n// 5. Merge entre o dado da API (Silver) e o Insight da IA (Gold)\nconst enrichedData = transactions.map(tx => {\n const insight = ai_insights.find(i => i.id === tx.transaction_id);\n const category = insight ? insight.category : \"Indefinido\";\n const esg = insight ? insight.esg_score : 5;\n \n fluxoCaixa += parseFloat(tx.amount);\n esgTotal += esg;\n \n if (category === \"Investment\") {\n qtdInvestimentos += 1; // Contabiliza a frequ\u00eancia de aportes\n }\n\n return {\n ...tx,\n category_enriched: category,\n esg_score: esg\n };\n});\n\n// 6. NORMALIZA\u00c7\u00c3O DAS GRANDEZAS (Teto 1000 para cada pilar)\n// Assume que R$ 5.000,00 de fluxo mensal \u00e9 o teto para nota m\u00e1xima neste pilar\nconst scoreFluxo = Math.min((fluxoCaixa / 5000) * 1000, 1000); \n\n// Assume que 5 aportes de investimento garantem nota m\u00e1xima neste pilar (200 pts por aporte)\nconst scoreInvestimento = Math.min(qtdInvestimentos * 200, 1000); \n\n// Fidelidade convertida para a escala de 1000 (ex: 850 = 85% de hist\u00f3rico positivo)\nconst scoreFidelidade = 850; \n\n// 7. Pesos e C\u00e1lculo da F\u00f3rmula\nconst W1 = 0.4;\nconst W2 = 0.3;\nconst W3 = 0.3;\n\nlet scoreFinal = (W1 * scoreFluxo) + (W2 * scoreInvestimento) + (W3 * scoreFidelidade);\n\n// 8. ESG Link (B\u00f4nus Verde)\nconst mediaEsg = transactions.length > 0 ? (esgTotal / transactions.length) : 5;\nif (mediaEsg >= 7) {\n scoreFinal *= 1.1; // B\u00f4nus Verde de 10%\n}\n\n// Garante que o score final jamais ultrapasse o limite regulat\u00f3rio de 1000\nconst scoreDefinitivo = Math.min(Math.round(scoreFinal), 1000);\n\nreturn [\n {\n json: {\n customer_id: \"USER-12345\",\n alternative_credit_score: scoreDefinitivo,\n average_esg_score: parseFloat(mediaEsg.toFixed(1)),\n transactions_enriched: enrichedData\n }\n }\n];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1904,
0
],
"id": "88cc9c69-7e90-4f9f-a777-1ef9b6ed9eed",
"name": "Fus\u00e3o de Dados e C\u00e1lculo do Score Alternativo (Camada Gold)"
},
{
"parameters": {
"jsCode": "return [\n{\njson: {\ndata: [\n{ \"id\": \"F0-01\", \"amount\": 3000.00, \"date\": \"2026-03-01\", \"description\": \"SALARIO EMPRESA TECH\", \"institution_name\": \"BANCO A\" },\n{ \"transactionIdentifier\": \"F0-02\", \"value\": \"1200.00\", \"bookingDate\": \"2026-03-02T10:00:00Z\", \"narrative\": \"BONUS PERFORMANCE\", \"institution_name\": \"BANCO A\" },\n{ \"txid\": \"F0-03\", \"amount\": 600.00, \"date\": \"2026-03-03\", \"description\": \"ALUGUEL APARTAMENTO\", \"institution_name\": \"BANCO A\" },\n{ \"transactionId\": \"F0-04\", \"value\": \"450.00\", \"bookingDate\": \"2026-03-04T12:00:00Z\", \"narrative\": \"SUPERMERCADO PREMIUM\", \"institution_name\": \"BANCO A\" },\n{ \"id\": \"F0-05\", \"amount\": 350.00, \"date\": \"2026-03-05\", \"description\": \"RESTAURANTES VARIOS\", \"institution_name\": \"BANCO A\" },\n{ \"transactionIdentifier\": \"F0-06\", \"value\": \"250.00\", \"bookingDate\": \"2026-03-06T18:00:00Z\", \"narrative\": \"VIAGEM AEREA\", \"institution_name\": \"BANCO A\" }\n]\n}\n}\n];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
928,
320
],
"id": "ad82c882-8fa4-46ac-949d-241bd94fd297",
"name": "Max Invest - Camada Bronze (Raw Data)1"
},
{
"parameters": {
"operation": "executeQuery",
"query": "INSERT INTO customer_scores (customer_id, credit_score, esg_score, transactions_enriched)\nVALUES (\n '{{ $json.customer_id }}', \n {{ $json.alternative_credit_score }}, \n {{ $json.average_esg_score }}, \n '{{ JSON.stringify($json.transactions_enriched) }}'::jsonb\n)\nON CONFLICT (customer_id) \nDO UPDATE SET \n credit_score = EXCLUDED.credit_score,\n esg_score = EXCLUDED.esg_score,\n transactions_enriched = EXCLUDED.transactions_enriched,\n last_updated = CURRENT_TIMESTAMP;",
"options": {}
},
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.6,
"position": [
2112,
0
],
"id": "ba4b237e-cd82-4f80-b5b3-6e3ae26c4f00",
"name": "Execute a SQL query",
"credentials": {
"postgres": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "executeQuery",
"query": "INSERT INTO customer_scores (customer_id, credit_score, esg_score, transactions_enriched)\nVALUES (\n '{{ $json.customer_id }}', \n {{ $json.alternative_credit_score }}, \n {{ $json.average_esg_score }}, \n '{{ JSON.stringify($json.transactions_enriched) }}'::jsonb\n)\nON CONFLICT (customer_id) \nDO UPDATE SET \n credit_score = EXCLUDED.credit_score,\n esg_score = EXCLUDED.esg_score,\n transactions_enriched = EXCLUDED.transactions_enriched,\n last_updated = CURRENT_TIMESTAMP;",
"options": {}
},
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.6,
"position": [
2768,
688
],
"id": "7a056cfb-d693-49bc-9bdd-80f545efb26c",
"name": "Seeding QUERY",
"credentials": {
"postgres": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "const categories = [\"Investment\", \"Essential\", \"Leisure\", \"Other\"];\n\nfunction generateTransactions(userIndex) {\n const txCount = Math.floor(Math.random() * 5) + 8; // 8 a 12 transa\u00e7\u00f5es\n const transactions = [];\n\n for (let i = 0; i < txCount; i++) {\n const category =\n categories[Math.floor(Math.random() * categories.length)];\n\nlet esg = 5.0;\n if (category === \"Investment\") esg = (Math.random() * (10 - 8) + 8).toFixed(1); // 8 a 10\n else if (category === \"Essential\") esg = (Math.random() * (7 - 5) + 5).toFixed(1); // 5 a 7\n else if (category === \"Leisure\") esg = (Math.random() * (5 - 2) + 2).toFixed(1); // 2 a 5\n else esg = (Math.random() * (6 - 4) + 4).toFixed(1); // 4 a 6\n\n transactions.push({\n id: `tx_${userIndex}_${i + 1}`,\n category_enriched: category,\n esg_score: parseFloat(esg) // <--- O CAMPO QUE ESTAVA FALTANDO!\n });\n }\n\n return transactions;\n}\n\nconst baseUsers = [\n { id: \"USER-901\", score: 985, esg: 9.1 },\n { id: \"USER-902\", score: 940, esg: 8.2 },\n { id: \"USER-903\", score: 910, esg: 7.8 },\n { id: \"USER-750A\", score: 880, esg: 7.5 },\n { id: \"USER-760B\", score: 845, esg: 6.8 },\n { id: \"USER-770C\", score: 810, esg: 6.2 },\n { id: \"USER-780D\", score: 775, esg: 7.2 },\n { id: \"USER-790E\", score: 760, esg: 5.9 },\n { id: \"USER-600A\", score: 690, esg: 5.5 },\n { id: \"USER-610B\", score: 670, esg: 4.8 },\n { id: \"USER-620C\", score: 655, esg: 6.1 },\n { id: \"USER-630D\", score: 640, esg: 5.2 },\n { id: \"USER-640E\", score: 615, esg: 5.0 },\n { id: \"USER-400A\", score: 590, esg: 6.4 },\n { id: \"USER-410B\", score: 560, esg: 4.3 },\n { id: \"USER-420C\", score: 530, esg: 5.7 },\n { id: \"USER-430D\", score: 505, esg: 3.9 },\n { id: \"USER-440E\", score: 480, esg: 6.0 },\n { id: \"USER-200A\", score: 390, esg: 4.1 },\n { id: \"USER-210B\", score: 360, esg: 3.5 },\n { id: \"USER-220C\", score: 330, esg: 2.9 },\n { id: \"USER-230D\", score: 295, esg: 3.2 },\n { id: \"USER-240E\", score: 250, esg: 2.8 },\n { id: \"USER-MIX1\", score: 720, esg: 8.0 },\n { id: \"USER-MIX2\", score: 810, esg: 9.0 },\n { id: \"USER-MIX3\", score: 670, esg: 6.9 },\n { id: \"USER-MIX4\", score: 590, esg: 7.2 },\n { id: \"USER-MIX5\", score: 880, esg: 8.7 },\n { id: \"USER-EDGE1\", score: 1000, esg: 9.9 },\n { id: \"USER-EDGE2\", score: 0, esg: 1.0 },\n { id: \"USER-EDGE3\", score: 850, esg: 6.99 },\n { id: \"USER-EDGE4\", score: 860, esg: 7.0 }\n];\n\nconst seedData = baseUsers.map((user, index) => ({\n customer_id: user.id,\n alternative_credit_score: user.score,\n average_esg_score: user.esg,\n transactions_enriched: generateTransactions(index)\n}));\n\nreturn seedData.map(item => ({ json: item }));"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
2432,
688
],
"id": "3ffb14ce-2571-4bea-9c95-a58b6bf730fb",
"name": "Seeding DATASET"
},
{
"parameters": {
"content": "\u2699\ufe0f Microsservi\u00e7o 2: Pipeline Medallion & IA (RF02 a RF04)\nPadr\u00e3o: ETL / Batch Processing / RAG\n\nEste \u00e9 o motor ass\u00edncrono (Background Worker) do Hub. N\u00e3o bloqueia a tela do usu\u00e1rio.\n\u2022 Camada Bronze: Ingest\u00e3o de dados brutos e despadronizados de m\u00faltiplos bancos.\n\u2022 Camada Silver: Limpeza, normaliza\u00e7\u00e3o de datas (ISO 8601) e tipagem (Float).\n\u2022 Agente IA (Vertex): Uso de RAG e Chain-of-Thought para categoriza\u00e7\u00e3o sem\u00e2ntica rigorosa e c\u00e1lculo de impacto ESG.\n\u2022 Camada Gold: Fus\u00e3o de dados (Merge) e c\u00e1lculo matem\u00e1tico do Score Alternativo de Cr\u00e9dito (0 a 1000).\n\u2022 Persist\u00eancia: Upsert do JSONB enriquecido no banco de dados PostgreSQL.",
"height": 192,
"width": 848,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1168,
-256
],
"id": "b6e353e8-b3eb-4708-b736-bb3232b70697",
"name": "Sticky Note"
},
{
"parameters": {
"inputSource": "passthrough"
},
"type": "n8n-nodes-base.executeWorkflowTrigger",
"typeVersion": 1.1,
"position": [
672,
0
],
"id": "ca692fd9-aa3d-4ee5-97ec-2d176f425273",
"name": "When Executed by Another Workflow"
}
],
"connections": {
"Camada Silver (Clean Data)": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Fus\u00e3o de Dados e C\u00e1lculo do Score Alternativo (Camada Gold)",
"type": "main",
"index": 0
}
]
]
},
"Google Vertex Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"S600 - Camada Bronze (Raw Data)": {
"main": [
[
{
"node": "Camada Silver (Clean Data)",
"type": "main",
"index": 0
}
]
]
},
"S200 - Camada Bronze (Raw Data)2": {
"main": [
[]
]
},
"S900 - Camada Bronze (Raw Data)": {
"main": [
[]
]
},
"Max Invest - Camada Bronze (Raw Data)1": {
"main": [
[]
]
},
"Fus\u00e3o de Dados e C\u00e1lculo do Score Alternativo (Camada Gold)": {
"main": [
[
{
"node": "Execute a SQL query",
"type": "main",
"index": 0
}
]
]
},
"Seeding DATASET": {
"main": [
[
{
"node": "Seeding QUERY",
"type": "main",
"index": 0
}
]
]
},
"When Executed by Another Workflow": {
"main": [
[
{
"node": "S600 - Camada Bronze (Raw Data)",
"type": "main",
"index": 0
}
]
]
}
},
"active": true,
"settings": {
"executionOrder": "v1",
"availableInMCP": false,
"timeSavedMode": "fixed",
"saveDataSuccessExecution": "all",
"callerPolicy": "workflowsFromSameOwner",
"executionTimeout": -1,
"binaryMode": "separate"
},
"versionId": "6076ccc2-b069-4da5-9902-7b9733666a3e",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "SGMCcgT3a5Ggi8JP",
"tags": []
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googleApipostgres
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Medallion. Uses lmChatGoogleVertex, agent, postgres, executeWorkflowTrigger. Event-driven trigger; 14 nodes.
Source: https://github.com/Apolo442/Open-Finance-ETL/blob/f68288e35424dcbff31e17adfea84903550b7c01/workflows/Medallion.json — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
6_Multi-Agent_4vaEvzlaMrgovhNz. Uses postgres, httpRequest, lmChatOpenAi, outputParserStructured. Event-driven trigger; 54 nodes.
Postgres, HTTP Request, OpenAI Chat +4
LinkedIn Growth & Intelligence Agent. Uses telegram, postgres, executeWorkflowTrigger, @brightdata/n8n-nodes-brightdata. Event-driven trigger; 46 nodes.
Telegram, Postgres, Execute Workflow Trigger +5
Template was created in n8n v1.90.2 Execute Sub-workflow Trigger node Chat Trigger node Redis node Postgres node AI Agent node If node, Switch node, Code node, Edit Fields (Set) Execute Sub-workflow T
Execute Workflow Trigger, Chat Trigger, Agent +7
Template was created in n8n v1.90.2 Execute Sub-workflow Trigger node Chat Trigger node Redis node Postgres node AI Agent node Calculator node If node, Switch node, Code node, Edit Fields (Set) Execut
Execute Workflow Trigger, Chat Trigger, Agent +5
SHEETS RAG. Uses googleDriveTrigger, toolWorkflow, lmChatGoogleGemini, postgres. Event-driven trigger; 23 nodes.
Google Drive Trigger, Tool Workflow, Google Gemini Chat +5