This workflow follows the Agent → HTTP Request 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": "DSAProjeto2",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 17
}
]
}
},
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
-944,
96
],
"id": "2f11fad4-5c06-4518-a1b4-817d32e3f301",
"name": "Execu\u00e7\u00e3o Agendada",
"notes": "www.datascienceacademy.com.br\n\nDSA Log - Node de in\u00edcio do workflow com agendamento."
},
{
"parameters": {
"content": "## IA e Fluxos Cognitivos Para Automa\u00e7\u00e3o de Processos de Neg\u00f3cios (BPA) com n8n\n\n### DSA - Projeto 2\n\n**Fluxo de Automa\u00e7\u00e3o com IA Para Limpeza e Processamento de Dados**\n\nwww.datascienceacademy.com.br",
"height": 192,
"width": 944,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-32,
-208
],
"typeVersion": 1,
"id": "13906dc5-7601-48c9-a243-42e348952cc4",
"name": "Sticky Note"
},
{
"parameters": {
"operation": "pdf",
"binaryPropertyName": "=data",
"options": {}
},
"id": "dc6156dd-353d-425c-9cf6-802fe9f803fb",
"name": "Extra\u00e7\u00e3o de Dados",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-496,
96
],
"typeVersion": 1,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de extra\u00e7\u00e3o de dados de texto do arquivo PDF."
},
{
"parameters": {
"language": "python",
"pythonCode": "data = _input.first().json.text\nreturn {\"data\": data}"
},
"id": "3092f448-13cd-43fc-be83-d289b60e3e39",
"name": "Filtro de Dados (Python)",
"type": "n8n-nodes-base.code",
"position": [
-272,
96
],
"typeVersion": 2,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de filtro de dados usando c\u00f3digo Python."
},
{
"parameters": {
"promptType": "define",
"text": "=Resuma os dados em 4-5 linhas ou menos: {{ $json.data }}",
"hasOutputParser": true,
"options": {}
},
"id": "98443db3-438f-4247-9510-bb9b2351f9b5",
"name": "Agente de IA",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-48,
96
],
"typeVersion": 2.2,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de execu\u00e7\u00e3o de um Agente de IA."
},
{
"parameters": {
"mode": "raw",
"jsonOutput": "={{ '{ \"summary\": \"' + $json.output + '\" }' }}",
"options": {}
},
"id": "24f03db7-44fd-460b-aa50-93ffb3428d2f",
"name": "Sumariza\u00e7\u00e3o e Limpeza dos Dados",
"type": "n8n-nodes-base.set",
"position": [
304,
96
],
"typeVersion": 3.4,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de sumariza\u00e7\u00e3o e limpeza usando formato JSON."
},
{
"parameters": {
"method": "POST",
"url": "https://qwen-qwen-tts-demo.hf.space/gradio_api/call/predict",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"data\": [\n \"{{ $json.summary }}\",\n \"Dylan\"\n ]\n} ",
"options": {
"response": {
"response": {}
}
}
},
"id": "70b76cba-2b5c-4521-b410-5ab03c784ea6",
"name": "Processamento de Dados 1",
"type": "n8n-nodes-base.httpRequest",
"position": [
528,
96
],
"typeVersion": 4.2,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de processamento de dados via API."
},
{
"parameters": {
"url": "=https://qwen-qwen-tts-demo.hf.space/gradio_api/call/predict/{{ $json.event_id }}",
"options": {}
},
"id": "c3ccfc4d-549f-4ffc-84ad-8d25cf294dd8",
"name": "Processamento de Dados 2",
"type": "n8n-nodes-base.httpRequest",
"position": [
752,
96
],
"typeVersion": 4.2,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de processamento de dados atrav\u00e9s de API."
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "b7efed1c-af46-4f2d-8da7-08b783248f19",
"name": "audioUrl",
"type": "string",
"value": "={{ $json.audioUrl }}"
},
{
"id": "924be2a2-9c3b-4d00-9e96-65b5d86e350c",
"name": "summary",
"type": "string",
"value": "={{ $('Sumariza\u00e7\u00e3o e Limpeza dos Dados').item.json.summary }}"
}
]
},
"options": {}
},
"id": "e74517b1-2829-4779-a4d8-b1ea336f2895",
"name": "Formata\u00e7\u00e3o dos Dados",
"type": "n8n-nodes-base.set",
"position": [
1200,
96
],
"executeOnce": false,
"typeVersion": 3.4,
"alwaysOutputData": false,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de formata\u00e7\u00e3o dos dados."
},
{
"parameters": {
"operation": "upload",
"bucketName": "=dsa-p2-n8n-491085408077",
"fileName": "=saida/{{ $json.audioUrl }}",
"binaryData": false,
"additionalFields": {}
},
"type": "n8n-nodes-base.awsS3",
"typeVersion": 2,
"position": [
1424,
96
],
"id": "017271d3-c76a-478f-8c61-4cce2eef513f",
"name": "Upload do Resultado Para o Destino na AWS",
"credentials": {
"aws": {
"name": "<your credential>"
}
},
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de upload do arquivo resultante para a AWS."
},
{
"parameters": {
"bucketName": "dsa-p2-n8n-491085408077",
"fileKey": "entrada/curriculo_exemplo.pdf"
},
"type": "n8n-nodes-base.awsS3",
"typeVersion": 2,
"position": [
-720,
96
],
"id": "fd73e634-e2a8-4a65-9c1f-9bd9f689ca3e",
"name": "Download da Fonte de Dados na AWS",
"credentials": {
"aws": {
"name": "<your credential>"
}
},
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de download do arquivo da fonte em um bucket S3.\n"
},
{
"parameters": {},
"type": "n8n-nodes-base.noOp",
"typeVersion": 1,
"position": [
1648,
96
],
"id": "d97b280d-c106-4566-bef7-985d60a50b87",
"name": "Fim"
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "const raw = $json.data || $json || \"\";\n\n// Caso 1: Se j\u00e1 foi analisado JSON com data[0].url\nif (Array.isArray($json.data)) {\n return {\n json: {\n audioUrl: $json.data[0]?.url || null,\n rawJson: $json.data\n }\n };\n}\n\n// Caso 2: Manipular texto SSE bruto\nconst match = typeof raw === \"string\" ? raw.match(/data:\\s*(\\[[^\\]]+\\])/s) : null;\n\nif (match) {\n try {\n const parsed = JSON.parse(match[1]);\n return {\n json: {\n audioUrl: parsed?.[0]?.url || null,\n rawJson: parsed\n }\n };\n } catch (e) {\n return {\n json: {\n error: \"DSA - Falha ao analisar SSE JSON\",\n raw\n }\n };\n }\n}\n\n// Caso 3: Se nenhum dos dois funcionou\nreturn {\n json: {\n error: \"DSA - URL do \u00e1udio n\u00e3o encontrada.\",\n raw\n }\n};\n"
},
"id": "ebc5cc7a-4d2f-424b-8739-437833d4ea63",
"name": "Processamento de Dados 3 (JavaScript)",
"type": "n8n-nodes-base.code",
"position": [
976,
96
],
"typeVersion": 2,
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 de processamento de dados via Python."
},
{
"parameters": {
"model": "openai/gpt-oss-20b",
"options": {}
},
"id": "3ec34044-f5ab-435f-af7f-2f47b07430b5",
"name": "LLM Groq",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
-64,
304
],
"typeVersion": 1,
"credentials": {
"groqApi": {
"name": "<your credential>"
}
},
"notes": "www.datascienceacademy.com.br\n\nDSA Log - N\u00f3 do LLM usado no Agente de IA."
}
],
"connections": {
"Execu\u00e7\u00e3o Agendada": {
"main": [
[
{
"node": "Download da Fonte de Dados na AWS",
"type": "main",
"index": 0
}
]
]
},
"Extra\u00e7\u00e3o de Dados": {
"main": [
[
{
"node": "Filtro de Dados (Python)",
"type": "main",
"index": 0
}
]
]
},
"Filtro de Dados (Python)": {
"main": [
[
{
"node": "Agente de IA",
"type": "main",
"index": 0
}
]
]
},
"Agente de IA": {
"main": [
[
{
"node": "Sumariza\u00e7\u00e3o e Limpeza dos Dados",
"type": "main",
"index": 0
}
]
]
},
"Sumariza\u00e7\u00e3o e Limpeza dos Dados": {
"main": [
[
{
"node": "Processamento de Dados 1",
"type": "main",
"index": 0
}
]
]
},
"Processamento de Dados 1": {
"main": [
[
{
"node": "Processamento de Dados 2",
"type": "main",
"index": 0
}
]
]
},
"Processamento de Dados 2": {
"main": [
[
{
"node": "Processamento de Dados 3 (JavaScript)",
"type": "main",
"index": 0
}
]
]
},
"Formata\u00e7\u00e3o dos Dados": {
"main": [
[
{
"node": "Upload do Resultado Para o Destino na AWS",
"type": "main",
"index": 0
}
]
]
},
"Download da Fonte de Dados na AWS": {
"main": [
[
{
"node": "Extra\u00e7\u00e3o de Dados",
"type": "main",
"index": 0
}
]
]
},
"Upload do Resultado Para o Destino na AWS": {
"main": [
[
{
"node": "Fim",
"type": "main",
"index": 0
}
]
]
},
"Processamento de Dados 3 (JavaScript)": {
"main": [
[
{
"node": "Formata\u00e7\u00e3o dos Dados",
"type": "main",
"index": 0
}
]
]
},
"LLM Groq": {
"ai_languageModel": [
[
{
"node": "Agente de IA",
"type": "ai_languageModel",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "c5d63b83-9ea6-424e-816e-bb87a4ca4d95",
"id": "KY6llzDL4azSEcWC",
"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.
awsgroqApi
Pro
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About this workflow
DSAProjeto2. Uses agent, httpRequest, awsS3, lmChatGroq. Scheduled trigger; 14 nodes.
Source: https://github.com/alexmabud/Pos-Graduacao-Engenharia-Automacao-Processos-IA-DSA/blob/3fc4147652a588f1ebf0b07d0ede5c168ca16ad2/projetos_dsa/modulo_4/projetos_2_modulo_4/DSAProjeto2.json — original creator credit. Request a take-down →
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