This workflow follows the HTTP Request → Notion 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 →
{
"id": "FU3MrLkaTHmfdG4n",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Hugging Face to Notion",
"tags": [],
"nodes": [
{
"id": "32d5bfee-97f1-4e92-b62e-d09bdd9c3821",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
-2640,
-300
],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1,
2,
3,
4,
5
],
"triggerAtHour": 8
}
]
}
},
"typeVersion": 1.2
},
{
"id": "b1f4078e-ac77-47ec-995c-f52fd98fafef",
"name": "If",
"type": "n8n-nodes-base.if",
"position": [
-1360,
-280
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "7094d6db-1fa7-4b59-91cf-6bbd5b5f067e",
"operator": {
"type": "object",
"operation": "empty",
"singleValue": true
},
"leftValue": "={{ $json }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "afac08e1-b629-4467-86ef-907e4a5e8841",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-1760,
-300
],
"parameters": {
"options": {
"reset": false
}
},
"typeVersion": 3
},
{
"id": "807ba450-9c89-4f88-aa84-91f43e3adfc6",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
-1960,
-300
],
"parameters": {
"options": {},
"fieldToSplitOut": "url, url"
},
"typeVersion": 1
},
{
"id": "08dd3f15-2030-48f2-ab0f-f85f797268e1",
"name": "Request Hugging Face Paper",
"type": "n8n-nodes-base.httpRequest",
"position": [
-2440,
-300
],
"parameters": {
"url": "https://huggingface.co/papers",
"options": {},
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "date",
"value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "f37ba769-d881-4aad-927d-ca1f4a68b9a1",
"name": "Extract Hugging Face Paper",
"type": "n8n-nodes-base.html",
"position": [
-2200,
-300
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "url",
"attribute": "href",
"cssSelector": ".line-clamp-3",
"returnArray": true,
"returnValue": "attribute"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "94ba99bf-a33b-4311-a4e6-86490e1bb9ad",
"name": "Check Paper URL Existed",
"type": "n8n-nodes-base.notion",
"position": [
-1540,
-280
],
"parameters": {
"filters": {
"conditions": [
{
"key": "URL|url",
"urlValue": "={{ 'https://huggingface.co'+$json.url }}",
"condition": "equals"
}
]
},
"options": {},
"resource": "databasePage",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
"cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83",
"cachedResultName": "huggingface-abstract"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "ece8dee2-e444-4557-aad9-5bdcb5ecd756",
"name": "Request Hugging Face Paper Detail",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1080,
-300
],
"parameters": {
"url": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "53b266fe-e7c4-4820-92eb-78a6ba7a6430",
"name": "OpenAI Analysis Abstract",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
-640,
-300
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-2024-11-20",
"cachedResultName": "GPT-4O-2024-11-20"
},
"options": {},
"messages": {
"values": [
{
"role": "system",
"content": "Extract the following key details from the paper abstract:\n\nCore Introduction: Summarize the main contributions and objectives of the paper, highlighting its innovations and significance.\nKeyword Extraction: List 2-5 keywords that best represent the research direction and techniques of the paper.\nKey Data and Results: Extract important performance metrics, comparison results, and the paper's advantages over other studies.\nTechnical Details: Provide a brief overview of the methods, optimization techniques, and datasets mentioned in the paper.\nClassification: Assign an appropriate academic classification based on the content of the paper.\n\n\nOutput as json\uff1a\n{\n \"Core_Introduction\": \"PaSa is an advanced Paper Search agent powered by large language models that can autonomously perform a series of decisions (including invoking search tools, reading papers, and selecting relevant references) to provide comprehensive and accurate results for complex academic queries.\",\n \"Keywords\": [\n \"Paper Search Agent\",\n \"Large Language Models\",\n \"Reinforcement Learning\",\n \"Academic Queries\",\n \"Performance Benchmarking\"\n ],\n \"Data_and_Results\": \"PaSa outperforms existing baselines (such as Google, GPT-4, chatGPT) in tests using AutoScholarQuery (35k academic queries) and RealScholarQuery (real-world academic queries). For example, PaSa-7B exceeds Google with GPT-4o by 37.78% in recall@20 and 39.90% in recall@50.\",\n \"Technical_Details\": \"PaSa is optimized using reinforcement learning with the AutoScholarQuery synthetic dataset, demonstrating superior performance in multiple benchmarks.\",\n \"Classification\": [\n \"Artificial Intelligence (AI)\",\n \"Academic Search and Information Retrieval\",\n \"Natural Language Processing (NLP)\",\n \"Reinforcement Learning\"\n ]\n}\n```"
},
{
"content": "={{ $json.abstract }}"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.8
},
{
"id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd",
"name": "Store Abstract Notion",
"type": "n8n-nodes-base.notion",
"position": [
-300,
-300
],
"parameters": {
"options": {},
"resource": "databasePage",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
"cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83",
"cachedResultName": "huggingface-abstract"
},
"propertiesUi": {
"propertyValues": [
{
"key": "URL|url",
"urlValue": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}"
},
{
"key": "title|title",
"title": "={{ $('Extract Hugging Face Paper Abstract').item.json.title }}"
},
{
"key": "abstract|rich_text",
"textContent": "={{ $('Extract Hugging Face Paper Abstract').item.json.abstract.substring(0,2000) }}"
},
{
"key": "scrap-date|date",
"date": "={{ $today.format('yyyy-MM-dd') }}",
"includeTime": false
},
{
"key": "Classification|rich_text",
"textContent": "={{ $json.message.content.Classification.join(',') }}"
},
{
"key": "Technical_Details|rich_text",
"textContent": "={{ $json.message.content.Technical_Details }}"
},
{
"key": "Data_and_Results|rich_text",
"textContent": "={{ $json.message.content.Data_and_Results }}"
},
{
"key": "keywords|rich_text",
"textContent": "={{ $json.message.content.Keywords.join(',') }}"
},
{
"key": "Core Introduction|rich_text",
"textContent": "={{ $json.message.content.Core_Introduction }}"
}
]
}
},
"credentials": {
"notionApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.2
},
{
"id": "d5816a1c-d1fa-4be2-8088-57fbf68e6b43",
"name": "Extract Hugging Face Paper Abstract",
"type": "n8n-nodes-base.html",
"position": [
-840,
-300
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "abstract",
"cssSelector": ".text-gray-700"
},
{
"key": "title",
"cssSelector": ".text-2xl"
}
]
}
},
"typeVersion": 1.2
}
],
"active": true,
"settings": {
"executionOrder": "v1"
},
"versionId": "4b0ec2a3-253d-46d5-a4d4-1d9ff21ba4a3",
"connections": {
"If": {
"main": [
[
{
"node": "Request Hugging Face Paper Detail",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Check Paper URL Existed",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Request Hugging Face Paper",
"type": "main",
"index": 0
}
]
]
},
"Store Abstract Notion": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Check Paper URL Existed": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Analysis Abstract": {
"main": [
[
{
"node": "Store Abstract Notion",
"type": "main",
"index": 0
}
]
]
},
"Extract Hugging Face Paper": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Request Hugging Face Paper": {
"main": [
[
{
"node": "Extract Hugging Face Paper",
"type": "main",
"index": 0
}
]
]
},
"Request Hugging Face Paper Detail": {
"main": [
[
{
"node": "Extract Hugging Face Paper Abstract",
"type": "main",
"index": 0
}
]
]
},
"Extract Hugging Face Paper Abstract": {
"main": [
[
{
"node": "OpenAI Analysis Abstract",
"type": "main",
"index": 0
}
]
]
}
}
}
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.
notionApiopenAiApi
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How this works
This workflow automates the discovery and organisation of new AI research papers from Hugging Face, saving you hours of manual searching and curation by delivering structured summaries directly into your Notion database. It's ideal for researchers, data scientists, or content creators who track advancements in machine learning to stay ahead in their field. The key step involves a scheduled trigger that fetches the latest papers via HTTP requests, extracts details using HTML parsing, and integrates with OpenAI to generate concise insights before storing everything neatly in Notion.
Use this workflow when you need a hands-off way to monitor Hugging Face for specific topics like NLP models, running daily or weekly to build a searchable knowledge base without interrupting your routine. Avoid it if your focus is on real-time alerts rather than batch processing, or if you prefer platforms other than Notion for storage. Common variations include tweaking the cron schedule for less frequent updates or adding filters to target only papers from certain authors.
About this workflow
Hugging Face to Notion. Uses scheduleTrigger, splitInBatches, splitOut, httpRequest. Scheduled trigger; 11 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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