Pulling data from services that n8n doesn’t have a pre-built integration for
You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming data in your workflow Main use cases: Connect with apps and services that n8n doesn’t have integration with Web scraping How it works This workflow can be divided into three branches, each serving a distinct purpose: 1.Splitting into Items (HTTP Request - Get Mock Albums): The workflow initiates with a manual trigger (On clicking 'execute'). It performs an HTTP request to retrieve mock albums data from "https://jsonplaceholder.typicode.com/albums." The obtained data is split into items using the Item Lists node, facilitating easier management. 2.Data Scraping (HTTP Request - Get Wikipedia Page and HTML Extract): Another branch of the workflow involves fetching a random Wikipedia page using an HTTP request to "https://en.wikipedia.org/wiki/Special:Random." The HTML Extract node extracts the article title from the fetched Wikipedia page. 3.Handling Pagination (The final branch deals with handling pagination for a GitHub API request): It sends an HTTP request to "https://api.github.com/users/that-one-tom/starred," with parameters like the page number and items per page dynamically set by the Set node. The workflow uses conditions (If - Are we finished?) to check if there are more pages to retrieve and increments the page number accordingly (Set - Increment Page). This process repeats until all pages are fetched, allowing for comprehensive data retrieval.
By Jonathan🔐🦙🤖 Private & local Ollama self-hosted AI assistant
Transform your local N8N instance into a powerful chat interface using any local & private Ollama model, with zero cloud dependencies ☁️. This workflow creates a structured chat experience that processes messages locally through a language model chain and returns formatted responses 💬. How it works 🔄 💭 Chat messages trigger the workflow 🧠 Messages are processed through Llama 3.2 via Ollama (or any other Ollama compatible model) 📊 Responses are formatted as structured JSON ⚡ Error handling ensures robust operation Set up steps 🛠️ 📥 Install N8N and Ollama ⚙️ Download Ollama 3.2 model (or other model) 🔑 Configure Ollama API credentials ✨ Import and activate workflow This template provides a foundation for building AI-powered chat applications while maintaining full control over your data and infrastructure 🚀.
By Joseph LePageAI powered web scraping with Jina, Google Sheets and OpenAI : the EASY way
Purpose of workflow: The purpose of this workflow is to automate scraping of a website, transforming it into a structured format, and loading it directly into a Google Sheets spreadsheet. How it works: Web Scraping: Uses the Jina AI service to scrape website data and convert it into LLM-friendly text. Information Extraction: Employs an AI node to extract specific book details (title, price, availability, image URL, product URL) from the scraped data. Data Splitting: Splits the extracted information into individual book entries. Google Sheets Integration: Automatically populates a Google Sheets spreadsheet with the structured book data. Step by step setup: Set up Jina AI service: Sign up for a Jina AI account and obtain an API key. Configure the HTTP Request node: Enter the Jina AI URL with the target website. Add the API key to the request headers for authentication. Set up the Information Extractor node: Use Claude AI to generate a JSON schema for data extraction. Upload a screenshot of the target website to Claude AI. Ask Claude AI to suggest a JSON schema for extracting required information. Copy the generated schema into the Information Extractor node. Configure the Split node: Set it up to separate the extracted data into individual book entries. Set up the Google Sheets node: Create a Google Sheets spreadsheet with columns for title, price, availability, image URL, and product URL. Configure the node to map the extracted data to the appropriate columns.
By Derek CheungAI-powered stock analysis assistant with Telegram, Claude & GPT-4O Vision
"Ade Technical Analyst" is a dual-workflow AI system combining conversational intelligence with visual chart analysis through Telegram. The system features 11 primary nodes for conversation management and 8 secondary nodes for chart generation and analysis. Core Components: Telegram Integration: Message handling with dynamic typing indicators AI Personality: "Ade" - a financial analyst with 50+ years NYSE/LSE experience using Claude 3.5 Sonnet Chart Generation: TradingView integration via Chart-IMG API with MACD and volume indicators Visual Analysis: GPT-4O vision for technical pattern recognition Memory System: Session-based conversation context retention Target Users Individual traders seeking professional-grade analysis without subscription costs Financial advisors wanting 24/7 AI-powered client support Investment educators needing interactive learning tools Fintech companies requiring white-label analysis solutions Setup Requirements Critical Security Fix Needed: Remove hardcoded API key from Chart-IMG node immediately Store all credentials securely in n8n credential manager Required APIs: OpenRouter (Claude 3.5 Sonnet) OpenAI (GPT-4O vision) Chart-IMG API Telegram Bot Token Technical Prerequisites: n8n version 1.7+ with Langchain nodes Webhook configuration for Telegram Dual-workflow setup with proper ID referencing Workflow Requirements Security Compliance: Never hardcode API keys in workflow JSON files Use n8n credential manager for all sensitive data Implement proper session isolation for user data Include mandatory financial disclaimers Performance Specifications: Model temperature: 0.8 for balanced responses Token limit: 500 for optimized performance Dark theme charts with professional indicators Session-based memory management Need help customizing? Contact me for consulting and support or add me on LinkedIn
By Femi AdAuto-create TikTok videos with VEED.io AI avatars, ElevenLabs & GPT-4
💥 Viral TikTok Video Machine: Auto-Create Videos with Your AI Avatar --- 🎯 Who is this for? This workflow is for content creators, marketers, and agencies who want to use Veed.io’s AI avatar technology to produce short, engaging TikTok videos automatically. It’s ideal for creators who want to appear on camera without recording themselves, and for teams managing multiple brands who need to generate videos at scale. --- ⚙️ What problem this workflow solves Manually creating videos for TikTok can take hours — finding trends, writing scripts, recording, and editing. By combining Veed.io, ElevenLabs, and GPT-4, this workflow transforms a simple Telegram input into a ready-to-post TikTok video featuring your AI avatar powered by Veed.io — speaking naturally with your cloned voice. --- 🚀 What this workflow does This automation links Veed.io’s video-generation API with multiple AI tools: Analyzes TikTok trends via Perplexity AI Writes a 10-second viral script using GPT-4 Generates your voiceover via ElevenLabs Uses Veed.io (Fabric 1.0 via FAL.ai) to animate your avatar and sync the lips to the voice Creates an engaging caption + hashtags for TikTok virality Publishes the video automatically via Blotato TikTok API Logs all results to Google Sheets for tracking --- 🧩 Setup Telegram Bot Create your bot via @BotFather Configure it as the trigger for sending your photo and theme Connect Veed.io Create an account on Veed.io Get your FAL.ai API key (Veed Fabric 1.0 model) Use HTTPS image/audio URLs compatible with Veed Fabric Other APIs Add Perplexity, ElevenLabs, and Blotato TikTok keys Connect your Google Sheet for logging results --- 🛠️ How to customize this workflow Change your Avatar: Upload a new image through Telegram, and Veed.io will generate a new talking version automatically. Modify the Script Style: Adjust the GPT prompt for tone (educational, funny, storytelling). Adjust Voice Tone: Tweak ElevenLabs stability and similarity settings. Expand Platforms: Add Instagram, YouTube Shorts, or X (Twitter) posting nodes. Track Performance: Customize your Google Sheet to measure your most successful Veed.io-based videos. --- 🧠 Expected Outcome In just a few seconds after sending your photo and theme, this workflow — powered by Veed.io — creates a fully automated TikTok video featuring your AI avatar with natural lip-sync and voice. The result is a continuous stream of viral short videos, made without cameras, editing, or effort. --- ✅ Import the JSON file in n8n, add your API keys (including Veed.io via FAL.ai), and start generating viral TikTok videos starring your AI avatar today! 🎥 Watch This Tutorial --- 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube
By Dr. FirasExtract trends, auto-generate social content with AI, Reddit, Google & post
Extract Trends and Auto-Generate Social Media Content with OpenAI, Reddit, and Google Trends: Approve and Post to Instagram, TikTok, and More --- Description What Problem Does This Solve? 🛠️ This workflow automates trend extraction and social media content creation for businesses and marketers. It eliminates manual trend research and content generation by fetching trends, scoring them with AI, and posting tailored content to multiple platforms. Target audience: Social media managers, digital marketers, and businesses aiming to streamline content strategies. What Does It Do? 🌟 Fetches trending topics from Reddit, X and Google Trends Scores trends for relevance using OpenAI. Generates content for Twitter/X, LinkedIn, Instagram and Facebook Posts to supported platforms Stores results in Google Sheets for tracking Key Features 📋 Real-time trend fetching from Reddit and Google Trends. AI-driven trend scoring and content generation (OpenAI). Automated posting to Twitter/X, LinkedIn, Instagram, and Facebook. Persistent storage in Google Sheets. --- Setup Instructions Prerequisites ⚙️ n8n Instance: Self-hosted or cloud n8n instance. API Credentials: Reddit API: Client ID and secret from Reddit. SerpApi (Google Trends): API key from SerpApi, stored in n8n credentials OpenAI API: API key with GPT model access. Twitter/X API: OAuth 1.0a credentials with write permissions. LinkedIn API: OAuth 2.0 credentials with worganizationsocial scope. Instagram/Facebook API: Meta Developer app with posting permissions. Google Sheets API: Credentials from Google Cloud Console. Installation Steps 📦 Import the Workflow: Copy the workflow JSON from the "Template Code" section below. Import it into n8n via "Import from File" or "Import from URL". Configure Credentials: Add API credentials in n8n’s Credentials section for Reddit, SerpApi, OpenAI, Twitter/X, LinkedIn, Instagram/Facebook, and Google Sheets. Assign credentials to respective nodes. For example: In the Fetch Google Trends node (HTTP Request), use n8n credentials for SerpApi instead of hardcoding the API key. Example: Set the API key in n8n credentials as SerpApiKey and reference it in the node’s query parameter: api_key={{ $credentials.SerpApiKey }}. Set Up Google Sheets with the following columns (exact column names are case-sensitive) -Timestamp | Trend | Score | BrandVoice | AudienceMood | Customize Nodes: OpenAI Nodes (Trend Relevance Scoring, Generate Social Media Content): Update the model (e.g., gpt-4o) and prompt as needed. HTTP Request Nodes (Post to Twitter/X, Post to LinkedIn, etc.): Verify URLs, authentication, and payloads. Brand Voice/Audience Mood: Adjust Prepare Trend Scoring Input for your desired brandvoice (e.g., "casual") and audiencemood (e.g., "curious"). Test the Workflow: Fetch Reddit Trends to Store Selected Trends- to score and store trends. Retrieve Latest Trends to end) to generate and post content Check Google Sheets for posting statuses --- How It Works High-Level Steps 🔍 Fetch Trends: Pulls trends from Reddit,X and Google Trends. Score Trends: Uses OpenAI to score trends for relevance. Generate Content: Creates platform-specific social media content. Post Content: Posts to LinkedIn, Facebook or X Detailed descriptions are available in the sticky notes within the workflow screenshot above. --- Node Names and Actions Trend Extraction and Scoring Daily Trigger Idea: Triggers the workflow daily. Set Default Inputs: Sets default brand_voice and inputs. Fetch Reddit Trends: Fetches Reddit posts. Extract Reddit Trends: Extracts trends from Reddit. Fetch Google Trends: Fetches Google Trends via SerpApi. Extract Google Trends2: Processes Google Trends data. Fetch Twitter Mentions: Fetches Twitter mentions. Translate Tweets to English: Translates tweets. Fix Tweet Translation Output: Fixes translation format. Detect Audience Mood: Detects audience mood. Fix Audience Mood Output: Fixes mood output format. Analyze News Sentiment: Analyzes news sentiment. Combine Data (Merge): Merges all data sources. Merge Items into Single Item: Combines data into one item. Combine Trends and UGC: Combines trends with UGC. Prepare Trend Scoring Input: Prepares data for scoring. Trend Relevance Scoring: Scores trends with OpenAI. Parse Trend Scores: Parses scoring output. Store Selected Trends: Stores trends in Google Sheets. Content Generation and Posting Retrieve Latest Trends: Retrieves trends from Google Sheets. Parse Retrieved Trends: Parses retrieved trends. Select Top Trends: Selects the top trend. Generate Social Media Content: Generates platform-specific content. Parse Social Media Content: Parses generated content. Generate Images: Generates images for posts (if applicable). -Handle Approvals/Rejection before Posting Post to Instagram: Posts to Instagram. Post to Facebook: Posts to Facebook. Post to LinkedIn: Posts to LinkedIn. --- Customization Tips Add Trend Sources 📡: Include more sources (e.g., Instagram trends) by adding nodes to Combine Data (Merge). Change Content Tone ✍️: Update the Generate Social Media Content prompt for a different tone (e.g., "humorous"). Adjust Schedule ⏰: Modify Daily Trigger Idea to run hourly or weekly. Automate TikTok/YouTube 🎥: Add video generation (e.g., FFMPEG) to post TikTok and YouTube Shorts ---
By ImmanuelAutomated Instagram reels workflow
This workflow automates the process of creating and posting Instagram Reels, combining Google Drive, AI, Airtable, and the Facebook Graph API. It supports two content creation paths: Scheduled Random Video Selection & Posting Selects a random video from a Google Drive folder named "Random video mover" based on a schedule. Moves the video to a processing folder for posting. Manual Upload Trigger & Posting Watches a specific Google Drive folder ("n8n reels automation on instagram"). Triggers the workflow when a new video is uploaded. Core Process (applies to both paths) Download Video from Google Drive. AI Caption Generation with Google Gemini, using the file name as context. The AI creates concise captions with hashtags and a call-to-action. Airtable Logging to store video name, caption, and URL. Instagram Reels Posting via the Facebook Graph API. Recent Change In early 2025, Meta tightened its requirements for videourl and imageurl parameters. URLs must now be direct, public links to the raw media file with no redirects or authentication. Google Drive links no longer work. Our Fix Store the binary file locally on the n8n server at /tmp/video.mp4. Serve the file through a public n8n webhook with the correct Content-Type. Use the webhook URL in the Facebook Graph API request. Upload succeeds without the “Media download has failed” error. Cleanup Deletes the temporary file after posting. Benefits Saves time with full automation. Improves engagement through AI-generated captions. Keeps content organized in Airtable. Works with Meta’s updated API requirements by hosting files directly from the n8n server.
By Iniyavan JCAutomate WhatsApp booking system with GPT-4 Assistant, Cal.com and SMS reminders
AI-powered WhatsApp booking system with instant SMS confirmations Who is this for? This workflow is designed for solo entrepreneurs, consultants, coaches, clinics, or any business that handles client appointments and wants to automate the entire scheduling experience via WhatsApp — without the need for live agents. What problem is this workflow solving? Responding to inbound messages, collecting booking details, suggesting available times, and sending reminders can be a huge time drain. This workflow eliminates manual handling by: Automating WhatsApp conversations with an AI assistant Booking appointments directly into Cal.com Sending timely SMS reminders before appointments It ensures you never miss a lead or a follow-up — even while you sleep. What this workflow does From a single WhatsApp message, the workflow: Triggers via a WhatsApp webhook Uses GPT-4 to handle conversation flow and qualify the prospect Collects name, email, selected service Calls Cal.com API to fetch available time slots Books the appointment and stores it in Google Sheets Sends a confirmation message via WhatsApp Periodically scans for upcoming appointments Sends SMS reminders to clients 2 hours before their session Setup Connect your Webhook node to a WhatsApp API (e.g., 360dialog, Twilio, or Ultramsg) Add your OpenAI API key for the GPT-4 nodes Configure your Cal.com API key and set your calendar ID Link your Google Sheets with fields like: name, email, date, time, status, reminder_sent Connect your SMS service (e.g., sms77) with API credentials Adjust the schedule in the reminder node as needed How to customize this workflow to your needs Change the language or tone of the AI assistant by editing the system prompt in the GPT node Filter available time slots by service, team member, or duration Modify the reminder timing (e.g., 1 hour before, 24h before, etc.) Add conditional logic to route users to different booking flows based on their responses Integrate additional CRMs or notification channels like email or Slack 📄 Documentation: Notion Guide --- Need help customizing? Contact me for consulting and support : Linkedin / Youtube
By Dr. FirasBuild a weekly AI trend alerter with arXiv and Weaviate
Build a Weekly AI Trend Alerter with arXiv and Weaviate Ditch the endless scroll for AI trends. Meet Archi, your personal AI research assistant that hits you up once a week with everyone you need to know. 🧑🏽🔬 This workflow scrapes AI and machine learning article abstracts from arXiv, enriches them with topic categories using a LLM, and embeds them in a Weaviate vector store. The vector store is then used as a tool for agentic RAG to write a concise, easy-to-read summary of the week in AI research. The final output is a short, weekly email sent to the address of your choice that summarizes key AI research trends and future research directions, with links directly to the most interesting and impactful arXiv papers of the week. Who it's for This workflow is for anyone who can't keep up with all the latest AI advances. Coding skills are not required. How it works This is a contiguous workflow that can be summarized in two main parts: a data pipeline that fetches and embeds articles in Weaviate, and an agentic workflow that generates a weekly email summary. Part 1: Automatically fetch newly published articles on a weekly basis Fetch article abstracts (and metadata) from arXiv's free API Pre-process abstract data Enrich each article with a primary topic, secondary topics, and estimated potential impact of the research using a LLM Post-process data Insert data and embeddings into Weaviate Part 2: Use an AI Agent and Weaviate to generate a weekly summary email Add Weaviate as a Tool to an AI agent node Query Weaviate, agentically, to generate a report on the most important research trends of the week Post-process data Send the summary via email Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use a combination of OpenRouter and OpenAI models. Feel free to switch out the models as you like. An email address with STMP privileges. This is the address the email will come from. In this demo we use a personal Gmail address. You can create a new credential to link a STMP Account using these instructions. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, creating STMP privileges for your email account, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Enter the email addresses you want to send the email from and to. Let it rip. Workflow output The output for this workflow is a weekly email that summarizes key research trends and future research directions based on AI and ML papers published on arXiv. Here's an example of a summary email: Hey there, Here's a quick rundown of the key trends in Machine Learning research from the past week. Key Research Trends This Week This week saw significant advancements in retrieval-augmented systems, foundation models for specialized domains, and techniques balancing efficiency with performance. Advanced RAG Architectures: Researchers are developing sophisticated RAG frameworks that go beyond simple document retrieval, with AdaPCR introducing passage combination retrieval and UrbanMind proposing a framework for urban intelligence with multilevel optimization. Foundation Models for Tabular Data: The Real-TabPFN shows that targeted continued pre-training on real-world datasets can significantly boost the performance of foundation models for tabular data, outperforming models trained on broader, potentially noisier datasets. Efficiency-Focused Techniques: Researchers are developing resourceful methods that maintain performance without expensive computations, like logit reweighting for topic-focused summarization and strategic querying for privacy-preserving personalization. Future Research Directions Based on current trends, we expect to see the following developments in the near future: Explainable RAG Systems: Following the source attribution work in RAG systems, we can expect more research into making complex retrieval systems transparent and explainable for users. Cross-Domain and Cross-Modal Fusion: The promising performance of vision-language and code-specialized LLMs in retrieval tasks points toward unified retrievers capable of handling text, code, images, and multimodal content. Data-Centric Synthetic Generation: As shown by work on synthetic relational tabular data, we'll likely see more sophisticated approaches to generating high-quality synthetic data for pre-training foundation models in specialized domains. This week highlights how researchers are making AI more efficient, explainable, and applicable to specialized domains. Look out for more developments in RAG systems, tabular foundation models, and privacy-preserving AI techniques in the coming weeks. Until next week, Archi Want to make it better? Feel free to tweak, build on, or completely reconfigure this workflow. If you come up with something cool, let us know and we might just share it with our community! 💚
By Mary NewhauserConvert HTML to PDF using ConvertAPI
Who is this for? For developers and organizations that need to convert HTML files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Converts HTML to file. Converts the HTML file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Use your API Token for authentication. Pass the token in the Authorization header as a Bearer token. You can manage your API Tokens in the User panel → Authentication. Optionally, additional Body Parameters can be added for the converter.
By ConvertAPISummarize Google Sheets form feedback via OpenAI's GPT-4
This n8n workflow was developed to collect and summarize feedback from an event that was collected via a Google Form and saved in a Google Sheets document. The workflow is triggered manually by clicking on the "Test workflow" button. The Google Sheets node retrieves the responses from the feedback form. The Aggregate node then combines all responses for each question into arrays and prepares the data for analysis. The OpenAI node processes the aggregated feedback data. System Prompt instructs the model to analyze the responses and generate a summary report that includes the overall sentiment regarding the event and constructive suggestions for improvement. The Markdown node converts the summary report, which is in Markdown format, into HTML. Finally, the Gmail node sends an HTML-formatted email to the specified email address.
By YuliaAutomate audio/video transcription in any language with the new ElevenLabs model
How it works 🗣️> 📖 I set up this workflow to convert any audio or video file into structured text using the new ElevenLabs Scribe model, one of the best Speech-to-Text AIs, available in 99+ languages. This workflow integrates seamlessly with n8n and leverages the ElevenLabs Scribe API to: This workflow seamlessly integrates with n8n to: ✅ Upload audio/video files automatically ✅ Transcribe them with industry-leading accuracy in any language ✅ Export the text for further processing (summaries, subtitles, SEO content, etc.) 👉 Try the new ElevenLabs Scribe model now: Convert speech to text instantly Business Cases 🔹 Podcast Transcriptions – Convert podcast episodes into blog posts for SEO and accessibility 🔹 YouTube Subtitles – Generate captions automatically for increased engagement 🔹 Legal & Compliance – Accurately transcribe meetings, interviews, or customer calls 🔹 E-learning – Turn lectures and webinars into structured course notes 🔹 SEO & Content Marketing – Repurpose videos into articles, quotes, and social media content 💡 Boost your productivity with the new Scribe model → Start with ElevenLabs Scribe Set up steps 🚀 Quick & simple setup in n8n – Upload your file, select the model (scribe_v1), and let the AI handle the rest via the ElevenLabs API. ⸻ 📢 Why I Chose the New ElevenLabs Scribe Model? I wanted the most accurate and reliable transcription tool for my workflow. After testing different options, Scribe outperformed Google Gemini & OpenAI Whisper in independent benchmarks. It delivers high-quality transcriptions, even in underserved languages like Serbian, Mongolian, and many more. ✅ Transcribes in 99+ languages ✅ Fast, accurate, and easy to integrate ✅ Suitable for content creators, businesses, and professionals 🔗 Get started now and revolutionize your workflow with the new Scribe model → Try Scribe AI today 🚀 Phil | Inforeole | Linkedin 🇫🇷 Contactez nous pour automatiser vos processus
By phil