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 JonathanAutomated 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 JCBuild 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 NewhauserListen on new emails on a IMAP mailbox
Listen on new emails on a given IMAP mailbox. Converts the binary XML attachment to a stringified xmlproperty on the $data object and converts it to JSON. The Setnode could be replaced with Function nodes for example to do a conversion/mapping/transformation. The JSON is ultimately sent to a HTTP Endpoint via POST. > This could be a starter for EDI purposes like receiving or transmitting XML data and conversion via JSON object property mapping.
By mailTurn any RSS feed into email
Use case This workflow automatically turns any RSS feed into a newsletter. It send an email every time a new post is published to an RSS feed in the last hour. I have been using this personally to follow Derek Sivers and Anil Dash, who don't have newsletters. Check them out 😉 How to setup Add your email and email creds Add the RSS feed URLs you want to follow How to adjust this template Add the feeds you want to follow. It does not have to be just email. Why not get a telegram message? Or Slack message? Ah the power of n8n 🚀
By MutasemAuto-generate & post AI images to Facebook using Gemini & Pollinations AI
This workflow is designed for content creators or AI artists who want to automate posting unique AI-generated images to their Facebook Page multiple times a day. It uses Google Gemini via LangChain to generate imaginative image prompts, and Pollinations AI to generate the images. Posts are published with hashtags and a clean caption. Who Is It For AI artists Facebook page managers Digital marketers looking for automated creative content What It Does Triggers 3x daily at 7:00, 11:00, and 17:00 (local time) Generates random AI image prompts in a retro-futuristic, cinematic, or surreal style using Google Gemini Fetches images from Pollinations AI using custom prompts Posts images automatically to your Facebook Page with hashtags Requirements n8n self-hosted or desktop (workflow uses schedule trigger) Pollinations API (no auth needed) Facebook Page with Facebook Graph API token: Required scopes: pagesmanageposts, pagesreadengagement, pagesshowlist Google Gemini API Key (used via LangChain node) How to Customize Change the prompt style in the Basic LLM Chain node (promptType: define) to suit your theme. Adjust Set The Generator Image node if you want: Different image sizes (width / height) Different seed randomness Other Pollinations models (&model=kontext) Add Telegram/Twitter nodes if you want cross-posting Use Set node to allow easy user-level configuration of models, hashtags, times, etc.
By Fahmi OktafianReceive messages from a topic via Kafka and send an SMS
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By Harshil AgrawalAuto-generate SEO articles in WordPress with Gemini AI and OpenAI images
This n8n template helps you automate the process of generating, uploading, and saving SEO-optimized articles as drafts in your Wordpress site using AI agents for both content and visuals. Use cases are many: Blog Owners: Effortlessly keep your Wordpress blog updated with automated, SEO-optimized articles and featured images. Affiliate Marketers: Rapidly publish keyword-rich Wordpress posts to boost traffic and affiliate earnings without manual writing. Newsrooms & Content Teams: Instantly draft and upload timely Wordpress articles to stay ahead on trending topics with minimal effort. Agencies: Automate content creation and publishing across multiple client Wordpress sites, scaling your agency’s output. How It Works The workflow begins with a manual trigger in n8n. The target Wordpress website domain - where the article will be published - is specified using the Fields – Set Website node. The Agent – Topic Chooser & Title Generator uses Google Gemini to randomly select one of your pre-defined website-related topics and requests the model to generate article elements in your specified format. The results are then parsed and routed to the next AI agent. The Agent – Article Generator (powered by Google Gemini) creates a complete, SEO-optimized article tailored to your settings and the selected topic. The workflow then uploads the AI-generated article as a draft post to your Wordpress site. Next, the article content is sent to the OpenAI to generate a unique, relevant featured image. The generated image is uploaded and set as the featured image in the draft post. (Optional) Finally, the workflow can notify you via Telegram (or any connected messaging app) that the post is ready as a draft on your Wordpress account for further review. ⚠️ Important Note: In the default prompt setup of the first Agent – Topic Chooser & Title Generator, the website domain is set to Agent Circle, the industry is Technology and Artificial Intelligence, and the 5 available topics for article generation are: Artificial Intelligence (AI); AI Agents; Automation; Workflow; Prompts). To get the best results for your site, please make sure to adjust this part of the prompt to match your own website domain, your industry, and the topics most relevant to your audience. How To Set Up Download and import the workflow into your n8n workspace. Set up the necessary credentials for each tool: Google Gemini API (for article elements generation) → connected to nodes Google Gemini Chat Model 1 and Google Gemini Chat Model 2. OpenAI API (for creating unique featured images) → connected to the node OpenAI - Generate Image. Wordpress API access (for posting, uploading, and setting featured images) → connected to the node Wordpress - Post Draft. (Optional) Telegram or your preferred chat app (for completion notifications). Adjust the Fields – Set Website node to match your target Wordpress website domain. Manually trigger the workflow by clicking Test Workflow or Execute Workflow. Wait for the process to complete. Receive a chat notification or check the draft post directly in your Wordpress dashboard. Requirements An n8n instance (self-hosted or cloud). OpenAI API access (for image generation). Wordpress API access with admin permissions. Access to Google Gemini (for advanced topic selection and article generation). (Optional) API credentials for Telegram or another messaging app if you want to receive notifications when the process is complete. How To Customize Modify topics, article elements and guildelines: You can easily customize the pre-defined topics in the instruction prompt within Node Agent – Topic Chooser & Title Generator, as well as adjust the descriptions or structure of each article element to fit your needs and style. Integrate other chat triggers: Link up with Slack, Discord, or any messaging tool to update progress. Choose your AI model: Swap between Google Gemini and OpenAI for content generation, or add your preferred LLM. Need Help? If you’d like this workflow customized, or if you’re looking to build a tailored AI Agent for your own business - please feel free to reach out to Agent Circle. We’re always here to support and help you to bring automation ideas to life. Join our community on different platforms for assistance, inspiration and tips from others. Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle
By Agent CircleGenerate videos from text descriptions using Wan 2.2 T2V Fast and Replicate
This workflow provides automated access to the Wan Video Wan 2.2 T2V Fast AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for video generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete video generation process using the Wan Video Wan 2.2 T2V Fast model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: A very fast and cheap PrunaAI optimized version of Wan 2.2 A14B text-to-video Key Capabilities AI-powered video generation and processing High-quality video synthesis from inputs Advanced video manipulation capabilities Fast, optimized processing for quick results Tools Used n8n: The automation platform that orchestrates the workflow Replicate API: Access to the Wan Video/wan-2.2-t2v-fast AI model Wan Video Wan 2.2 T2V Fast: The core AI model for video generation Built-in Error Handling: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Video Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Video Content Creation: Generate videos for social media, marketing, and presentations Animation & Motion Graphics: Create animated content and visual effects Video Editing: Enhance and transform existing video content Educational Content: Produce instructional and explainer videos Connect with Me Website: https://www.nofluff.online YouTube: https://www.youtube.com/@YaronBeen/videos LinkedIn: https://www.linkedin.com/in/yaronbeen/ Get Replicate API: https://replicate.com (Sign up to access powerful AI models) n8n automation ai replicate aiautomation workflow nocode videogeneration aivideo videoai motion videoautomation videocreation machinelearning artificialintelligence aitools automation digitalart contentcreation productivity innovation
By Yaron BeenCreate 2 XML files: with and without XML attributes
This workflow demonstrates two ways of exporting data from SQL to XML. First, several random records are received from the MySQL database. Then, in the upper part of the workflow, the structure of an XML is defined in the Set node. After that, the ItemLists node combines all items into an array. This allows an XML node to create a simple XML file. The lower part of the workflow shows how to create an XML with attributes. It is almost identical except that a $ (dollar sign) JSON key is used to define XML attributes. Finally, both files are saved locally.
By n8n TeamGenerate written content with GPT Recursive Writing & Editing agents
Who is this for? Content creators, writers, and automation enthusiasts experimenting with recursive AI workflows for content generation and refinement. Ideal for those exploring AI agents that collaborate in cycles of writing and editing. What problem does this solve? This template introduces a fully automated, recursive writing‑editing loop using multi‑agent collaboration. A “Writing Agent” generates content based on an input topic. An “Editing Agent” reviews it, suggests improvements, and determines whether the work is complete. The loop continues until the editor is satisfied—allowing for high‑quality, iterative AI‑assisted writing with minimal human input. How it works This template is a foundational setup to help you build custom recursive writing workflows: Trigger: Activated by an n8n chat message containing a topic. You can customize this to work with webhooks, forms, or other input sources. Edit Handler: A code node checks for previous edits and sets a default empty string if none are found. Writing Agent: Generates a blurb based on the topic and any edits. Customize the prompt in this node by editing the user/system instructions to fit your tone, domain, or style preferences. Editing Agent: Suggests specific edits and outputs a structured JSON object: json { "status": "incomplete", "edits": "Replace passive voice with active voice in the second sentence. Clarify the main idea in the opening line." } You can adjust the JSON format or editing criteria in the prompt field. Customize the prompt in this node by editing the user/system instructions to fit your tone, domain, or style preferences. Recursive Loop: If the status is “incomplete,” the edits are passed back to the Writing Agent, which revises the blurb. Completion: Once the Editing Agent outputs a status of “complete,” the workflow ends, and the final blurb is returned to the n8n chat. Setup Steps Import the Template into your n8n workspace. Configure API Credentials: Link your OpenAI API key (or your preferred LLM like Claude or Gemini) in the credentials section. Customize the Prompts (Optional but recommended): In the Writing Agent, you can instruct it to mimic a specific tone, format, or genre. In the Editing Agent, specify your editing standards (e.g., concise, persuasive, technical). Modify the JSON output structure in the Structured Output Parser node if needed. Test and Iterate: Run a test by sending a topic via the chat trigger and observe the loop behavior. Example Output Input Topic: “The future of remote work” Final Blurb: “Remote work is here to stay. As companies embrace flexible setups, productivity and employee satisfaction are reaching new highs. The challenge now is to build culture and collaboration tools that keep up.” This template offers a powerful starting point for recursive AI writing. Expand it with additional agents, tone shifts, formatting layers, or sentiment analysis as needed.
By Matty ReedGenerate Job-Specific ATS Resumes with Perplexity AI and PDF Export
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. ATS Resume Maker Workflow Explanation Aim The aim of the ATS Resume Maker according to JD workflow is to automate the creation of an ATS-friendly resume by tailoring a candidate’s resume to a specific job description (JD). It streamlines the process of aligning resume content with JD requirements, producing a professional, scannable PDF resume that can be stored in Google Drive. Goal The goal is to: Allow users to input their resume (text or PDF) and a JD (PDF) via a web form. Extract and merge the text from both inputs. Use AI to customize the resume, prioritizing JD keywords while maintaining the candidate’s truthful information. Generate a clean, ATS-optimized HTML resume and convert it to a downloadable PDF. Upload the final PDF to Google Drive for easy access. This ensures the resume is optimized for Applicant Tracking Systems (ATS), which are used by employers to screen resumes, by incorporating relevant keywords and maintaining a simple, scannable format. Requirements The workflow relies on specific components and configurations: n8n Platform: The automation tool hosting the workflow. Node Requirements: Form Trigger: A web form to collect user inputs (resume text/PDF, JD PDF). Process one binary file1: JavaScript to rename and organize PDF inputs. Extracting resume1: Extracts text from PDF files. Merge Resume + JD1: Combines resume and JD text into a single string. Customize resume1: Uses Perplexity AI to generate an ATS-friendly HTML resume. HTML format1: Cleans the HTML output by removing newlines. HTML3: Processes HTML for potential display or validation. HTML to PDF: Converts the HTML resume to a PDF file. Upload file: Saves the PDF to a specified Google Drive folder. Credentials: CustomJS account for the HTML-to-PDF conversion API. Google Drive account for file uploads. Perplexity account for AI-driven resume customization. Input Requirements: Resume (plain text or PDF). Job description (PDF). Output: A tailored, ATS-friendly resume in PDF format, uploaded to Google Drive. API Usage The workflow integrates multiple APIs to achieve its functionality: Perplexity API: Used in the Customize resume1 node to leverage the sonar-reasoning model for generating an ATS-optimized HTML resume. The API processes the merged resume and JD text, aligning content with JD keywords while adhering to strict HTML and CSS guidelines (e.g., Arial font, no colors, single-column layout). [Ref: Workflow JSON] CustomJS API: Used in the HTML to PDF node to convert the cleaned HTML resume into a PDF file. This API ensures the resume is transformed into a downloadable format suitable for ATS systems. [Ref: Workflow JSON] Google Drive API: Used in the Upload file node to store the final PDF in a designated Google Drive folder (Resume folder in My Drive). This API handles secure file uploads using OAuth2 authentication. [Ref: Workflow JSON] These APIs are critical for AI-driven customization, PDF generation, and cloud storage, ensuring a seamless end-to-end process. HTML to PDF Conversion The HTML-to-PDF conversion is a key step in the workflow, handled by the HTML to PDF node: Process: The node takes the cleaned HTML resume ($json.cleanedResponse) from the HTML format1 node and uses the @custom-js/n8n-nodes-pdf-toolkit.html2Pdf node to convert it into a PDF. API: Relies on the CustomJS API for high-fidelity conversion, ensuring the PDF retains the ATS-friendly structure (e.g., no graphics, clear text hierarchy). Output: A binary PDF file passed to the Upload file node. Relevance: This step ensures the resume is in a widely accessible format, suitable for downloading or sharing with employers. The use of a dedicated API aligns with industry practices for HTML-to-PDF conversion, as seen in services like PDFmyURL or PDFCrowd, which offer similar REST API capabilities for converting HTML to PDF with customizable layouts. [Ref:,][](https://pdfmyurl.com/)[](https://pdfcrowd.com/api/html-to-pdf-api/) Download from Community Link The workflow does not explicitly include a community link for downloading the final PDF, but the Upload file node stores the PDF in Google Drive, making it accessible via a shared folder or link. To enable direct downloads: Workflow Summary The ATS Resume Maker according to JD workflow automates the creation of a tailored, ATS-friendly resume by: Collecting user inputs via a web form (Form Trigger). Processing and extracting text from PDFs (Process one binary file1, Extracting resume1). Merging and customizing the content using Perplexity AI (Merge Resume + JD1, Customize resume1). Formatting and converting the resume to PDF (HTML format1, HTML3, HTML to PDF). Uploading the PDF to Google Drive (Upload file). The workflow leverages APIs for AI processing, PDF conversion, and cloud storage, ensuring a professional output optimized for ATS systems. Community sharing can be enabled via Google Drive links or external platforms, as discussed in related web resources. [Ref:,,][](https://pdfmyurl.com/)[](https://www.servicenow.com/community/servicenow-ai-platform-forum/allow-user-to-download-a-pdf-file-on-click-of-a-button/td-p/1082457) Timestamp: 02:54 PM IST, Wednesday, August 20, 2025
By Yashraj singh sisodiya