Agents & Automations

How to Sell Your Used Car Easily with Airtop Selling a used car traditionally involves tedious manual steps: listing your vehicle online, filling repetitive forms, answering buyer inquiries, and comparing multiple offers—often leading to lost time, inaccuracies, and missed opportunities. Dealers' platforms and marketplace websites can introduce additional friction, occasionally requiring manual interactions and delivering inconsistent offers. The Sell a Used Car Agent powered by Airtop and n8n eliminates this friction by automating the selling process. Simply input your car's details once, and the automation quickly navigates through popular used car marketplaces like Carvana, CarMax, and others, efficiently fetching competitive buying offers. Leveraging Airtop’s powerful real-browser automation capabilities, this setup interacts smoothly with the forms and queries specific to each sales platform, eliminating error-prone manual entries and tedious repetition. This Airtop automation employs sophisticated data extraction features, ensuring accurate, formatted data ready for immediate review. Say goodbye to cumbersome listing processes, and hello to streamlined, effortless car selling. Who is this Automation for? - Automation engineers streamlining marketplace interactions - Developers building user-friendly car selling apps - Technical teams optimizing inventory resale processes - Dealership IT departments automating valuation systems Key Benefits - No-code automation setup using Make - Saves significant time with instant multi-platform offers - Real browser sessions ensuring reliable site interactions - Structured JSON output simplifying data integration Use Cases - Car dealership teams quickly pricing used vehicles - Individuals efficiently comparing offers from online dealers - Developers building consumer-friendly automated car resale apps - Technical consultants streamlining used car valuation processes How this Airtop Automation Works When started, Airtop opens automated, real browser sessions to intelligently fill out required car details on leading resale marketplaces (e.g., Carvana and CarMax). It captures instantly generated vehicle offers, structures them into clean output, and returns organized buying proposals. All of this is accomplished without manual intervention. What You’ll Need - A free Airtop API key - Basic details describing your vehicle (make, model, condition, mileage) Setting up the workflow 1. Create a new Airtop connection with your API key. 2. Enter the information about the car you are selling into the "Variables" node. For example: - VIN: 1FTRF17253NB81140 - Mileage: 221081 - Zip code: 01952 - Condition: Perfect, no interior or exterior damages, all tires are inflated, have 2 keys, working battery, has an attached catalytic converter, Airbags not deployed, No flood or fire damage. - Ownership: full clean title with no debt; answer yes to all other questions. 3. Run the workflow. Customize the Automation - Add or remove marketplaces based on regional availability or preference. - Extend outputs to your internal inventory or sales systems. - Schedule regular automation runs to track market pricing trends. - Integrate additional valuation platforms for broader market coverage. Automation Best Practices - Regularly review marketplace URLs to maintain accuracy and consistency. - Verify structured data to ensure consistent outputs. - Maintain clearly documented scenarios for easy debugging. - Periodically update your car's condition description for accurate pricing. Happy Automating!

This n8n template shows you how to create an MCP server out of your existing n8n workflows. With this, any MCP client connected can get more done with powerful end-to-end workflows rather than just simple tools. Designing agent tools for outcome rather than utility has been a long recommended practice of mine and it applies well when it comes to building MCP servers; in gist, agents should be making the least amount of calls possible to complete a task. This is why n8n can be a great fit for MCP servers! This template connects your agent/MCP client (like Claude Desktop) to your existing workflows by allowing the AI to discover, manage and run these workflows indirectly. How it works An MCP trigger is used and attaches four custom workflow tools to discover and manage existing workflows to use and one custom workflow tool to execute them. We'll introduce an idea of "available" workflows which the agent is allowed to use. This will help limit and avoid some issues when trying to use every workflow, such as clashes or non-production. The n8n node is a core node that taps into your n8n instance API and is able to retrieve all workflows or filter by tag. For our example, we've tagged the workflows we want to use with "mcp" and these are exposed through the tool "search workflows." Redis is used as our main memory for keeping track of which workflows are "available." The tools we have are "add Workflow," "remove workflow," and "list workflows." The agent should be able to manage this autonomously. Our approach to allow the agent to execute workflows is to use the Subworkflow trigger. The tricky part is figuring out the input schema for each but it was eventually solved by pulling this information out of the workflow's template JSON and adding it as part of the "available" workflow's description. To pass parameters through the Subworkflow trigger, we can do so via the passthrough method - which is that incoming data is used when parameters are not explicitly set within the node. When running, the agent will not see the "available" workflows immediately but will need to discover them via "list" and "search." The human will need to make the agent aware that these workflows will be preferred when answering queries or completing tasks. How to use First, decide which workflows will be made visible to the MCP server. This example uses the tag of "mcp" but you can filter in other ways. Next, ensure these workflows have Subworkflow triggers with input schema set. This is how the MCP server will run them. Set the MCP server to "active," which turns on production mode and makes it available to the production URL. Use this production URL in your MCP client. For Claude Desktop, see the instructions in the documentation. There is a small learning curve which will shape how you communicate with this MCP server, so be patient and test. The MCP server will work better if there is a focused goal in mind, i.e., Research and report, rather than just a collection of unrelated tools. Customising this workflow If your targeted workflows do not use the Subworkflow trigger, it is possible to amend the executeTool to use HTTP requests for webhooks. Managing available workflows helps if you have many workflows where some may be too similar for the agent. If this isn't a problem for you, however, feel free to remove the concept of "available" and let the agent discover and use all workflows!

This workflow includes advanced features like text summarization and tokenization. It's ideal for automating document processing tasks that require parsing and summarizing text data from Google Drive. To use this template, you need to be on n8n version 1.19.4 or later.

Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands-on example and developers looking to integrate Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data on the MCP Server is time-consuming and error-prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Airtable (such as adding, deleting, or modifying records) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention. Setup Requirements - An active n8n account. - Access to Airtable API. - A sample base and rows in Airtable that you can use to test. - An API key from your preferred LLM to power the AI agent. Step-by-step guide 1. Create a new workflow in n8n: Log in to your n8n account and create a new workflow. 2. Add Airtable nodes: Search for and add the Airtable nodes to your workflow that you wish the MCP client to have access to. 3. Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Airtable nodes to the MCP nodes as required. 4. Activate and test the workflow: Talk to the chat trigger once all credentials have been updated and table data synced and try adding some rows, deleting, or finding and updating cells. How to customize this workflow to your needs If you want to customize this workflow, you can: - Modify the triggers: You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect changes only in specific fields or based on certain record values in Airtable. - Integrate with other services: You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or triggering further actions based on the detected Airtable changes. Need Assistance or Want to Explore Advanced Integrations? This template provides a solid starting point for integrating Airtable with your MCP server in n8n. If you have questions about this workflow, encounter any challenges, or are looking to build more complex AI automation solutions, we're here to help. At 1 Node, we specialize in empowering businesses with AI integrations.

This n8n template builds a simple automation to ensure no JIRA issues go unassigned for more than a week to prevent them from falling through the cracks. It uses AI to perform searching tasks against a Supabase Vector Store. This can be one way to help reduce the amount of manual work in managing the issue backlog for busy teams with little effort. How it works This template contains 2 separate flows which run continuously via schedule triggers. The first populates our Supabase vector store with resolved issues within the last day. This helps keep our vector store up-to-date and relevant for the purpose of finding similar issues. It does this by pulling the latest resolved issues from JIRA and populating the Supabase vector store with carefully chosen metadata. This will come in handy later. The second flow watches for stale, unassigned issues for the purpose of auto-assigning them to a relevant team member. It does this by comparing the stale issue against our vector store of resolved issues with the goal of identifying which team member would have the best context regarding the issue. In a busy team, this may net a few team members as possible candidates to assign. Therefore, we can introduce additional logic to count each team member's assigned, in-progress issues. This is intended to not overload our busiest members. The team member with the least assigned issues is presumed to have the most capacity and therefore is assigned. A comment is left in the issue to notify the team member that they've been auto-assigned due to the age of the issue. How to use Modify the project and interval parameters to match those of your use-case and team members. Add additional criteria before assigning to a team member, e.g., department, as required. Customizing this workflow Not using JIRA or Supabase? The beauty of these AI templates is that these components are entirely interchangeable with competing services. Try Linear and Qdrant instead! Auto-assigning logic is simplified in this template. Expand criteria as required for your team and organization. E.g., it might be a good idea to pull in annual leave information from the HR system to prevent assigning to someone who is currently on holiday!

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process. What problem does this workflow solve? Manually screening resumes is time-consuming and prone to human error. This workflow automates the process, providing consistent and objective analysis of CVs against job descriptions. It helps filter out unsuitable candidates early, reducing workload and improving the overall efficiency of the recruitment process. What this workflow does This workflow automates the resume screening process using OpenAI for analysis. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. - Retrieve Resume: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox). - Extract Data: Extracts text data from PDF or DOC files for analysis. - Analyze with OpenAI: Sends the extracted data and job description to OpenAI to: - Generate a matching score. - Summarize candidate strengths and weaknesses. - Provide actionable insights into their suitability for the job. SetupPreparation - Create Accounts: - N8N: For workflow automation. - OpenAI: For AI-powered CV analysis. - Get CV Link: Upload CV files to Supabase storage or Dropbox to generate a direct link for processing. - Prepare Artifacts for OpenAI: - Define Metrics: Identify the metrics you want from the analysis (e.g., matching percentage, strengths, weaknesses). - Generate JSON Schema: Use OpenAI to structure responses, ensuring compatibility with your database. - Write a Prompt: Provide OpenAI with a clear and detailed prompt to ensure accurate analysis. N8N Scenario - Download File: Fetch the CV using its direct URL. - Extract Data: Use N8N’s PDF or text extraction nodes to retrieve text from the CV. - Send to OpenAI: - URL: POST to OpenAI’s API for analysis. - Parameters: - Include the extracted CV data and job description. - Use JSON Schema to structure the response. Summary This workflow provides a seamless, automated solution for CV screening, helping recruitment agencies and HR teams save time while maintaining consistency in candidate evaluation. It enables organizations to focus on the most suitable candidates, improving the overall hiring process.

This WooCommerce-integrated chatbot is designed to transform post-sales customer support by combining automation and artificial intelligence to deliver fast, secure, and personalized assistance. The chatbot retrieves real-time order information, including shipping details and tracking numbers, after verifying the customer's identity through a strict email-based authentication system. Beyond order management, the chatbot answers frequently asked questions about return policies, delivery times, and terms of service using RAG. If a request is too complex, the system seamlessly escalates it to a human operator via Telegram, guaranteeing no customer query goes unresolved. Key Features of the Chatbot: - Order Tracking: Retrieves real-time tracking information for WooCommerce orders, including carrier URLs and pickup dates. - Order Details Retrieval: Provides customers with past/current order information after strict email verification. - Policy & FAQ Assistance: Answers questions about shipping, returns, and store policies using a vectorized knowledge base (ToS tool). - Identity Verification: Ensures privacy by requiring exact email-order matches before sharing sensitive data. - Human Escalation: Transfers complex issues to human agents via Telegram when the AI cannot resolve them. - Context-Aware Conversations: Maintains dialogue context using memory buffers for seamless interactions. Who Benefits from This Chatbot? - E-commerce Stores: WooCommerce businesses needing 24/7 automated post-sales support. - Customer Support Teams: Reduces ticket volume by handling repetitive queries (tracking, policies). - SMBs: Small-to-medium businesses lacking resources for full-time support staff. - Customers: Shoppers who want instant, self-service access to order status and FAQs. How It Works: Customer Interaction: The workflow starts when a customer sends a chat message, triggering the AI agent. Identity Verification: The agent requests the order number and associated email, strictly verifying the match before proceeding. Order & Tracking Retrieval: Using WooCommerce API tools (get_order, get_tracking), it fetches order details and tracking information. Policy & Support: The ToS tool answers shipping and policy questions, while human_assistance escalates unresolved issues to a human agent via Telegram. Memory & Context: A buffer memory retains conversation context for coherent interactions. Set Up Steps: 1. Configure Qdrant Vector Store: Replace QDRANTURL and COLLECTION in the nodes to set up document storage. 2. Add Telegram Chat ID: Insert your Telegram CHAT_ID in the human_assistance node for escalations. 3. Integrate WooCommerce Tracking Plugin: Install the YITH WooCommerce Order Tracking plugin and update the HTTP request URL in the tracking node. 4. Test & Activate: Verify the workflow by testing order queries and ensuring proper email verification. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Vector Database as a Big Data Analysis Tool for AI Agents This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant 1. The first pipeline uploads an image dataset to Qdrant. 2. The second pipeline sets up cluster (class) centers and cluster (class) threshold scores needed for anomaly detection. Anomaly Detection Tool This is the third pipeline, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly in the uploaded dataset. KNN (k nearest neighbours) Classification 1. The first pipeline uploads an image dataset to Qdrant. 2. The second is the KNN classifier tool, which takes any image as input and classifies it based on the uploaded dataset in Qdrant. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket and recreate APIs/connections to Qdrant Cloud (you can use the Free Tier cluster), Voyage AI API, and Google Cloud Storage. Anomaly Detection Tool This tool can be used directly for anomalous images (crops) detection. It takes as input any image URL and returns a text message indicating whether the image is anomalous in relation to the crop dataset stored in Qdrant. An image URL is received via the Execute Workflow Trigger, which is used to generate embedding vectors using the Voyage AI Embeddings API. The returned vectors are used to query the Qdrant collection to determine if the given crop is known by comparing it to threshold scores of each image class (crop type). If the image scores lower than all thresholds, then the image is considered an anomaly for the dataset.

🧵 Generate Conversational Twitter/X Threads with GPT-4o AI (n8n Workflow) This workflow uses OpenAI (GPT-4o) and Twitter/X to automatically generate and publish engaging, conversational threads in response to a trigger (e.g., from a chatbot or form). 🚀 What Does It Do? - Listens for an incoming message (e.g., via webhook or another n8n input). - Uses GPT-4o to craft a narrative-style Twitter thread in a personal, friendly tone. - Publishes the first tweet, then automatically posts each following tweet as a reply—building a full thread. 🛠️ What Do You Need to Configure? Before using this template, make sure to set up the following credentials: - OpenAI: Add your OpenAI API key in the OpenAI Chat Model node. This is used to generate the thread content. - Twitter/X: Add your Twitter OAuth2 credentials to the First Tweet and Thread Reply nodes. This allows the workflow to publish tweets on your behalf. ✨ Who Is This For? This template is perfect for: - Content creators who want to share ideas regularly. - Personal brands looking to grow their presence. - Social media managers automating thread creation. 🔧 How to Customize It You can easily adjust the tone, structure, or length of the threads by modifying the system prompt in the OpenAI node. For example: - To create threads with humor, change the prompt to “Write in a witty and humorous tone.” - To tailor it for marketing, prompt it with “Write a persuasive product-focused Twitter thread.” You can also integrate this workflow with: - Telegram bots - Web forms (e.g., Typeform, Tally) - CRM tools or newsletter platforms 📋 Sample OutputPrompt sent to the workflow: “Tips for growing on Twitter in 2025” Generated thread: ++Tweet 1:++ Thinking of growing your presence on Twitter/X in 2024? Here's a thread with the most effective strategies that actually work 🧵 ++Reply 1:++ Engage, don’t broadcast. Twitter is a conversation platform. Reply to others, quote-tweet, and start discussions instead of just posting links. ++Reply 2:++ 2. Consistency beats virality. Tweeting regularly builds trust and visibility. You don't need to go viral — just show up.

Automatically draft emails with OpenAI GPT-3 when new email triggers occur. Streamline communication by generating content and creating drafts ready for review effortlessly.

Use GPT-3 to classify email responses in lemlist. And automate: - Slack alerts when a lead is interested - Task creation when a lead is OOO - Unsubscription of leads when they request it

Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMsHow the flow runs: Fill the form with the job position you're hunting for. Bright Data's scraper will scrape Indeed based on your requirements. The workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLM writes YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need: - Google Sheets with our template. - Bright Data dataset and API key. - OpenAI key for GPT‑4o mini (or any other LLM). - n8n with required nodes. Form fields To Fill: - Job Location – city or region. - Keyword – role or skills. - Country – two‑letter code. Setup steps: 1. Copy the sheet template link. 2. Import the JSON workflow. 3. Add your credentials in nodes. 4. Test the form manually. 5. Add a schedule if desired. Bright Data filter example:json [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips: - Choose "Last 24 hours" often. - Increase wait time for big snapshots. - Narrow keywords to save credits. Need help? Email me anytime: [email protected] YouTube: @YaronBeen LinkedIn: https://www.linkedin.com/in/yaronbeen/

🎥 AI Video Generator with HeyGen 🚀 Create AI-Powered Videos in n8n with HeyGen This workflow enables you to generate realistic AI videos using HeyGen, an advanced AI platform for video automation. Simply input your text, choose an AI avatar and voice, and let HeyGen generate a high-quality video for you – all within n8n! ✅ Ideal for: - Content creators & marketers 🏆 - Automating personalized video messages 📩 - AI-powered video tutorials & training materials 🎓 🔧 How It Works 1️⃣ Provide a text script – This will be spoken in the AI-generated video. 2️⃣ Select an Avatar & Voice – Choose from a variety of AI-generated avatars and voices. 3️⃣ Run the workflow – HeyGen processes your request and generates a video. 4️⃣ Download your video – Get the direct link to your AI-powered video! ⚡ Setup Instructions 1️⃣ Get Your HeyGen API Key Sign up for a HeyGen account. Go to your account settings and retrieve your API Key. 2️⃣ Configure n8n Credentials In n8n, create new credentials and select "Custom Auth" as the authentication type. In the Name provide: X-Api-Key And in the value paste your API key from Heygen. Update the 2 http node with the right credentials. 3️⃣ Select an AI Avatar & Voice Browse available avatars & voices in your HeyGen account. Copy the Avatar ID and Voice ID for your video. 4️⃣ Run the Workflow Enter your text, avatar ID, and voice ID. Execute the workflow – your video will be generated automatically! 🎯 Why Use This Workflow? ✔️ Fully Automated – No manual editing required! ✔️ Realistic AI Avatars – Choose from a variety of digital avatars. ✔️ Seamless Integration – Works directly within your n8n workflow. ✔️ Scalable & Fast – Generate multiple videos in minutes. Start automating AI-powered video creation today with n8n & HeyGen!

This workflow allows you to ask questions about data stored in a database using AI. To use it, you'll need an OpenAI API key (although you could also swap in a model from another service). Supported databases: - Postgres - MySQL - SQLite The workflow uses n8n's embedded chat, but you could also modify it to work with a chat service such as Slack, MS Teams, or WhatsApp. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow demonstrates the use of the HTTP Request node to upload binary files for form-data-multipart type. This example workflow updates the Twitter banner. The HTTP Request node fetches an image from Unsplash. Replace this node with any other node to fetch the image file. The HTTP Request1 node uploads the Twitter Profile Banner. The Twitter API requires OAuth 1.0 authentication. Follow the Twitter documentation to learn how to configure the authentication.

HR Job Posting and Evaluation with AI The HR Job Posting and Evaluation with AI workflow is designed to streamline and enhance recruitment for technical roles, such as Automation Specialists. By automating key stages in the hiring process, this workflow ensures a seamless experience for both candidates and HR teams. From collecting applications to evaluating candidates using AI and scheduling interviews, this workflow provides an end-to-end solution for recruitment challenges. Who is this for? This workflow is ideal for: - HR Professionals: Managing multiple job postings and candidates efficiently. - Recruitment Teams: Handling large volumes of applications for technical positions. - Hiring Managers: Ensuring structured and objective candidate evaluations. What problem does this workflow solve? - Time-Consuming Processes: Automates repetitive tasks like data entry, CV management, and scheduling. - Fair Candidate Evaluation: Leverages AI to provide objective insights based on resumes and job descriptions. - Streamlined Communication: Ensures timely and personalized candidate interactions, improving their experience. What this workflow does This workflow automates the following steps: - Form Submission: Collects candidate information via a structured application form. - Data Storage: Stores applicant details in Airtable for centralized tracking. - CV Management: Automatically uploads resumes to Google Drive for easy access and organization. - AI-Powered Candidate Evaluation: Scores candidates based on their resumes and job descriptions using OpenAI, providing actionable insights. - Interview Scheduling: Automates scheduling based on candidate and interviewer availability. - Communication: Sends customized emails to candidates for interview invitations and feedback. SetupPrerequisites To use this workflow, you’ll need: - n8n Account: To create and run the workflow. - Airtable Account: For managing applicant data. - Google Drive Account: For storing candidate CVs. - OpenAI API Key: For AI-powered candidate scoring. - SMTP Email Account: For sending candidate communications. Setup Process - Airtable Configuration: Create a base in Airtable with tables for Applicants and Job Positions. - Google Drive Setup: Create a folder for CV storage and ensure you have write permissions. - Integrate Airtable in n8n: Use the Airtable API key to connect Airtable to n8n. - Integrate Google Drive in n8n: Authorize Google Drive to enable CV storage automation. - OpenAI Integration: Add your OpenAI API key to n8n for candidate scoring. - Email Configuration: Set up your SMTP email account in n8n for sending notifications and invitations. How to customize this workflow Tailor the workflow to fit your unique recruitment needs: - Edit Job Descriptions: Adjust the form parameters to match the specific role and qualifications. - Refine AI Evaluation Criteria: Modify OpenAI prompts to reflect the skills and competencies for the desired position. - Personalize Email Templates: Update email content to match your organization’s tone and branding. - Add New Features: Incorporate additional steps like feedback collection or integration with other HR tools. Conclusion The HR Job Posting and Evaluation with AI workflow simplifies and automates the recruitment process, enabling HR teams to focus on engaging with candidates rather than handling administrative tasks. With its powerful integrations and customization options, this workflow helps organizations hire efficiently while improving the candidate experience.

Welcome to my Automated Image Metadata Tagging Workflow! DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-exif-data Community Node! An alternative for cloud usage is this workflow. This workflow automatically analyzes the image content with the help of AI and writes it directly back into the image file as keywords. This workflow has the following steps: - Google Drive trigger (scan for new files added in a specific folder) - Download the added image file - Analyze the content of the image - Merge Metadata and image file - Write the Keywords into the Metadata (dc:subject/keywords) and create a new image file - Update the original file in the Google Drive folder The following accesses are required for the workflow: - You have to install the n8n-nodes-exif-data Community Node - Google Drive: Documentation - AI API access (e.g. via OpenAI, Anthropic, Google, or Ollama) You can contact me via LinkedIn if you have any questions.

This guide will show you how to use a workflow as a reusable tool in n8n, such as integrating an AI Agent or other specialized processes into your workflows. By the end of this example, you'll have a simple, reusable workflow that can be easily plugged into larger projects, making your automations more efficient and scalable. With this approach, you can create reusable workflows like "Scrape a Page," "Search Brave," or "Generate an Image," which you can then call whenever needed. While n8n makes it easy to build these workflows from scratch, setting them up as reusable components saves time as your automations grow in complexity. Setup - Add the "Execute Workflow Trigger" node. - Add the node(s) to perform the desired tasks in the workflow. - Add a final "Set" or "Edit Fields" node at the end to ensure all external workflows return a consistent output format. Details In this example, the "Execute Workflow Trigger" expects input in the following JSON format: [ { "query": { "url": "<https://en.wikipedia.org/wiki/some_info>" } } ] Once your external workflow is ready, you can instruct the AI Agent to use this tool by connecting it to the external workflow. Set up the schema type to "Generate from JSON Example" using this structure: { "url": "URL_TO_GET" } Finally, ensure your external workflow includes a "Set" or "Edit Fields" node at the end to define the response format. This helps keep the outputs of your reusable workflows consistent and predictable.

Automatically fetch new articles via RSS, convert HTML to text, generate summaries with OpenAI, and send updates to Slack for instant notifications.

LangChain is a framework for building AI functionality that utilizes large language models. By leveraging the functionality of LangChain, you can write even more powerful workflows. This workflow shows how you can write LangChain code within n8n, including importing LangChain modules. The workflow itself produces a summary of a YouTube video when given the video's ID. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Open Deep Research - AI-Powered Autonomous Research Workflow This workflow automates deep research by leveraging AI-driven search queries, web scraping, content analysis, and structured reporting. It enables autonomous research with iterative refinement, allowing users to collect, analyze, and summarize high-quality information efficiently. How it works 🔹 User Input The user submits a research topic via a chat message. 🧠 AI Query Generation A Basic LLM generates up to four refined search queries to retrieve relevant information. 🔎 SERPAPI Google Search The workflow loops through each generated query and retrieves top search results using the SerpAPI API. 📄 Jina AI Web Scraping Extracts and summarizes webpage content from the URLs obtained via SerpAPI. 📊 AI-Powered Content Evaluation An AI Agent evaluates the relevance and credibility of the extracted content. 🔁 Iterative Search Refinement If the AI finds insufficient or low-quality information, it generates new search queries to improve results. 📜 Final Report Generation The AI compiles a structured markdown report, including sources with citations. Set Up Instructions 🚀 Estimated setup time: ~10-15 minutes ✅ Required API Keys: SerpAPI → For Google Search results Jina AI → For text extraction OpenRouter → For AI-driven query generation and summarization ⚙️ n8n Components Used: AI Agents with memory buffering for iterative research Loops to process multiple search queries efficiently HTTP Requests for direct API interactions with SerpAPI and Jina AI 📝 Recommended Enhancements: Add sticky notes in n8n to explain each step for new users Implement Google Drive or Notion Integration to save reports automatically 🎯 Ideal for: ✔️ Researchers & Analysts - Automate background research ✔️ Journalists - Quickly gather reliable sources ✔️ Developers - Learn how to integrate multiple AI APIs into n8n ✔️ Students - Speed up literature reviews 🔗 Completely free and open-source! 🚀