Agents & Automations

Instantly search and create Airtable records using iterated data when a task finishes in Browse AI.

What does the workflow do? This workflow is designed to generate high-quality short videos, primarily using GPT-4o-mini (unofficial), Midjourney (unofficial), and Kling (unofficial) APIs from PiAPI and Creatomate API. It is mainly for content creators, social media bloggers, and short-form video creators. Through this short video workflow, users can quickly validate their creative ideas and focus more on enhancing the quality of their video concepts. Who is the workflow for? - Social Media Influencers: Produce content videos based on inspiration efficiently. - Vloggers: Generate vlogs based on inspiration. - Educational Creators: Explain specific topics via animated short videos or demonstrate a specific imagined scenario to students for enhanced educational impact. - Advertising Agencies: Generate short videos based on specific products. - AI Tool Developers: Automatically generate product demo videos. Step-by-step Instructions 1. Fill in the X-API-key of the PiAPI account in the Basic Params node. 2. Fill in the scenario of the image and video prompt. 3. Set a video template on Creatomate and make an API call in the final node with core and processing modules provided in Creatomate. Before full video generation, you can first use basic assets in Creatomate for a prototype demo, then integrate with n8n after verifying the expected results. 4. Fill in your Creatomate account settings following the image guideline. 5. Click Test Workflow and wait for a generation (within 10-20 minutes). In this workflow, we've established a basic structure for image-to-video generation with subtitle integration. You can further enhance it by adding music nodes using either PiAPI's audio models or your preferred music solution. All video elements will ultimately be composited through Creatomate. For best practice, please refer to PiAPI's official API documentation or Creatomate's API documentation to comprehend more use cases. Use CaseParams Settings - style: a children’s book cover, ages 6-10. --s 500 --sref 4028286908 --niji 6 - character: A gentle girl and a fluffy rabbit explore a sunlit forest together, playing by a sparkling stream. - situational_keywords: Butterflies flutter around them as golden sunlight filters through green leaves. Warm and peaceful atmosphere.

This n8n template monitors active support issues in Linear.app to track the mood of their ongoing conversation between reporter and assignee using Sentiment Analysis. When sentiment dips into the negative, a notification is sent via Slack to alert the team. How it works A scheduled trigger is used to fetch recently updated issues in Linear using the GraphQL node. Each issue's comments thread is passed into a simple Information Extractor node to identify the overall sentiment. The resulting sentiment analysis combined with some issue details are uploaded to Airtable for review. When the template is re-run at a later date, each issue is re-analysed for sentiment. Each issue's new sentiment state is saved to the Airtable whilst its previous state is moved to the "previous sentiment" column. An Airtable trigger is used to watch for recently updated rows. Each matching Airtable row is filtered to check if it has a previous non-negative state but now has a negative state in its current sentiment. The results are sent via notification to a team Slack channel for priority. The Airtable also serves to give a snapshot of sentiment across support tickets for a given period. It's possible to use this to assess the daily operations. How to use Modify the GraphQL filter to fetch issues to a relevant issue type, team, or person. Update the Slack channel to ensure messages are sent to the correct location or persons. Customizing the workflow - Add more granular levels of sentiment to reduce the number of alerts. - Explore different types of sentiment based on issue types and customer types. This may help prioritize alerts and response. - Run across teams or categories of issues to get an overview of sentiment across the support organization.

Automatically engage with Instagram media by generating comments using ChatGPT. Watch media, list comments, and create new ones effortlessly.

This n8n template lets you summarize individual team member activity on MS Teams for the past week and generates a report. For remote teams, chat is a crucial communication tool to ensure work gets done. However, with so many conversations happening at once and in multiple threads, ideas, information, and decisions usually live in the moment and get lost just as quickly - and all together forgotten by the weekend! Using this template, this doesn't have to be the case. Have AI crawl through last week's activity, summarize all messages and replies, and generate a casual and snappy report to bring the team back into focus for the current week. A project manager's dream!How it works A scheduled trigger is set to run every Monday at 6 AM to gather all team channel messages within the last week. Messages are grouped by user. AI analyses the raw messages and replies to pull out interesting observations and highlights. This is referred to as the individual reports. All individual reports are then combined and summarized together into what becomes the team weekly report. This allows understanding of group and similar activities. Finally, the team weekly report is posted back to the channel. The timing is important as it should be the first message of the week and ready for the team to glance over coffee. How to use Ideally works best per project and where most of the communications happen on a single channel. Avoid combining channels and instead duplicate this workflow for more channels. You may need to filter for specific team members if you want specific team updates. Customize the report to suit your organization, team, or the channel. You may prefer to be more formal if clients or external stakeholders are also present. Requirements - MS Teams for chat platform - OpenAI for LLM Customizing this workflow If the teams channel is busy enough already, consider posting the final report to email. Pull in project metrics to include in your report. As extra context, it may be interesting to tie the messages to production performance. Use an AI Agent to query for knowledge base or tickets relevant to the messages. This may be useful for attaching links or references to add context.

Automate message creation and audio generation by integrating Claude and ChatGPT. Send personalized messages with audio content effortlessly to your app via HTTP.

Who this is for? The LinkedIn Company Story Generator is an automated workflow that extracts company profile data from LinkedIn using Bright Data's web scraping infrastructure, then transforms that data into a professionally written narrative or story using a language model (e.g., OpenAI, Gemini). The final output is sent via webhook notification, making it easy to publish, review, or further automate. This workflow is tailored for: - Marketing Professionals: Seeking to generate compelling company narratives for campaigns. - Sales Teams: Aiming to understand potential clients through summarized company insights. - Content Creators: Looking to craft stories or articles based on company data. - Recruiters: Interested in obtaining concise overviews of companies for talent acquisition strategies. What problem is this workflow solving? Manually gathering and summarizing company information from LinkedIn can be time-consuming and inconsistent. This workflow automates the process, ensuring: - Efficiency: Quick extraction and summarization of company data. - Consistency: Standardized summaries for uniformity across use cases. - Scalability: Ability to process multiple companies without additional manual effort. What this workflow does The workflow performs the following steps: 1. Input Acquisition: Receives a company's name or LinkedIn URL as input. 2. Data Extraction: Utilizes Bright Data to scrape the company's LinkedIn profile. 3. Information Parsing: Processes the extracted HTML content to retrieve relevant company details. 4. Summarization: Employs AI Google Gemini to generate a concise company story. 5. Output Delivery: Sends the summarized content to a specified webhook or email address. How to customize this workflow to your needs - Input Variations: Modify the Set LinkedIn URL node to accept a different company LinkedIn URL. - Data Points: Adjust the HTML Data Extractor Node to retrieve additional details like employee count, industry, or headquarters location. - Summarization Style: Customize the AI prompt to generate summaries in different tones or formats (e.g., formal, casual, bullet points). - Output Destinations: Configure the output node to send summaries to various platforms, such as Slack, CRM systems, or databases.

About The LinkedIn Profile Discovery Automation Are you tired of manually searching for LinkedIn profiles or paying expensive data providers for often outdated information? If you spend countless hours trying to find accurate LinkedIn URLs for your prospects or candidates, this automation will change your workflow forever. Just give this workflow the information you have about a contact, and it will automatically augment it with a LinkedIn profile. How to find a LinkedIn Profile Link In this guide, you'll learn how to automate LinkedIn profile link discovery using Airtop's built-in node in n8n. Using this automation, you'll have a fully automated workflow that saves you hours of manual searching while providing accurate, validated LinkedIn URLs. What You'll Need - A free Airtop API key - A Google Workspace account. If you have a Gmail account, you’re all set - Estimated setup time: 10 minutes Understanding the Process This automation leverages the power of intelligent search algorithms combined with LinkedIn validation to ensure accuracy. Here's how it works: - Takes your input data (name, company, etc.) and constructs intelligent search queries - Utilizes Google search to identify potential LinkedIn profile URLs - Validates the discovered URLs directly against LinkedIn to ensure accuracy - Returns confirmed, accurate LinkedIn profile URLs Setting Up Your Automation Getting started with this automation is straightforward: 1. Prepare Your Google Sheet - Create a new Google Sheet with columns for input data (name, company, domain, etc.) - Add columns for the output LinkedIn URL and validation status (see this example) 2. Configure the Automation - Connect your Google Workspace account to n8n if you haven't already - Add your Airtop API credentials - (Optionally) Configure your Airtop Profile and sign-in to LinkedIn in order to validate profile URLs 3. Run Your First Test - Add a few test entries to your Google Sheet - Run the workflow - Check the results in your output columns Customization Options While the default setup uses Google Sheets, this automation is highly flexible: - Webhook Integration: Perfect for integrating with tools like Clay, Instantly, or your custom applications - Alternatives: Replace Google Sheets with Airtable, Notion, or any other tools you already use for more robust database capabilities - Custom Output Formatting: Modify the output structure to match your existing systems - Batch Processing: Configure for bulk processing of multiple profiles Real-World Applications This automation has the potential to transform how organizations handle profile enrichment. Recruiting Firm Success Story With this automation, a recruiting firm could save hundreds of dollars a month in data enrichment fees, achieve better accuracy, and eliminate subscription costs. They would also be able to process thousands of profiles weekly with near-perfect accuracy. Sales Team Integration A B2B sales team could integrate this automation with their CRM, automatically enriching new leads with validated LinkedIn profiles and saving their SDRs hours per week on manual research. Best Practices To maximize the accuracy of your results: - Always include company information (domain or company name) with your search queries - Use full names rather than nicknames or initials when possible - Consider including location data for more accurate results with common names - Implement rate limiting to respect LinkedIn's usage guidelines - Keep your input data clean and standardized for best results - Use the integrated proxy to navigate more effectively through Google and LinkedIn What's Next? Now that you've automated LinkedIn profile discovery, consider exploring related automations: - Automated lead scoring based on LinkedIn profile data - Email finder automation using validated LinkedIn profiles - Integration with your CRM for automated contact enrichment

Analyze your competitors using ChatGPT, conducting a thorough SWOT assessment and easily sending valuable insights into an Airtable base for strategic decision-making.

Every time a new image containing text is posted to your Telegram channel, Make will detect the text with Google Cloud Vision (OCR), translate it into the language you want using Google Translate, and send the translated text as a new message to a selected Telegram channel.

Overview This template is designed for Amazon sellers and advertisers who want to automate their campaign performance analysis and bidding strategy. It solves the common challenge of manually reviewing Sponsored Products reports and guessing how to adjust keywords, placements, and budgets. By combining Amazon Advertising reports with OpenAI's GPT-4o, this workflow delivers real-time, personalized optimization instructions — automatically. Features 📥 Automatically downloads Sponsored Products reports from Google Drive 🧠 Uses AI to analyze campaign, keyword, placement, targeting, and budget performance 📊 Supports both .csv and .xlsx report formats 🔁 Handles multiple ASINs and scales easily across ad accounts 📧 Sends structured optimization recommendations to your inbox via Gmail 🗂 Built-in logic to normalize filenames and correctly map reports 🧹 Includes error handling and formatting cleanup for AI-ready input Requirements To use this workflow, you’ll need: - An Amazon Ads account with access to Sponsored Products reports - A Google Drive folder where Amazon Ads reports are delivered (manually or via Gmail automation) - A Gmail account (for sending summaries) - An OpenAI API key with access to GPT-4o - Optional: a developer account for the Amazon Ads API to fully automate report generation in the future Setup Instructions 📂 Connect your Amazon Ads reports folder in the Google Drive node 🔐 Add your credentials to the OpenAI and Gmail nodes 📝 Schedule five reports in the Amazon Ads Console: - Search Term Report → Detailed - Targeting Report → Detailed - Campaign Report → Summary - Placement Report → Summary - Budget Report → Summary Use “Last 30 Days”, “Daily”, and .xlsx or .csv format 🔁 (Optional) Automate report ingestion using Gmail + Drive workflows 🧪 Test with one account, then replicate across additional ad accounts as needed ⏱️ Setup time: 15–30 minutes 📌 All field-specific guidance is included in workflow notes

This workflow reads PDF textual content and sends the text to OpenAI. Attachments of interest will then be uploaded to a specified Google Drive folder. For example, you may wish to send invoices received from an email to an inbox folder in Google Drive for later processing. This workflow has been designed to easily change the search term to match your needs. How it works: - Triggers off on the On email received node. - Iterates over the attachments in the email. - Uses the OpenAI node to filter out the attachments that do not match the search term set in the Configure node. You could match on various PDF files (i.e. invoice, receipt, or contract). - If the PDF attachment matches the search term, the workflow uses the Google Drive node to upload the PDF attachment to a specific Google Drive folder.

Who is this for? This workflow is built for marketers, sales teams, agencies, virtual assistants, and anyone who regularly researches or contacts local businesses. It's ideal for building lead lists, tracking competitors, or creating location-specific outreach campaigns. What problem is this workflow solving? Instead of manually searching Google Maps and copying business info into spreadsheets, this automation pulls structured business data (e.g. restaurants, gyms, service providers) and logs it directly into Google Sheets. It saves hours of work and ensures cleaner, more usable data. What this workflow does The workflow takes a Google Maps search query (like "best restaurants in New York") and sends it to Dumpling AI. It returns a list of places including their name, address, website, phone number, rating, and more. Each result is split into a row and automatically added to a Google Sheet. SetupDumpling AI - Sign up at Dumpling AI - Generate your API key - In the HTTP Request node, select Header Auth and paste your key in the Authorization field Google Sheets - Create a sheet with tab name Leads - Add the following column headers to row 1: Name, Address, Phone number, Website, Rating, Price Level, Type, Booking Link, Position - Connect your Google Sheets account and link this sheet in the node Customize the Query - In the HTTP node, replace the query string (e.g., "best+restaurants+in+New+York") with your own search term Run It - Use the manual trigger to test - Optionally swap in a Schedule or Webhook node to run it automatically How to customize this workflow to your needs - Change the search query to target different cities or business types - Use filters to only save leads with a minimum rating or price level - Add GPT to summarize listings or qualify leads - Swap Google Sheets for Airtable or a CRM system for deeper integration

This n8n workflow is designed to automate the aggregation, processing, and reporting of community statistics related to n8n creators and workflows. Its primary purpose is to generate insightful reports that highlight top contributors, popular workflows, and key trends within the n8n ecosystem. How It WorksData Retrieval: The workflow fetches JSON data files from a GitHub repository containing statistics about creators and workflows. It uses HTTP requests to access these files dynamically based on pre-defined global variables. Data Processing: The data is parsed into separate streams for creators and workflows. It processes the data to identify key metrics such as unique weekly and monthly inserters/visitors. Ranking and Filtering: The workflow sorts creators by their weekly inserts and workflows by their popularity. It selects the top 10 creators and top 50 workflows for detailed analysis. Report Generation: Using AI tools like GPT-4 or Google Gemini, the workflow generates a Markdown report summarizing trends, contributors, and workflow statistics. The report includes tables with detailed metrics (e.g., unique visitors, inserters) and insights into why certain workflows are popular. Distribution: The report is saved locally or uploaded to Google Drive. It can also be shared via email or Telegram for broader accessibility. Automation: A schedule trigger ensures the workflow runs daily or as needed, keeping the reports up-to-date. Why It's ImportantCommunity Insights: This workflow provides actionable insights into the n8n community by identifying impactful contributors and popular workflows. This fosters collaboration and innovation within the ecosystem. Time Efficiency: By automating data collection, processing, and reporting, it saves significant time and effort for community managers or administrators. Recognition of Contributors: Highlighting top creators encourages engagement and recognizes individuals driving value in the community. Trend Analysis: The workflow helps uncover patterns in usage, enabling better decision-making for platform improvements or feature prioritization. Scalability: With its modular design, this workflow can be easily adapted to include additional metrics or integrate with other tools.

Enhance Query Resolution with the Knowledge Base Tool! Our KB Tool - Confluence KB is crafted to seamlessly integrate into the IT Ops AI SlackBot Workflow, enhancing the IT support process by enabling sophisticated search and response capabilities via Slack. Workflow Functionality: - Receive Queries: Directly accepts user queries from the main workflow, initiating a dynamic search process. - AI-Powered Query Transformation: Utilizes OpenAI's models or local AI to refine user queries into searchable keywords that are most likely to retrieve relevant information from the Knowledge Base. - Confluence Integration: Executes searches within Confluence using the refined keywords to find the most applicable articles and information. - Deliver Accurate Responses: Gathers essential details from the Confluence results, including article titles, links, and summaries, preparing them to be sent back to the parent workflow for final user response. Quick Setup Guide: - Ensure correct configurations are set for OpenAI and Confluence API integrations. - Customize query transformation logic as per your specific Knowledge Base structure to improve search accuracy. Need Help? Dive into our Documentation or get support from the Community Forum! Deploy this tool to provide precise and informative responses, significantly boosting the efficiency and reliability of your IT support workflow.

Protect your content from intellectual theft by instantly identifying plagiarism with a single click in Airtable, leveraging the power of Eden AI to maintain digital integrity.

Create personalized outreach videos at scale using tools like Pitchlane and Clipio, along with ElevenLabs and Airtable. This approach allows for efficient and effective communication, helping to engage potential clients and enhance outreach efforts. Utilizing these tools can significantly improve the personalization of videos, making them more appealing to the target audience.

CallForge - AI Gong Transcript PreProcessor Transform your Gong.io call transcripts into structured, enriched, and AI-ready data for better sales insights and analytics. Who is This For? This workflow is designed for: ✅ Sales teams looking to automate call transcript formatting. ✅ Revenue operations (RevOps) professionals optimizing AI-driven insights. ✅ Businesses using Gong.io that need structured, enriched call transcripts for better decision-making. What Problem Does This Workflow Solve? Manually processing raw Gong call transcripts is inefficient and often lacks essential context for AI-driven insights. With CallForge, you can: ✔ Extract and format Gong call transcripts for structured AI processing. ✔ Enhance metadata using sales data from Salesforce. ✔ Classify speakers as internal (sales team) or external (customers). ✔ Identify external companies by filtering out free email domains (e.g., Gmail, Yahoo). ✔ Enrich customer profiles using PeopleDataLabs to identify company details and locations. ✔ Prepare transcripts for AI models by structuring conversations and removing unnecessary noise. What This Workflow Does 1. Retrieves Gong Call Data Calls the Gong API to extract call metadata, speaker interactions, and collaboration details. Fetches call transcripts for AI processing. 2. Processes and Cleans Transcripts Converts call transcripts into structured, speaker-based dialogues. Assigns each speaker as either Internal (Sales Team) or External (Customer). 3. Extracts Company Information Retrieves Salesforce data to match customers with existing sales opportunities. Filters out free email domains to determine the customer’s actual company domain. Calls the PeopleDataLabs API to retrieve additional company data and location details. 4. Merges and Enriches Data Combines Gong metadata, Salesforce customer details, and insights. Ensures all necessary data is available for AI-driven sales insights. 5. Final Formatting for AI Processing Merges all call transcript data into a single structured format for AI analysis. Extracts the final cleaned, enriched dataset for further AI-powered insights. How to Set Up This Workflow 1. Connect Your APIs 🔹 Gong API Access – Set up your Gong API credentials in n8n. 🔹 Salesforce Setup – Ensure API access if you want customer enrichment. 🔹 PeopleDataLabs API – Required to retrieve company and location details based on email domains. 🔹 Webhook Integration – Modify the webhook call to push enriched call data to an internal system. How to Customize This Workflow 💡 Modify Data Sources – Connect different CRMs (e.g., HubSpot, Zoho) instead of Salesforce. 💡 Expand AI Analysis – Add another AI model (e.g., OpenAI GPT, Claude) for advanced conversation insights. 💡 Change Speaker Classification Rules – Adjust internal vs. external speaker logic to match your team’s structure. 💡 Filter Specific Customers – Modify the free email filtering logic to better fit your company’s needs. Why Use CallForge? 🚀 Automate Gong call transcript processing to save time. 📊 Improve AI accuracy with enriched, structured data. 🛠 Enhance sales strategy by extracting actionable insights from calls. Start optimizing your Gong transcript analysis today!

Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database. How It Works This workflow creates a question-answering system based on Paul Graham essays. It has two main steps: 1. Data Collection & Processing: - Scrapes Paul Graham essays. - Extracts text content. - Loads them into a Milvus vector store. 2. Chat Interaction: - Provides a question-answering interface using the stored vector embeddings. - Utilizes OpenAI embeddings for semantic search. Set Up Steps - Set up a Milvus server following the official guide. - Create a collection named "my_collection." - Run the workflow to scrape and load Paul Graham essays. - Start chatting with the QA system. The workflow handles the entire process from fetching essays, extracting content, generating embeddings via OpenAI, storing vectors in Milvus, and providing retrieval for question answering.

This workflow allows you to easily evaluate and compare the outputs of two language models (LLMs) before choosing one for production. In the chat interface, both model outputs are shown side by side. Their responses are also logged into a Google Sheet, where they can be evaluated manually or automatically using a more advanced model. Use Case You're developing an AI agent, and since LLMs are non-deterministic, you want to determine which one performs best for your specific use case. This template is designed to help you compare them effectively. How It Works The user sends a message to the chat interface. The input is duplicated and sent to two different LLMs. Each model processes the same prompt independently, using its own memory context. Their answers, along with the user input and previous context, are logged to Google Sheets. You can review, compare, and evaluate the model outputs manually (or automate it later). In the chat, both responses are also shown one after the other for direct comparison. How To Use It Copy this Google Sheets template (File > Make a Copy). Set up your System Prompt and Tools in the AI Agent node to suit your use case. Start chatting! Each message will trigger both models and log their responses to the spreadsheet. Note: This version is set up for two models. If you want to compare more, you’ll need to extend the workflow logic and update the sheet. About Models You can use OpenRouter or Vertex AI to test models across providers. If you're using a node for a specific provider, like OpenAI, you can compare different models from that provider (e.g., gpt-4.1 vs gpt-4.1-mini). Evaluation in Google Sheets This is ideal for teams, allowing non-technical stakeholders (not just data scientists) to evaluate responses based on real-world needs. Advanced users can automate this evaluation using a more capable model (like o3 from OpenAI), but note that this will increase token usage and cost. Token Considerations Since each input is processed by two different models, the workflow will consume more tokens overall. Keep an eye on usage, especially if working with longer prompts or running multiple evaluations, as this can impact cost.

Who is this for? This n8n workflow is designed for Customer Success Managers (CSM), marketers, sales teams, and data administrators who need to automate the process of uploading and processing CSV data in HubSpot. It is ideal for users who regularly import contact lists, update CRM records, or sync data between systems. What problem does this workflow solve? Manually uploading and processing CSV files in HubSpot can be time-consuming and error-prone, especially when dealing with large datasets or complex field mappings. This workflow automates data validation, indexing, and field mapping, reducing manual effort and ensuring data consistency. What this workflow does: - Generating the list of the fields directly from HubSpot API - Indexing: Organizes and prepares CSV data for HubSpot import. - Data Processing: Cleanses and transforms data. - Field Mapping: Maps CSV columns to HubSpot fields dynamically. - Import Execution: Uploads processed data to HubSpot. SetupPrerequisites: - HubSpot API credentials (Private App token). - Google Sheets credentials. - n8n instance (cloud or self-hosted). Installation: 1. Import the workflow JSON into n8n. 2. Configure the HubSpot nodes and the Google Sheets nodes with your API credentials. 3. Upload your CSV file to the workflow via the form. Customization - Data Filters: Add nodes to filter/transform data (e.g., deduplication, formatting). - Fields Filters: according to your needs. - Add a HubSpot Object: To the list in "Define array of objects" node. - Workflow Triggers: Set up triggers (e.g., schedule, webhook) for automated runs.