Workflow Templates

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

What Does This Flow Do? This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. Use Cases You can use it in presentations or requesting for chart generation from a software with HTTP requests. Automated report generation (e.g., daily sales charts). Visualizing data fetched from APIs or databases. Simple monitoring dashboards. Adding charts to internal tools or notifications. How it Works Trigger: The workflow starts manually when you click 'Test workflow'. Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains: reportTitle: A title (not used in the chart generation in this example, but useful for context). labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]). salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]). Generate Chart: The QuickChart node is configured to: Create a line chart. Dynamically read labels from the jsonData.labels array (Labels Mode: From Array). Use the jsonData.salesData array as the input data (Note: This configuration places data in the top-level 'Data' field. For more complex charts with multiple datasets or specific dataset options, configure datasets under 'Dataset Options' instead). The node outputs the generated chart image as binary data in a field named data. Upload to Google Drive: The Google Drive node (Google Drive: Upload File): Takes the binary data (data) from the QuickChart node. Uploads the image to your specified Google Drive folder. Dynamically names the file based on its extension (e.g., chart.png). Setup Steps Import: Import this template into your n8n instance. Configure Google Drive Credentials: Select the Google Drive: Upload File node. You MUST configure your own Google Drive credentials. Click on the 'Credentials' dropdown and either select existing credentials or create new ones by following the authentication prompts. (Optional) Customize Google Drive Folder: In the Google Drive: Upload File node, you can change the Drive ID and Folder ID to specify exactly where the chart should be uploaded. Activate: Activate the workflow if you want it to run automatically based on a different trigger. How to Use & Customize Change Input Data:** Modify the labels and salesData arrays within the Edit Fields: Set JSON data to test node to use your own data. Ensure the number of labels matches the number of data points. Use Real Data Sources:** Replace the Edit Fields: Set JSON data to test node with nodes that fetch data from real sources like: HTTP Request (APIs) Postgres / MongoDB nodes (Databases) Google Sheets node Ensure the output data from your source node is formatted similarly (providing labels and salesData arrays). You might need another Set node to structure the data correctly before the QuickChart node. Change Chart Type:** In the QuickChart node, modify the Chart Type parameter (e.g., change from line to bar, pie, doughnut, etc.). Customize Chart Appearance:** Explore the Chart Options parameter within the QuickChart node to add titles, change colors, modify axes, etc., using QuickChart's standard JSON configuration options. Use Datasets (Recommended for Complex Charts):** For multiple lines/bars or more control, configure datasets explicitly in the QuickChart node: Remove the expression from the top-level Data field. Go to Dataset Options -> Add option -> Add dataset. Set the Data field within the dataset using an expression like {{ $json.jsonData.salesData }}. You can add multiple datasets this way. Change Output Destination:** Replace the Google Drive: Upload File node with other nodes to handle the chart image differently: Write Binary File: Save the chart to the local filesystem where n8n is running. Slack / Discord / Telegram: Send the chart to messaging platforms. Move Binary Data: Convert the image to Base64 to embed in HTML or return via webhook response. Nodes Used Manual Trigger Set QuickChart Google Drive Tags: (Suggestions for tags field) QuickChart, Chart, Visualization, Line Chart, Google Drive, Reporting, Automation

Who is this for? Sales professionals looking to build lead lists from target company domains Business development teams conducting outreach campaigns Marketers building contact databases for account-based marketing Recruiters searching for potential candidates at specific companies Anyone needing to transform a list of company domains into actionable contact information What problem is this workflow solving? Finding business email addresses for outreach is a time-consuming process. The Apollo API doesn't provide a direct way to extract email contacts from domains in a single call. This workflow bridges that gap by: Automating the two-step process required by Apollo's API Processing multiple domains in batches without manual intervention Extracting, enriching, and storing contact information in a structured format Eliminating hours of manual data entry and API interaction What this workflow does This workflow creates an automated pipeline between Google Sheets and Apollo's API to: Pull a list of target domains from a Google Sheet Submit each domain to Apollo's search API to find associated people Loop through each person found and enrich their profile data Extract key information: name, title, email address, and LinkedIn URL Write the enriched contact information back to a results sheet Process the next domain automatically until all are complete Setup Prerequisites: An n8n instance (cloud or self-hosted) Apollo.io account with API access Google account with access to Google Sheets Google Sheets Setup: Create a new Google Sheet with two tabs: Tab 1: "Target Domains" with a column named "Domain To Enrich" Tab 2: "Results" with columns: Company, First Name, Last Name, Title, Email, LinkedIn n8n Setup: Import the workflow JSON into your n8n instance Set up Google Sheets credentials in n8n Update the Google Sheets document ID in both Google Sheets nodes Add your Apollo API key to both HTTP Request nodes Review and adjust API rate limits if needed Testing: Add a few test domains to your "Target Domains" sheet Run the workflow manually to verify it's working correctly Check the "Results" sheet to confirm data is being properly populated How to customize this workflow to your needs Adding More Contact Fields: Modify the "Clean Up" node to extract additional fields from the Apollo API response Add corresponding columns to your "Results" sheet Update the "Results To Results Sheet" node mapping to include the new fields Filtering Results: Add a Filter node after "Clean Up" to include only contacts with specific roles Create conditions based on title, seniority, or other fields returned by Apollo Automating Workflow Execution: Replace the manual trigger with a Schedule Trigger to run daily/weekly Add a Filter node to process only domains with "Not Processed" status Update the status field in Google Sheets after processing Additional Notes This workflow respects Apollo's API rate limits by processing one contact at a time The Apollo API may not return contact information for all domains or all employees Consider legal and privacy implications when collecting and storing contact information Made with ❤️ by Hueston

This workflow enriches new accounts in Pipedrive using Datagma API by adding data about ICP (ideal customer profile). Instead of Pipedrive, you can use any other CRM. In this example, ideal buyers are heads of sales/business development. Prerequisites Pipedrive account and Pipedrive credentials How it works Pipedrive trigger node starts the workflow when a new company is created. HTTP Request node queries data from Datagma. Pipedrive node updates Pipedrive contact with new data from Datagma. The Item Lists node simplifies returned data from Datagma that contain lists (arrays), enabling you to easily modify the structure for further processing without the need to use Function nodes and write custom JavaScript. IF node identifies if the lead corresponds ICP. HTTP Request node searches for emails in Datagma. Set node prepares data for further merging. Merge node combines data from multiple streams. Pipedrive node adds a new person in Pipedrive.

Receive realtime call-event data from telli Purpose and Problem Solved This template automates the process of receiving and acting upon real-time call event data from telli, an AI-powered voice agent platform. It solves the challenge of manually updating CRM records and initiating follow-up actions based on call outcomes. By leveraging webhooks and n8n's powerful workflow capabilities, this template enables businesses to instantly update their Airtable CRM and trigger appropriate follow-up actions, enhancing efficiency and responsiveness in customer interactions. Prerequisites An active telli account with API access and webhook capabilities An Airtable base set up as your CRM n8n instance (cloud or self-hosted) Airtable Specifications Create an Airtable base with the following table and fields: Table: Contacts Fields: Name (Single line text) Phone (Phone number) Email (Email) Appointment_Booked (Checkbox) Interest (Single select: High, Medium, Low) Last_Call_Date (Date) Notes (Long text) Step-by-Step Setup Instructions Webhook Configuration in telli: Log into your telli dashboard Navigate to the webhook settings Set the endpoint URL to your n8n Webhook node URL Select the "call_ended" event to trigger the webhook n8n Workflow Setup: Create a new workflow in n8n Add a Webhook node as the trigger Configure the Webhook node to receive POST requests Parse Webhook Data: Add a Set node to extract relevant information from the webhook payload Map fields such as call_outcome, appointment_booked, and interest Decision Logic: Add a Switch node to create different paths based on the call outcome Create branches for scenarios like "Appointment Booked", "Interested", and "Not Interested" Airtable Integration: Add Airtable nodes for each outcome to update the Contacts table Configure the nodes to update fields like Appointment_Booked, Interest, and Last_Call_Date Follow-up Actions: For "Interested" but not booked outcomes, add an Email node to trigger a follow-up email campaign For "Appointment Booked", add a node to create a calendar event or task Testing and Activation: Use the n8n testing feature to simulate webhook calls and verify each path Once satisfied, activate the workflow Example Workflow Webhook receives a "call_ended" event from telli Set node extracts call_outcome: appointment_booked = true, interest = true Switch node directs to the "Appointment Booked" path Airtable node updates the contact record: Set Appointment_Booked to true Set Interest to "High" Update Last_Call_Date Calendar node creates an appointment for the booked slot Example Payload Below is an example of the payload you might receive from telli when a call ends: { "event": "call_ended", "call": { "call_id": "b4a05730-2abc-4eb0-8066-2e4d23b53ba9", "attempt": 1, "from_number": "+17755719467", "to_number": "+16506794960", "external_contact_id": "external-123", "contact_id": "6bd1e7e0-6d00-4c0b-ad5b-daa72457a27d", "agent_id": "d8931604-92ad-45cf-9071-d9cd2afbad0c", "triggered_at": 1731956924302, "started_at": 1731956932264, "booked_slot_for": "2025-02-24T15:30:00Z", "ended_at": 1731957002078, "call_length_min": 2, "call_status": "COMPLETED", "transcript": "Agent: Hello...", "transcriptObject": [ { "role": "agent", "content": "Hello..." } ], "call_analysis": { "summary": { "value": true, "details": "A call between an agent and a customer talking about buying an ice cream machine" }, "appointment": { "value": true, "details": "2025-02-18T15:30:00Z" }, "interest": { "value": true, "details": "The customer is interested in buying an ice cream machine" } } } } In this example, you can see that the call resulted in a booked appointment and showed customer interest. Your n8n workflow would process this data, updating the Airtable CRM and triggering any necessary follow-up actions. By implementing this template, businesses can automate their post-call processes, ensuring timely follow-ups and accurate CRM updates. This real-time integration between telli's AI voice agents and your Airtable CRM streamlines operations, improves customer engagement, and increases the efficiency of your sales and support teams.

🤖 AI Agent Web Search using SearchApi & LLM Who is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does Connects an AI agent node to SearchApi.io as an integrated search tool. Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. Ensures responses include clearly cited sources for validation and further exploration. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk prompt, and hit Install. It should now appear as a node when you search for it. API Configuration: Set up your SearchApi.io credentials in n8n. Add your preferred LLM provider credentials (e.g., OpenRouter API). Input Requirements: Provide the YouTube video ID (e.g., wBuULAoJxok). Connect LLM Integration: Configure the summarization chain with your chosen model and parameters for text splitting. How to customize this workflow to your needs Integrate additional nodes to structure or store search results (e.g., saving to databases, Notion, Google Sheets). Extend chatbot capabilities to integrate with messaging platforms (Slack, Discord) or email notifications. Adjust search parameters and filters within the AI agent node to tailor information retrieval. Example Usage Input**: User asks, "What are the latest developments in AI regulation?" Output**: AI retrieves, summarizes, and cites recent, authoritative articles and news sources from the web.

Edit an existing image with OpenAI ImageGen1 via API Request Transform your creative pipeline by letting n8n call OpenAI ImageGen1’s edit image endpoint, automatically replacing or augmenting parts of any image you supply and returning a brand-new version in seconds. Designers, marketers, and product teams can eliminate repetitive manual edits and test more variations, faster. Who is this for? Content creators who need quick, on-brand image tweaks Marketers running A/B visual tests at scale Developers exploring the new ImageGen1 API inside low-code automations Use case / problem solved Opening design software to mask, fill, or swap objects is slow and error-prone. This workflow feeds an input image plus a prompt to OpenAI ImageGen1, receives the edited output, and passes it on to any service you like—perfect for bulk-editing product shots, social visuals, or UI mocks. What this workflow does Read or receive the source image (Webhook → Binary Data). Call OpenAI ImageGen1 with an HTTP Request node, sending the image and edit prompt. Parse the JSON response to capture the returned image URL. Download & hand off the edited file (e.g., upload to S3, post to Slack, or store in Drive). Setup Add your OpenAI API key in the API KEY node. Follow the notes on the workflow for more information. (Optional) Point the final node to your preferred storage or chat tool. > 📝 A sticky note in the workflow summarizes these steps and links to the OpenAI documentation. How to customize this workflow Trigger alternatives**: Replace the Chat with Google Drive, Airtable, etc. Chained edits**: Loop the output back for successive prompts. Conditional flows**: Add an If node to branch actions by image size or category. With renamed nodes, color-coded sticky notes, and a concise setup guide, you’ll be editing images via OpenAI ImageGen1 in under five minutes—no code, maximum creativity.

This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose). It automatically retrieves your Google Calendar events and feeds them to the model. The workflow uses Google’s Gemini AI for response generation. How it works The workflow uses a Scheduled Trigger Node as the main trigger. The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data. The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash). The Google Gemini Chat Model generates a summary and informative response based on today’s meetings. ++Setup Steps++ Google Cloud Project and Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API Key: Obtain a Google AI API key from Google AI Studio. Credentials in n8n: Configure credentials in your n8n environment for: Google Gemini (PaLM) API (using your Google AI API key). Import the Workflow: Import this workflow into your n8n instance. Configure the Workflow: Update both Slack and Gemini nodes with your credentials.

Community Node Disclaimer: This workflow uses KlickTipp community nodes. How It Works This workflow automates personalized WhatsApp message template delivery triggered by events in KlickTipp or by messages sent to the Whatsapp Business account. When a contact triggers an Outbound, the workflow uses a pre-approved WhatsApp message template to send dynamic, real-time messages through the WhatsApp Business Cloud API. When receiving messages it checks whether a cancellation should be processed or if a auto-response is sent. This setup is ideal for time-sensitive campaigns such as: Birthday greetings Discount or promo notifications Follow-ups on product or service interest Key Features KlickTipp Trigger Starts the workflow when a specific outbound is triggered Typical use case: subcriber receives activation Tag and triggers an Outbound which sends a webhook call to trigger WhatsApp messaging. WhatsApp Business Cloud - Message Trigger Listens to messages from the contact and processes answers with answering auto-responder or by tagging the contact in KlickTipp. WhatsApp Business Cloud - Sending Template Messages Sends WhatsApp message templates using a pre-approved template. Template placeholders are filled with data from KlickTipp custom fields. Setup Instructions Set up the KlickTipp and Whatsapp nodes in your n8n instance. Authenticate your WhatsApp and KlickTipp accounts. Create the necessary custom fields to match the data structure Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup. | Field Label | Field Type | |--------------------------------|-------------| | Whatsapp_Produkt/Dienstleistung | Single line | | | Whatsapp_Name/Unternehmen | Single line | | Whatsapp_Link_Endung | Single line | Testing & Deployment Use a real test contact with all required fields filled. Trigger the Outbound in KlickTipp using the activation tag and answer with a message to the template. Run the scenario once in n8n to verify successful delivery of the whatsapp message template to your test contact as well as the reception of the auto-responder and the subscription and tagging in KlickTipp to stop further messages. Campaign Expansion Ideas Connect campaign to process keywords like "STOP" from WhatsApp messages Pair WhatsApp with welcome email series for onboarding. Use tags like product_interest_X for precise segmentation. A/B test templates with different CTA formats or timings. Monitor CTRs via dynamic URLs in WhatsApp templates. Benefits Multi-channel engagement:** Adds WhatsApp to your marketing toolkit. Dynamic content:** Personalizes messages using contact data. KlickTipp campaign control** Whatsapp contacts can for example signal with messages like "STOP" to receive the according Tag in KlickTipp in order to start/end automations. > 💡 Pro Tip: Customize the domain link ending per campaign or product line. This allows targeted redirects, e.g., meinshop.de/ProduktA or `mein Ressources: Send WhatsApp Templates with KlickTipp Use KlickTipp Community Node in n8n Automate Workflows: KlickTipp Integration in n8n

This workflow provides a robust solution for automatically backing up all your n8n workflows to a designated GitHub repository on a daily basis. By leveraging the n8n API and GitHub API, it ensures your workflows are version-controlled and securely stored, safeguarding against data loss and facilitating disaster recovery. How it works The automation follows these key steps: Scheduled trigger: The workflow is initiated automatically every day at a pre-configured time. List existing backups: It first connects to your GitHub repository to retrieve a list of already backed-up workflow files. This helps in determining whether a workflow's backup file needs to be created or updated. Retrieve n8n workflows: The workflow then fetches all current workflows directly from your n8n instance using the n8n REST API. Process and prepare: Each retrieved workflow is individually processed. Its data is converted into JSON format. This JSON content is then encoded to base64, a format suitable for GitHub API file operations. Commit to GitHub: For each n8n workflow: A standardized filename is generated (e.g., workflow-name-tag.json). The workflow checks if a file with this name already exists in the GitHub repository (based on the list fetched in step 2). If the file exists: It updates the existing file with the latest version of the workflow. If it's a new workflow (file doesn't exist): A new file is created in the repository. Each commit is timestamped for clarity. This process ensures that you always have an up-to-date version of all your n8n workflows stored securely in your GitHub version control system, providing peace of mind and a reliable backup history. Pre-requisites Before you can use this template, please ensure you have the following: An active n8n instance (self-hosted or cloud). A GitHub account. A GitHub repository created where you want to store the workflow backups. A GitHub Personal Access Token with repo scope (or fine-grained token with read/write access to the specific backup repository). This token will be used for GitHub API authentication. n8n API credentials (API key) for your n8n instance. Set up steps Setting up this workflow should take approximately 10-15 minutes if you have your credentials ready. Import the template: Import this workflow into your n8n instance. Configure n8n API credentials: Locate the "Retrieve workflows" node. In the "Credentials" section for "n8n API", create new credentials (or select existing ones). Enter your n8n instance URL and your n8n API Key (you can create your n8n api key in the settings of your n8n instance) Configure GitHub credentials: Locate the "List files from repo" node (and subsequently "Update file" / "Upload file" nodes which will use the same credential). In the "Credentials" section for "GitHub API", create new credentials. Select OAuth2/Personal Access Token authentication method. Enter the GitHub Personal Access Token you generated as per the pre-requisites. Specify repository details: In the "List files from repo", "Update file", and "Upload file" GitHub nodes: Set the Owner: Your GitHub username or organization name. Set the Repository: The name of your GitHub repository dedicated to backups. Set the Branch (e.g., main or master) where backups should be stored. (Optional) Specify a Path within the repository if you want backups in a specific folder (e.g., n8n_backups/). Leave blank to store in the root. Adjust schedule (Optional): Select the "Schedule Trigger" node. Modify the trigger interval (e.g., change the time of day or frequency) as needed. By default, it's set for a daily run. Activate the workflow: Save and activate the workflow. Explanation of nodes Here's a detailed breakdown of each node used in this workflow: Schedule trigger** Type: n8n-nodes-base.scheduleTrigger Purpose: This node automatically starts the workflow based on a defined schedule (e.g., daily at midnight). List files from repo** Type: n8n-nodes-base.github Purpose: Connects to your specified GitHub repository and lists all files, primarily to check for existing workflow backups. Aggregate** Type: n8n-nodes-base.aggregate Purpose: Consolidates the list of file names obtained from the "List files from repo" node into a single item for easier lookup later in the "Check if file exists" node. Retrieve workflows** Type: n8n-nodes-base.n8n Purpose: Uses the n8n API to fetch a list of all workflows currently present in your n8n instance. Json file** Type: n8n-nodes-base.convertToFile Purpose: Takes the data of each workflow (retrieved by the "Retrieve workflows" node) and converts it into a structured JSON file format. To base64** Type: n8n-nodes-base.extractFromFile Purpose: Converts the binary content of the JSON file (from the "Json file" node) into a base64 encoded string. This encoding is required by the GitHub API for file content. Commit date & file name** Type: n8n-nodes-base.set Purpose: Prepares metadata for the GitHub commit. It generates: commitDate: The current date and time for the commit message. fileName: A standardized file name for the workflow backup (e.g., my-workflow-vps-backups.json), typically using the workflow's name and its first tag. Check if file exists** Type: n8n-nodes-base.if Purpose: A conditional node. It checks if the fileName (generated by "Commit date & file name") is present in the list of files aggregated by the "Aggregate" node. This determines if the workflow backup already exists in GitHub. Update file** Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does exist, this node updates that existing file in your GitHub repository with the latest workflow content (base64 encoded) and a commit message. Upload file** Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does not exist, this node creates and uploads a new file to your GitHub repository with the workflow content and a commit message. Customization Here are a few ways you can customize this template to better fit your needs: Backup path**: In the GitHub nodes ("List files from repo", "Update file", "Upload file"), you can specify a Path parameter to store backups in a specific folder within your repository (e.g., workflows/ or daily_backups/). Filename convention**: Modify the "Commit date & file name" node (specifically the expression for fileName) to change how backup files are named. For example, you might want to include the workflow ID or a different date format. Commit messages**: Customize the commit messages in the "Update file" and "Upload file" GitHub nodes to include more specific information if needed. Error handling**: Consider adding error handling branches (e.g., using the "Error Trigger" node or checking for node execution failures) to notify you if a backup fails for any reason. Filtering workflows**: If you only want to back up specific workflows (e.g., those with a particular tag or name pattern), you can add a "Filter" node after "Retrieve workflows" to include only the desired workflows in the backup process. Backup frequency**: Adjust the "Schedule Trigger" node to change how often the backup runs (e.g., hourly, weekly, or on specific days). Template was created in n8n v1.92.2

Define Your ICP from Customer LinkedIn Profiles Use Case This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it generates a clear ICP definition and scoring methodology for future targeting. What This Automation Does This automation analyzes LinkedIn profiles of your existing customers and produces: A structured ICP definition A scoring model to evaluate future prospects A Google Boolean search string to find similar prospects Input: LinkedIn profile URLs of existing high-fit customers (e.g., https://www.linkedin.com/in/amirashkenazi/) Output: A Google Doc containing the ICP analysis and scoring methodology How It Works Trigger: Waits for a chat message containing one or more LinkedIn profile URLs. AI Agent: Parses and processes the URLs. Airtop Data Enrichment: Uses Airtop to extract structured information from each LinkedIn profile (e.g., job title, company, experience, skills). Memory: Maintains state between inputs for consistent analysis. LLM Analysis: Uses Claude 3.7 Sonnet to synthesize enriched data into a meaningful ICP. Google Docs: Automatically creates a new doc with a timestamped title and appends the ICP definition. Setup Requirements Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool Airtop API credentials. Get it free here If you choose to activate saving the profiles in Google Docs you will need OAuth2 credentials (or just copy the ICP definition from the chat) Next Steps Use the ICP for Scoring**: Feed new LinkedIn profiles through the same Airtop enrichment and use the scoring function to evaluate fit. Automate Target Discovery**: Plug the Boolean search output into LinkedIn, Google, or People Data Labs for ICP-matching lead generation. Refine Continuously**: Repeat the workflow as your customer base grows or segments evolve. Read more about how to Define ICP from Customer Examples

Monitor X for Relevant Posts Use Case This automation monitors X (formerly Twitter) search pages in real time and extracts high-signal posts that match your categories of interest. It’s ideal for community engagement, lead discovery, thought leadership tracking, or competitive analysis. What This Automation Does Given a search URL and a list of categories, it: Logs into X using Airtop Opens the specified search URL Scrolls through the results Extracts up to 10 valid, English-language posts Filters and classifies each post by category (or marks as [NA] if unrelated) Returns the structured results as JSON Input parameters: airtop_profile** — An Airtop browser profile authenticated on X x_url** — X search URL (e.g., https://x.com/search?q=ai agents&f=live) relevant_categories** — Text-based list of categories to classify posts (e.g., "Web automation use cases", "Thought leadership") Output: A JSON array of posts, each with: writer time text url category How It Works Trigger: This workflow is triggered by another workflow (e.g., a community engagement pipeline). Input Setup: Accepts the Airtop profile, search URL, and categories to use for classification. Session: Starts a browser session using the Airtop profile. Window Navigation: Opens the provided X search URL. Extraction: Scrapes up to 10 posts with /status/ in the URL and text in English. Classification: Each post is labeled with a category if relevant, or [NA] otherwise. Filtering: Discards [NA] posts. Output: Returns the list of classified posts. Setup Requirements Airtop profile with an active X login. Airtop API key connected in n8n. List of category definitions to guide post classification (used in prompt). Next Steps Feed into Engagement Workflows**: Pass the results to workflows that reply, retweet, or track posts. Use in Slack Alerts**: Push classified posts into Slack channels for review and reaction. Customize Classifier**: Refine the categorization logic to include sentiment or company mentions. Read more about Monitoring X for Relevant Posts