Back to Catalog

Extract LinkedIn Sales Navigator contacts to Google Sheets with auto pagination

Naveen ChoudharyNaveen Choudhary
685 views
2/3/2026
Official Page

Complete Template Description

Automate LinkedIn Sales Navigator contact extraction to Google Sheets

This workflow scrapes LinkedIn Sales Navigator search results and automatically saves contact details to Google Sheets with pagination support and rate limiting protection.

Who's it for

Sales teams, recruiters, and business development professionals who need to extract and organize LinkedIn contact data at scale without manual copy-pasting.

What it does

The workflow connects to a LinkedIn scraping API to fetch contact information from Sales Navigator search results. It handles pagination automatically, extracts contact details (name, title, company, location, profile URL), and appends them to a Google Sheet. Built-in rate limiting (30-60 second delays) prevents API blocks and mimics natural browsing behavior.

Requirements

  • Self-hosted n8n instance (this workflow will NOT work on n8n Cloud due to cookie requirements and third-party API usage)
  • LinkedIn Sales Navigator account
  • Google Sheets account
  • EditThisCookie browser extension
  • API access from the creator (1 month free trial available)

How to set up

Step 1: Get API Access Email the creator to request 1 month of free API access using the link in the workflow. You'll receive your API key within 24 hours.

Step 2: Configure API Authentication

  1. Click the "Scrape LinkedIn Contacts API" node
  2. Under Authentication, select "Header Auth"
  3. Create new credential with Name: x-api-key and your received API key as the Value
  4. Save the credential

Step 3: Extract LinkedIn Cookies

  1. Install the EditThisCookie extension
  2. Navigate to LinkedIn Sales Navigator
  3. Click the cookie icon in your browser toolbar
  4. Click "Export" and copy the cookie data
  5. Paste into the cookies field in the "Set Search Parameters" node

Step 4: Configure Your Search In the "Set Search Parameters" node, update:

  • cookies: Your exported LinkedIn cookies
  • url: Your LinkedIn Sales Navigator search URL
  • total_pages: Number of pages to scrape (default: 2, each page = ~25 contacts)

Step 5: Set Up Google Sheets

  1. Make a copy of the template Google Sheet (or create your own with matching column headers)
  2. In the "Save Contacts to Google Sheets" node, connect your Google Sheets account
  3. Select your destination spreadsheet and sheet name

Important Security Note: Keep your LinkedIn cookies private. Never share them with others or commit them to public repositories.

Customization options

  • Adjust total_pages to control how many contacts you scrape
  • Modify the delay in "Rate Limit Delay Between Requests" node (default: 30-60 seconds random) - do not lower this to avoid API blocks
  • Customize which contact fields to save in the Google Sheets column mapping
  • Change the search URL to target different prospect segments or filters

Extract LinkedIn Sales Navigator Contacts to Google Sheets with Auto-Pagination

This n8n workflow automates the process of extracting contacts from LinkedIn Sales Navigator and saving them into a Google Sheet, handling pagination automatically. This is particularly useful for sales professionals, recruiters, and marketers who need to export large lists of leads from Sales Navigator for further analysis or outreach.

What it does

This workflow performs the following steps:

  1. Manual Trigger: Initiates the workflow upon manual execution.
  2. HTTP Request (LinkedIn Sales Navigator API): Makes an API call to LinkedIn Sales Navigator to fetch contact data. This node is configured to handle pagination, iterating through multiple pages of results until all contacts are retrieved.
  3. Edit Fields (Set): Transforms and cleans the extracted data, mapping relevant fields from the Sales Navigator API response to a standardized format suitable for Google Sheets.
  4. If: Checks for a specific condition (e.g., if there are more pages to fetch) to control the pagination loop.
  5. Wait: Introduces a delay between API calls, which can be crucial for respecting API rate limits and preventing IP blocking.
  6. Code: Executes custom JavaScript code, likely to manage pagination logic, update page numbers, or process data in a specific way before writing to Google Sheets.
  7. Google Sheets: Appends the processed contact data as new rows to a specified Google Sheet.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • LinkedIn Sales Navigator Account: Access to LinkedIn Sales Navigator with the necessary permissions to retrieve contact data via API.
  • Google Account: A Google account with access to Google Sheets to store the extracted data.
  • n8n Credentials for Google Sheets: Configured Google Sheets OAuth2 credentials in n8n.
  • API Key/Authentication for LinkedIn Sales Navigator: Details on how to authenticate with the LinkedIn Sales Navigator API (this typically involves cookies, tokens, or other session management, which would be configured within the HTTP Request node).

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets credentials in n8n.
    • Configure the HTTP Request node with the appropriate LinkedIn Sales Navigator API endpoint and authentication details (e.g., headers, cookies, or query parameters). You will need to inspect how Sales Navigator makes its requests to replicate this.
  3. Specify Google Sheet: In the Google Sheets node, specify the Spreadsheet ID and Sheet Name where you want the contact data to be written.
  4. Adjust Pagination Logic: Review the Code and If nodes to understand and potentially adjust the pagination logic based on the specific Sales Navigator API response structure (e.g., how nextPageToken or offset are handled).
  5. Run the workflow: Execute the workflow manually using the "When clicking โ€˜Execute workflowโ€™" trigger. The workflow will then fetch contacts page by page and append them to your Google Sheet.

Related Templates

Automate Dutch Public Procurement Data Collection with TenderNed

TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch ๐Ÿ”— LinkedIn โ€“ Wessel Bulte

Wessel BulteBy Wessel Bulte
247

AI multi-agent executive team for entrepreneurs with Gemini, Perplexity and WhatsApp

This workflow is an AI-powered multi-agent system built for startup founders and small business owners who want to automate decision-making, accountability, research, and communication, all through WhatsApp. The โ€œvirtual executive team,โ€ is designed to help small teams to work smarter. This workflow sends you market analysis, market and sales tips, It can also monitor what your competitors are doing using perplexity (Research agent) and help you stay a head, or make better decisions. And when you feeling stuck with your start-up accountability director is creative enough to break the barrier ๐ŸŽฏ Core Features ๐Ÿง‘โ€๐Ÿ’ผ 1. President (Super Agent) Acts as the main controller that coordinates all sub-agents. Routes messages, assigns tasks, and ensures workflow synchronization between the AI Directors. ๐Ÿ“Š 2. Sales & Marketing Director Uses SerpAPI to search for market opportunities, leads, and trends. Suggests marketing campaigns, keywords, or outreach ideas. Can analyze current engagement metrics to adjust content strategy. ๐Ÿ•ต๏ธโ€โ™€๏ธ 3. Business Research Director Powered by Perplexity AI for competitive and market analysis. Monitors competitor moves, social media engagement, and product changes. Provides concise insights to help the founder adapt and stay ahead. โฐ 4. Accountability Director Keeps the founder and executive team on track. Sends motivational nudges, task reminders, and progress reports. Promotes consistency and discipline โ€” key traits for early-stage success. ๐Ÿ—“๏ธ 5. Executive Secretary Handles scheduling, email drafting, and reminders. Connects with Google Calendar, Gmail, and Sheets through OAuth. Automates follow-ups, meeting summaries, and notifications directly via WhatsApp. ๐Ÿ’ฌ WhatsApp as the Main Interface Interact naturally with your AI team through WhatsApp Business API. All responses, updates, and summaries are delivered to your chat. Ideal for founders who want to manage operations on the go. โš™๏ธ How It Works Trigger: The workflow starts from a WhatsApp Trigger node (via Meta Developer Account). Routing: The President agent analyzes the incoming message and determines which Director should handle it. Processing: Marketing or sales queries go to the Sales & Marketing Director. Research questions are handled by the Business Research Director. Accountability tasks are assigned to the Accountability Director. Scheduling or communication requests are managed by the Secretary. Collaboration: Each sub-agent returns results to the President, who summarizes and sends the reply back via WhatsApp. Memory: Context is maintained between sessions, ensuring personalized and coherent communication. ๐Ÿงฉ Integrations Required Gemini API โ€“ for general intelligence and task reasoning Supabase- for RAG and postgres persistent memory Perplexity API โ€“ for business and competitor analysis SerpAPI โ€“ for market research and opportunity scouting Google OAuth โ€“ to connect Sheets, Calendar, and Gmail WhatsApp Business API โ€“ for message triggers and responses ๐Ÿš€ Benefits Acts like a team of tireless employees available 24/7. Saves time by automating research, reminders, and communication. Enhances accountability and strategy consistency for founders. Keeps operations centralized in a simple WhatsApp interface. ๐Ÿงฐ Setup Steps Create API credentials for: WhatsApp (via Meta Developer Account) Gemini, Perplexity, and SerpAPI Google OAuth (Sheets, Calendar, Gmail) Create a supabase account at supabase Add the credentials in the corresponding n8n nodes. Customize the system prompts for each Director based on your startupโ€™s needs. Activate and start interacting with your virtual executive team on WhatsApp. Use Case You are a small organisation or start-up that can not afford hiring; marketing department, research department and secretar office, then this workflow is for you ๐Ÿ’ก Need Customization? Want to tailor it for your startup or integrate with CRM tools like Notion or HubSpot? You can easily extend the workflow or contact the creator for personalized support. Consider adjusting the system prompt to suite your business

ShadrackBy Shadrack
331

๐ŸŽ“ How to transform unstructured email data into structured format with AI agent

This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From โ€“ senderโ€™s email address To โ€“ recipientโ€™s email address Subject โ€“ email subject line Summary โ€“ short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits โœ… Full Automation: Eliminates manual reading and data entry from incoming emails. โœ… Multi-Source Integration: Handles data from different email providers seamlessly. โœ… AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. โœ… Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. โœ… Time Efficiency: Processes emails in real time, improving productivity and response speed. *โœ… Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.

DavideBy Davide
1616