AI Agent: Find the Right LinkedIn Profiles in Seconds

Published 12 days ago

Created by

Badr

Template description

Automate LinkedIn Prospecting with AI Agent and Get Results Straight to Google Sheets!

A practical AI-powered workflow that helps you find relevant LinkedIn profiles using natural language queries and saves the results automatically to Google Sheets.

🎯 What It Actually Does

Accepts natural language requests (e.g., "Find marketing managers in Paris")
Uses AI to extract search parameters:
- Job titles
- Industry
- Location
Searches for matching LinkedIn profiles
Saves profile URLs and titles to Google Sheets

🛠️ Technical Components

AI Query Processor
- Converts natural language to structured search parameters
- Handles various input formats and languages
- Maintains context for accurate parameter extraction
Search Engine Integration
- Uses Google Custom Search API
- Handles pagination for multiple results
- Filters for actual LinkedIn profile URLs
Data Storage
- Automatically saves to Google Sheets
- Stores profile titles and URLs
- Easy to access and export

📋 Prerequisites

OpenAI API key
Google Custom Search API credentials
Google Sheets access

💡 Adaptation Possibilities

This template can be modified to scrape other websites by:

Changing the search domain in the HTTP Request node
Adjusting the URL filter in the Code node
Modifying the column structure in Google Sheets
Updating the AI prompt for different parameter extraction

Example adaptations:

Twitter profile finder
GitHub repository search
Company website finder
Professional blog discovery

🔧 Setup Instructions

API Setup
- Configure Google Custom Search API
- Set up OpenAI API
- Prepare Google Sheets
Workflow Configuration
- Import both workflows
- Connect your API credentials
- Set up your Google Sheet
- Test with a sample query

📝 Usage Example

Input: "Find software developers in London working in fintech"
Output: A Google Sheet containing:

LinkedIn profile URLs of software developers
Profile titles/headlines
Ready for your review and outreach

#AIAgent #WebScraping #Automation #n8n #Workflow #LinkedInProspecting

Share Template

More AI workflow templates

AI agent chat

This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.

n8n Team

Scrape and summarize webpages with AI

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.

n8n Team

AI agent that can scrape webpages

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.

Eduard

+10

Building Your First WhatsApp Chatbot

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

Jimleuk

Telegram AI Chatbot

The workflow starts by listening for messages from Telegram users. The message is then processed, and based on its content, different actions are taken. If it's a regular chat message, the workflow generates a response using the OpenAI API and sends it back to the user. If it's a command to create an image, the workflow generates an image using the OpenAI API and sends the image to the user. If the command is unsupported, an error message is sent. Throughout the workflow, there are additional nodes for displaying notes and simulating typing actions.