Hi everyone! 👋 I’ve been exploring Lovable and decided to create a user-friendly interface for my AI agents on N8N. After just 3 hours of work, here’s what I built (see screenshots): 🔹 Chat with N8N and get formatted responses with images. 🔹 Switch between multiple agents in the same conversation. If all agents are yours, they can share chat history for better context. • Example: I asked my YouTube agent to find channels and then had my DB agent save the data in a database (pic. 1). 🔹 Add private agents via webhook (pic. 2). • You’ll need to slightly adjust your agent flow to standardize inputs from different triggers (pic. 3). 🔹 Make agents public so others can use them (pic. 4). • Public agents can’t access others’ data in chat. 🚀 Would you like to see how I built this? Or maybe use it for your own AI agents? Let me know

UPDATE: I made a video guide and template based on this case that you can find in Classroom - link to guide 2025 is shaping up to be the year of the solo entrepreneur. Why? Because with AI automations and agents, you can operate an entire company by yourself. Let me show you how. Last week, I worked on a real case that’s a perfect example of this. A client needed to hire for multiple positions for their clients but didn’t want to hire an HR team. Instead, they had: 1. A list of jobs with specific requirements. 2. A Gmail account receiving hundreds of CVs for these positions. After just a few days, they had over 500 emails sitting in their inbox. Manually processing all of them would take a full week of non-stop work. So we built an AI automation system that handled everything in one day. Here’s how we did it: 1️⃣ Email Processing: Every 5 minutes, our automation fetched new emails. It downloaded attached CVs in PDF format. If the document was in a different format (like .doc or image-based PDFs), we sent a message asking the candidate to resend their CV as a single, readable PDF. For better parsing, we could have added LlamaParse, but the standard n8n node worked for this case. 2️⃣ Filtering Non-CVs: If an email wasn’t a CV, the system marked it as read and left it in the inbox. No manual intervention needed. 3️⃣ Job Matching: Each CV was automatically analyzed to find the best matching job. If no match was found, the email was marked as read, saving time on irrelevant applications. 4️⃣ Scoring Candidates: Once a CV was matched to a job, we applied a custom scoring system tailored to the job requirements. Different positions required different scoring logic, so we built a separate AI-powered scenario for this step. 5️⃣ Personalized Feedback: • If a candidate didn’t meet the required score, the system generated a personalized rejection email, including constructive feedback and highlighting their strengths.

⚠️ Link to guide and templates 🔍 Real Business Case: As an advertising agency, you need to sift through countless audition tapes to find the perfect talent for your chocolate ad. Manual reviews can consume hours, leaving you with little time for creative strategy. ⚡️ The Solution: By integrating N8N with Google Drive and Airtable, you can set up an automation that evaluates audition videos based on specific criteria like smiles and emotional delivery. Once the video is uploaded, the AI processes it and provides you with a detailed rating and comments, streamlining your selection process. Why It Works: - Improves talent selection accuracy by leveraging AI to assess key video attributes - Significantly reduces the time spent on reviewing audition tapes - Enhances your creative team's efficiency, allowing more focus on impactful campaigns 🗣️ Question: Have you faced similar challenges in video assessments? What automation solutions have you considered?

⚠️ Link to guide and templates 👋 Hey everyone! Are you ready to transform how you interact with your data? In today's video, we're diving into how to build AI agents that communicate with your Airtable data! 🚀 Here’s what you’ll learn: - 📊 How to fetch and analyze data from Airtable, whether for tracking sales, generating graphs, or creating maps based on addresses! - 🤖 Set up a chat agent that utilizes OpenAI to process requests and respond with insights derived from your Airtable data. - 🗺️ Learn to generate maps using extract coordinates from your data to enhance your analyses. What’s Included: 1. Setting up and integrating a chat agent with Airtable to interact with your records seamlessly. 2. A demonstration on using the agent to perform mathematical operations, generate graphs and more. 3. Options for complex queries and manipulation of data using various tools available in Airtable. This tutorial is not only about building an AI but enhancing your workflows for better efficiency and insight.

Hi everyone! Just wanted to share my very first Database AI Agent with you all! I built it using the provided template. You’ll find the n8n Cloud workflow file attached below. Have fun exploring, and happy creating!