Agents & Automations

Overview This n8n workflow automates the creation of 9:16 aspect ratio images optimized for short-form video content and thumbnails. It integrates multiple tools to retrieve content, generate scripts, and create AI-generated imagery. Key Features - Trigger Workflow Manually The workflow starts when triggered manually in n8n. - Retrieve Brand Guidelines Fetch brand elements like style, tone, and guidelines from Airtable. - SEO Keywords and Blog Post Retrieval Retrieves blog posts and associated SEO keywords from Airtable to form the basis of image content. - Content Preparation Uses GPT-4 to prepare a 4-scene script and thumbnail prompts for short-form videos. - AI Image Generation Uses Leonardo.ai API to generate: - Thumbnail Images - Scene-specific Images (9:16 Aspect Ratio) - Airtable Asset Management Generated assets (images) are saved back into Airtable with metadata like URLs and file sizes. Tools and Integrations - n8n: Workflow automation platform. - OpenAI: Generates scripts and prompts (GPT-4O-MINI). - Leonardo.ai: AI tool for improving prompts and generating high-quality images. - Airtable: Used as a data source for brand guidelines, blog posts, and to store generated assets. Workflow Steps 1. Manual Trigger Initiate the workflow. 2. Retrieve Brand and SEO Guidelines Fetch essential brand elements like tone, style, and keywords. 3. Filter and Fetch Blog Content Search for blog posts relevant to selected SEO keywords. 4. Script Preparation Use GPT-4 to generate a script with image prompts for scenes and thumbnails. 5. Image Generation Call Leonardo.ai to create: - Scene Images in 9:16 Aspect Ratio. - A Thumbnail Image with an improved prompt. 6. Store Assets Save generated assets (images) to Airtable for future use. Workflow Structure - Nodes Breakdown: - Manual Trigger: Start the workflow. - Get Brand Guidelines: Pull brand-related information (style, tagline, tone, etc.) from Airtable. - Set Guidelines: Prepare fetched data. - Get SEO Keywords: Retrieve keywords to filter relevant content. - Keyword Filter: Filter results for specified keywords (e.g., "AI Automation"). - Script Prep: Generate 4-scene scripts and prompts with GPT-4. - Leo - Improve Prompt: Improve image prompts for clarity and detail. - Leo - Generate Image: Create AI-generated images for scenes and thumbnails. - Wait Nodes: Ensures Leonardo image generation is complete. - Add Asset Info: Store the generated images back into Airtable with metadata. API Credentials Required Ensure the following credentials are configured in n8n: - OpenAI API Key - Leonardo.ai API Key - Airtable API TokenOutput - Generated Images: High-quality AI-generated images with a 9:16 aspect ratio. - Saved Metadata: Asset details (URLs, sizes, types) stored in Airtable. Usage - Import this workflow into n8n. - Set up your Airtable API, Leonardo.ai API, and OpenAI API credentials. - Run the workflow manually. - Monitor image generation and check the Airtable output for results. Tags OpenAI RunwayML Leonardo Airtable Video AutomationAuthor AlexK1919 AI-Native Workflow Architect

This n8n workflow template is designed to integrate a DeepSeek AI agent with Telegram, incorporating long-term memory capabilities for personalized and context-aware responses. Here's a detailed breakdown: Core FeaturesTelegram Integration Uses a webhook to receive messages from Telegram users. Validates user identity and message content before processing. AI-Powered Responses Employs DeepSeek's AI models for conversational interactions. Includes memory capabilities to personalize responses based on past interactions. Error Handling Sends an error message if the input cannot be processed. Model Options - DeepSeek-V3 Chat: Handles general conversational tasks. - DeepSeek-R1 Reasoning: Provides advanced reasoning capabilities for complex queries. - Memory Buffer Window: Maintains session context for ongoing conversations. Quick SetupTelegram Webhook Configuration Set up a webhook using the Telegram Bot API:https://api.telegram.org/bot{my_bot_token}/setWebhook?url={url_to_send_updates_to} Replace {my_bot_token} with your bot's token and {url_to_send_updates_to} with your n8n webhook URL. Verify the webhook setup using:https://api.telegram.org/bot{my_bot_token}/getWebhookInfoDeepSeek API Configuration Base URL: https://api.deepseek.com Obtain your API key from the DeepSeek platform. Implementation DetailsUser Validation The workflow validates the user's first name, last name, and ID using data from incoming Telegram messages. Only authorized users proceed to the next steps. Message Routing Routes messages based on their type (text, audio, or image) using a switch node. Ensures appropriate handling for each message format. AI Agent Interaction Processes text input using DeepSeek-V3 or DeepSeek-R1 models. Customizable system prompts define the AI's behavior and rules, ensuring user-centric and context-aware responses. Memory Management Retrieves long-term memories stored in Google Docs to enhance personalization. Saves new memories based on user interactions, ensuring continuity across sessions.

Periodically search and filter Google Sheets data, aggregate text, generate responses with ChatGPT, and send replies via Telegram for streamlined communication.

This n8n template builds a meeting assistant that compiles timely reminders of upcoming meetings filled with email history and recent LinkedIn activity of other people on the invite. This is then discreetly sent via WhatsApp, ensuring the user is always prepared, informed, and ready to impress! How it works A scheduled trigger fires hourly to check for upcoming personal meetings. When found, the invite is analyzed by an AI agent to pull email and LinkedIn details of the other invitees. Two subworkflows are then triggered for each invitee to (1) search for last email correspondence with them and (2) scrape their LinkedIn profile + recent activity for social updates. Using both available sources, another AI agent is used to summarize this information and generate a short meeting prep message for the user. The notification is finally sent to the user's WhatsApp, allowing them ample time to review. How to use There are a lot of moving parts in this template, so in its current form, it's best to use this for personal rather than team calendars. The LinkedIn scraping method used in this workflow requires you to paste in your LinkedIn cookies from your browser, which essentially lets n8n impersonate you. You can retrieve this from the dev console or ask someone technical for help! Note: It may be wise to switch to other LinkedIn scraping approaches that do not impersonate your own account for production. Customizing this workflow Try adding information sources that are relevant to you and your invitees, such as company search or other social media sites. Create an on-demand version that doesn't rely on the scheduled trigger. Sometimes you want to prepare for meetings hours or days in advance, where this could help immensely.

Periodically search for and update Airtable records using 2 ChatGPT completions.Streamline data management with AI-driven insights and Airtable integration.

The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works:Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp. Set up steps: 1. Create Qdrant Collection: - Update the QDRANTURL and COLLECTION variables in the workflow. - Use the Create collection HTTP request node to initialize the collection in Qdrant. 2. Vectorize Documents: - Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. - Use the Embeddings OpenAI node to generate embeddings for the downloaded files. - Store the vectorized documents in Qdrant using the Qdrant Vector Store node. 3. Configure Webhooks: - Ensure both Verify and Respond webhooks have the same URL. - Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. 4. Set Up AI Agent: - Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. - Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. 5. Test Workflow: - Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. - Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.

LinkedIn Enrichment & Ice Breaker Generator For SDRs, growth marketers, and founders looking to scale personalized outreach. This workflow enriches LinkedIn profile data using Bright Data and generates AI-powered ice breakers using Claude (Anthropic). It automates research and messaging to help you connect smarter and faster — without manual effort. 🧩 How It Works This workflow combines Google Sheets, Bright Data, and Claude (Anthropic) to fully automate your outreach research: Trigger Manually trigger the workflow or run it on a schedule (via Manual Trigger or Schedule Trigger). Read Input Sheet Fetches rows from a Google Sheet. Each row must contain at least a Linkedin_URL_Person and row_number. Prepare Input Formats each row for Bright Data’s API using Set and SplitInBatches nodes. Enrich Profile (Bright Data API) Sends LinkedIn URLs to Bright Data’s Dataset API via HTTP Request. Waits for snapshot to be ready using polling logic with Wait, If, and Snapshot Progress nodes. Once ready, retrieves the enriched profile data including: - Name - City - Current company - About section - Recent postsUpdate Sheet with Profile Data Writes the retrieved enrichment data into the corresponding row in Google Sheets (via row_number). Generate Ice Breaker (Claude AI) Sends enriched profile content to Claude (Anthropic) using a custom prompt. Focuses on recent posts for crafting relevant, respectful, 1–4-line ice breakers. Update Sheet with Ice Breaker Writes the generated ice breaker to the Ice Breaker 1 column in the original row. ✅ Requirements To use this workflow, you must have the following: - Google Sheets - A Google account - A Google Sheet with at least one sheet/tab containing: - Column: Linkedin_URL_Person - Column: row_number (used for mapping input and output rows) - Bright Data - A Bright Data account with access to the Dataset API - An active dataset that accepts LinkedIn URLs - API key with Dataset API access - Anthropic Claude - An Anthropic API key (for Claude 3.5 Haiku or other Claude models) - n8n Environment - Access to HTTP Request, Set, Wait, SplitInBatches, If, and Google Sheets nodes - Access to Claude integration (via LangChain nodes: @n8n/n8n-nodes-langchain) - Credential manager properly configured with: - Google Sheets OAuth2 credentials - Bright Data API key - Anthropic API key⚙️ Setup InstructionsStep 1: Copy the Google Sheets Template Fill the Linkedin_URL_Person column with LinkedIn profile URLs you want to enrich. Do not modify headers or add filters to the sheet. Leave other columns (name, city, about, posts, ice breaker) blank — the workflow fills them. Step 2: Connect Your Accounts in n8n - Google Sheets: Create a credential under Google Sheets OAuth2 API - Bright Data: Add your API key as a credential under HTTP Request (Authorization header) - Anthropic: Create a credential for Anthropic API with your Claude keyStep 3: Import and Configure the Workflow Import the workflow into your n8n instance. In each Google Sheets node: - Select the copied Google Sheet - Select the correct tab (usually input or Sheet1) In the HTTP Request node to Bright Data: - Paste your Bright Data dataset ID In the Claude prompt node: - Optionally adjust the tone and length of the ice breaker promptStep 4: Run the Workflow Test it using the Manual Trigger node. For daily automation, enable the Schedule Trigger and configure interval settings. Watch your Google Sheet populate with enriched data and tailored ice breakers. 🧠 Tips & Best Practices - Bright Data Delay: Snapshots may take time. The workflow polls the status until complete. - Retry Protection: If and Wait nodes avoid infinite loops by checking snapshot status. - Mapping via row_number: Critical to ensure data is updated in the right row. - Prompt Engineering: You can fine-tune Claude's behavior by editing the text prompt.🧾 Output Example Once complete, each row in your Google Sheet will contain: - Linkedin_URL_Person - Name - City - Company - Recent Post - Ice Breaker Example:linkedin.com/... Jane Doe NYC ACME Corp “Why AI should replace meetings” "Loved your post about AI and meetings — finally someone said it!"💬 Support & Feedback Questions? Want to tweak the prompt or expand the enrichment? - 📧 Email: [email protected] - 📺 YouTube: @YaronBeen - 🔗 LinkedIn: linkedin.com/in/yaronbeen

Track your Google keyword rankings effortlessly with a prebuilt Browse AI robot. This template fetches the top 10 rankings, logs the data into a Google Sheet, and instantly shares the information with your team on Slack. Ensure your robot is configured to fetch 10 rankings for optimal performance.

🔍 Competitor Review Scraper & Ad Copy Generator(Trustpilot + Bright Data + GPT-4o-mini) 📌 Who It's For Marketers, business owners, and agencies looking to: - Analyze competitor pain points - Generate high-impact Facebook ad copy - Automate manual data processing 🧩 How It Works This n8n-based workflow combines Bright Data, Google Sheets, and OpenAI to scrape, process, and transform Trustpilot reviews into ready-to-use ad copy. 🔹 Step-by-Step BreakdownTrigger (Manual Form Submission) Input required: - Competitor’s Trustpilot URL - Review timeframe (30d, 3m, 6m, 12m)Fetch Reviews Calls Bright Data’s Dataset API with URL & timeframe. Polls until snapshot is ready.Retrieve & Store Extracts all reviews. Saves them into a structured Google Sheet.Filter & Aggregate Filters to only 1–2 star reviews. Summarizes common negative feedback.Generate Ad Copy Sends the summary to OpenAI GPT-4o-mini. Produces 3 variations of ad copy targeting pain points.Distribute Insights Sends ad copy + summary via email to the marketing team. ✅ Requirements - LLM Account - Google Sheets - Copy this sheet - Bright Data account ⚙️ Setup InstructionsStep 1: Google Sheets Copy this Google Sheets template. Do not change column headers.Step 2: n8n Credential Setup - Google Sheets: OAuth2 - Bright Data: Authorization Header - OpenAI: API Key for GPT-4o-miniStep 3: Import Workflow Import the .json file into n8n. Configure your sheet + dataset ID. Adjust GPT prompts as needed.Step 4: Run the Workflow Trigger via form. Receive ad copy + review insights via email. 🧠 Tips & Best Practices - Bright Data snapshots may take time — polling is handled. - Focusing on 1–2 star reviews yields the most actionable pain points. - You can customize GPT-4o-mini prompts for tone or vertical. 💬 Support & Feedback Need help or customization? 📧 Email: [email protected] 📺 YouTube: @YaronBeen 🔗 LinkedIn: linkedin.com/in/yaronbeen 📚 Bright Data Docs: docs.brightdata.com/introduction

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI, and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works:Data loading and indexing: - Fetches links to PDF earnings documents from a Google Sheet containing a list of file links. - Downloads the PDFs from Google Drive. - Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). - Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent: - Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. - The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery: - Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps: 1. Google Cloud Project & Vertex AI API: - Create a Google Cloud project. - Enable the Vertex AI API for your project. 2. Google AI API key: - Obtain a Google AI API key from Google AI Studio. 3. Pinecone account and API key: - Create a free account on the Pinecone website. - Obtain your API key from your Pinecone dashboard. - Create an index named company-earnings in your Pinecone project. 4. Google Drive - download and save financial documents: - Go to a company you want to analyze and download their quarterly earnings PDFs. - Save the PDFs in Google Drive. - Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive. 5. Configure credentials in your n8n environment for: - Google Sheets OAuth2 - Google Drive OAuth2 - Google Docs OAuth2 - Google Gemini(PaLM) API (using your Google AI API key) - Pinecone API (using your Pinecone API key) 6. Import and configure the workflow: - Import this workflow into your n8n instance. - Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. - Update the Download File From Google Drive to point to the column where the file URLs are. - Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

⚠️ DISCLAIMER: This workflow uses the HDW LinkedIn community node, which is only available on self-hosted n8n instances. It will not work on n8n.cloud. This workflow automates the entire LinkedIn lead generation process from finding prospects that match your Ideal Customer Profile (ICP) to sending personalized messages. It uses AI to analyze lead data, score potential clients, and prioritize your outreach efforts. Key Features - AI-Driven Lead Generation: Convert ICP descriptions into LinkedIn search parameters. - Comprehensive Data Enrichment: Analyze company websites, LinkedIn posts, and news. - Intelligent Lead Scoring: Prioritize leads based on AI analysis of intent signals. - Automated Outreach: Connect with prospects and send personalized messages. Requirements - Self-hosted n8n instance with the HDW LinkedIn community node installed. - OpenAI API access (for GPT-4o). - Google Sheets access. - HDW API key (available at app.horizondatawave.ai). - LinkedIn account. Setup Instructions 1. Install Required Nodes Ensure the HDW LinkedIn community node is installed on your n8n instance. Command: npm install n8n-nodes-hdw (or use this instruction) 2. Configure Credentials OpenAI: Add your OpenAI API key. Google Sheets: Set up Google account access. HDW LinkedIn: Configure your API key from horizondatawave.ai. 3. Set Up Google Sheet Create a new Google Sheet with the following columns (or copy template): Name, URN, URL, Headline, Location, Current company, Industry, etc. The workflow will populate these columns automatically. 4. Customize Your ICP Use chat to provide the AI Agent with your Ideal Customer Profile. Example: "Target marketing directors at SaaS companies with 50-200 employees." 5. Adjust Scoring Criteria Modify the lead scoring prompt in the "Company Score Analysis" node to match your specific product/service. Tune the evaluation criteria based on your unique business needs. 6. Configure Message Templates Update the HDW LinkedIn Send Message node with your custom message. How It Works - ICP Translation: AI converts your ICP description into LinkedIn search parameters. - Lead Discovery: Workflow searches LinkedIn using these parameters. - Data Collection: Results are saved to Google Sheets. - Enrichment: System collects additional data about each lead: - Company website analysis - Lead's LinkedIn posts - Company's LinkedIn posts - Recent company news - Intent Analysis: AI analyzes all data to identify buying signals. - Lead Scoring: Leads are scored on a 1-10 scale based on likelihood of interest. - Connection Requests: Top-scoring leads receive connection requests. - Follow-Up: When connections are accepted, automated messages are sent. Customization - Search Parameters: Adjust the AI Agent prompt to refine your target audience. - Scoring Criteria: Modify scoring prompts to highlight indicators relevant to your product. - Message Content: Update message templates for personalized outreach. - Schedule: Configure when connection requests and messages are sent. Rate Limits & Best Practices - LinkedIn has connection request limits (approximately 100-200 per week). - The workflow includes safeguards to avoid exceeding these limits. - Consider spacing your outreach for better response rates. Note: Always use automation tools responsibly and in accordance with LinkedIn's terms of service.

Every time a new mp3 audio file longer than 1 minute is added to Google Drive, Make will automatically transcribe it with Google Cloud Speech and save the text to a Google Docs document.

This n8n template shows how anyone can build a simple newsletter-like subscription service where users can enroll themselves to receive messages/content on a regular basis. It uses n8n forms for data capture, Airtable for the database, AI for content generation, and Gmail for email sending. How it works An n8n form is set up to allow users to subscribe with a desired topic and interval of which to receive messages. This information is then added to the Airtable. A scheduled trigger is executed every morning and searches for subscribers to send messages based on their desired intervals. Once found, subscribers are sent to a subworkflow which performs the text content generation via an AI agent and also uses a vision model to generate an image. Both the text and image are attached to an email that is sent to the subscriber. This email also includes an unsubscribe link. The unsubscribe flow works similarly via the n8n form interface, which when submitted, disables further scheduled emails to the user. How to use Make a copy of the sample Airtable. Make sure the workflow is "activated" and the forms are available and reachable by your audience. Requirements - Airtable for Database - OpenAI for LLM (but compatible with others) - Gmail for Email (but can be replaced with others)Customizing this workflow This simple use case can be extended to deliver any types of content such as your company newsletter, promotions, social media posts, etc. It doesn't have to be limited to just email—try social messaging, WhatsApp, Telegram, and others.

How many times have you missed a meeting or forgotten an appointment because a calendar reminder got lost in the noise? Traditional notifications are often dry, easy to ignore, or scattered across different apps—leaving you scrambling at the last minute. This smart Google Calendar workflow fixes that by sending you a clear, friendly reminder exactly 1 hour before your event starts—delivered through Telegram as if a personal assistant were looking out for you. Powered by AI, it transforms cold calendar alerts into warm, conversational nudges you won't ignore. Why This Works Better: ✅ No More Overlooked Alerts – Consolidates reminders into one clear, accessible place (Telegram), so you never miss them. ✅ Friendly & Engaging – AI transforms robotic calendar entries into natural, human-like reminders that are harder to ignore. ✅ Works Everywhere – Whether you're on your phone, laptop, or tablet, you’ll get the same clear notification, no matter the platform. How It WorksScheduled Trigger: The workflow starts with a Schedule Trigger node that runs every minute to check for upcoming events. Google Calendar Check: The "Get upcoming event" node queries Google Calendar for events starting within the next hour (between timeMin and timeMax). Duplicate Prevention: The "Already sent?" node ensures reminders are not sent multiple times for the same event by filtering out duplicates. AI-Powered Reminder: The "Secretary Agent" node, powered by GPT-4, crafts a friendly and professional reminder message. It includes event details like name, description, location, start/end time, and creator, formatted in a conversational tone. Telegram Notification: The final "Send reminder" node delivers the reminder via Telegram, ensuring the user receives it in a clear and accessible format. Set Up Steps 1. Configure Schedule Trigger: Set the interval (e.g., every minute) to check for events. 2. Connect Google Calendar: Link your Google Calendar account and specify the calendar to monitor. 3. Set Up AI Agent: Customize the "Secretary Agent" with the provided system message to ensure reminders are warm, professional, and detailed. 4. Link Telegram: Add your Telegram credentials and specify the CHAT_ID where reminders will be sent. 5. Activate Workflow: Ensure the workflow is active and set to the correct timezone (e.g., Europe/Rome). Why It’s Useful Never Miss an Event: Traditional calendar reminders can be easy to overlook, especially when scattered across platforms. This workflow consolidates reminders into a single, accessible channel (Telegram). Clear and Friendly: The AI agent transforms generic calendar alerts into personalized, conversational reminders, making them harder to ignore. Cross-Platform Accessibility: By delivering reminders via Telegram, users receive them in a consistent format, regardless of the device or platform they’re using. No more missed events due to unclear notifications! Need help customizing? Contact me for consulting and support or add me on Linkedin.

This template integrates OpenAI's image generation and editing endpoints via the GPT-Image-1 model to visually create and manipulate images based on prompts. It features base64 conversion, binary handling, and prompt chaining. Perfect for marketing, design, product visuals, and creative workflows. 🛠️ Requirements - OpenAI account with access to gpt-image-1 (probably you need organization verifications for access to that model) - OpenAI API credentials configured in n8n - A self-hosted or cloud n8n instance - Basic familiarity with the n8n UI (no programming required) 🔧 Step-by-step InstructionsStep 1: Manual Trigger Starts the workflow on click. Ideal for testing the generation and edit logic. Step 2: Generate Image The Create image call node sends a prompt to OpenAI and returns a base64 image. Example prompt:A cyberpunk city at night with flying cars and neon lightsStep 3: Convert to Binary The base64 image is converted into a usable binary PNG file with the Convert json binary to File node. Step 4: Edit the Image The binary file is passed to OpenAI’s /images/edits endpoint. A new prompt applies changes to the image. Example:Add a glowing robot in the foreground with a neon sword ✅ Supports model: gpt-image-1 ⚠️ Requires binary file (not base64)Step 5: Final Conversion Converts the final edited image from base64 to file so it can be downloaded or used in other nodes. 🎯 Real-World Use Cases 🎨 Artists & Creators: concept art and illustration variations 🛍️ E-commerce: auto-generate product mockups 📰 Marketing: create eye-catching blog or social visuals 💡 Bonus Ideas - Add a Telegram or Slack node to generate or edit images via chat - Use a Webhook to feed prompts from a form or frontend - Add a mask to restrict edits to specific areas (e.g., background only)

This n8n template watches a Gmail inbox for support messages and creates an equivalent issue item in Linear. How it works A scheduled trigger fetches recent Gmail messages from the inbox which collects support requests. These support requests are filtered to ensure they are only processed once, and their HTML body is converted to markdown for easier parsing. Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority, and summarizes a title and description of the original request. Finally, the AI-generated values are used to create an issue in Linear to be actioned. How to use Ensure the messages fetched are solely support requests; otherwise, you'll need to classify messages before processing them. Specify the labels and priorities to use in the system prompt of the AI agent. Requirements - Gmail for incoming support messages - OpenAI for LLM - Linear for issue management Customizing this workflow Consider automating more steps after the issue is created, such as attempting issue resolution or capacity planning.

CallForge - AI-Powered Sales Call Data Processor Automate sales call analysis and store structured insights in Notion with AI-powered intelligence. Who is This For? This workflow is ideal for: ✅ Sales teams looking to automate call insight processing. ✅ Sales operations managers managing AI-driven call analysis. ✅ Revenue teams using Gong, Fireflies.ai, Otter.ai, or similar transcription tools. It streamlines sales call intelligence, ensuring that insights such as competitor mentions, objections, and customer pain points are efficiently categorized and stored in Notion for easy access. What Problem Does This Workflow Solve? Manually reviewing and documenting sales call takeaways is time-consuming and error-prone. With CallForge, you can: ✔ Identify competitors mentioned in sales calls. ✔ Capture objections and customer pain points for follow-up. ✔ Track sales call outcomes and categorize insights automatically. ✔ Store structured sales intelligence in Notion for future reference. ✔ Improve sales strategy with AI-driven, automated call analysis. Key Features & Workflow Steps 🎙️ AI-Powered Call Data Processing This workflow processes AI-generated sales call insights and structures them in Notion databases: - Triggers automatically when AI call analysis data is received. - Extracts competitor mentions from the call transcript and logs them in Notion. - Identifies and categorizes sales objections for better follow-ups. - Processes integration mentions, capturing tools or platforms referenced in the call. - Extracts customer use cases, categorizing pain points and feature requests. - Aggregates all extracted insights and updates relevant Notion databases. 📊 Notion Database Integration - Competitors → Logs mentioned competitors for sales intelligence. - Objections → Tracks and categorizes common objections from prospects. - Integrations → Captures third-party tools & platforms discussed in calls. - Use Cases → Stores customer challenges & product feature requests. How to Set Up This Workflow 1. Prepare Your AI Call Analysis Data Ensure AI-generated sales call data is passed into the workflow. Compatible with Gong, Fireflies.ai, Otter.ai, and other AI transcription tools. 2. Connect Your Notion Database Set up Notion databases for: 🔹 Competitors (tracks competing products) 🔹 Objections (logs customer objections & concerns) 🔹 Integrations (captures mentioned platforms & tools) 🔹 Use Cases (categorizes customer pain points & feature requests) 3. Configure n8n API Integrations Connect your Notion API key in n8n under “Notion API Credentials.” Set up webhook triggers to receive data from your AI transcription tool. Test the workflow using a sample AI-generated call transcript. How to Customize This Workflow 💡 Modify Notion Data Structure – Adjust fields to match your company’s CRM setup. 💡 Enhance AI Data Processing – Align fields with different AI transcription providers. 💡 Expand with CRM Integration – Sync insights with HubSpot, Salesforce, or Pipedrive. 💡 Add Notifications – Send alerts via Slack, email, or webhook when key competitor mentions or objections are detected. Key Nodes Used in This Workflow 🔹 If Nodes – Checks if AI-generated data includes competitors, integrations, objections, or use cases. 🔹 Notion Nodes – Creates or updates entries in Notion databases. 🔹 Split Out & Aggregate Nodes – Processes multiple insights and consolidates AI outputs. 🔹 Wait Nodes – Ensures smooth sequencing of API calls and database updates. 🔹 HTTP Request Node – Sends AI-extracted insights to Notion for structured storage. Why Use This Workflow? ✔ Eliminates manual data entry and speeds up sales intelligence processing. ✔ Ensures structured and categorized sales insights for decision-making. ✔ Improves team collaboration with AI-powered competitor tracking & objections logging. ✔ Seamlessly integrates with Notion to centralize and manage sales call insights. ✔ Scalable for teams using n8n Cloud or self-hosted deployments. This workflow empowers sales teams with automated AI insights, streamlining sales strategy and follow-ups with minimal effort. 🚀

Workflow Description: This n8n workflow automates the drafting of email replies for Fastmail using OpenAI's GPT-4 model. Here’s the overall process: Email Monitoring: The workflow continuously monitors a specified IMAP inbox for new, unread emails. Email Data Extraction: When a new email is detected, it extracts relevant details such as the sender, subject, email body, and metadata. AI Response Generation: The extracted email content is sent to OpenAI's GPT-4, which generates a personalized draft response. Get Fastmail Session and Mailbox IDs: Connects to the Fastmail API to retrieve necessary session details and mailbox IDs. Draft Identification: Identifies the "Drafts" folder in the mailbox. Draft Preparation: Compiles all the necessary information to create the draft, including the generated response, original email details, and specified recipient. Draft Uploading: Uploads the prepared draft email to the "Drafts" folder in the Fastmail mailbox. Prerequisites: - IMAP Email Account: You need to configure an IMAP email account in n8n to monitor incoming emails. - Fastmail API Credentials: A Fastmail account with JMAP API enabled. You should set up HTTP Header authentication in n8n with your Fastmail API credentials. - OpenAI API Key: An API key from OpenAI to access GPT-4. Make sure to configure the OpenAI credentials in n8n. Configuration Steps:Email Trigger (IMAP) Node: Provide your email server settings and credentials to monitor emails. HTTP Request Nodes for Fastmail: Set up HTTP Header authentication in n8n using your Fastmail API credentials. Replace the httpHeaderAuth credential IDs with your configured credential IDs. OpenAI Node: Configure the OpenAI API key in n8n. Replace the openAiApi credential ID with your configured credential ID. By following these steps and setting up the necessary credentials, you can seamlessly automate the creation of email drafts in response to new emails using AI-generated content. This workflow helps improve productivity and ensures timely, personalized communication.

This n8n workflow builds another example of creating a knowledge base assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot. In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user. Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries. Example Human: "Tell me about what the tax code says about cargo for intentional commerce?" AI: "Section 11.25 of the Texas Property Tax Code pertains to 'MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE.' In this section, a person who is a citizen of a foreign country or an..." How it works The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters. Each chapter is then parsed and split into its sections using data manipulation expressions. Each section is then inserted into our Qdrant vector store tagged with its source, chapter, and section numbers as metadata. When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant. Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node. When the AI Agent needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document. Requirements A Qdrant instance is required for the vector store and specifically for its filtering functionality. Mistral.ai account for Embeddings and AI models. Customising this workflow Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust. Not using Mistral? You are able to replace but note to match the distance and dimension size of the Qdrant collection to your chosen embedding model.

Overview This workflow helps you compare Claude 3.5 Sonnet and Gemini 2.0 Flash when extracting data from a PDF. This workflow extracts and processes the data within a PDF in one single step, instead of calling an OCR and then an LLM. How it works The initial 2 steps download the PDF and convert it to base64. This base64 string is then sent to both Claude 3.5 Sonnet and Gemini 2.0 Flash to extract information. This workflow is made to let you compare results, latency, and cost (in their dedicated dashboard). How to use it 1. Set up your Google Drive if not already done. 2. Select a document on your Google Drive. 3. Modify the prompt in "Define Prompt" to extract the information you need and transform it as wanted. 4. Get a Claude API key and/or Gemini API key. Note that you can deactivate one of the 2 API calls if you don't want to try both. 5. Test the Workflow.

🚀 WhatsApp Automation Template Designed & Developed by Infridet Solutions Private Limited 🔧 Objective: Automate your lead nurturing and sales process from YouTube/Instagram → Landing Page → CRM → Email → WhatsApp → Sales → Deal Closure using tools like: 🌐 WordPress (Landing Page + Fluent Forms) 🧾 Google Sheets (Backup Log) 📩 FluentCRM (Lead Tagging + Email Sequences) 💬 Whinta.com (WhatsApp Messaging API) ⚙️ N8N (Workflow Automation Engine) 🧩 System Flow Overview:Lead Source: YouTube or Instagram CTALanding Page: Built on WordPress with a story-driven designForm Capture: Fluent Forms with dynamic input fieldsData Sync: - Backup to Google Sheets - Push lead to FluentCRM and tag as New LeadEmail Sequence: - Warm-up emails (1 to 5) - Introduce offer or serviceWhatsApp Outreach: Send personalized message via Whinta Triggered 1 hour after form fill or last emailSales Follow-Up: Sales team handles replies manually CRM tag updated to Customer upon closing 📁 Folder Structure (Optional Git/Zip File): 📦 WhatsApp-Automation-Infridet/ │ ├── whatsapp-automation-n8n.json # N8N Flowchart Import File ├── email-templates.docx # Warm-up Email Scripts ├── whinta-api-integration.pdf # API Documentation ├── crm-tagging-notes.txt # CRM Tag Setup Details └── readme.md # This Instruction File 🛠️ Required Integrations & Setup ✅ Fluent Forms (WordPress) Embed form with Name, Email, Phone Enable webhook to N8N: /lead-capture ✅ Google Sheets Use n8n-nodes-base.googleSheets node Capture name, email, phone, source, timestamp ✅ FluentCRM REST API enabled Push contact and assign tag New Lead Setup Email Automation via tag trigger ✅ SMTP Email (Optional) Use Gmail SMTP or Brevo Trigger email on form submission ✅ Whinta.com (WhatsApp API) Send POST request Payload includes phone, message, sender_id Customize message with personalization 💬 Sample WhatsApp Message: Hey {{name}}, Gyan here from Account Craft 👋 I saw your form submission – would you like help in starting your YouTube journey this week? Let me know. I'm just one text away. ✅ 📧 Sample Email (Warmup Day 1):Subject: Welcome to Account Craft 🚀Body: Hi {{name}}, I’m Gyan from Account Craft. Thanks for joining us! Here’s what’s coming next: exclusive videos, personalized tips, and real support to get your YouTube channel earning. Let’s go! – Gyan 🔁 CRM Tag Updates:Action | Tag Assigned On form fill | New Lead After WhatsApp | Engaged After sale closed | Customer 📌 Final Output: Once completed, the system will: - Log all leads into a database - Automatically send emails and WhatsApp messages - Notify your sales team - Update lead status without manual entryAutomation Template Designed & Deployed by Infridet Solutions Private LimitedSmart Integrations. Seamless Business. 🌐 www.infridetsolutions.com | 📞 +91-8853354829