Agents & Automations

This workflow allows you to ask questions about a PDF document. The answers are provided by an AI model of your choice, and the answer includes a citation pointing to the information it used. You can use n8n’s built-in chat interface to ask the questions, or you could customize this workflow to use another one (e.g. Slack, Teams, etc.). Example: The workflow is set up with the Bitcoin whitepaper. So you could ask things like:Question: “Which email provider does the creator of Bitcoin use?“Answer: “GMX [Bitcoin whitepaper.pdf, lines 1-35]” Requirements: - A Pinecone account (they have a free tier at the time of writing that is easily enough for this workflow) - Access to a large language model (e.g. an OpenAI account)Customizing this workflow: The workflow only reads in one document, but you could customize it to read in all the documents in a folder (or more). The workflow is set up to use GPT 3.5, but you could swap that out for any other model (including self-hosted ones).

Get your WordPress posts generated on auto-pilot from 2 Anthropic Claude prompts (title and content) when a Google Sheets row updates, streamlining content creation.

This workflow is a prototype of an AI-powered image editing interface, similar to Photoshop's Generative Fill feature, but running entirely in the browser. It provides a web-based editor that allows users to: - Select areas in images using an adjustable brush tool - Input text prompts to guide the AI generation - Compare original and generated images side by side - Iterate on edits with different prompts and settings - Save or reuse generated images 🎨 Perfect for product catalog management, seasonal content updates, and creative image editing tasks! 📋 Requirements - FLUX API Access: You'll need API credentials from FLUX to use this workflow. - Configure the HTTP Header Auth credential in n8n with your FLUX API key. 🔧 Key Components - FLUX Fill API for AI-powered image generation - Konva.js for canvas manipulation - img-comparison-slider for result visualization - Custom CSS/JS for editor functionality - Simple Editor Interface - HTML page with an editor is served on the Webhook call - Adjustable brush selection tool - Provides several mock examples and allows uploading custom images - Basic prompt and FLUX model parameter controls Image Processing Pipeline - Handles image and mask separately - Processes FLUX Fill API requests - Delivers results back to the editor Result Viewer - Split-screen comparison of original and generated images - Interactive slider for before/after comparison - Options to save or continue editing - Support for multiple iteration cycles 🎯 Use Cases This prototype is particularly useful for: - Testing AI-powered image editing concepts - Quick product visualization experiments - Exploring creative image variations - Demonstrating inpainting capabilities 💡 Pro Tip: Save masks for frequently edited areas to quickly generate variations with different prompts! The workflow can be extended to integrate with various data sources and can be customized for specific business needs.

What problem does this solve? It fetches LinkedIn profiles for a multitude of purposes based on a keyword and location via Google search and stores them in an Excel file for download and in a NocoDB database. It tries to avoid using costly services and should be n8n beginner friendly. It uses the serpapi.com to avoid being blocked by Google Search and to process the data in an easier way. What does it do? Based on criteria input, it searches LinkedIn profiles. It discards unnecessary data and turns the follower count into a real number. The output is provided as an Excel table for download and in a NocoDB database. How does it do it? Based on criteria input, it uses serpAPI.com to conduct a Google search of the respective LinkedIn profiles. With OpenAI.com, the name of the respective company is added. With OpenAI.com, the follower number (e.g., 300+) is turned into a real number: 300. All unnecessary metadata is discarded. As an output, an Excel file is created, and the output is stored in a nocodb.com table. Step-by-step instruction 1. Import the Workflow: - Copy the workflow JSON from the "Template Code" section below. - Import it into n8n via "Import from File" or "Import from URL". 2. Set up a free account at serpapi.com and get API credentials to enable good Google search results. 3. Set up an API account at openai.com and get the API key. 4. Set up a nocodb.com account (or self-host) and get the API credentials. 5. Create the credentials for serpapi.com, openai.com, and nocodb.com in n8n. 6. Set up a table in NocoDB with the fields indicated in the note above the NocoDB node. 7. Follow the instructions as detailed in the notes above individual nodes. 8. When the workflow is finished, open the Excel node and click download if you need the Excel file.

AI-Powered Social Media Content Automation 🧑‍💻 Who is this for? This workflow is perfect for social media managers, content creators, and digital marketers who want to save time by automating social media post generation and publishing across platforms. 📌 What problem does this solve? Manually generating and scheduling social media content is time-consuming and repetitive. This workflow automates content creation and publishing, allowing you to: - Streamline content generation using AI - Ensure consistent posting across social media platforms - Track published posts in Google Sheets 🔍 What this workflow does: - Fetches content ideas from a Google Sheet. - Generates social media posts using OpenAI's GPT-4. - Checks the target platform (e.g., Twitter/X, LinkedIn). - Posts the content to the chosen social media platform. - Updates the Google Sheet with the generated post and timestamp. 🛠️ Setup Guide: 1. Connect Google Sheets: Ensure you have a Google Sheet with content ideas (columns: Idea, Status, Generated Post). 2. Set up OpenAI API Key: Provide your OpenAI API key for GPT-4. 3. Configure Social Media Accounts: Link your Twitter/X or other social media accounts using n8n's built-in nodes. 4. Test the Workflow: Run the workflow to verify automation. 5. Schedule Automation: Set a recurring trigger (e.g., daily) to automate posting. 🔧 Customization Tips: - Adjust prompt inputs in the OpenAI node to tailor the tone and style. - Add more platforms (e.g., Instagram, Facebook) by duplicating the social media node. - Include analytics tracking for engagement insights. 📊 Example Use Cases: - Automatically generate and share daily motivational quotes. - Post product updates and announcements. - Share curated industry news and insights. This workflow saves time and keeps your social media presence active and engaging effortlessly. 🚀

In this automation, we first make a screenshot with a screenshot API called URLbox and then send this screenshot into the OpenAI API for analysis. You can extend this automation by the way you want to ingest the website URLs & names into this workflow. Options as data sources: - Postgres - Google Sheets - Your CRM - ... Setup: - Replace Website & URL in Setup Node - Put in your URLbox API Key - Put in your OpenAI credentials

Enhance content authenticity by identifying copied content in Google Docs with Trello and Eden AI. Just move your card, enter the Google Doc ID, and let the system automatically check and update you on the plagiarism status.

This workflow collects client feedback through a form (Tally, Typeform, or Google Forms) and uses AI to analyze it. It automatically generates a summary of the positive points, highlights areas for improvement, and drafts a short social media post based on the feedback. Ideal for: - Freelancers - Customer support teams - Online service providers - Coaches and educators Setup steps 1. Connect your form tool to the Webhook node (POST method) and make sure it sends a feedback field. 2. Add your DeepSeek (or other GPT-compatible) API key to the AI request node. 3. Configure the email node with your SMTP credentials and desired recipient address. 4. Replace the Telegram node with Slack, Buffer, or another integration if you prefer. 5. (Optional) Customize the prompt in the function node for different tone/language. 🕐 Estimated setup time: ~15 minutes 💬 Sticky notes are included and clearly positioned to guide you. Use cases - Analyze feedback from onboarding or satisfaction surveys - Create ready-to-publish social media content from real customer praise - Help support or marketing teams act on feedback immediately

What it is Chat with your event schedule from Google Sheets in Telegram: - "When is the next meetup?" - "How many events are there next month?" - "Who presented most often?" - "Which future meetups have no presenters yet?" This workflow lets you chat with a Telegram bot about past, present, and future events that are scheduled in a Google Spreadsheet. Who it is for If you want an easy way for your audience to get information about your events, you can use this workflow for the same purpose or easily adapt it to your needs and different use cases where you want to query smaller amounts of tabular data in natural language. How it works Upon getting triggered by a chat message to a Telegram bot, the schedule of meetups is retrieved from Google Spreadsheets, converted into a markdown table syntax, and fed into the system prompt of an LLM (we're using OpenRouter in this example), whose output is posted back as an answer into the same Telegram chat. Setup steps As the reviewer of this workflow, you can temporarily use it via an existing Telegram bot. Simply point your Telegram client to https://t.me/AiDaNangBot and start to ask questions like: - "When is the next meetup?" - "What future meetings do not have presenters?" - "Who presented on Future of Human Relationships?" To build upon this workflow: - Import the workflow - Customize the Google Docs credentials for your individual access - Create a Telegram bot and connect it to the workflow by entering its API token into the credentials used in the Telegram trigger node - In the "Settings" node, replace the "scheduleURL" with the URL of your own copy of the Google Spreadsheet or a copy of the Event Schedule Template Sheet to spin off your own – whereby the structure of the spreadsheet doesn't matter, it's just important that you semantically structure your information in dedicated columns clearly labeled in the header row.

🎦🚀 YouTube Video Comment Analysis Agent This n8n workflow is designed to help YouTube creators analyze video details and comments to generate a comprehensive and actionable report. The workflow provides insights into video performance, audience engagement, and viewer feedback, helping creators identify trends, interests, and opportunities for future content creation. ✨ Key Features - Video Performance Analysis: Extracts metrics like views, likes, and comments to evaluate the video's success. - Comment Sentiment Analysis: Determines the tone of comments (positive, neutral, or negative) to understand audience sentiment. - Recurring Themes Detection: Identifies common topics or questions in comments to highlight viewer interests. - Engagement Drivers: Pinpoints what aspects of the video resonated most with viewers. - Actionable Recommendations: Offers strategies for creating follow-up content or improving future videos. - Keyword Suggestions: Extracts frequently mentioned terms for better discoverability on YouTube. - Collaboration Opportunities: Suggests potential partnerships based on viewer feedback or related channels. 🛠️ How to Use 1. Set Up Workflow Variables: - Add your GOOGLE_API_KEY and the VIDEO_ID of the YouTube video you want to analyze in the "Workflow Variables" node. - Ensure your Google API key has access to the YouTube Data API. 2. Run the Workflow: - Trigger the workflow manually or through another workflow using the "Execute Workflow Trigger" node. - The workflow will fetch video details and comments using pagination to ensure all data is captured. 3. Generate Insights: - The workflow processes video details and comments to create a detailed report with actionable insights. - Outputs include sentiment analysis, engagement drivers, content opportunities, and audience profiling. 4. View or Share Results: - The report is converted into Markdown and can be emailed via Gmail or saved to Google Drive as a document. 🌟 Value from This Workflow - Gain a deeper understanding of your audience's preferences and feedback. - Identify trends and engagement drivers to replicate success in future videos. - Discover new content opportunities based on viewer questions and suggestions. - Improve discoverability by leveraging keyword suggestions extracted from comments. - Build stronger connections with your audience by addressing their needs effectively.

This n8n workflow demonstrates a simple multi-agent setup to perform the task of competitor research. It showcases how using the HTTP request tool could reduce the number of nodes needed to achieve a workflow like this. How it works For this template, a source company is defined by the user which is sent to Exa.ai to find competitors. Each competitor is then funneled through 3 AI agents that will go out onto the internet and retrieve specific data points about the competitor: company overview, product offering, and customer reviews. Once the agents are finished, the results are compiled into a report which is then inserted into a Notion database. Requirements - An OpenAI account for the LLM. - Exa.ai account for access to their AI search engine. - SerpAPI account for Google search. - Firecrawl.dev account for web scraping. - Notion.com account for the database to save final reports. Customizing the workflow Add additional agents to gather more data points such as SEO keywords and metrics. Not using Notion? Feel free to swap this out for your own database.

Who this is for? This workflow automates the process of Wikipedia data extraction using the Bright Data Web Unlocker, parsing and cleaning the data, and then sending the results to a specified webhook URL for downstream processing, reporting, or integration. - Researchers who need structured information from Wikipedia pages regularly. - Data Engineers building knowledge bases or enriching datasets with factual data. - Digital Marketers or Content Writers automating fact-checking or content sourcing. - Automation Enthusiasts who want to trigger external systems with rich context from Wikipedia. What this workflow does This workflow addresses the challenges of manually retrieving, structuring, and using data from Wikipedia at scale. Workflow BreakdownTrigger - Type: Scheduled or Manual - Purpose: Starts the workflow either on a fixed schedule (e.g., daily) or on-demand via a manual trigger or incoming webhook. Bright Data Wikipedia Scraping - Tool Used: Bright Data Web Unlocker - Action: Scrape the HTML content of one or multiple Wikipedia article URLs. Parse & Extract Structured Data The Basic LLM Chain node is responsible for producing human-readable content. Summarization Summarize the Wikipedia content by utilizing the Summarization Chain node. Send to Webhook Initiates a Webhook notification to the specified URL as part of the "Summary Webhook Notifier" node. Setup - Sign up at Bright Data. - Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. - In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). - The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. - In n8n, configure the Google Gemini (PaLM) API account with the Google Gemini API key (or access through Vertex AI or proxy). - Update the Set Wikipedia URL with Bright Data Zone node with the Wikipedia URL and Bright Data Zone. - Update the Summary Webhook Notifier node with the Webhook endpoint of your choice. How to customize this workflow to your needsUpdate Wikipedia URL Replace with your own Wikipedia URL of your interest. Make sure to set the Wikipedia URL as part of the "Set Wikipedia URL with Bright Data Zone" node. Modify Data Extraction Logic Extract entire article content or just specific sections by extending the "LLM Data Extractor" node prompt. Extend AI Summarization Extract key bullet points or entities. Create short-form summaries by extending the "Concise Summary Generator" node. Extend Summary Webhook Notifier Send to Slack, Discord, Telegram, MS Teams via the Webhook notification mechanism. Connect to your internal database/API via the Webhook notification mechanism.

Enrich your company lists with OpenAI GPT-3 You’ll get valuable information such as: - Market (B2B or B2C) - Industry - Target Audience - Value Proposition This will help you to: - Add more personalization to your outreach - Make informed decisions about which accounts to target I've made the process easy with an n8n workflow. Here is what it does: - Retrieve website URLs from Google Sheets - Extract the content for each website - Analyze it with GPT-3 - Update Google Sheets with GPT-3 data

This workflow streamlines the process of managing content by automatically identifying and fetching the most recently added Google Doc file from your Google Drive. It extracts the content of the document for processing and leverages an AI model to generate a concise and meaningful summary of the extracted text. The summarized content is then stored in a designated Google Sheet, alongside relevant details like the document name and the date it was added, providing an organized and easily accessible reference for future use. This automation simplifies document handling, enhances productivity, and ensures seamless data management. Steps: 1. Fetch the Most Recent Document from Google Drive - Action: Use the Google Drive Node. - Details: List files, filter by date to fetch the most recently added .doc file, and retrieve its file ID and metadata. 2. Extract Content from the Document - Action: Use the Google Docs Node. - Details: Set the operation to "Get Content," pass the file ID, and extract the document's text content. 3. Summarize the Document Using an AI Model - Action: Use an AI Model Node (e.g., OpenAI, ChatGPT). - Details: Provide the extracted text to the AI model, use a prompt to generate a summary, and capture the result. 4. Store the Summarized Content in Google Sheets - Action: Use the Google Sheets Node. - Details: Append a new row to the target sheet with details such as the original document name, summary, and date added. About WeblineIndia: WeblineIndia specializes in delivering innovative and custom AI solutions to simplify and automate business processes. If you need any help, please reach out to us.

Transform simple queries into comprehensive, well-structured content with this n8n workflow that leverages Perplexity AI for research and GPT-4 for content transformation. Create professional blog posts and HTML content automatically while maintaining accuracy and depth. Intelligent Research & Analysis 🚀 Automated Research Pipeline - Harnesses Perplexity AI's advanced research capabilities - Processes complex topics into structured insights - Delivers comprehensive analysis in minutes instead of hours 🧠 Smart Content Organization - Automatically structures content with clear hierarchies - Identifies and highlights key concepts - Maintains technical accuracy while improving readability - Creates SEO-friendly content structure Content Transformation Features 📝 Dynamic Content Generation - Converts research into professional blog articles - Generates clean, responsive HTML output - Implements proper semantic structure - Includes metadata and categorization 🎨 Professional Formatting - Responsive Tailwind CSS styling - Clean, modern HTML structure - Proper heading hierarchy - Mobile-friendly layouts - Blockquote highlighting for key insights Perfect For 📚 Content Researchers Save hours of manual research by automating the information gathering and structuring process. ✍️ Content Writers Focus on creativity while the workflow handles research and technical formatting. 🌐 Web Publishers Generate publication-ready HTML content with modern styling and proper structure. Technical Implementation ⚡ Workflow Components - Webhook endpoint for query submission - Perplexity AI integration for research - GPT-4 powered content structuring - HTML transformation engine - Telegram notification system (optional) Transform your content creation process with an intelligent system that handles research, writing, and formatting while you focus on strategy and creativity.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey InsightsCustomer InsightsCommunity Insights This template demonstrates the Customer Insights scenario where Trustpilot reviews can be quickly grouped by similarity, and an AI agent can generate insights on those groupings. With this workflow, marketers can save days and even weeks of work breaking down their own or competitor reviews and identify frequently mentioned positives and negatives. How it works Trustpilot reviews are scraped for a particular company using the HTTP request node. Reviews are then inserted into a Qdrant collection carefully tagged with the question and Trustpilot metadata. Reviews are fetched and put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the marketer. Requirements - Qdrant Vectorstore for storing embeddings. - OpenAI account for embeddings and LLM. Customising the Template - Adjust clustering parameters which make sense for your data. - Consider expanding the date range of reviews for insights over common intervals: 3mth, 6mth, and YTD.

Who is this template for? This workflow template is designed for any professionals seeking relevant data from a database using natural language. How it works Each time a user asks a question using the n8n chat interface, the workflow runs. Then the message is processed by the AI Agent using relevant tools - Execute SQL Query, Get DB Schema and Tables List, and Get Table Definition, if required. The agent uses these tools to form and run SQL queries that are necessary to answer the questions. Once the AI Agent has the data, it uses it to form an answer and returns it to the user. Set up instructions Complete the Set up credentials step when you first open the workflow. You'll need PostgreSQL credentials and an OpenAI API key.

🧾 Personal Invoice Processor This N8N workflow automates the extraction and organization of personal invoices in Colombia received via Gmail. It includes the following key steps: 🔁 Flow SummaryEmail Trigger Polls Gmail every 30 minutes for emails with .zip attachments (assumed to contain invoices). Expects ZIP file following DIAN standards. ZIP File Handling Extracts all files. Filters only PDF and XML files for processing. Data Extraction & Processing Uses LangChain Agent + OpenAI (GPT-4o-mini) to extract: - Tipo de documento (Factura / Nota Crédito) - Número de factura - Fecha de emisión (YYYY-MM-DD) - NIT emisor y receptor (sin dígito de verificación) - Razón social del emisor - Subtotal, IVA, Total - CUFE - Resumen de compra (max 20 words, formatted sentence) Validation Ensures Total = Subtotal + IVA using a calculator node. Storage Uploads the original PDF to Google Drive. Renames the file to: YYYY-MM-DD-NUMERO_FACTURA.pdf. Inserts or updates invoice details in Google Sheets using a unique Key (NIT_Emisor + Numero_Factura) to prevent duplication. ⚙️ Designed for personal use with minimal latency tolerance and high automation reliability.

A Discord bot that responds to mentions by sending messages to n8n workflows and returning the responses. Connects Discord conversations with custom automations, APIs, and AI services through n8n. The Discord bot listens for mentions, forwards questions to an n8n workflow, processes responses, and replies in Discord. This workflow is intended for all Discord users who want to offer AI interactions with their respective channels. What do you need? You need a Discord account as well as a Google Cloud Project. Key Features 1. Listens for Mentions The bot monitors Discord channels for messages that mention it. Optional Configuration: Can be set to respond only in a specific channel. 2. Forwards Questions to n8n When a user mentions the bot and asks a question: The bot extracts the question. Sends the question, along with channel and user information, to an n8n webhook URL. 3. Processes Data in n8n The n8n workflow receives the question and can: - Interact with AI services (e.g., generating responses). - Access databases or external APIs. - Perform custom logic. n8n formats the response and sends it back to the bot. 4. Replies to Discord with n8n's Response The bot receives the response from n8n. It replies to the user's message in the Discord channel with the answer. Long Responses: Handles responses exceeding Discord's 2000-character limit by chunking them into multiple messages. 5. Error Handling Includes error handling for: - Issues with n8n communication. - Response formatting problems. Manages cases where: - No question is asked. - An invalid response is received from n8n. 6. Typing Indicator While waiting for n8n's response, the bot sends a "typing..." indicator to the Discord channel. 7. Status Update For lengthy n8n processes, the bot sends a message to the Discord channel to inform the user that it is still processing their request.Key Takeaways You’ll configure an n8n webhook to receive Discord messages, process them with your workflow, and respond. You’ll set up a Discord application and bot, grant the right permissions/intents, and invite it to your server. You’ll prepare your server environment (Node.js), scaffold the project, and wire up environment variables. You’ll implement message‐chunking, “typing…” indicators, and robust error handling in your bot code. You’ll deploy with PM2 for persistence and know how to test and troubleshoot common issues.

This n8n demonstrates how to build a simple PostgreSQL MCP server to manage your PostgreSQL database, such as HR, Payroll, Sale, Inventory, and more! This MCP example is based on an official MCP reference implementation. How it works A MCP server trigger is used and connected to 5 tools: 2 PostgreSQL and 3 custom workflows. The 2 PostgreSQL tools are simple read-only queries, and as such, the PostgreSQL tool can be simply used. The 3 custom workflow tools are used for select, insert, and update queries, as these are operations that require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input, which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template, which has a switch to route the operation to the correct handler. Finally, we use our standard PostgreSQL node to handle select, insert, and update operations. The responses are then sent back to the MCP client. How to use This PostgreSQL MCP server allows any compatible MCP client to manage a PostgreSQL database by supporting select, create, and update operations. You will need to have a database available before you can use this server. Try the following queries in your MCP client: - "Please help me check if Alex has an entry in the users table. If not, please help me create a record for her." - "What was the top selling product in the last week?" - "How many high priority support tickets are still open this morning?" Requirements - PostgreSQL for the database. This can be an external database such as Supabase or one you can host internally. - MCP Client or Agent for usage. Customizing this workflow If the scope of schemas or tables is too open, try to restrict it so the MCP serves a specific purpose for business operations. For example, confine the querying and editing to HR-only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

Every time a new image containing text is posted to Telegram, Make will detect the text using Google Cloud Vision (OCR) and send it as a new message to a specified Telegram channel.