Actioning Your Meeting Next Steps using Transcripts and AI

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.

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

How it works: The workflow starts by sending a request to a website to retrieve its HTML content. It then parses the HTML extracting the relevant information The extracted data is storted and converted into a CSV file. The CSV file is attached to an email and sent to your specified address. The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use. Set-up steps: Change the website to scrape in the "Fetch website content" node Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node) Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).

Who is this template for? This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved. Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses. You can watch the video tutorial here: https://youtu.be/HaiO-UeiKBA How it works This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates: Input Queries You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in. Iterates through Queries The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks. Scrapes Google Maps for URLs Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria. Fetches HTML Content The workflow then fetches the HTML pages of the collected URLs for further processing. Extracts Emails Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses. Setup Add Queries: Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region. Configure the Google Sheets Node: Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved. Run the workflow: Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses. Customize as Needed: You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.

Who is this for? This workflow is for all sales reps and lead generation manager who need to prepare their prospecting activities, and find relevant information to personalize their outreach. Use Case This workflow allows you to do account research with the web using AI. It has the potential to replace manual work done by sales rep when preparing their prospecting activities by searching complex information available online. What this workflow does The advanced AI module has 2 capabilities: Research Google using SerpAPI Visit and get website content using a sub-workflow From an unstructured input like a domain or a company name. It will return the following properties: domain company Linkedin Url cheapest plan has free trial has entreprise plan has API market (B2B or B2C) The strength of n8n here is that you can adapt this workflow to research whatever information you need. You just have to precise it in the prompt and to precise the output format in the "Strutured Output Parser" module. Detailed instructions + video guide can be found by following this link.

This workflow automatically generates draft replies in Gmail. It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses. Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out. How It Works: Email Trigger: activates when new emails reach the Gmail inbox Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary. Reply Generation: crafts a reply with OpenAI GPT-4 Turbo Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message Set Up Overview (~10 minutes): OAuth Configuration (follow n8n instructions here): Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope. Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings. OpenAI Configuration: add OpenAI API Key in the credentials Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs Detailed Walkthrough Check out this blog post where I go into more details on how I built this workflow. Reach out to me here if you need help building automations for your business.

Task: Create a simple API endpoint using the Webhook and Respond to Webhook nodes Why: You can prototype or replace a backend process with a single workflow Main use cases: Replace backend logic with a workflow

Task: Merge two datasets into one based on matching rules Why: A powerful capability of n8n is to easily branch out the workflow in order to process different datasets. Even more powerful is the ability to join them back together with SQL-like joining logic. Main use cases: Appending data sets Keep only new items Keep only existing items

This workflow will backup your workflows to Github. It uses the public api to export all of the workflow data using the n8n node. It then loops over the data checks in Github to see if a file exists that uses the workflow name. Once checked it will then update the file on Github if it exists, Create a new file if it doesn't exist and if it's the same it will ignore the file. Config Options repo_owner - Github owner repo_name - Github repository name repo_path - Path within the Github repository >This workflow has been updated to use the n8n node and the code node so requires at least version 0.198.0 of n8n

This workflow allows extracting data from multiple pages website. The workflow: 1) Starts in a country list at https://www.theswiftcodes.com/browse-by-country/. 2) Loads every country page (https://www.theswiftcodes.com/albania/) 3) Paginates every page in the country page. 4) Extracts data from the country page. 5) Saves data to MongoDB. 6) Paginates through all pages in all countries. It uses getWorkflowStaticData('global') method to recover the next page (saved from the previous page), and it goes ahead with all the pages. There is a first section where the countries list is recovered and extracted. Later, I try to read if a local cache page is available and I recover the cached page from the disk. Finally, I save data to MongoDB, and we paginate all the pages in the country and for all the countries. I have applied a cache system to save a visited page to n8n local disk. If I relaunch workflow, we check if a cache file exists to discard non-required requests to the webpage. If the data present in the website changes, you can apply a Cron node to check the website once per week. Finally, before inserting data in MongoDB, the best way to avoid duplicates is to check that swift_code (the primary value of the collection) doesn't exist. I recommend using a proxy for all requests to avoid IP blocks. A good solution for proxy plus IP rotation is scrapoxy.io. This workflow is perfect for small data requirements. If you need to scrape dynamic data, you can use a Headless browser or any other service. If you want to scrape huge lists of URIs, I recommend using Scrapy + Scrapoxy.

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.

This workflow allows to scrape Google Maps data in an efficient way using SerpAPI. You'll get all data from Gmaps at a cheaper cost than Google Maps API. Add as input, your Google Maps search URL and you'll get a list of places with many data points such as: phone number website rating reviews address And much more. Full guide to implement the workflow is here: https://lempire.notion.site/Scrape-Google-Maps-places-with-n8n-b7f1785c3d474e858b7ee61ad4c21136?pvs=4

This n8n workflow automates the process of parsing and extracting data from PDF invoices. With this workflow, accounts and finance people can realise huge time and cost savings in their busy schedules. Read the Blog: https://blog.n8n.io/how-to-extract-data-from-pdf-to-excel-spreadsheet-advance-parsing-with-n8n-io-and-llamaparse/ How it works This workflow will watch an email inbox for incoming invoices from suppliers It will download the attached PDFs and processing them through a third party service called LlamaParse. LlamaParse is specifically designed to handle and convert complex PDF data structures such as tables to markdown. Markdown is easily to process for LLM models and so the data extraction by our AI agent is more accurate and reliable. The workflow exports the extracted data from the AI agent to Google Sheets once the job complete. Requirements The criteria of the email trigger must be configured to capture emails with attachments. The gmail label "invoice synced" must be created before using this workflow. A LlamaIndex.ai account to use the LlamaParse service. An OpenAI account to use GPT for AI work. Google Sheets to save the output of the data extraction process although this can be replaced for whatever your needs. Customizing this workflow This workflow uses Gmail and Google Sheets but these can easily be swapped out for equivalent services such as Outlook and Excel. Not using Excel? Simple redirect the output of the AI agent to your accounting software of choice.

This flow is supported by a Chrome plugin created with Cursor AI. The idea was to create a Chrome plugin and a backend service in N8N to do chart analytics with OpenAI. It's a good sample on how to submit a screenshot from the browser to N8N. Who is it for? N8N developers who want to learn about using a Chrome plugin, an N8N webhook and OpenAI. What opportunity does it present? This sample opens up a whole range of N8N connected Chrome extensions that can analyze screenshots by using OpenAI. What this workflow does? The workflow contains: a webhook trigger an OpenAI node with GPT-4O-MINI and Analyze Image selected a response node to send back the Text that was created after analysing the screenshot. All this is needed to talk to the Chrome extension which is created with Cursor AI. The idea is to visit the tradingview.com crypto charts, click the Chrome plugin and get back analytics about the shown chart in understandable language. This is driven by the N8N flow. With the new image analytics capabilities of OpenAI this opens up a world of opportunities. Requirements/setup OpenAI API key Cursor AI installed The Chrome extension. Download The N8N JSON code. Download How to customize it to your needs? Both the Chrome extension and N8N flow can be adapted to use on other websites. You can consider: analyzing a financial screen and ask questions about the data shown analyzing other charts extending the N8N workflow with other AI nodes With AI and image analytics the sky is the limit and in some cases it saves you from creating complex API integrations. Download Chrome extension

This n8n workflow demonstrates how to manage your Qdrant vector store when there is a need to keep it in sync with local files. It covers creating, updating and deleting vector store records ensuring our chatbot assistant is never outdated or misleading. Disclaimer This workflow depends on local files accessed through the local filesystem and so will only work on a self-hosted version of n8n at this time. It is possible to amend this workflow to work on n8n cloud by replacing the local file trigger and read file nodes. How it works A local directory where bank statements are downloaded to is monitored via a local file trigger. The trigger watches for the file create, file changed and file deleted events. When a file is created, its contents are uploaded to the vector store. When a file is updated, its previous records are replaced. When the file is deleted, the corresponding records are also removed from the vector store. A simple Question and Answer Chatbot is setup to answer any questions about the bank statements in the system. Requirements A self-hosted version of n8n. Some of the nodes used in this workflow only work with the local filesystem. Qdrant instance to store the records. Customising the workflow This workflow can also work with remote data. Try integrating accounting or CRM software to build a managed system for payroll, invoices and more. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/189F1fNOiw6naNSlSwnyLVEm_Ho_IFfdM/view?usp=sharing

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.

This workflow will check a mailbox for new emails and if the Subject contains Expenses or Reciept it will send the attachment to Mindee for processing then it will update a Google sheet with the values. To use this node you will need to set the Email Read node to use your mailboxes credentials and configure the Mindee and Google Sheets nodes to use your credentials.

Purpose of workflow: The purpose of this workflow is to create an AI-powered technical analysis agent capable of analyzing financial charts, specifically for cryptocurrencies like Bitcoin or equity stocks. This agent provides users with insights into market trends, price movements, and technical indicators to assist in making informed trading decisions. How it works: The agent uses the Sonnet model from Anthropic for LLM It integrates with TradingView charts through the chart-img.com API to generate and download financial charts. The agent analyzes the chart using AI vision capabilities, examining candlestick patterns, pricing trends, and technical indicators like Relative Strength Index (RSI) and Directional Movement Index (DMI). It provides a detailed analysis of the chart, including support and resistance levels, market trends, and volume analysis. The agent generates a visual representation of the analysis, displaying candlesticks, volume, RSI, and DMI. Step by step setup: Create free API key from chart-img.com Set the exchange for the ticker (defaults to NYSE) Tutorial

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

Who is this for This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup. Check out the Notion template this Assistant is set up to use: https://www.notion.so/templates/knowledge-base-ai-assistant-with-n8n How it works The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or question and retrieves content from inside individual Notion pages for additional context. Key features include: Querying the database with flexible filters. Searching within individual Notion pages and extracting relevant blocks. Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification. This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets. Set up Find basic set up instructions inside the workflow itself or watch a quickstart video 👇

How it Works This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates: Data Collection and Extraction: The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node. Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV. Processing and Evaluation: A Merge node combines the extracted personal and qualification data into a single output. This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile. An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring. Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document via the Google Sheets node for further review or reporting purposes [[9]]. Set Up Steps To replicate this workflow within your own n8n environment, follow these steps: Configuration: Begin by setting up an n8n instance if you haven't already; you can sign up directly on their website or self-host the application. Import the provided JSON configuration into your n8n workspace. Ensure that all necessary credentials (e.g., Google Drive, Google Sheets, OpenAI API keys) are correctly configured under the Credentials section since some nodes require external service integrations like Google APIs and OpenAI for language processing tasks. Customization: Adjust the parameters of each node according to your specific requirements. For example, modify the fields in the formTrigger node to match what kind of information you wish to collect from applicants. Customize the prompts given to AI models in nodes like Qualifications, Summarization Chain, and HR Expert so they align with the type of analyses you want performed on the candidates' profiles. Update the destination settings in the Google Sheets node to point towards your own spreadsheet where you would like the final outputs recorded.

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It Works Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up Steps Estimated Time**: 20–25 minutes Prerequisites**: Create an account with an AI provider (e.g., OpenAI). Connect WhatsApp or Telegram credentials in n8n. Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. Import the workflow template into n8n and update environment variables for your credentials.

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process. What problem does this workflow solve? Manually screening resumes is time-consuming and prone to human error. This workflow automates the process, providing consistent and objective analysis of CVs against job descriptions. It helps filter out unsuitable candidates early, reducing workload and improving the overall efficiency of the recruitment process. What this workflow does This workflow automates the resume screening process using OpenAI for analysis. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. Retrieve Resume: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox). Extract Data: Extracts text data from PDF or DOC files for analysis. Analyze with OpenAI: Sends the extracted data and job description to OpenAI to: Generate a matching score. Summarize candidate strengths and weaknesses. Provide actionable insights into their suitability for the job. Setup Preparation Create Accounts: N8N: For workflow automation. OpenAI: For AI-powered CV analysis. Get CV Link: Upload CV files to Supabase storage or Dropbox to generate a direct link for processing. Prepare Artifacts for OpenAI: Define Metrics: Identify the metrics you want from the analysis (e.g., matching percentage, strengths, weaknesses). Generate JSON Schema: Use OpenAI to structure responses, ensuring compatibility with your database. Write a Prompt: Provide OpenAI with a clear and detailed prompt to ensure accurate analysis. N8N Scenario Download File: Fetch the CV using its direct URL. Extract Data: Use N8N’s PDF or text extraction nodes to retrieve text from the CV. Send to OpenAI: URL: POST to OpenAI’s API for analysis. Parameters: Include the extracted CV data and job description. Use JSON Schema to structure the response. Summary This workflow provides a seamless, automated solution for CV screening, helping recruitment agencies and HR teams save time while maintaining consistency in candidate evaluation. It enables organizations to focus on the most suitable candidates, improving the overall hiring process.

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.

Create a Branded AI Website Chatbot Engage website visitors with an intelligent chat widget powered by OpenAI. This template includes: 💬 Natural conversation handling 📅 Microsoft Outlook calendar integration 📝 Lead capture and information gathering 🔄 Human handoff capabilities Simply add a JavaScript snippet to your website and configure the workflow to match your needs. Follow our detailed setup guide to get started in minutes. > Note: Widget includes a "Powered By" affiliate link

This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot. After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message. In case user sends a message to the bot, the text and user data is stored in Google Sheets. To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node. Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉 Setup Instructions To get this workflow up and running, you'll need to: 👇 Create a WhatsApp template message on the Meta Business portal. Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages. Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal. Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates. Connect your Google Sheets account for data storage and management. Check out the documentation page. ⚠️ Important Notes WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent. The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.

Important Notes: Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! Workflow Description: 😮‍💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping ? This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured. This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy. Key Features: Google Sheets Integration**: Manage URLs to scrape and store structured results. ScrapingBee**: Capture full-page screenshots and retrieve HTML data for fallback extraction. AI-Powered Data Parsing**: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON. Token Efficiency**: HTML is converted to Markdown to optimize processing costs. This template is designed for e-commerce scraping but can be customized for various use cases.

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

Who is this template for? This workflow template is designed for any professionals seeking relevent data from database using natural language. How it works Each time user ask's question using the n8n chat interface, the workflow runs. Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions. Once AI Agent has the data, it uses it to form 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 a Postgresql Credentials, and OpenAI api key. Template was created in n8n v1.77.0

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.

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.

⚙️🛠️🚀🤖🦾 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.

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.

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.

The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.

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

How it works: The workflow starts by sending a request to a website to retrieve its HTML content. It then parses the HTML extracting the relevant information The extracted data is storted and converted into a CSV file. The CSV file is attached to an email and sent to your specified address. The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use. Set-up steps: Change the website to scrape in the "Fetch website content" node Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node) Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).

Who is this template for? This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved. Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses. You can watch the video tutorial here: https://youtu.be/HaiO-UeiKBA How it works This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates: Input Queries You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in. Iterates through Queries The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks. Scrapes Google Maps for URLs Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria. Fetches HTML Content The workflow then fetches the HTML pages of the collected URLs for further processing. Extracts Emails Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses. Setup Add Queries: Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region. Configure the Google Sheets Node: Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved. Run the workflow: Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses. Customize as Needed: You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.

Easily generate images with Black Forest's Flux Text-to-Image AI models using Hugging Face’s Inference API. This template serves a webform where you can enter prompts and select predefined visual styles that are customizable with no-code. The workflow integrates seamlessly with Hugging Face's free tier, and it’s easy to modify for any Text-to-Image model that supports API access. Try it Curious what this template does? Try a public version here: https://devrel.app.n8n.cloud/form/flux Set Up Watch this quick set up video 👇 Accounts required Huggingface.co account (free) Cloudflare.com account (free - used for storage; but can be swapped easily e.g. GDrive) Key Features: Text-to-Image Creation**: Generates unique visuals based on your prompt and style. Hugging Face Integration**: Utilizes Hugging Face’s Inference API for reliable image generation. Customizable Visual Styles**: Select from preset styles or easily add your own. Adaptable**: Swap in any Hugging Face Text-to-Image model that supports API calls. Ideal for: Creators**: Rapidly create visuals for projects. Marketers**: Prototype campaign visuals. Developers**: Test different AI image models effortlessly. How It Works: You submit an image prompt via the webform and select a visual style, which appends style instructions to your prompt. The Hugging Face Inference API then generates and returns the image, which gets hosted on Cloudflare S3. The workflow can be easily adjusted to use other models and styles for complete flexibility.

Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video. Time-Saving Features 🚀 Instant Processing Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis. 🤖 AI-Powered Analysis Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points. Smart Processing Pipeline 📝 Automated Transcript Extraction Supports public YouTube video Handles multiple URL formats Extracts complete video transcripts automatically 🧠 Intelligent Content Organization Breaks down content into main topics Highlights key concepts and terminology Maintains technical accuracy while improving clarity Structures information logically with markdown formatting Perfect For 📚 Researchers & Students Quick comprehension of educational content and lectures without watching entire videos. 💼 Business Professionals Efficient analysis of industry talks, presentations, and training materials. 🎯 Content Creators Rapid research and competitive analysis of video content in your niche. Technical Implementation 🔄 Workflow Components Webhook endpoint for URL submission YouTube API integration for video details Transcript extraction system GPT-4 powered analysis engine Telegram notification system (optional) Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.

This workflow automatically generates draft replies in Gmail. It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses. Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out. How It Works: Email Trigger: activates when new emails reach the Gmail inbox Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary. Reply Generation: crafts a reply with OpenAI GPT-4 Turbo Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message Set Up Overview (~10 minutes): OAuth Configuration (follow n8n instructions here): Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope. Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings. OpenAI Configuration: add OpenAI API Key in the credentials Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs Detailed Walkthrough Check out this blog post where I go into more details on how I built this workflow. Reach out to me here if you need help building automations for your business.