Agents & Automations

This workflow demonstrates how to connect an open-source model to a Basic LLM node. The workflow is triggered when a new manual chat message appears. The message is then run through a Language Model Chain that is set up to process text with a specific prompt to guide the model's responses. Note that open-source LLMs with a small number of parameters require slightly different prompting with more guidance to the model. You can change the default Mistral-7B-Instruct-v0.1 model to any other LLM supported by HuggingFace. You can also connect other nodes, such as Ollama. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Automatically filter Google Sheets rows, generate ChatGPT completions, and update rows with insights. Streamline data processing and enhance decision-making.

Printify Automation - Update Title and Description Workflow This n8n workflow automates the process of retrieving products from Printify, generating optimized product titles and descriptions, and updating them back to the platform. It leverages OpenAI for content generation and integrates with Google Sheets for tracking and managing updates. Features - Integration with Printify: Fetch shops and products through Printify's API. - AI-Powered Optimization: Generate engaging product titles and descriptions using OpenAI's GPT model. - Google Sheets Tracking: Log and manage updates in Google Sheets. - Custom Brand Guidelines: Ensure consistent tone by incorporating brand-specific instructions. - Loop Processing: Iteratively process each product in batches. Workflow StructureNodes Overview - Manual Trigger: Manually start the workflow for testing purposes. - Printify - Get Shops: Retrieves the list of shops from Printify. - Printify - Get Products: Fetches product details for each shop. - Split Out: Breaks down the product list into individual items for processing. - Loop Over Items: Iteratively processes products in manageable batches. - Generate Title and Desc: Uses OpenAI GPT to create optimized product titles and descriptions. Google Sheets Integration: - Trigger: Monitors Google Sheets for changes. - Log Updates: Records product updates, including old and new titles/descriptions. Conditional Logic: - If Nodes: Ensure products are ready for updates and stop processing once completed. - Printify - Update Product: Sends updated titles and descriptions back to Printify. - Brand Guidelines + Custom Instructions: Sets brand tone and seasonal instructions. Setup InstructionsPrerequisites - n8n Instance: Ensure n8n is installed and configured. - Printify API Key: Obtain an API key from your Printify account. Add it to n8n under HTTP Header Auth. - OpenAI API Key: Obtain an API key from OpenAI. Add it to n8n under OpenAI API. - Google Sheets Integration: Share your Google Sheets with the Google API service account. Configure Google Sheets credentials in n8n. Workflow Configuration - Set Brand Guidelines: Update the Brand Guidelines + Custom Instructions node with your brand name, tone, and seasonal instructions. - Batch Size: Configure the Loop Over Items node for optimal batch sizes. - Google Sheets Configuration: Set the correct Google Sheets document and sheet names in the integration nodes. - Run the Workflow: Start manually or configure the workflow to trigger automatically. Key Notes - Customization: Modify API calls to support other platforms like Printful or Vistaprint. - Scalability: Use batch processing for efficient handling of large product catalogs. - Error Handling: Configure retries or logging for any failed nodes. Output ExamplesOptimized Content Example - Input Title: "Classic White T-Shirt" - Generated Title: "Stylish Classic White Tee for Everyday Wear" - Input Description: "Plain white T-shirt made of cotton." - Generated Description: "Discover comfort and style with our classic white tee, crafted from premium cotton for all-day wear. Perfect for casual outings or layering." Next Steps - Monitor Updates: Use Google Sheets to review logs of updated products. - Expand Integration: Add support for more Printify shops or integrate with other platforms. - Enhance AI Prompts: Customize prompts for different product categories or seasonal needs. Feel free to reach out for additional guidance or troubleshooting!

Workflow Overview: Extracting text from images using AI is valuable because it requires no coding. It incorporates Google Gemini 2.0 Flash model for important text extraction from images. If you code without AI, you may have to use multiple conditions, which can cause a lot of bugs. However, with Google Gemini, you don't need any coding, and even if the Pay Slip is different, Gemini will extract it automatically. Workflow Description: Users utilize the Line Messaging API to send a Pay Slip image or message to the chatbot. 1. Create a Line Business ID. 2. Classify the message as either an image or text. 3. If the message is a Pay Slip image, it will process using Gemini 2.0 Flash EXP and extract important information, responding in JSON format with the following values: Status, From, To, Date, Amount. If the message is text, it will process using the Gemini 2.0 Flash EXP model as the AI Assistant. If the message is an image, it will extract the important fields, reply to the user, and insert the data into Google Sheets. Key Features: - Extract text from images with No Code: Without N8N, you would have to write code to extract text from images, but with N8N and Google Gemini 2.0 Flash EXP together, there's no need for coding. It can process all slip vendors or other document vendors. - Multipurpose Chatbot: This chatbot accepts both text and images, so there’s no need to create multiple chatbot accounts. - Reduce human error: This workflow allows any officer to verify document status when the job ends. Note: You can change the information by modifying your prompt and the Google Sheets column names accordingly.

This workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. 💪 Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file, and saves locally (chinook.db). Chatting with Your Data Send a message in a chat window. The locally saved SQLite database loads automatically. The user's chat input is combined with the binary data. The LangChain Agent node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️ Example Queries Try these sample queries to see the AI Agent in action: - "Please describe the database" - Get a high-level overview of the database structure; only one or two queries are needed. - "What are the revenues by genre?" - Retrieve revenue information grouped by genre; the LangChain agent iterates several times before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬

This n8n template automates triaging of newly opened support tickets and issue resolution via JIRA. If your organisation deals with a large number of support requests daily, automating triaging is a great use-case for introducing AI to your support teams. Extending the idea, we can also get AI to give a first attempt at resolving the issue intelligently. How it works A scheduled trigger picks up newly opened JIRA support tickets from the queue and discards any seen before. An AI agent analyses the open ticket to add labels, priority on the seriousness of the issue, and simplifies the description for better readability and understanding for human support. Next, the agent attempts to address and resolve the issue by finding similar issues (by tags) which have been resolved. Each similar issue has its comments analysed and summarised to identify the actual resolution and facts. These summaries are then used as context for the AI agent to suggest a fix to the open ticket. How to use Simply connect your JIRA instance to the workflow and activate to start watching for open tickets. Depending on frequency, you may need to increase or decrease the intervals. Define labels to use in the agent's system prompt. Restrict to certain projects or issue types to suit your organisation. Customising this workflow Not using JIRA? Try swapping out the nodes for Linear or your issue management system of choice. Try a different approach for issue resolution. You might want to try RAG approach where a knowledge base is used.

Make will watch your WooCommerce orders at regular intervals and synthesize speech for order detail values designated by you via Google Cloud Text-to-Speech. It will upload them to your Google Drive, create a Google Drive share link, and send the audio file as a campaign to a list of mobile phones via Slybroadcast.

This workflow sends an OpenAI GPT reply when an email is received from specific email recipients. It then saves the initial email and the GPT response to an automatically generated Google spreadsheet. Subsequent GPT responses will be added to the same spreadsheet. Additionally, when feedback is given for any of the GPT responses, it will be recorded to the spreadsheet, which can then be used later to fine-tune the GPT model. How it works This workflow is essentially a two-in-one workflow. It triggers off from two different nodes and has very different functionality from each trigger. The flow triggered from the On email received node is as follows: - Triggers off on the On email received node. - Extract the email body from the email. - Generate a response from the email body using the OpenAI node. - Reply to the email sender using the Send reply to recipient node. A feedback link is also included in the email body, which will trigger the On feedback given node. This is used to fine-tune the GPT model. - Save the email body and OpenAI response to a Google Sheet. If a sheet does not exist, it will be created. The flow triggered from the On feedback given node is as follows: - Triggers off when a feedback link is clicked in the emailed GPT response. - The feedback, either positive or negative, for that specific GPT response is then recorded to the Google Sheet.

Empower Your AI Chatbot with Long-Term Memory and Dynamic Tool Routing This n8n workflow equips your AI agent with long-term memory and a dynamic tools router, enabling it to provide intelligent, context-aware responses while managing tasks across multiple tools. By combining persistent memory and modular task routing, this workflow makes your AI smarter, more efficient, and highly adaptable. 👥 Who Is This For? - AI Developers & Automation Enthusiasts: Integrate advanced AI features like long-term memory and task routing without coding expertise. - Businesses & Teams: Automate tasks while maintaining personalized, context-aware interactions. - Customer Support Teams: Improve user experience with chatbots that remember past interactions. - Marketers & Content Creators: Streamline communication across platforms like Gmail and Telegram. - AI Researchers: Experiment with persistent memory and multi-tool integration. 🚀 What Problem Does This Solve? This workflow simplifies the creation of intelligent AI systems that retain memory, manage tasks dynamically, and automate notifications across tools like Gmail and Telegram—saving time and improving efficiency. 🛠️ What This Workflow Does - Save & Retrieve Memories: Uses Google Docs for long-term storage to recall past interactions or user preferences. - Dynamic Task Routing: Routes tasks to the right tools (e.g., saving/retrieving memories or sending notifications). - AI-Powered Context Understanding: Combines OpenAI GPT-based short-term memory with long-term memory for smarter responses. - Multi-Channel Notifications: Sends updates via Gmail or Telegram. 🔧 Setup - API Credentials: Connect to OpenAI (AI processing), Google Docs (memory storage), Gmail/Telegram (notifications). - Customize Parameters: Adjust the AI agent's system message for your use case. Define task-routing rules in the tools router node. - Test & Deploy: Verify memory saving/retrieval, task routing, and notification delivery. 💡 How to Customize - Modify the system message in the OpenAI node to tailor your agent’s behavior. - Add or adjust routing rules for additional tools. - Update notification settings to match your communication preferences.

Supercharge your productivity with this N8N workflow blueprint that connects Meta AI Smart Glasses, Twilio SMS, and smart response channels like WhatsApp and Messenger. Built for hands-free automation with Meta AI Smart Glasses—fast, flexible, and ready to deploy.

Whenever a new event is scheduled on your Google Calendar, this workflow generates a Meeting Briefing email, giving an overview of each person on the call and the company they work for. It makes use of the web search tool on the OpenAI Responses API to make lookups. The workflow triggers when a new event is added to the calendar, loops over each attendee, generating reports on each person and their company, collates the results, and sends the briefing as an email. Set up steps: 1. Add your credentials for Google Calendar (for viewing events) and Gmail (to send the email). 2. Add your OpenAI credentials as a Header Auth on the Company Search and Person Search nodes. - Name: Authorization - Value: Bearer {{ YOUR_API_KEY }} 3. Edit the "Edit Fields" node with the email that you want to send the briefing to, and a short bit of context about yourself.

Make will watch your Shopify orders at regular intervals and synthesize speech for order detail values designated by you via Google Cloud Text-to-Speech. The audio files will be uploaded to your Google Drive, where a share link will be created. Finally, the audio file will be sent as a campaign to a list of mobile phones via Slybroadcast.

Every time a new page is captured with HTML/CSS to Image, Make will automatically analyze the screenshot and the text content will be extracted using Google Cloud Vision (OCR).

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. 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 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. 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.

Use case In this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags. This is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example. What this workflow does In this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval. The assistant will perform the file search within the OpenAI infrastructure and will return the content with citations. We will make an HTTP request to retrieve all the details we need to format the text output. How to adjust it to your needs At the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.

CallForge - AI-Powered Product Insights Processor from Sales Calls Automate product feedback extraction from AI-analyzed sales calls and store structured insights in Notion for data-driven product decisions. 🎯 Who is This For? This workflow is designed for: ✅ Product managers tracking customer feedback and feature requests. ✅ Engineering teams identifying usability issues and AI/ML-related mentions. ✅ Customer success teams monitoring product pain points from real sales conversations. It streamlines product intelligence gathering, ensuring customer insights are structured, categorized, and easily accessible in Notion for better decision-making. 🔍 What Problem Does This Workflow Solve? Product teams often struggle to capture, categorize, and act on valuable feedback from sales calls. With CallForge, you can: ✔ Automatically extract and categorize product feedback from AI-analyzed sales calls. ✔ Track AI/ML-related mentions to gauge customer demand for AI-driven features. ✔ Identify feature requests and pain points for product development prioritization. ✔ Store structured feedback in Notion, reducing manual tracking and increasing visibility across teams. This workflow eliminates manual feedback tracking, allowing product teams to focus on innovation and customer needs. 📌 Key Features & Workflow Steps 🎙️ AI-Powered Product Feedback Processing This workflow processes AI-generated sales call insights and organizes them in Notion databases: Triggers when AI sales call data is received. Detects product-related feedback (feature requests, bug reports, usability issues). Extracts key product insights, categorizing feedback based on customer needs. Identifies AI/ML-related mentions, tracking customer interest in AI-driven solutions. Aggregates feedback and categorizes it by sentiment (positive, neutral, negative). Logs insights in Notion, making them accessible for product planning discussions. 📊 Notion Database Integration Product Feedback → Logs feature requests, usability issues, and bug reports. AI Use Cases → Tracks AI-related discussions and customer interest in machine learning solutions. 🛠 How to Set Up This Workflow 1. Prepare Your AI Call Analysis Data Ensure AI-generated sales call insights are available. Compatible with Gong, Fireflies.ai, Otter.ai, and other AI transcription tools. 2. Connect Your Notion Database Set up Notion databases for: 🔹 Product Feedback (logs feature requests and bug reports). 🔹 AI Use Cases (tracks AI/ML mentions and customer demand). 3. Configure n8n API Integrations Connect your Notion API key in n8n under “Notion API Credentials.” Set up webhook triggers to receive AI-generated sales insights. Test the workflow using a sample AI sales call analysis. 🔧 How to Customize This Workflow 💡 Modify Notion Data Structure – Adjust fields to align with your product team's workflow. 💡 Refine AI Data Processing Rules – Customize how feature requests and pain points are categorized. 💡 Integrate with Slack or Email – Notify teams when recurring product issues emerge. 💡 Expand with Project Management Tools – Sync insights with Jira, Trello, or Asana to create product tickets automatically. ⚙️ Key Nodes Used in This Workflow 🔹 If Nodes – Detect if product feedback, AI mentions, or feature requests exist in AI data. 🔹 Notion Nodes – Create and update structured feedback entries in Notion. 🔹 Split Out & Aggregate Nodes – Process multiple insights and consolidate AI-generated data. 🔹 Wait Nodes – Ensure smooth sequencing of API calls and database updates. 🚀 Why Use This Workflow? ✔ Eliminates manual sales call review for product teams. ✔ Provides structured, AI-driven insights for feature planning and prioritization. ✔ Tracks AI/ML mentions to assess demand for AI-powered solutions. ✔ Improves product development strategies by leveraging real customer insights. ✔ Scalable for teams using n8n Cloud or self-hosted deployments. This workflow empowers product teams by transforming sales call data into actionable intelligence, optimizing feature planning, bug tracking, and AI/ML strategy. 🚀

Vector Database as a Big Data Analysis Tool for AI Agents This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (Image) Datasets to Qdrant 1. The first pipeline to upload an image dataset to Qdrant. 2. The second pipeline sets up cluster (class) centres and cluster (class) threshold scores needed for anomaly detection. Anomaly Detection Tool The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (K Nearest Neighbours) Classification 1. The first pipeline to upload an image dataset to Qdrant. 2. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded Qdrant dataset. To Recreate Both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket and recreate APIs/connections to Qdrant Cloud (you can use the Free Tier cluster), Voyage AI API, and Google Cloud Storage. Workflow for Setting Up Cluster (Class) Centres & Cluster (Class) Threshold Scores for Anomaly Detection This preparatory workflow sets cluster centres and cluster threshold scores so anomalies can be detected based on these thresholds. Here, we're using two approaches to set up these centres: the "distance matrix approach" and the "multimodal embedding model approach."

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Chat Conversations with Bright Data MCP Search Engines & Google Gemini workflow is designed for users who need real-time, AI-enhanced conversations powered by live search engine results. This workflow is tailored for: - Data Analysts - Who want live, search-based data fused with AI reasoning. - Marketing Researchers - Seeking up-to-the-minute market or competitor insights via conversational AI. - Product Managers - Exploring user needs, market trends, and competitor analysis in real time. - AI Developers - Building dynamic applications that combine live search data with intelligent conversation agents. - Growth Hackers - Who need fast, conversational research tools for campaign ideation, outreach, or content creation.What problem is this workflow solving? Traditional chatbots and AI systems often rely on static, outdated data. This workflow enables AI agents to fetch live search engine data and converse intelligently about it, making interactions dynamic, accurate, and highly contextual. This workflow solves the major gaps of: - Outdated Knowledge: Regular chatbots lack up-to-date information from live web searches. - Manual Search Fatigue: Manually searching for information and interpreting it is time-consuming. - Context Bridging: Connecting search results into meaningful, conversational replies requires human-level reasoning.What this workflow does? - Accepts a user's conversational query input. - Triggers a search request to Bright Data’s MCP Search Engines API (Google, Bing, etc.) based on the query. - Waits for the search task to complete. - Retrieves real-time search results. - Feeds the search results and original question into Google Gemini. - Generates a human-like, contextually accurate AI response combining live information and conversational flow. - Outputs the response back into a chat app.Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol. You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio. You need to install the Bright Data MCP Server @brightdata/mcp. You need to install the n8n-nodes-mcp.Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp. Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Also, do "Account Setup" as mentioned in the @brightdata/mcp URL. 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 Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data Web Unlocker API Token within the Environments textbox above. Update the HTTP Request for Webhook Notification node for sending the Webhook notification for chat responses.How to customize this workflow to your needs - Change Search Engine: Add or Remove the Search Engine MCP tools based upon the Bright Data MCP Server updates. - Expand Outputs: Send AI chat responses to Slack, Discord, custom chat UIs, WhatsApp, or CRM systems. Store conversation logs in a database (PostgreSQL, MongoDB, etc.) for future audits or training.

This n8n template builds upon a simple appointment request form design which uses AI to qualify if the incoming enquiry is suitable and/or time-worthy of an appointment. This demonstrates a lighter approach to using AI in your templates but handles a technically difficult problem - contextual understanding! This example can be used in a variety of contexts where figuring out what is and isn't relevant can save a lot of time for your organisation. How it works We start with a form trigger which asks for the purpose of the appointment. Instantly, we can qualify this by using a text classifier node which uses AI's contextual understanding to ensure the appointment is worthwhile. If not, an alternative is suggested instead. Multi-page forms are then used to set the terms of the appointment and ask the user for a desired date and time. An acknowledgement is sent to the user while an approval by email process is triggered in the background. In a subworkflow, we use Gmail with the wait for approval operation to send an approval form to the admin user who can either confirm or decline the appointment request. When approved, a Google Calendar event is created. When declined, the user is notified via email that the appointment request was declined. How to use Modify the enquiry classifier to determine which contexts are relevant to you. Configure the wait for approval node to send to an email address which is accessible to all appropriate team members. Customising this workflow Not using Google Mail or Calendar? Feel free to swap this with other services. The wait for approval step is optional. Remove if you wish to handle appointment request resolution in another way.

This workflow automates the real estate lead qualification process by leveraging property data from BatchData. The automation follows these steps: When a new lead is received through your CRM webhook, the workflow captures their address information. It then makes an API call to BatchData to retrieve comprehensive property details. A sophisticated scoring algorithm evaluates the lead based on property characteristics like: - Property value (higher values earn more points) - Square footage (larger properties score higher) - Property age (newer constructions score higher) - Investment status (non-owner occupied properties earn bonus points) - Lot size (larger lots receive additional score) Leads are automatically classified into categories (high-value, qualified, potential, or unqualified). The workflow updates your CRM with enriched property data and qualification scores. High-value leads trigger immediate follow-up tasks for your team. Notifications are sent to your preferred channel (Slack in this example). The entire process happens within seconds of receiving a new lead, ensuring your sales team can prioritize the most valuable opportunities immediately. --- Who It's For This workflow is perfect for: - Real estate agents and brokers looking to prioritize high-value property leads - Mortgage lenders who need to qualify borrowers based on property assets - Home service providers (renovators, contractors, solar installers) targeting specific property types - Property investors seeking specific investment opportunities - Real estate marketers who want to segment audiences by property value - Home insurance agents qualifying leads based on property characteristics Any business that bases lead qualification on property details will benefit from this automated qualification system. --- About BatchData BatchData is a comprehensive property data provider that offers detailed information about residential and commercial properties across the United States. Their API provides: - Property valuation and estimates - Ownership information - Property characteristics (size, age, bedrooms, bathrooms) - Tax assessment data - Transaction history - Occupancy status (owner-occupied vs. investment) - Lot details and dimensions By integrating BatchData with your lead management process, you can automatically verify and enrich leads with accurate property information, enabling more intelligent lead scoring and routing based on actual property characteristics rather than just contact information. This workflow demonstrates how to leverage BatchData's property API to transform your lead qualification process from manual research into an automated, data-driven system that ensures high-value leads receive immediate attention.

This n8n workflow is a fun way to query and search over your credentials on your n8n instance. Good to know: Your credentials should remain safe as this workflow does not decrypt or use any decrypted data. Example Usage: - "Which workflows are using Slack and Google Calendar?" - "Which workflows have AI in their name but are not using openAI?" How it works: Using the n8n API, it fetches all workflow data on the instance. Workflow data contains references to credentials used, so this will be extracted. With some necessary reformatting, the workflows and their credentials metadata are stored to a SQLite database. Next, an AI agent is used with a custom SQL tool that reads the SQLite database created in the previous step. The AI agent is instructed to perform SQL queries against our workflow credential table when asked about credentials by the user. Requirements: You'll need an n8n API key. Please note that only workflows will be scoped to your API key. Customising the workflow: Add extra table fields to the SQLite database to answer even more complex queries such as workflow status to differentiate between active and inactive workflows.