Baserow and HTTP Request integration

This n8n template demonstrates how to automatically create tasks (or in general, records) in Baserow based on template or blueprint tables. The first blueprint table is the master table that holds the general information about the template. For example: a standard procedure to handle incidents. The second table is the details table that holds multiple records for the template. Each record in that table is a specific task that needs to be assigned to someone with a certain deadline. This makes it easy to streamline task creation for recurring processes. Use cases are many: Project management (generate tasks for employees based on a project template) HR & onboarding (generate tasks for employee onboarding based on a template) Operations (create checklists for maintenance, audits, or recurring procedures) Good to know The Baserow template for handling Standard Operating Procedures works perfect as a base schema to try out this workflow. Authentication is done through a database token. Check the documentation on how to create such a token. Tasks are inserted using the HTTP request node instead of a dedicated Baserow node. This is to support batch import instead of importing records one by one. Requirements Baserow account (cloud or self-hosted) A Baserow database with at least the following tables: Assignee / employee table. This is required to be able to assign someone to a task. Master table with procedure or template information. This is required to be able to select a certain template Details table with all the steps associated with a procedure or template. This is required to convert each step into a specific task. A step must have a field Days to complete with the number of days to complete the step. This field will be used to calculate the deadline. Tasks table that contains the actual tasks with an assignee and deadline. How it works Trigger task creation (webhook)** The automation starts when the webhook is triggered through a POST request. It should contain an assignee, template, date and note in the body of the request. It will send a succes or failure response once all steps are completed. Configure settings and ids** Stores the ids of the involved Baserow database and tables, together with the API credentials and the data from the webhook. Get all template steps** Gets all the steps from the template Details table that are associated with the id of the Master template table. For example: the master template can have a record about handling customer complaints. The details table contains all the steps to handle this procedure. Calculate deadlines for each step** Prepares the input of the tasks by using the same property names as the field of the Tasks table. Adjust this names, add or remove fields if this is required for your database structure. The deadline of each step is calculated by adding the number of days a step can take based on the deadline of the first step. This is done through a field Days to complete in the template Details table. For example. If the schedule_date property in the webhook is set to 2025-10-01 and the Days to complete for the step is 3, then the deadline will be 2025-10-04 Avoid scheduling during the weekend** It might happen that the calculated deadline is on a Saturday or Sunday. This Code node moves those dates to the first Monday to avoid scheduling during the weekend. Aggregate tasks for insert** Aggregates the data from the previous nodes as an array in a property named items. This matches perfect with the Baserow API to insert new records in batch. Generate tasks in batch** Calls the API endpoint /api/database/rows/table/{table_id}/batch/ to insert multiple records at once in the tasks table. Check the Baserow API documentation for further details. Success / Error response** Sends a simple text response to indicate the success or failure of the record creation. This is to offer feedback when triggering the automation from a Baserow application, but can be replaced with a JSON response. How to use Call the Trigger task creation node with the required parameters through a POST request. This can be done from any web application. For example: the application builder in Baserow supports an action to send an HTTP request. The Procedure details page in the Standard Operating Procedures template demonstrates this action. The following information is required in the body of the request. This information is required to create the actual tasks. { "assignee_id": integer refering to the id of the assignee in the database, "template_id": integer refering to the id of the template or procedure in the master table, "schedule_date": the date the tasks need to start scheduling, "note": text with an optional note about the tasks } Set the corresponding ids in the Configure settings and ids node. Check the names of the properties in the Calculate deadlines for each step node. Make sure the names of those properties match the field names of your Tasks table. You can replace the text message in the Success response and Failure response with a more structured format if this is necessary in your application. Customising this workflow Add support for public holidays (e.g., using an external calendar API). Modify the task assignment logic (e.g., pre-assign tasks in the details table). Combine with notifications (email, Slack, etc.) to alert employees when new tasks are generated.

This n8n template demonstrates how to automatically generate personalized calendar views in Baserow, based on a chosen date field and a filter. Having a personalized view with only information that is relevant to you makes it easy to integrate with external calendar tools like Outlook or Google Calendar. Use cases are many: Task management (deadlines per staff member) Customer management (appointments per customer) Inventory management (delivery dates per supplier) Good to know You only need a Date field (e.g., a task deadline, due date, appointment date) and a Link to table field (e.g., a customer, employee, product) to make this work. The generated calendar views can be shared as .ics files and imported into any external calendar application. Authentication is done through a JWT token constructed from your Baserow username and password. How it works Set Baserow credentials**: Allows you to enter your Baserow credentials (username + password) and the API host path. The host is by default https://api.baserow.io, but you can change this in case you are self-hosting. The information is required to generate a JWT token that authenticates all future HTTP request nodes to create and configure the view. Create a token**: Generates a JWT token based on the information provided in the previous node. Set table and field ids**: Stores the generated JWT token and allows you to enter the ids of the tables and fields required to run the automation. Get all records from filter table** Gets all the records from the table you want to filter on. This is the table that has a Link to table field referencing the table with the Date field. Each record from this table will get it’s own view. Some examples: Customers, Employees and Products. Create new calendar view** Calls the API endpoint /api/database/views/table/{table_id} to create a new view. Check the Baserow API documentation for further details. The body of this requests configures the new view by setting among other things a name and the date field Create filter** Calls the API endpoint /api/database/views/{view_id}/filters/ to set a filter on the view so that it only shows the records that are relevant. This filter is based on the Link to table field that is set in earlier steps. Check the Baserow API documentation for further details. Set background color** Calls the API endpoint /api/database/views/{view_id}/decorations/ to set a a color on the background or left side of each item. By default, the color is based on a single select field, but it is also possible to use a condition. Check the Baserow API documentation for further details. Share the view** Calls the API endpoint /api/database/views/{view_id} to update the current view. It updates the ical_public property to true so that an ics link is created. Check the Baserow API documentation for further details. Update the url’s** Updates all the records in the table you want to filter on to fill in the url to the new generated view and the url to the ics file. This can be useful if you want to build an application on top of your database. How to use The Manual Trigger node is provided as an example, but you can replace it with other triggers such as a webhook The included Baserow SOP template works perfectly as a base schema to try out this workflow. Requirements Baserow account (cloud or self-hosted) A Baserow database with a table that has a Date field and a Link to Table field Customising this workflow Change the date field used to generate the calendars (e.g., deadline → appointment date). Adjust the filters to match your context (staff, customer, product, etc.). Configure which fields are shown using the /api/database/view/{view_id}/field-options/ endpoint. Check the Baserow API documentation for further details. Add or remove optional steps such as coloring by status or sharing the ics feed. Extend the workflow to notify staff when a new view has been created for them.

Automatically track your Android app’s keyword rankings on Google Play. This workflow checks ranks via SerpApi, updates a Baserow table, and posts a heads-up in Slack so your team can review changes quickly. 💡 Perfect for ASO teams tracking daily keyword positions Growth & marketing standups that want quick rank visibility Lightweight historical logging without a full BI stack 🧠 What it does Runs on a schedule (e.g., weekly) Queries SerpApi for each keyword’s Play Store ranking Saves results to Baserow: Current Rank, Previous Rank, Last modified Sends a Slack alert: “Ranks updated — review in Baserow” ⚡ Requirements SerpApi account & API key Baserow account + API token Slack connection (bot/app or credential in n8n) ⚙️ Setup Instructions 1) Create a Baserow table Create a new table (any name). Add user-field names exactly: Keywords (text) Current Rank (number) Previous Rank (number) Last modified (date/time) Optional fields you can add later: Notes, Locale, Store Country, App Package ID. 2) Connect credentials in n8n Baserow: add your API token and select your Database and Table in the Baserow nodes. Slack: connect your Slack account/workspace in the Slack node. SerpApi: open the HTTP Request node and put your API key under Query Parameters → api_key = YOUR_KEY. 3) Verify field mapping In the Baserow (Update Row) node, map: Row ID → {{$json.id}} Current Rank → {{$json["Current Rank"]}} Previous Rank → your code node should set this (the template copies the old “Current Rank” into “Previous Rank” before writing the new one) Last modified → {{$now}} (or the timestamp you compute) 🛟 Notes & Tips If you prefer a single daily Slack summary instead of multiple pings, add a Code node after updates to aggregate lines and send one message. Treat 0 or missing ranks as “not found” and flag them in Slack if helpful. For multi-country tracking, include hl/gl (locale/country) in your SerpApi query params and store them as columns. 🤝 Need a hand? I’m happy to help you get this running smoothly—or tailor it to your brand. Reach out to me via email: [email protected]

VEXA: AI-Powered Meeting Intelligence I'll be honest, I built this because I was getting lazy in meetings and missing key details. I started with a simple VEXA integration for transcripts, then added AI to pull out summaries and tasks. But that just solved part of the problem. The real breakthrough came when we integrated Mem0, creating a persistent memory of every conversation. Now, you can stop taking notes and actually focus on the person you're talking to, knowing a system is tracking everything that matters. This is the playbook for how we built it. How It Works This isn't just one workflow; it's a two-part system designed to manage the entire meeting lifecycle from start to finish. Bot Management: It starts when you flick a switch in your CRM (Baserow). A command deploys or removes an AI bot from Google Meet. No fluff—it's there when you need it, gone when you don't. The workflow uses a quick "digital sticky note" in Redis to remember who the meeting is with and instantly updates the status in your Baserow table. AI Analysis & Memory: Once the meeting ends, VEXA sends the transcript over. Using the client ID (thank god for redis) , we feed the conversation to an AI model (OpenAI). It doesn't just summarize; it extracts actionable next steps and potential risks. All this structured data is then logged into a memory layer (Mem0), creating a permanent, searchable record of every client conversation. Setup Steps: Your Action Plan This is designed for rapid deployment. Here's what you do: Register Webhook: Run the manual trigger in the workflow once. This sends your n8n webhook URL to VEXA, telling it where to dump transcripts after a call. Connect Your CRM: Copy the vexa-start webhook URL from n8n. Paste it into your Baserow automation so it triggers when you set the "Send Bot" field to Start_Bot. Integrate Your Tools: Plug your VEXA, Mem0, Redis, and OpenAI API credentials into n8n. Use the Baserow Template: I've created a free Baserow template to act as your control panel. Grab it here: https://baserow.io/public/grid/t5kYjovKEHjNix2-6Rijk99y4SDeyQY4rmQISciC14w. It has all the fields you need to command the bot. Requirements An active n8n instance or cloud account. Accounts for VEXA.ai, Mem0.ai, Baserow, and OpenAI. A Redis database . Your Baserow table must have these fields: Meeting Link, Bot Name, Send Bot, and Status. Next Steps: Getting More ROI This workflow is the foundation. The real value comes from what you build on top of it. Automate Follow-ups:** Use the AI-identified next steps to automatically trigger follow-up emails or create tasks in your project management tool. Create a Unified Client Memory:** Connect your email and other communication platforms. Use Mem0 to parse and store every engagement, building a complete, holistic view of every client relationship. Build a Headless CRM:** Combine these workflows to build a fully AI-powered system that handles everything from lead capture to client management without any manual data entry. Copy the workflow and stop taking notes

Master Outbound WhatsApp: Baserow & WasenderAPI This workflow integrates with your Baserow 'Messages' table, triggering on 'Sent' status. Messages fire via WasenderAPI, rigorously logged as 'Outbound' in Baserow. Gain total control; drive results. How it works Monitors Baserow 'Messages' table for 'Sent' status. Sends messages via WasenderAPI. Logs outbound details in Baserow. Who's it for For teams dominating outbound WhatsApp and centralizing Baserow logging. Demand communication efficiency? This is your solution. Setup Steps Rapid implementation. Action plan: Activate all critical workflow nodes. Copy Sent_whatsapp webhook URL. Configure Baserow automation (on 'Sent' status) to trigger webhook. Ensure Baserow 'Messages' table includes 'Status' ('Sent' option), linked 'WhatsApp Number', and 'Message Content' fields. (Optional: Baserow Message Form for input). Embed WasenderAPI and Baserow API tokens in n8n Credentials. Security is non-negotiable. Requirements Active n8n instance (self-hosted/cloud). WasenderAPI.com trial/subscription. Baserow account with pre-configured 'Contacts' (link) and 'Messages' (link) tables.

WhatsApp Micro-CRM with Baserow & WasenderAPI Struggling to manage WhatsApp client communications? This n8n workflow isn't just automation; it's your centralized CRM solution for small businesses and freelancers. How it works Capture Every Message:** Integrates WhatsApp messages directly via WasenderAPI. Effortless Contact Management:** Automates contact data standardization and intelligently manages records (creating new or updating existing profiles). Rich Client Profiles:** Retrieves profile pictures and decrypts image media, giving you full context. Unified Data Hub:** Centralizes all conversations and media in Baserow, no more scattered interactions. Setup Steps Setup is incredibly fast; you can deploy this in under 15 minutes. Here's what you'll do: Link WasenderAPI:** Connect your WasenderAPI webhooks directly to n8n. Set up Baserow:** Duplicate our pre-built 'Contacts' (link) and 'Messages' (link) Baserow table templates. Secure Your Data:** Input your API credentials (WasenderAPI and Baserow) directly into n8n. Every single step is fully detailed in the workflow's sticky notes – we've made it foolproof. Requirements What do you need to get started? An active n8n instance (self-hosted or cloud). A WasenderAPI.com subscription or trial. A Baserow account. Note: Keep the flow layout as is! This will ensure that the flow is running in the correct order.

This workflow automates the process of monitoring multiple RSS feeds, intelligently identifying new articles, maintaining a record of processed content, and delivering timely notifications to a designated Slack channel. It leverages AI to ensure only truly new and relevant articles are dispatched, preventing duplicate alerts and information overload. 🚀 Main Use Cases Automated News Aggregation:** Continuously monitor industry news, competitor updates, or specific topics from various RSS feeds. 📈 Content Curation:** Filter and deliver only new, unprocessed articles to a team or personal Slack channel. 🎯 Duplicate Prevention:** Maintain a persistent record of seen articles to avoid redundant notifications. 🛡️ Enhanced Information Delivery:** Provide a streamlined and intelligent way to stay updated without manual checking. 📧 How it works The workflow operates in distinct, interconnected phases to ensure efficient and intelligent article delivery: RSS Feed Data Acquisition 📥 Initiation:** The workflow is manually triggered to begin the process. 🖱️ RSS Link Retrieval:** It connects to a Baserow database to fetch a list of configured RSS feed URLs. 🔗 Individual Feed Processing:** Each RSS feed URL is then processed independently. 🔄 Content Fetching & Parsing:** An HTTP Request node downloads the raw XML content of each RSS feed, which is then parsed into a structured JSON format for easy manipulation. 📄➡️🌳 Historical Data Management 📚 Seen Articles Retrieval:** Concurrently, the workflow queries another Baserow table to retrieve a comprehensive list of article GUIDs or links that have been previously processed and notified. This forms the basis for duplicate detection. 🔍 Intelligent Article Filtering with AI 🧠 Data Structuring for AI:** A Code node prepares the newly fetched articles and the list of already-seen articles into a specific JSON structure required by the AI Agent. 🏗️ AI-Powered Filtering:** An AI Agent, powered by an OpenAI Chat Model and supported by a Simple Memory component, receives this structured data. It is precisely prompted to compare the new articles against the historical "seen" list and return only those articles that are genuinely new and unprocessed. 🤖 Output Validation:** A Structured Output Parser ensures that the AI Agent's response adheres to a predefined JSON schema, guaranteeing data integrity for subsequent steps. ✅ JSON Cleaning:** A final Code node takes the AI's raw JSON string output, parses it, and formats it into individual n8n items, ready for notification and storage. 🧹 Notification & Record Keeping 🔔 Persistent Record:** For each newly identified article, its link is saved to the Baserow "seen products" table, marking it as processed and preventing future duplicate notifications. 💾 Slack Notification:** The details of the new article (title, content, link) are then formatted and sent as a rich message to a specified Slack channel, providing real-time updates. 💬 Summary Flow: Manual Trigger → RSS Link Retrieval (Baserow) → HTTP Request → XML Parsing | Seen Articles Retrieval (Baserow) → Data Structuring (Code) → AI-Powered Filtering (AI Agent, OpenAI, Memory, Parser) → JSON Cleaning (Code) → Save Seen Articles (Baserow) → Slack Notification 🎉 Benefits: Fully Automated:** Eliminates manual checking of RSS feeds and Slack notifications. ⏱️ Intelligent Filtering:** Leverages AI to accurately identify and deliver only new content, avoiding duplicates. 💡 Centralized Data Management:** Utilizes Baserow for robust storage of RSS feed configurations and processed article history. 🗄️ Real-time Alerts:** Delivers timely updates directly to your team or personal Slack channel. ⚡ Scalable & Customizable:** Easily adaptable to monitor various RSS feeds and integrate with different Baserow tables and Slack channels. ⚙️ Setup Requirements: Baserow API Key:** Required for accessing and updating your Baserow databases. 🔑 OpenAI API Key:** Necessary for the AI Agent to function. 🤖 Slack Credentials:** Either a Slack OAuth token (recommended for full features) or a Webhook URL for sending messages. 🗣️ Baserow Table Configuration:** A table with an rssLink column to store your RSS feed URLs. A table with a Nom column to store the links of processed articles. For any questions or further assistance, feel free to connect with me on LinkedIn: https://www.linkedin.com/in/daniel-shashko/

Send personalized welcome messages to your new Bluesky followers automatically, helping you maintain engagement while saving time. This workflow monitors your follower list and sends customized direct messages, creating a warm welcome for new connections without manual intervention. How it works Checks your Bluesky followers list daily at 9 AM Identifies new followers by comparing against a database Extracts the follower's first name** when available Sends a personalized welcome message with optional link Prevents duplicate messages** through double-verification Maintains a record of sent messages to avoid repetition Set up steps (10-15 minutes) Create a Bluesky account if you haven't already Generate an app password in your Bluesky settings Enter your Bluesky handle and app password in the "Set Bluesky Credentials" node Set up your database (Baserow, or adapt for Airtable/Google Sheets) Customize your welcome message in the "Create Welcome Message" node Optional: Adjust the regular check time (default: 9 AM) Features Personalized messaging** using follower's first name when available Database tracking** to prevent duplicate messages Basic rate limiting protection** to stay within API limits Customizable welcome message with clickable links Ability to handle up to 100 new followers per check Perfect for creators who want to Welcome new followers consistently Save time** on manual messaging Build early engagement with followers Share important links or resources Maintain a professional presence Scale** their community management Suggested enhancements Add message templates for different follower types Include email/Slack notifications for errors Add analytics tracking for message success rates Implement dynamic timing based on follower activity Create A/B testing for different welcome messages Add follower segmentation based on profile data

Who's this for? If you own a website and need to analyze your keyword rankings If you need to create a keyword report on your rankings If you want to grow your keyword positions SerpBear is an opensourced SEO tool specifically for keyword analytics. Click here to watch youtube tutorial Example output of A.I. Key Observations about Ranking Performance: The top-performing keyword is “Openrouter N8N” with a current position of 7 and an improving trend. Two keywords, “Best Docker Synology” and “Bitwarden Synology”, are not ranking in the top 100 and have a stable trend. Three keywords, “Obsidian Second Brain”, “AI Generated Reference Letter”, and “Actual Budget Synology”, and “N8N Workflow Generator” are not ranking well and have a declining trend. Keywords showing the most improvement: “Openrouter N8N” has an improving trend and a relatively high ranking of 7. Keywords needing attention: “Obsidian Second Brain” has a declining trend and a low ranking of 69. “AI Generated Reference Letter” has a declining trend and a low ranking of 84. “Actual Budget Synology”, “N8N Workflow Generator”, “Best Docker Synology”, and “Bitwarden Synology” are not ranking in the top 100. Use case Instead of hiring an SEO expert, I run this report weekly. It checks the keyword rankings of the past week and gives me recommendations on what to improve. How it works The workflow gathers SerpBear analytics for the past 7 days. It passes the data to openrouter.ai for A.I. analysis. Finally it saves to baserow. How to use this Input your SerpBearcredentials Enter your domain name Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Date, Note, Blog Created by Rumjahn

Who's this for? If you own a website and need to analyze your Matomo analytics data so you can increse the number of frequent visitors If you need to create an SEO report on what are the common trends amongst your most frequent visitors If you want to grow your site based on suggestions from data Matomo is an analytics tool that can give you details of each individual visitor. Much more powerful than Google analytics. Watch youtube tutorial here Get my SEO A.I. agent system here Here's the A.I. output: Keywords showing the most improvement: Openrouter N8N. Keywords needing attention: Ai Generated Reference Letter Obsidian Second Brain Suggested actions for improvement: Optimize for "best Docker Synology" despite stable ranking, an improvement to top 10 is an achievable goal. Since "2nd brain app for developer" is of interest to a developer. Consider writing a blog post on how the app addresses the specific pain points of developers. Use case Instead of hiring an SEO expert, I run this report weekly. It looks at the data for the past week and looks for visitors with more than 3 visits and recommends ideas to convert more visitors into frequent visitors. How it works The workflow gathers matomo analytics for the past 7 days. We then parse the data The data is sent to Openrouter and using a FREE LLM, it analyses the data. It stores the results in baserow How to use this Input your Matomo analytics credentials Input your Matomo site ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Dates, Notes, Blog. Created by Rumjahn

Who's this for? Anyone who wants to improve the SEO of their website Umami users who want insights on how to improve their site SEO managers who need to generate reports weekly Case study Watch youtube tutorial here Get my SEO A.I. agent system here How it works This workflow calls the Umami API to get data Then it sends the data to A.I. for analysis It saves the data and analysis to Baserow How to use this Input your Umami credentials Input your website property ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Date, Summary, Top Pages, Blog (name of your blog). Future development Use this as a template. There's alot more Umami stats you can pull from the API. Change the A.I. prompt to give even more detailed analysis. Created by Rumjahn

This workflow uses a number of technologies to track the value of ETFs, stocks and other exchange-traded products: Baserow: To keep track of our investments n8n’s Cron node: To trigger the workflow compiling our daily morning briefing Webscraping: The HTTP Request & HTML Extract nodes to fetch up-to-date prices from the relevant stock exchange and structure this infromation Javascript: We’ll use the Function node to build a custom HTML body with all the relevant information Sendgrid: The Email Service Provider in this workflow to send out our email Thanks to n8n, the steps in this workflow can easily be changed. Not a Sendgrid user? Simply remove the Sendgrid node and add a Gmail node instead. The stock exchange has a REST API? Just throw away the HTML Extract node. Here’s how it works: Data Source In this scenario, our data source is Baserow. In our table, we’ll track all information needed to identify each investment product: We have two text type columns (Name and ISIN) as well as two number type columns (Count and Purchase Price). Workflow Nodes Cron The Cron node will trigger our workflow to run each work day in the morning hours. Baserow The Baserow node will fetch our investments from the database table shown above. HTTP Request Using the HTTP Request node we can fetch live data from the stock exchange of our choice based on the ISIN. This example uses Tradegate, which is used by many German fintechs. The basic approach should also work for other exchanges, as long as they provide the required data to the public. HTML Extract Since our HTTP Request node fetches full websites, we’re using the HTML Extract node to extract the information we’re looking for from each website. If an exchange other than Tradegate is used, the selectors used in this node will most likely need to be updated. + 6. Set The Set nodes helps with setting the exact columns we’ll use in our table. In this case we’re first formatting the results from our exchange, then calculate the changes based on the purchase price. Function Here were using a bit of Javascript magic to build an HTML email. This is where any changes to the email content would have to be made. Sendgrid Finally we send out the email built in the previous step. This is where you can configure sender and recipients. Result The basic email generated by this workflow will look like so: