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322 n8n Postgres workflow templates

The Postgres node runs parameterised queries, inserts and upserts against a Postgres database, and can poll for new rows as a trigger. These are the templates that treat a database as the system of record rather than as an append-only log, so most of them read existing rows, derive something, and write it back. Parameterised queries are the node’s default path, which is what keeps a workflow-built query from being string-concatenated.

  • Generate Instagram Content from Top Trends with AI Image Generation

    by mustafa kendigüzelweb71,977 clicks

    How it works This automated workflow discovers trending Instagram posts and creates similar AI-generated content. Here's the high-level process: 1. Content Discovery & Analysis Scrapes trending posts from specific hashtags Analyzes visual elements using AI Filters out videos and duplicates 2. AI Content Generation Creates unique images based on trending content Generates engaging captions with relevant hashtags Maintains brand consistency while being original 3. Automated Publishing Posts content directly to Instagram Monitors publication status Sends notifications via Telegram Set up steps Setting up this workflow takes approximately 15-20 minutes: 1. API Configuration (7-10 minutes) Instagram Business Account setup Telegram Bot creation API key generation (OpenAI, Replicate, Rapid Api) 2. Database Setup (3-5 minutes) Create required database table Configure PostgreSQL credentials 3. Workflow Configuration (5-7 minutes) Set scheduling preferences Configure notification settings Test connection and permissions Detailed technical specifications and configurations are available in sticky notes within the workflow. An n8n automation workflow template by mustafa kendigüzel.

    • 6 nodes
    • 71,977 views

    Workflow detailsImport from n8n.ioAutomation, AI

  • AI Agent for realtime insights on meetings

    by Mark Shcherbakovweb15,744 clicks

    Video Guide I prepared a detailed guide explaining how to build an AI-powered meeting assistant that provides real-time transcription and insights during virtual meetings. Youtube Link Who is this for? This workflow is ideal for business professionals, project managers, and team leaders who require effective transcription of meetings for improved documentation and note-taking. It's particularly beneficial for those who conduct frequent virtual meetings across various platforms like Zoom and Google Meet. What problem does this workflow solve? Transcribing meetings manually can be tedious and prone to error. This workflow automates the transcription process in real-time, ensuring that key discussions and decisions are accurately captured and easily accessible for later review, thus enhancing productivity and clarity in communications. What this workflow does The workflow employs an AI-powered assistant to join virtual meetings and capture discussions through real-time transcription. Key functionalities include: Automatic joining of meetings on platforms like Zoom, Google Meet, and others with the ability to provide real-time transcription. Integration with transcription APIs (e.g., AssemblyAI) to deliver seamless and accurate capture of dialogue. Structuring and storing transcriptions efficiently in a database for easy retrieval and analysis. Real-Time Transcription: The assistant captures audio during meetings and transcribes it in real-time, allowing participants to focus on discussions. Keyword Recognition: Key phrases can trigger specific actions, such as noting important points or making prompts to the assistant. Structured Data Management: The assistant maintains a database of transcriptions linked to meeting details for organized storage and quick access later. Setup Preparation Create Recall.ai API key Setup Supabase account and table create table public.data ( id uuid not null default gen_random_uuid (), date_created timestamp with time zone not null default (now() at time zone 'utc'::text), input jsonb null, output jsonb null, constraint data_pkey primary key (id), ) tablespace pg_default; Create OpenAI API key Development Bot Creation: Use a node to create the bot that will join meetings. Provide the meeting URL and set transcription options within the API request. Authentication: Configure authentication settings via a Bearer token for interacting with your transcription service.

    • 4 nodes
    • 15,744 views

    Workflow detailsImport from n8n.ioAutomation, AI

  • WordPress - AI Chatbot to enhance user experience - with Supabase and OpenAI

    by Datakiweb10,875 clicks

    This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content.

    • 11 nodes
    • 10,875 views

    Workflow detailsImport from n8n.ioAutomation, AI

  • 🤖 Advanced Slackbot with n8n

    by Mutasemweb9,377 clicks

    Use case Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do. This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases. In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly. This bot can create a new thread on an alerts channel and respond there. Or reply directly to the user. It responds for help request to return a help page. It automatically handles unknown commands. It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow. How to setup Add Slack command and point it up to the webhook. For example. Add the following to the Set config node alerts_channel with alerts channel to start threads on instance_url with this instance url to make it easy to debug slack_token with slack bot token to validate request slack_secret_signature with slack secret signature to validate request help_docs_url with help url to help users understand the commands Build other workflows to call and add them to commands in Set Config. Each command must be mapped to a workflow id with an Execute Workflow Trigger node Activate workflow 🚀 How to adjust Add your own commands. Depending on your need, you might need to lock down who can call this. An n8n automation workflow template by Mutasem.

    • 4 nodes
    • 9,377 views

    Workflow detailsImport from n8n.ioAutomation

  • ETL pipeline for text processing

    by Lorenaweb8,734 clicks

    This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score. An n8n automation workflow template by Lorena.

    • 5 nodes
    • 8,734 views

    Workflow detailsImport from n8n.ioAutomation

  • Query Google Sheets/CSV data through an AI Agent using PostgreSQL

    by Leonardo Grigorioweb8,069 clicks

    Want to see it in action? Watch the full breakdown here: 📺 Video Link Template Description This n8n workflow empowers you to query structured financial data from Google Sheets or CSV files using AI-generated SQL. Unlike traditional vector database solutions that falter with numerical queries, this template leverages PostgreSQL for efficient data storage and an AI agent to dynamically create optimized SQL queries from natural language inputs. What It Does Retrieves data from Google Sheets or CSV files Infers the data schema and builds a PostgreSQL table Populates the table with your data Uses an AI agent to translate natural language questions into SQL queries Returns precise numerical results quickly and efficiently Why Use This? No SQL knowledge required—the AI generates queries for you Bypasses the inefficiencies and costs of vector database approaches Scales effortlessly without overwhelming the language model Fully free and open-source Setup Requirements Pre-Conditions PostgreSQL Database**: A running PostgreSQL instance (no specific extensions required beyond standard installation). Google Sheets Access**: A publicly accessible or shared Google Sheet URL with structured data (e.g., financial records). Need a starting point? Use this Sample Google Sheet Template. n8n Instance**: A working n8n setup with access to the Google Drive and PostgreSQL nodes. Step-by-Step Instructions Add Your Google Sheets URL Open the "Google Drive Trigger" node. Replace the placeholder URL with your Google Sheet’s link. Verify the sheet name matches your data source. Configure PostgreSQL Update the "PostgreSQL" nodes with your database credentials (host, database, user, password). The workflow automatically creates and populates the table based on your data schema. Run the Workflow Execute the workflow manually to set up the database. Once initialized, use the AI agent by asking questions like: "How much did I sell last week?" "What were the total sales for Product X in February?" (Optional) Automate Updates Add a "Schedule Trigger" node to sync your Google Sheets data with PostgreSQL on a regular basis. How It Works Schema Detection**: The workflow analyzes your Google Sheets or CSV data to infer its structure and create an appropriate PostgreSQL table. AI-Powered Queries**: An optimized AI agent converts your natural language questions into precise SQL queries, ensuring accurate results.

    • 6 nodes
    • 8,069 views

    Workflow detailsImport from n8n.ioAutomation, AI

  • Suspicious Login Detection

    by n8n Teamweb6,442 clicks

    This n8n workflow is designed for security monitoring and incident response when suspicious login events are detected. It can be initiated either manually from within the n8n UI for testing or automatically triggered by a webhook when a new login event occurs. The workflow first extracts relevant data from the incoming webhook payload, including the IP address, user agent, timestamp, URL, and user ID. It then splits into three parallel processing paths. In the first path, it queries GreyNoise's Community API to retrieve information about the investigated IP address. Depending on the classification and trust level received from GreyNoise, the alert is given a High, Medium, or Low priority. This priority is assigned based on the best practices documentation from GreyNoise on how to apply their data to analysis. Once a priority is assigned, a message is sent to a Slack channel to notify users about the alert. The second path involves fetching geolocation data about the IP address using IP-API's Geolocation API and merging it with data from the UserParser node. This data is then combined with the data obtained from GreyNoise. In the third path, the UserParser node queries the Userparser IP address and user agent lookup API to obtain information about the user's IP and user agent. This data is merged with the IP-API data and GreyNoise data. The workflow then checks if the IP address is considered an unknown threat by examining both the noise and riot fields from GreyNoise. If it is considered an unknown threat, the workflow proceeds to retrieve the last 10 login records for the same user from a Postgres database. If there are any discrepancies in the login information, indicating a new location or device/browser, the user is informed via email. Potential issues when setting up this workflow include ensuring that credentials are correctly entered for GreyNoise and UserParser nodes, and addressing any discrepancies in the data sources that could lead to false positives or negatives in threat detection. Additionally, the usage of hardcoded API keys should be replaced with credentials for security and flexibility. Thorough testing and validation with sample data are crucial to ensure the workflow performs as expected and aligns with security incident response procedures. An n8n automation workflow template by n8n Team.

    • 6 nodes
    • 6,442 views

    Workflow detailsImport from n8n.ioAutomation

  • Synchronize your Google Sheets with Postgres

    by Belaweb6,087 clicks

    Sync your Google Sheets Data with your Postgres database table, requiring minimal adjustments. Follow these steps: Retrieve Data: Pull data from Google Sheets and PostgreSQL. Compare Datasets: Identify differences, focusing on new or updated entries. Update PostgreSQL: Apply changes to ensure both platforms mirror each other. Automate this process to regularly synchronize data. Before starting, grant necessary access to both Google Sheets and PostgreSQL, and specify the data details for synchronization. This streamlined workflow enhances data consistency across platforms. This example is a one-way synchronization from Google Sheets into your Postgres. With small adjustments, you can make it the other way around, or 2-way. An n8n automation workflow template by Bela.

    • 2 nodes
    • 6,087 views

    Workflow detailsImport from n8n.ioAutomation

  • Generate and insert data into a Postgres database

    by tanaypantweb5,969 clicks

    This is Workflow 1 in the blog tutorial Database activity monitoring and alerting. Prerequisites A Postgres database set up and credentials. Basic knowledge of JavaScript and SQL. Nodes Cron node starts the workflow every minute. Function node generates sensor data (sensor id (preset), a randomly generated value, timestamp, and notification (preset as false) ) Postgres node inserts the data into a Postgres database. You can create the database for this workflow with the following SQL statement: CREATE TABLE n8n (id SERIAL, sensor_id VARCHAR, value INT, time_stamp TIMESTAMP, notification BOOLEAN);. An n8n automation workflow template by tanaypant.

    • 1 nodes
    • 5,969 views

    Workflow detailsImport from n8n.ioAutomation

  • Build your own PostgreSQL MCP server

    by Jimleukweb5,672 clicks

    This n8n demonstrates how to build a simple PostgreSQL MCP server to manage your PostgreSQL database such as HR, Payroll, Sale, Inventory and More! This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/postgres How it works A MCP server trigger is used and connected to 5 tools: 2 postgreSQL and 3 custom workflow. The 2 postgreSQL tools are simple read-only queries and as such, the postgreSQL tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our standard PostgreSQL node to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This PostgreSQL MCP server allows any compatible MCP client to manage a PostgreSQL database by supporting select, create and update operations. You will need to have a database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me check if Alex has an entry in the users table. If not, please help me create a record for her." "What was the top selling product in the last week?" "How many high priority support tickets are still open this morning?" Requirements PostgreSQL for database. This can be an external database such as Supabase or one you can host internally. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department.

    • 2 nodes
    • 5,672 views

    Workflow detailsImport from n8n.ioAutomation, AI

  • Youtube Outlier Detector (Find trending content based on your competitors)

    by Leonardo Grigorioweb5,386 clicks

    Video explanation This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a channel's average views. It automates the process of monitoring competitor channels, saving time and streamlining content research. Included in the Workflow Automated Competitor Video Tracking Monitors videos from specified competitor channels, fetching data directly from the YouTube API. Outlier Detection Based on Channel Averages Compares each video’s performance against the channel’s historical average to identify significant spikes in viewership. Historical Video Data Management Stores video statistics in a PostgreSQL database, allowing the workflow to only fetch new videos and optimize API usage. Short Video Filtering Automatically removes short videos based on duration thresholds. Flexible Video Retrieval Fetches up to 3 months of historical data on the first run and only new videos on subsequent runs. PostgreSQL Database Integration Includes SQL queries for database setup, video insertion, and performance analysis. Configurable Outlier Threshold Focuses on videos published within the last two weeks with view counts at least twice the channel's average. Data Output for Analysis Outputs best-performing videos along with their engagement metrics, making it easier to identify trending topics. Requirements n8n installed on your machine or server A valid YouTube Data API key Access to a PostgreSQL database This workflow is intended for educational and research purposes, helping content creators gain insights into what topics resonate with audiences without manual daily monitoring. An n8n automation workflow template by Leonardo Grigorio.

    • 4 nodes
    • 5,386 views

    Workflow detailsImport from n8n.ioAutomation

  • 🐶 AI Agent for PetShop Appointments (Agente de IA para agendamentos de PetShop)

    by Bruno Diasweb4,761 clicks

    🐶🤖 AI Agent for Pet Shops – Automate Customer Service & Bookings! 🐾💡 Transform Your Pet Shop with AI-Powered Automation! 🚀 Enhance customer experience and optimize operations with this n8n AI Agent designed for pet shops. 📲🐾 Automate client interactions, appointment scheduling, and service recommendations—saving time and increasing revenue! 🔹 Key Features: ✅ Instant WhatsApp responses – AI-powered chatbot handles customer inquiries. 💬 ✅ Automated appointment scheduling – Clients can book services hassle-free. 📅✂️ ✅ Personalized reminders – Reduce no-shows with automated notifications. 📢🐾 ✅ Customer data & service history management – Track interactions effortlessly. 📊📁 ✅ Product & service recommendations – Improve sales with smart suggestions. 🎁🐶 📌 How It Works 1️⃣ The workflow captures customer inquiries via WhatsApp. 2️⃣ AI processes requests, provides information, and offers booking options. 3️⃣ Clients can schedule grooming, vet visits, or other services in seconds. 4️⃣ Automated reminders ensure appointments are remembered. 5️⃣ Customer data is stored for better service personalization. ⚙️ Setup & Customization 🔧 Connect your WhatsApp API (evolution) for instant messaging. 🔧 Integrate with Google Calendar for appointment booking. 🔧 Customize reminders, services, and pricing rules to fit your business. 💡 Reduce manual work, improve customer satisfaction, and scale your pet shop with AI automation! 🐶🤖 [PT-BR] Agente de IA para Pet Shops – Atendimento e Agendamentos Automatizados! 🐾💡 Transforme Seu Pet Shop com Automação Inteligente! 🚀 Otimize o atendimento ao cliente e agilize processos com este Agente de IA para n8n. 📲🐾 Automatize interações, agendamentos e recomendações de serviços—economizando tempo e aumentando as vendas! 🔹 Principais Funcionalidades: ✅ Atendimento automático no WhatsApp – IA responde clientes instantaneamente. 💬 ✅ Agendamento de serviços automatizado – Clientes marcam banho, tosa ou consultas facilmente. 📅✂️ ✅ Lembretes personalizados – Reduza faltas com notificações automáticas. 📢🐾 ✅ Gestão de clientes e histórico de serviços – Controle dados de forma eficiente. 📊📁 ✅ Sugestão de produtos e serviços – Venda mais com recomendações inteligentes. 🎁🐶 📌 Como Funciona 1️⃣ O fluxo recebe perguntas dos clientes via WhatsApp. 2️⃣ A IA processa os pedidos e fornece opções de agendamento. 3️⃣ O cliente escolhe o serviço desejado e agenda em segundos.

    • 22 nodes
    • 4,761 views

    Workflow detailsImport from n8n.ioAutomation, AI

All n8n workflow automations — every integration, filterable by platform and complexity.