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3.557 fertige Workflow-Dateien zum Download, plus 10.661 quellenverknüpfte n8n-Referenzen.
S🤖🧠 AI Agent Chatbot + LONG TERM Memory + Note Storage + Telegram
This workflow template creates an AI agent chatbot with long-term memory and note storage using Google Docs and Telegram integration. Google Docs Integration 📄 n8n Google Docs Node Setup Google Credentials Telegram Integration 💬 Telegram Setup Core Features 🌟 AI Agent Integration 🤖 Implements a sophisticated AI agent with memory management capabilities Uses GPT-4o-mini and DeepSeek models for intelligent conversation handling Maintains context awareness through session management Memory System 🧠 Long-term memory storage using Google Docs Separate note storage system for specific information Window buffer memory for maintaining conversation context Intelligent memory retrieval and storage mechanisms Communication Interface 💬 Telegram integration for message handling Real-time message processing and response generation Technical Components 🔧 Memory Architecture 📚 Dual storage system separating memories from notes Automated memory retrieval before each interaction Structured memory saving with timestamps AI Models 🤖 Primary GPT-4o-mini mini model for general interactions DeepSeek-V3 Chat for specialized processing Custom agent system with tool integration Storage Integration
RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini
Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document c
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Test Webhooks in n8n Without Changing WEBHOOK_URL (PostBin & BambooHR Example)
Using PostBin to Test Webhooks Without Changing WEBHOOK_URL How it Works Many new n8n users struggle with testing webhooks when running n8n on localhost, as external services cannot reach localhost. This workflow introduces a technique using PostBin, which provides a temporary, publicly accessible URL to receive webhook requests. Generates a temporary webhook endpoint via PostBin. Uses this endpoint in place of localhost to test webhooks. Captures and displays the incoming webhook request data. Enables debugging and iterating without modifying the WEBHOOK_URL environment variable. Set Up Steps Estimated time:** ~5–10 minutes Create a PostBin instance to generate a publicly accessible webhook URL. Copy the PostBin URL and use it as the webhook destination in n8n. Trigger the webhook from an external service or manually. Inspect the request payload in PostBin to verify data reception. (EXAMPLE) Using PostBin for Webhook Testing in a BambooHR Integration How it Works In this example, we apply the PostBin technique to a BambooHR integration. Instead of manually configuring a webhook in BambooHR, this workflow automates webhook registration using the BambooHR API. The workflow: Uses the
WBackup your workflows to GitHub -- in (subfolders)
Based on Jonathan & Solomon work. > The only addition I've made is a Set node. This node organizes workflows into subfolders within the GitHub repository based on their respective tags. How it works This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. Who is this for? People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.
WScrape Latest Github Trending Repositories
Scrape Latest 20 TechCrunch Articles Who is this for? This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page. What problem is this workflow solving? Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. What this workflow does This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Run the workflow manually or schedule it to execute at regular intervals. (Optional) Customize the extracted data or integrate it with other systems. How to customize this workflow to your
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Automate Pinterest Analysis & AI-Powered Content Suggestions With Pinterest API
Automate Pinterest Analysis & AI-Powered Content Suggestions With Pinterest API This workflow automates the collection, analysis, and summarization of Pinterest Pin data to help marketers optimize content strategy. It gathers Pinterest Pin performance data, analyzes trends using an AI agent, and delivers actionable insights to the Marketing Manager via email. This setup is ideal for content creators and marketing teams who need weekly insights on Pinterest trends to refine their content calendar and audience engagement strategy. Prerequisites Before setting up this workflow, ensure you have the following: Pinterest API Access & Developer Account Sign up at Pinterest Developers and obtain API credentials. Ensure you have access to both Organic and Paid Pin data. Airtable Account & API Key Create an account at Airtable and set up a database. Obtain an API key from Account Settings. AI Agent for Trend Analysis An AI-powered agent (such as OpenAI's GPT or a custom ML model) is required to analyze Pinterest trends. Ensure integration with your workflow automation tool (e.g., Zapier, Make, or a custom Python script). Email Automation Setup Configure an SMTP email service (e.g., Gmail, Ou
S🐋🤖 DeepSeek AI Agent + Telegram + LONG TERM Memory 🧠
This n8n workflow template is designed to integrate a DeepSeek AI agent with Telegram, incorporating long-term memory capabilities for personalized and context-aware responses. Here's a detailed breakdown: Core Features Telegram Integration Uses a webhook to receive messages from Telegram users. Validates user identity and message content before processing. AI-Powered Responses Employs DeepSeek's AI models for conversational interactions. Includes memory capabilities to personalize responses based on past interactions. Error Handling Sends an error message if the input cannot be processed. Model Options 🧠 DeepSeek-V3 Chat**: Handles general conversational tasks. DeepSeek-R1 Reasoning**: Provides advanced reasoning capabilities for complex queries. Memory Buffer Window**: Maintains session context for ongoing conversations. Quick Setup 🛠️ Telegram Webhook Configuration Set up a webhook using the Telegram Bot API: https://api.telegram.org/bot{my_bot_token}/setWebhook?url={url_to_send_updates_to} Replace {my_bot_token} with your bot's token and {url_to_send_updates_to} with your n8n webhook URL. Verify the webhook setup using: https://api.telegram.org/bot{my_bot_token}/getWebhookInf
Effortless Email Management with AI-Powered Summarization & Review
How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. A
AI-powered email processing autoresponder and response approval (Yes/No)
How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, i
AI Automated HR Workflow for CV Analysis and Candidate Evaluation
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 v
SChat with Postgresql Database
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
Automate GitLab Merge Requests Using APIs with n8n
Who is this template for? This template is designed for developers, DevOps engineers, and automation enthusiasts who want to streamline their GitLab merge request process using n8n, a low-code workflow automation tool. It eliminates manual intervention by automating the merging of GitLab branches through API calls. How it works ? Trigger the workflow: The workflow can be triggered by a webhook, a scheduled event, or a GitLab event (e.g., a new merge request is created or approved). Fetch Merge Request Details: n8n makes an API call to GitLab to retrieve merge request details. Check Merge Conditions: The workflow validates whether the merge request meets predefined conditions (e.g., approvals met, CI/CD pipelines passed). Perform the Merge: If all conditions are met, n8n sends a request to the GitLab API to merge the branch automatically. Setup Steps 1. Prerequisites An n8n instance (Self-hosted or Cloud) A GitLab personal access token with API access A GitLab repository with merge requests enabled 2. Create the n8n Workflow Set up a trigger: Choose a trigger node (Webhook, Cron, or GitLab Trigger). Fetch merge request details: Add an HTTP Request node to call GET /merge_requests/:i
Pattern for Multiple Triggers Combined to Continue Workflow
Overview This template describes a possible approach to handle a pseudo-callback/trigger from an independent, external process (initiated from a workflow) and combine the received input with the workflow execution that is already in progress. This requires the external system to pass through some context information (resumeUrl), but allows the "primary" workflow execution to continue with BOTH its own (previous-node) context, AND the input received in the "secondary" trigger/process. Primary Workflow Trigger/Execution The workflow path from the primary trigger initiates some external, independent process and provides "context" which includes the value of $execution.resumeUrl. This execution then reaches a Wait node configured with Resume - On Webhook Call and stops until a call to resumeUrl is received. External, Independent Process The external, independent process could be anything like a Telegram conversation, or a web-service as long as: it results in a single execution of the Secondary Workflow Trigger, and it can pass through the value of resumeUrl associated with the Primary Workflow Execution Secondary Workflow Trigger/Execution The secondary workflow execution can start wi
Spotify to YouTube Playlist Synchronization
Spotify to YouTube Playlist Synchronization A workflow that maintains a YouTube playlist in sync with a Spotify playlist, featuring smart video matching and persistent synchronization. Key Features One-way Sync**: Spotify playlist → YouTube playlist (additions and deletions) Continuous Monitoring**: Automatic synchronization (every hour by default, but you can put any time you want) Smart Video Matching**: Considers video length and content relevance Auto-Recovery**: Automatically handles deleted YouTube videos Database Backup**: Persistent storage using Supabase Prerequisites Supabase project with the following table structure: CREATE TABLE IF NOT EXISTS musics ( id TEXT PRIMARY KEY, title TEXT NOT NULL, artist TEXT NOT NULL, duration INT8 NOT NULL, youtube_video_id TEXT, to_delete BOOLEAN DEFAULT FALSE ); Empty YouTube playlist (recommended as duplicates are not handled) Configured credentials for YouTube, Spotify, and Supabase APIs Properly set variables in all "variables" nodes (variables, variables1, variables2, variables3, variables4 (all the same)) Activate the workflow !
AI-Powered Email Automation for Business: Summarize & Respond with RAG
This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted respo
Modular & Customizable AI-Powered Email Routing: Text Classifier for eCommerce
How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message. Text Classification: The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories: Request Quote: For quote requests. Product info: For general product inquiries. General problem: For issues or problems related to products. Order: For questions about placed orders. Other: For any messages that don’t fit the above categories. Email Routing: Based on the classification, the workflow routes the message to the appropriate department via email: Prod. Dep.: For product-related inquiries. Quote Dep.: For quote requests. Gen. Dep.: For general problems. Order Dep.: For order-related questions. Other Dep.: For all other inquiries. Each email includes the user's name, email, message, and the classified category. Data Logging: The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including: User’s name, email, and message. Submission date and t
BBambooHR AI-Powered Company Policies and Benefits Chatbot
How it works This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits: Retrieves company policies, benefits, and HR documents from BambooHR. Uses AI to analyze and answer employee questions based on company records. Identifies the most relevant contact person for escalations. Seamlessly integrates with company systems to provide real-time HR assistance. Set up steps: Estimated time: ~20 minutes Connect your BambooHR account to allow policy retrieval. Configure AI parameters and access control settings. (Optional) Set up the employee lookup tool for personalized responses. Test the chatbot to ensure accurate responses and seamless integration. Benefits This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries. Outperform BambooHR's "Ask BambooHR" Chatbot #1. Superior specificity of replies to general inquiries #2. More appropriate escalations when responding to sensitive employee concerns
AI Voice Chatbot with ElevenLabs & OpenAI for Customer Service and Restaurants
The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interactio
Complete business WhatsApp AI-Powered RAG Chatbot using OpenAI
The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o
WAutomate Sports Betting Data with the Odds API
Automate Sports Betting Data with TheOddsAPI This workflow enables you to create and update a table using TheOddsAPI for sports betting data. It automatically pulls upcoming Ice Hockey games at the start of the day and updates the table with results at the end of the day. You can modify it to retrieve odds and game data for any sport. This setup is particularly useful for sports betting applications, such as tracking the results of a predictive model. It leverages scheduled triggers to activate HTTP requests, which then create or update fields in Airtable by matching on the game ID. Prerequisites Before implementing this workflow, ensure you have the following: TheOddsAPI Account & API Key Sign up at TheOddsAPI and obtain an API key. Ensure you have the correct API permissions to access sports odds and results. Airtable Account & API Key Create an account at Airtable and set up a database. Obtain an API key from the Account Settings page. API Access & Rate Limits Review TheOddsAPI’s rate limits and ensure your account tier allows for scheduled API calls. Confirm that Airtable API limits align with your expected data retrieval frequency. Step-by-Step Guide to Integrating TheOddsAPI
Automate SIEM Alert Enrichment with MITRE ATT&CK, Qdrant & Zendesk in n8n
n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant Who is this for? This workflow is ideal for: Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**. IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system. Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**. Anyone using n8n and Qdrant* to build *AI-powered security workflows**. What problem does this workflow solve? Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by: ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs. ✔ Tagging & classifying alerts based on known attack techniques. ✔ Providing remediation steps to guide the response team. ✔ Enhancing security tickets in Zendesk with relevant threat intelligence. What this workflow does 1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk). 2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques. 3️⃣ Extracts relevant TTPs (Tactics, Techni
Calculate the Centroid of a Set of Vectors
n8n Workflow: Calculate the Centroid of a Set of Vectors Overview This workflow receives an array of vectors in JSON format, validates that all vectors have the same dimensions, and computes the centroid. It is designed to be reusable across different projects. Workflow Structure Nodes and Their Functions: Receive Vectors (Webhook): Accepts a GET request containing an array of vectors in the vectors parameter. Expected Input: vectors parameter in JSON format. Example Request: /webhook/centroid?vectors=[[2,3,4],[4,5,6],[6,7,8]] Output: Passes the received data to the next node. Extract & Parse Vectors (Set Node): Converts the input string into a proper JSON array for processing. Ensures vectors is a valid array. If the parameter is missing, it may generate an error. Expected Output Example: { "vectors": [[2,3,4],[4,5,6],[6,7,8]] } Validate & Compute Centroid (Code Node): Validates vector dimensions and calculates the centroid. Validation: Ensures all vectors have the same number of dimensions. Computation: Averages each dimension to determine the centroid. If validation fails: Returns an error message indicating inconsistent dimensions. Successful Output Example: { "centroid": [4,5,
Automate Rank Math SEO Field Updates for Wordpress or Woocommerce
This workflow automates the process of updating important Rank Math SEO fields (SEO Title, Description, and Canonical URL) directly via n8n. By leveraging a custom WordPress plugin that extends the WordPress REST API, this workflow ensures that you can programmatically manage SEO metadata for your posts and WooCommerce products efficiently. Bulk version available here. How it works: Sends a POST request to a custom API endpoint exposed by the Rank Math plugin. Updates SEO Title, Description, and Canonical URL fields for a specified post or product. Setup steps: Install and activate the Rank Math API Manager Extended plugin on WordPress. Provide the post or product ID you want to update in the workflow. Run the workflow to update the metadata automatically. Benefits: Full automation of SEO optimizations. Works for both standard posts and WooCommerce products. Simplifies large-scale SEO management tasks. To understand exactly how to use it in detail, check out my comprehensive documentation here. Rank Math API Manager Extended plugin on WordPress // ATTENTION: Replace the line below with <?php - This is necessary due to display constraints in web interfaces. <?php /** Plugin Na
Docker Registry Cleanup Workflow
Docker Registry Cleanup Template This template is designed to automatically clean up old image tags in the Docker registry and perform garbage collection. Features List all images in the registry Preserve the last 10 tags for each image (latest tag is always preserved) Delete old tags Email notification for Successful/Excused cancellation Registry garbage collection automation Failure notification in error conditions Prerequisites Docker Registry v2 API access Basic Authentication credentials SMTP email settings (for notifications) SSH node installed on n8n (for garbage collection) Installation 1. Identity Information Add the following credentials in n8n: HTTP Basic Auth**: For Registry access SSH Private Key**: For Garbage collection command Email SMTP**: For notifications 2. Set Variables Replace your-registry-url with your actual registry URL on all nodes: ‘url": ‘https://your.registry.com/v2/_catalog’. Customisation Retention Policy: Set the number of tags to be retained by changing the slice(0, 10) value in the Identify Tags to Remove node Schedule: Change the frequency of operation at the Trigger node Notification Content: Customise email templates according to your needs Not