Workflow Templates

Description This n8n workflow template allows users to check if a Tron wallet address is blacklisted on the USDT contract via a Telegram bot. When a user sends the command {walletAddress} through the Telegram bot, the workflow queries the Tronscan API to determine if the provided wallet address is blacklisted. The result is then sent back to the user via the Telegram bot. Detailed Description Workflow Overview This workflow is designed to interact with users through a Telegram bot and check if a given Tron wallet address is blacklisted on the USDT contract. The workflow consists of four main nodes: Telegram Trigger Node: Listens for messages from the Telegram bot. HTTP Request Node: Sends a GET request to the Tronscan API to check the blacklist status of the provided wallet address. Function Node: Processes the API response and formats the message to be sent back to the user. Telegram Send Message Node: Sends the formatted message back to the user via the Telegram bot. Nodes Configuration 1.Telegram Trigger Node Event: Message Update Types: Message Command: /sorgu Description: This node listens for the {walletAddress} command followed by a wallet address from the user. 2.HTTP Request Node Method: GET URL: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress={{ $json.message.text }} Response Format: JSON Description: This node sends a GET request to the Tronscan API using the wallet address provided by the user. 3.Code Node Check Api Response: let message; if (response.total && response.total > 0) { message = 🚨🛑 This Wallet is Blacklisted! 🛑🚨: ${response.data[0].blackAddress}; } else { message = ✅💚 This Wallet is NOT Blacklisted! 💚✅.; } return [ { json: { text: message, }, }, ]; Description:** This node processes the API response to determine if the wallet address is blacklisted and formats the message to be sent back to the user. 4.Telegram Send Message Node Resource: Message Operation: Send Chat ID: ={{$json["chat_id"]}} Text: ={{$json["text"]}} Description: This node sends the formatted message back to the user via the Telegram bot. How to Use Set Up Telegram Bot: Create a Telegram bot and obtain the API token. Configure the bot to listen for the {walletAddress} command. Import Workflow: Import this workflow into your n8n instance. Configure Credentials: Add your Telegram API credentials to the Telegram Trigger and Telegram Send Message nodes. Run Workflow: Start the workflow. Users can now send the {walletAddress} command to the Telegram bot to check if a Tron wallet address is blacklisted. Example Usage User Telegram Command: {TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t} API Request: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress=TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t API Response: "total": 1, "data": [ { "blackAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t", "tokenName": "USDT", "num": "367583344429", "time": 1593184959, "transHash": "af4bc4d793f82ca5ba500cf13cf93ca3e7a56fccc2aabf8b09e55fc756500ea8", "contractAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t" } ] } Bot Response: 🚨🛑 This Wallet is Blacklisted! 🛑🚨: TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t > This workflow provides a simple and efficient way to check the blacklist status of Tron wallet addresses via a Telegram bot, making it easy for users to stay informed about the status of their wallets.

Description Automates error detection and notification to prevent production downtime. Monitors incoming webhooks, filters critical errors, and triggers alerts or bug reports. Ensures rapid response to critical issues in real-time. Essential Information Processes webhook triggers to detect errors instantly. Filters and categorizes errors as critical or non-critical. Sends Slack alerts for critical errors and creates Jira bugs as needed. System Architecture Error Detection Pipeline**: Webhook Trigger: Captures incoming error data via POST requests. Filter Critical Errors: Identifies and separates critical errors. Alert Generation Flow**: Send Slack Alert: Notifies the team via Slack for critical errors. Create Jira Bug: Logs critical errors as Jira issues. Non-Critical Handling**: No Action for Non-Critical: Skips non-critical errors with no further action. Implementation Guide Import the workflow JSON into n8n. Configure webhook URL and test with sample error data. Set up Slack and Jira credentials for alerts and bug creation. Test error filtering and notification flows. Monitor alert accuracy and adjust filter rules as needed. Technical Dependencies Webhook service for error data ingestion. Slack API for real-time notifications. Jira API for bug tracking and issue creation. n8n for workflow automation. Customization Possibilities Adjust Filter Critical Errors node to refine error severity rules. Customize Slack alert messages in Send Slack Alert node. Modify Jira issue templates in Create Jira Bug node. Add logging node to track all errors for analysis. Integrate with additional notification tools (e.g., email).

> Disclaimer: this workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes. This template is intended for use by those interested in learning more about Agentic AI workflow development, as well as those interested in learning how to use the Couchbase Search Vector Store node for practical applications. This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search. How it Works This template contains two workflows: The Data Ingestion workflow uses the following nodes Webhook node (to listen for HTTP requests) OpenAI Embeddings node (to generate embeddings on document insertion) Note: You’ll need to configure OpenAI credentials for this node Couchbase Vector node (configured for document insertion) Default Data Loader and Recursive Character Text Splitter The Chat Application workflow uses the following nodes Chat Trigger node AI Tools Agent node connect to: Gemini (as the Chat Model, for generating responses) Note: You will have to configure Gemini credentials for this node Simple Memory (as the Memory, to maintain conversation context) Couchbase Search Vector node (as the Tool, for search) OpenAI Embeddings node (as the Embedding model for the Couchbase Search Vector node, to convert queries to vectors) Note: You’ll need to configure OpenAI credentials for this node Set up Setting up this workflow is easy and only takes around 10 minutes. Prerequisites A Couchbase Cluster running the Search Service, and corresponding database access credentials Be sure the Couchbase cluster allows the incoming IP address for n8n Create a Vector Search Index using this index definition Create a bucket (called travel-agent), scope (called vectors), and collection (called points-of-interest) in your Cluster OpenAI API Key Gemini API Key Steps Configure all necessary credentials (Couchbase, OpenAI, and Gemini) Select your bucket, scope, and collection for each of the Couchbase vector nodes Ingest data, either using the cURL statements found on the sticky note within the workflow, or using this shell script to ingest 6 points of interest Open the chat and test out your travel agent! Customization and Next Steps This workflow template can be made more robust by enhancing the data model to include more information about each point of interest. For example, the addition of price ranges, ideal seasons to visit, activity types, and accomodation options can help inform the LLM further about each destination, and in turn allow it to provide a more tailored response and be more helpful for travel planning. Alternatively, the data model could be entirely re-configured to suit a wide variety of other use cases. This template can serve as a building block for all sorts of AI Agent applications using RAG and is not limited to only travel recommendations.

🎬 YouTube Video to Blog – Multilingual Blog Generator Convert YouTube videos into SEO-friendly blog posts in just seconds using this fully automated n8n workflow. Perfect for content creators, marketers, educators, and bloggers looking to repurpose video content without manual transcription or formatting. 🔧 What It Does 📥 Accepts a YouTube video URL and preferred language via a simple form 🧠 Uses a third-party API to convert the video into a blog-style article 📄 Automatically inserts the generated content into a Google Docs document 🌍 Supported Languages Supports all major languages, including but not limited to: English Hindi French German Gujarati 🎯 The workflow is flexible and can generate blog content in any language supported by the API. Just select your language when submitting the form. 🚀 Benefits ⏱️ Time-Saving: Eliminate manual video transcription and formatting 🌐 Multilingual: Easily generate blogs in multiple languages 📚 Centralized Storage: Store all generated blogs in a single Google Docs file 🔧 Customizable: Extend the flow to auto-publish, email, or analyze content 🧠 Use Cases Repurpose YouTube content into keyword-rich blog posts Generate multilingual content for global reach Convert educational videos into study guides or summaries Create email newsletters or social media posts from video content 🛠️ Requirements ✅ An n8n instance (self-hosted or cloud) 🔑 RapidAPI key for youtube-to-blog.p.rapidapi.com 🧾 A Google Docs account with API access 🚨 Note: Be sure to update the API key and Google Docs URL with your own credentials before activating the workflow. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

0xdino Cyberrealistic Pony V125 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the 0xdino/cyberrealistic-pony-v125 model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Optional Parameters cfg** (number, default: 4): CFG scale seed** (integer, default: 0): Random seed (0 = random) steps** (integer, default: 40): Sampling steps width** (integer, default: 768): Image width height** (integer, default: 1152): Image height prompt** (string, default: score_9, score_8_up, score_7_up, super-detailed fashion portrait of a young woman in ripped denim shorts and ribbed tank top, colorful accessories, RAW photography style, soft cinematic lighting, dramatic shadows across her face and body, brown hair gently tousled, (fine-art editorial atmosphere), moody tone, high-resolution textures and rich natural detail, solo subject): Positive prompt denoise** (number, default: 0.98): Denoise strength scheduler** (string, default: karras): Scheduler type facerestore** (boolean, default: True): Enable face restoration sampler_name** (string, default: dpmpp_3m_sde): Sampler name How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: 0xdino/cyberrealistic-pony-v125 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

This workflow builds a conversational AI chatbot agent using Claude 3.7 Sonnet model with the new . It enhances standard LLM capabilities with Anthropic’s features: Web Search and Think: Real-time web search**, to answer up-to-date factual queries. A “Think” function, to support internal reasoning and memory-like behavior by Anthropic. A memory buffer, allowing the agent to maintain conversation history. A system prompt defining clear ethical, functional, and formatting rules for interaction. When a user sends a message (trigger), the chatbot evaluates the query, optionally performs a web search if needed, processes the result using Claude, and responds accordingly. ✅ Advantages 🧠 Enhanced Reasoning Abilities** The Think tool allows the agent to simulate deep thought processes or contextual memory storage, improving conversational intelligence. 🌐 Real-Time Knowledge via Web Search** The integrated web_search tool enables the agent to fetch the latest information from the internet, making it ideal for dynamic or news-driven use cases. 🧾 Contextual Responses with Memory Buffer** The inclusion of a memory buffer allows the agent to maintain state across messages, improving dialogue flow and continuity. 🛡️ Built-in Ethical Guidelines** The system prompt enforces privacy, factual integrity, neutrality, and ethical response generation, making the agent safe for public or enterprise use. How It Works Chat Trigger: The workflow begins when a chat message is received via a webhook. This triggers the AI Agent to process the user's query. AI Agent Processing: The AI Agent analyzes the query to determine if it requires information from the website or external sources. It follows a structured approach: For website-related queries, it uses the provided context. For external information, it employs the web_search tool to fetch up-to-date data from the internet. The Think tool is used for internal reasoning or caching thoughts without altering data. Language Model: The Anthropic Chat Model (Claude 3.7 Sonnet) generates responses based on the analyzed query, incorporating website context or web search results. Memory: A simple memory buffer retains context from previous interactions to maintain continuity in conversations. Output: The final response is delivered to the user, excluding internal processes like web searches or reasoning steps. Set Up Steps Configure Nodes: Chat Trigger: Set up the webhook to receive user messages. AI Agent: Define the system message and rules for handling queries. Anthropic Chat Model: Select the Claude 3.7 Sonnet model and configure parameters like maxTokensToSample. Memory: Initialize the memory buffer to store conversation context. Tools: web_search: Configure the HTTP request to the Anthropic API for web searches, including headers and authentication. Think: Set up the tool for internal reasoning. Connect Nodes: Link the Chat Trigger to the AI Agent. Connect the Anthropic Chat Model, Memory, and Tools (web_search and Think) to the AI Agent. Credentials: Ensure the Anthropic API credentials are correctly configured for both the chat model and the web_search tool. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Reduce human delays between malware detection and remediation in MSSP/SOC environments. This workflow automates full endpoint antivirus scanning immediately after high-severity endpoint infection wazuh alerts, closing the gap between alerting and action. Why Use This Workflow? Malware alerts are only effective if acted upon swiftly. Manual follow-ups are slow or often missed, letting threats persist. Automates detection, triage, scan initiation, and notification—all within one minute of alerting. Ensures consistent, auditable actions across endpoints running Linux or Windows. 🔑 Key Features Listens for high-severity Wazuh AV infection alerts (e.g., rule 52502). Uses GPT-4 for AI-powered alert summaries to speed triage and decision making. Extracts exact infected file paths using AI and regex for targeted scanning. Runs ClamAV/defender scans directly on endpoints via SSH with least-privilege credentials. Sends real-time scan results and remediation updates through Telegram, Slack, or email. Runs locally with limited permissions—no need for elevated Wazuh manager access. 🎯 Impact Eliminates manual lag—scans start automatically and immediately. Standardizes response playbooks for reliable, repeatable remediation. Reduces threat dwell time, minimizing risk exposure. Provides full event-to-remediation visibility via logs and notifications. 🚀 Get Started Configure Wazuh Manager to forward AV alerts to this n8n webhook. Import this workflow JSON into your n8n instance. Set up required credentials: OpenAI API, SSH access for ClamAV scanning, notification channels (Telegram/Slack/email). Activate the workflow and monitor alerts triggering automated scans and reports. 📂 Enjoy customizing Swap ClamAV with your preferred antivirus commands (e.g., Defender) as needed. Integrate with your existing communication or ticketing systems. Extend or adapt for multi-endpoint orchestration or other alert rules. Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast

Workflow: Publish to Contentful with Rich Text Formatting ⚡ About the Creators This workflow was created by Varritech Technologies, an innovative agency that leverages AI to engineer, design, and deliver software development projects 500% faster than traditional agencies. Based in New York City, we specialize in custom software development, web applications, and digital transformation solutions. If you need assistance implementing this workflow or have questions about content management solutions, please reach out to our team. 🏗️ Architecture Overview This workflow takes a JSON article payload, splits its markdown content into logical chunks, converts each chunk into Contentful Rich Text JSON via an AI agent, merges the resulting rich text nodes back into a single document, formats the entire entry according to Contentful's field schema, and finally publishes it to Contentful. Trigger → Executes when called by another workflow Split by Headings → Breaks markdown into ##-delimited chunks Markdown → Rich Text → AI agent converts each chunk to Contentful Rich Text JSON Combine Rich Text Objects → Aggregates all chunk outputs into one document Format Entry → Wraps metadata and rich-text content into Contentful schema Publish Entry → HTTP POST to Contentful API 📦 Node-by-Node Breakdown flowchart LR A[When Executed by Another Workflow] --> B[Split by Headings] B --> C[Markdown to Contentful format] C --> D[Combine Rich Text Objects] D --> E[Merge1] E --> F[Format1] F --> G[Create newly formatted Contentful Entry] 1. When Executed by Another Workflow Type: Execute Workflow Trigger Input Example: title, slug, category.id, description, keywords, content, metaTitle, metaDescription, readingTime, difficulty Purpose: Receives the JSON payload from the upstream workflow. 2. Split by Headings Type: Code Logic: Splits input.content into an array of markdown chunks at each second-level heading (##). Emits one item per chunk with index, slug, title, and contentChunk. 3. Markdown to Contentful format Type: LangChain Agent (+ OpenAI Chat model) System Prompt: Defines rules for generating valid Contentful Rich Text JSON (must include nodeType, data:{}, content:[], etc.). Provides examples for paragraphs, headings, lists, links, and images. User Prompt: Here is the markdown content to convert: Purpose: Converts each markdown chunk into an array of rich-text nodes. 4. Combine Rich Text Objects Type: Code Logic: Parses and merges all content arrays returned by the AI agent into one combined content array under a document root. 5. Merge1 Type: Merge Purpose: Joins the original item (with metadata) and the combined rich-text document into a single data stream. 6. Format1 Type: Code Logic: Maps workflow data into the Contentful entry schema by setting each field (title, slug, category link, description, keywords, rich-text content, metaTitle, metaDescription, readingTime, difficulty) under the appropriate locale and structure required by Contentful. 7. Create newly formatted Contentful Entry Type: HTTP Request Method: POST URL: https://api.contentful.com/spaces Headers: Authorization: Bearer token for Contentful Management API Content-Type: application/vnd.contentful.management.v1+json X-Contentful-Version: entry version number X-Contentful-Content-Type: content type ID Body: The formatted fields object produced by the previous node Purpose: Publishes the new entry with rich-text content to Contentful. 🔍 Design Rationale & Best Practices Chunked Conversion Splitting by headings prevents AI context limits and keeps conversions modular. Strict Rich Text Schema Enforcing nodeType, data, and content structure avoids validation errors on Contentful. Two-Phase Merge Separating "combine AI outputs" and "format entry" keeps transformations clear and testable. Idempotent Publish Uses explicit versioning and content type headers to ensure correct entry creation.

Bulk Contact Deletion from HubSpot via Uploaded Excel / CSV File This workflow allows you to automate the deletion of HubSpot contacts based on email addresses provided in an uploaded Excel (.xlsx) file. It's ideal for bulk-cleaning outdated or invalid contact data. ✅ Prerequisites Before using this workflow, ensure you have the following: A valid HubSpot App Token with permissions to search and delete contacts. An Excel (.xlsx) file with a column labeled emails containing the contact emails to be deleted. n8n self-hosted or cloud environment with: Webhook node enabled and accessible. HubSpot node credentials configured. Basic familiarity with n8n node configuration for custom adjustments (optional). 📃Sample Document Download 🧠 n8n Workflow: Delete HubSpot Contacts from an Uploaded Excel File This n8n workflow allows you to upload an Excel file containing contact email addresses. It will check each one in HubSpot and delete the contact if it exists. 🔗 Workflow Overview 📥 1. Trigger via Webhook (POST) The workflow starts when a .xlsx file is uploaded via an HTTP POST request to the webhook. This Excel file should contain a column with contact email addresses. 📄 2. Extract Data from Excel The uploaded file is parsed, and its rows are converted into structured JSON items, making each email address available for further processing. 🧹 3. Normalize Data The data is cleaned and normalized — for example, mapping column headers (e.g., emails) into a standard email field, ensuring consistent downstream logic. 🔁 4. Loop Through Contacts Each row (contact) is processed individually using batch looping. This allows for fine-grained error handling and sequential processing. 🔎 5. Search for Contact in HubSpot For each contact, a search query is made in HubSpot based on the email address. The workflow only fetches the first result (if any). 🧪 6. Check if Contact Exists An IF condition checks whether the contact was found (i.e., if a HubSpot contact ID exists): ✅ Yes → proceed to delete the contact. ❌ No → skip deletion and continue to the next. 🗑️ 7. Delete Contact If a contact exists, it is deleted from HubSpot using its internal contact ID. 🛠️ 8. Optional Placeholder for Post-Processing A placeholder node named “Replace Me” is included for any custom logic you may want to add after the deletion step, such as: Logging Notifications Writing to external storage ✅ Use Cases Bulk delete old or bounced email addresses from HubSpot. Clean up contacts based on external suppression lists. Automate regular CRM hygiene processes. 💡 Suggested Enhancements ✍️ Log results to Google Sheets or a database 📬 Send completion report via email or Slack 🔁 Add retry logic for temporary API failures 🔍 Validate email format before making requests 📎 Requirements n8n (self-hosted or cloud) HubSpot App Token (set up in n8n credentials) Excel file (.xlsx) with a column for email 📦 Files No external files are required. All logic is contained within the n8n workflow. 🚀 Getting Started Deploy the workflow in n8n. Copy the webhook URL and use it in your app or API client (like Postman). Upload an Excel file containing contact emails via POST request. Watch as it searches and deletes matches in HubSpot.

This workflow is aimed at generating keywords for SEO and articles To get started, you need to use the workflow as it is. You just call the webhook URL with a query parameter as q={{ $keywords}} For example, you can call it using ?q=keyword research This will give you a list of keywords back as an array. This system can be used by SEO pros, content marketers and also social media marketers to generate relevant keywords for their user needs

This n8n workflow automatically tracks assignment deadlines and sends reminders to students and teachers. It checks for upcoming assignments daily, organizes the data, and sends email notifications to ensure deadlines are met. Good to Know Fully Automated**: Runs daily at 9 AM on weekdays to check assignments. Regular Updates**: Sends reminders for upcoming deadlines. Clear Notifications**: Emails a list of assignments to students and teachers. Error Handling**: Skips execution if no assignments are due. Scalable**: Works for multiple assignments and users. How It Works Reminder and Tracking Flow Set Schedule for Trigger: Starts the workflow daily at 9 AM on weekdays. Get Assignments: Retrieves assignment data from Notion database. IF Assignments Exist: Checks if there are any upcoming assignments. Split Items: Breaks down the assignment list for individual processing. Send Email Reminder: Emails reminders to students and teachers. No Assignments: Stops the workflow if no assignments are found. Example Database Columns Assignment ID**: Unique identifier for each assignment. Title**: Name of the assignment. Due Date**: Deadline for submission. Student ID**: Unique identifier for the student. Teacher ID**: Unique identifier for the teacher. Status**: Current status (e.g., Pending, Completed). How to Use Import Workflow: Add the workflow to n8n using the “Import Workflow” option. Set Up Notion: Configure n8n with Notion API credentials to fetch assignments. Configure Email: Add student and teacher email addresses and set up an email service (e.g., Gmail). Activate Workflow: Save and turn on the workflow in n8n. Check Logs: Verify reminders are sent and tracked. Requirements n8n Instance**: Self-hosted or cloud-based n8n setup. Notion Database**: API access with assignment data. Email Service**: SMTP setup (e.g., Gmail) for sending reminders. Admin Oversight**: Someone to monitor and adjust as needed. Customizing This Workflow Change Schedule**: Adjust the trigger to run at a different time or frequency. Add More Data**: Include additional fields like priority or notes. Custom Email**: Modify the email template for specific details.

This n8n template demonstrates how to add a tie form data to a new PDF. The idea is to automate the creation of a professional looking job posting. Use cases would be organizations who need to automate the creation of job postings. How it Works The trigger is a form that asks for job position, salary, office location, and responsiblities When the form is posted, it kicks off the workflow's next steps A Word document is downloaded from a Dropbox folder. This Word document is used as the template for the posting. The Word document is converted to base64. A call to Foxit's Document Generation endpoint includes the encoded Word document along with the form information. The resulting PDF is downloaded and converted from base64 into binary. At this point, the PDF is just there, but it could be emailed, sent to another workflow, etc. Requirements A Dropbox account. The workflow's first step points to a Word template. See our doc gen APIs for information on how to craft the Word doc, but the easiest way is to copy text like so: Job Position We are pleased to announce the opening of a new job, {{ jobPosition }}. This job pays ${{ salary }} per year and is in our {{ office }} location. The details of this job are: {{ responsibilities }} Foxit developer account (https://developer-api.foxit.com) Next Steps As mentioned above, you could do anything with the resulting PDF when done.