by Humble Turtle
Manage Jira Issues with Natural Language via Telegram and GPT-4o Overview The Jira Agent is an AI-powered assistant that allows users to interact with Jira directly through messaging platform Telegram. It leverages OpenAI's GPT-4o model to interpret natural language commands and perform various Jira-related actions. On Telegram, it enables users to create Jira stories by triggering a guided form when prompted with "create story." Additionally, it provides more extensive functionality, including creating, updating, searching, and transitioning Jira issues through natural language commands. How it works Normal interaction Using messages as "Please give all my issues". Standardized process of creating stories: Message: "create story" Open the Form that Telegram responds back to you Fill in the essential story information in the form The story automatically gets created in your backlog. Required Connections To use the Jira Agent effectively, users need access to: A Telegram account, Telegram setup involves deploying the bot and starting a chat; story creation is triggered with a simple text command. A connected Jira workspace Permissions to create and modify Jira issue Access to GPT-4o API-key Detailed configuration instructions are provided in the workflow Setup Time <15 minutes Customising this workflow Try adding more details to the form for more complete Jira ticket creation. Try connecting a Google Calendar node to plan your work
by Joseph LePage
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 💾 Google Docs integration for persistent storage Separate document management for memories and notes Automated document updates and retrievals
by Dhruv from Saleshandy
🧠 How it works This workflow automates QA review of Intercom support conversations by: Triggering on conversation.admin.closed events via a webhook Fetching full conversation data using Intercom API Structuring and summarizing the conversation into a readable transcript Using GPT to evaluate: Response time Clarity Tone & behavior Urgency handling Ownership & resolution Logging structured QA scores in a Google Sheet Providing coaching-style feedback if the rating is 3 or below ⚙️ Set up steps 🔐 Configure your Intercom and OpenAI credentials in n8n 📩 Set up the webhook in Intercom to post on conversation close 🧠 Use your OpenAI API key for the GPT-based nodes 🗃️ Connect your Google Sheet (or replace with another data sink) ✅ Add your own filtering logic for spam/promotional tickets if needed Note: This workflow contains a sticky notes to explain each step inside the n8n canvas.
by Wyeth
Learn n8n: Interactive Lesson 1 This interactive tutorial teaches you how to build in n8n from scratch, using a live walkthrough with real-time examples. Rather than static documentation, this guided workflow explains key n8n concepts while you execute each step. It is ideal for developers new to n8n but experienced with programming, JSON, and APIs. Requirements An active n8n instance (cloud or self-hosted) Basic programming experience (JavaScript or TypeScript, JSON, and APIs) Web browser with console access (for log inspection) What This Workflow Covers Triggers, Form nodes, and data flow How n8n executes nodes one step at a time How data moves between nodes (variables, context, side effects) Merge, Split, Aggregate, and Loop patterns Code nodes in single vs multiple execution modes Debugging using Logs and console output Step-by-Step Setup Manual Setup Before starting, create your n8n account and optionally enable dark mode. A video link is included with suggested background material. Form-Based Progression The tutorial uses Form Trigger and Form nodes as interactive checkpoints. You will execute the workflow, follow the browser prompts, and observe what happens in the visual editor. Live Code and Flow Examples Key concepts like branching, merging, and data references are shown in action. Sticky notes in the workflow explain what to look for and how things work. Execution Behavior You will see how multiple items affect execution count, and how to control it using options like Execute Once, batching, and aggregation. Debugging with Logs Toward the end, the workflow encourages you to inspect inputs and outputs of each node, and use console.log() inside Code nodes to understand the data being passed around. How to Use This Workflow This workflow is meant to be a long-term reference. If you get stuck building in n8n, return to it. Each section focuses on a core concept such as how data flows, how execution counts behave, or how to merge parallel branches. You can copy and paste working examples from this tutorial directly into your own workflows to solve common problems. This is not just a lesson. It's a toolbox.
by Mike
Use case LLMs have provided a lot of value for several use cases. Especially some OpenAI models are proving to be quite valuable. However, it's sometimes not super accessible to chat with these models. This workflow enables you to chate directly with OpenAI's GPT-3.5 via Telegram. How it works A simple telegram bot that connects to your botfather bot to give AI responses, using OpenAI's GPT 3.5 model, to a user's messages with emojis. What to do Add your telegram API key and your OpenAI api key and have fun!
by Polina Medvedieva
This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file (JSON) for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. Customer support: Generate consistent and detailed answers for common customer queries. Product managers: Easily maintain up-to-date documentation as products evolve. Content creators: Streamline the process of creating informative content about various offerings. Accounts required Google account (for Google Sheets and Google Drive) OpenAI API account (for AI-enhanced content generation) n8n.io account (for workflow execution) Set up instructions Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. Prepare your Google Sheets document with the service/category information. Here's an example of Google Sheet. Fill the "Define Sheets" node with your sheets Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. Test the workflow with a small subset of data before running it on your entire dataset. Adjust the questions asked in the "Create your Q&A templates" section After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input and support when building this workflow. Your approach to AI workflows is always super helpful!
by Jorge Martínez
Automate tweet engagement on X (formerly Twitter) Description Automate professional engagement on X (formerly Twitter) by searching for, filtering, liking, and replying to tweets that match your key topics. This workflow enables you to engage consistently and efficiently with relevant conversations, using your defined professional role and the power of GPT for filtering and replies. Save time and maintain high-quality interactions, while staying focused on your business or personal brand interests. How it Works Rotating Topic Selection The workflow selects one search term from your list on each run, using a rotating index based on the date. Search Tweets & Extract Essentials Searches X (formerly Twitter) for tweets matching the chosen topic, then extracts only the tweet id and text for further processing. GPT‑Based Filtering with Role Context Filters tweets based on your role and strict criteria, removing non-English tweets, memes, spam, Grok-generated content, political posts, internships, and more. Engagement Loop For every filtered tweet, the workflow likes the post, generates a professional, concise reply with GPT (matching language and context), and posts the reply. Wait nodes ensure compliance with Twitter’s API rate limits (can be adjusted for paid API tiers). Requirements X (Twitter) API credentials (for searching, liking, and replying to tweets) OpenAI API key (for GPT-based steps) Setup Steps Obtain your X (Twitter) API credentials. Obtain your OpenAI API key. Configure the schedule in the trigger node to your desired frequency (e.g., every 3 days or daily). Set your list of topics and professional role in the variables node. How to Customize the Workflow (Optional) Adjust prompts** in the GPT nodes to fine-tune filtering and reply style. Upgrade your Twitter API plan** to increase request limits and search for more tweets per run. Change tweet processing logic:** For high-volume engagement (e.g., analyzing 100+ tweets per run), consider switching to a per-tweet loop for advanced filtering and response handling. This workflow enables scalable, professional, and targeted engagement on X (formerly Twitter), fully customizable to your audience and objectives.
by Jimleuk
This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.
by Nukeador
Who is this for? BlueSky users who are looking to send a "welcome message" to their new followers as a private message. What this workflow does This worflow will check for new followers on BlueSky every 60 minutes and send a private message to the new ones. Setup You need to create a BlueSky app password with private messages access. Fill your credentials and the message text on the corresponding nodes (see sticky notes). Manually run once the `Save followers to file` node to generate your initial followers list. Enable the workflow How to customize this workflow to your needs You can adjust the check frecuency, but be careful to avoid hitting the 100 createSession per day rate limit Feedback or comments You can leave comments, feedback or improvements about this workflow on the n8n forums
by Angel Menendez
Phishing Email Detection and Reporting with n8n Who is this for? This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails. What problem is this workflow solving? Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira. What this workflow does This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens: Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook. Email Analysis: Extracts email content, headers, and metadata for analysis. HTML Screenshot: Converts the email’s HTML body into a visual screenshot. AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators. Jira Integration: Automatically creates a Jira ticket with detailed analysis and attaches the email screenshot for review by the security team. Customizable Reports: Includes options to customize ticket descriptions and adapt the workflow to organizational needs. Setup Authentication: Set up Gmail and Microsoft Outlook OAuth credentials in n8n to access your email accounts securely. API Keys: Add API credentials for the HTML screenshot service (hcti.io) and ChatGPT. Jira Integration: Configure your Jira project and issue types in the workflow. Workflow Configuration: Update sticky notes and nodes to include any additional setup or configuration details unique to your system. How to customize this workflow to your needs Email Filters**: Modify email triggers to filter specific subjects or sender addresses. Analysis Scope**: Adjust the ChatGPT prompt to refine phishing detection logic. Integration**: Replace Jira with your preferred ticketing system or modify the ticket fields to include additional information. This workflow provides an end-to-end automated solution for phishing email management, enhancing efficiency and reducing security risks. It’s perfect for teams looking to minimize manual effort and improve incident response times.
by Mario
Purpose This ensures that executions of scheduled workflows do not overlap when they take longer than expected. How it works This is a separate workflow which monitors the execution of the main workflow Stores a flag in Redis (key dynamically named after workflow ID) which indicates if the main workflow is running or idle Only calls the main workflow if the last execution has finished Setup Update the credentials suitable for your Redis instance Replace the Schedule Trigger of your main workflow by an Execute Workflow Trigger Copy the workflow ID from the URL Paste the workflow ID in the Execute Workflow Node of this workflow Configure the Schedule Trigger Node
by Jimleuk
This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths. The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py How it works This evaluation works best where the agent's response is allowed to be more verbose and conversational. For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not ground truth) and False Negative (not in answer but in ground truth). We also calculate an average similarity score on the agent's response against all ground truths. The classification and the similarity score is then averaged to give the final score. A high score indicates the agent is accurate whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing