Workflow Templates

Unlock a new level of sophistication for your AI agents with this template. While the native n8n Think Tool is great for giving an agent an internal monologue, it's limited to one instance. This workflow provides a clever solution using a sub-workflow to create multiple, custom thinking tools, each with its own specific purpose. This template provides the foundation for building agents that can plan, act, and then reflect on their actions before proceeding. Instead of just reacting, your agent can now follow a structured, multi-step reasoning process that you design, leading to more reliable and powerful automations. Who is this for? AI and automation developers:** Anyone looking to build complex, multi-tool agents that require robust logic and planning capabilities. LangChain enthusiasts:** Users familiar with advanced agent concepts like ReAct (Reason-Act) will find this a practical way to implement similar frameworks in n8n. Problem solvers:** If your current agent struggles with complex tasks, giving it distinct steps for planning and reflection can dramatically improve its performance. What problem does this solve? Bypasses the single "Think Tool" limit:** The core of this template is a technique that allows you to add as many distinct thinking steps to your agent as you need. Enables complex reasoning:** You can design a structured thought process for your agent, such as "Plan the entire process," "Execute Step 1," and "Reflect on the result," making it behave more intelligently. Improves agent reliability and debugging:** By forcing the agent to write down its thoughts at different stages, you can easily see its line of reasoning, making it less prone to errors and much easier to debug when things go wrong. Provides a blueprint for sophisticated AI:** This is not just a simple tool; it's a foundational framework for building state-of-the-art AI agents that can handle more nuanced and multi-step tasks. How it works The re-usable "Thinking Space": The magic of this template is a simple sub-workflow that does nothing but receive text. This workflow acts as a reusable "scratchpad." Creating custom thinking tools: In the main workflow, we use the Tool (Workflow) node to call this "scratchpad" sub-workflow multiple times. We give each of these tools a unique name (e.g., Initial thoughts, Additional thoughts). The power of descriptions: The key is the description you give each of these tool nodes. This description tells the agent when and how it should use that specific thinking step. For example, the Initial thoughts tool is described as the place to create a plan at the start of a task. Orchestration via system prompt: The main AI Agent's system prompt acts as the conductor, instructing the agent on the overall process and telling it about its new thinking abilities (e.g., "Always start by using the Initial thoughts tool to make a plan..."). A practical example: This template includes two thinking tools to demonstrate a "Plan and Reflect" cycle, but you can add many more to fit your needs. Setup Add your own "action" tools: This template provides the thinking framework. To make it useful, you need to give the agent something to do. Add your own tools to the AI Agent, such as a web search tool, a database lookup, or an API call. Customize the thinking tools: Edit the description of the existing Initial thoughts and Additional thoughts tools. Make them relevant to the new action tools you've added. For example, "Plan which of the web search or database tools to use." Update the agent's brain: Modify the system prompt in the main AI Agent node. Tell it about the new action tools you've added and how it should use your customized thinking tools to complete its tasks. Connect your AI model: Select the OpenAI Chat Model node and add your credentials. Taking it further Create more granular thinking steps:** Add more thinking tools for different stages of a process, like a "Hypothesize a solution" tool, a "Verify assumptions" tool, or a "Final answer check" tool. Customize the thought process:* You can change *how the agent thinks by editing the prompt inside the fromAI('Thoughts', ...) field within each tool. You could ask for thoughts in a specific format, like bullet points or a JSON object. Change the workflow trigger:** Switch the chat trigger for a Telegram trigger, email, Slack, whatever you need for your use case! Integrate with memory:** For even more power, combine this framework with a long-term memory solution, allowing the agent to reflect on its thoughts from past conversations.

🧾 Generate Project Summary from meeting transcript Who’s it for 🤝 Project managers looking to automate client meeting summaries Client success teams needing structured deliverables from transcripts Agencies and consultants who want consistent, repeatable documentation How it works / What it does ⚙️ Trigger: Manual or webhook trigger kicks off the workflow. Get meeting transcript: Reads the raw transcript from a specified Google Docs file. Generate summary: Sends transcript + instructions to OpenAI (gpt-4.1-mini) to produce a structured project summary. Convert to HTML: Transforms the LLM-generated Markdown into styled HTML. Prepare request: Wraps HTML and metadata into a multipart request body. Create Google Doc: Uploads the new “Project Summary” document into your Drive folder. How to set up 🛠️ Credentials Google Docs & Drive OAuth2 credentials OpenAI API key (gpt-4.1-mini) Nodes configuration Manual Trigger / webhook node Google Docs “Get meeting transcript” node: set documentURL AI Chat Model node: select gpt-4.1-mini Markdown node: enable tables & emoji Google Drive “CreateGoogleDoc” node: set target folder ID Paste in your IDs Update documentURL to your transcript doc Update google_drive_folder_id in the Set node Execute Click “Execute Workflow” or call via webhook Requirements 📋 n8n Google OAuth2 scopes for Docs & Drive OpenAI account with GPT-4.1-mini access A Google Drive folder to store summaries How to customize ✨ Output format**: Edit the Markdown prompt in the ChainLlm node to adjust headings or tone Timeline section**: Extend LLM prompt template with your own phase table Styling**: Tweak inline CSS in the Code node (Prepare_Request) for fonts or margins Trigger**: Swap Manual Trigger for HTTP/Webhook trigger to integrate with other tools Language model**: Upgrade to a different model by changing model.value in the AI node

How it Works This workflow fetches top news headlines every 10 minutes from NewsAPI, summarizes them using OpenAI's GPT-4o model, and sends a concise email digest to a list of recipients defined in a Google Spreadsheet. It's ideal for anyone who wants to stay updated with the latest news in a short, digestible format. 🎯 Use Case Professionals who want summarized daily news Newsletters or internal communication updates Teams that require contextual summaries of the latest events Setup Instructions 1. Upload the Spreadsheet File name: Emails Column: Email with recipient addresses 2. Configure Google Sheets Nodes Connect your Google account to: Email List Send Email 3. Add API Credentials NewsAPI Key** → for fetching top headlines OpenAI API Key** → for summarizing headlines Gmail Account** → for sending the email digest 4. Activate the Workflow Once active, the workflow runs every 10 minutes via a cron trigger Summarized news is sent to the list of emails in the spreadsheet 🔁 Workflow Logic Trigger: Every 10 minutes via Cron Fetch News: HTTP request to NewsAPI for top headlines Summarize: Headlines are passed to OpenAI's GPT-4o for 5-bullet summary Read Recipients: Google Sheet is used to collect email recipients Send Email: Summary is formatted and sent via Gmail 🧩 Node Descriptions | Node Name | Description | |-----------|-------------| | Cron | Triggers the workflow every 10 minutes. | | HTTP Request - NewsAPI | Fetches top news headlines using NewsAPI. | | Set | Formats or structures raw news data before processing. | | AI Agent | Summarizes the news content using OpenAI into 5 bullet points. | | Email List | Reads recipient email addresses from the 'Emails' Google Spreadsheet. | | Send Email | Sends the email digest to all recipients using Gmail. | 🛠️ Customization Tips Modify the AI prompt for tone, length, or content type Send summaries to Slack, Telegram, or Notion instead of Gmail Adjust cron interval for more/less frequent updates Change email formatting (HTML vs plain text) 📎 Required Files | File Name | Purpose | |-----------|---------| | Emails spreadsheet | Google Sheet containing the list of email recipients | | daily_news.json | Main n8n workflow file to automate daily news digest | 🧪 Testing Tips Add 1–2 test email addresses in your spreadsheet Temporarily change the Cron node to run every minute for testing Check email inbox for delivery and formatting Inspect the execution logs for API errors or formatting issues 🏷 Suggested Tags & Categories #News #OpenAI #Automation #Email #Digest #Marketing

Daily Email Pulse Summary: This agent summarizes a user's daily emails into a clean, actionable summary. It uses OpenAI to analyze content and sends a formatted "Daily Pulse" email at the end of each day. Main use cases: Keep track of open loops and next steps across all email conversations Identify high-potential leads and flag conversations going nowhere Eliminate the need to manually review your inbox at day’s end Build a smart summary layer using AI without hallucination or noise How it works This workflow can be divided into eight core nodes, each serving a distinct purpose in helping a user stay on top of their day. The result is a curated, AI-generated summary delivered to your inbox — crafted from real message content, not guesswork. Schedule Trigger (Trigger Node – Runs Daily at Set Time) Kicks off the workflow at a specific time each day (e.g. 6:00 PM). Ensures you receive your Daily Pulse consistently, without needing to run it manually. Date Transformer (Function Node – Define Today & Tomorrow Range) Uses JavaScript to calculate the current day’s date range: today: Start of day (00:00:00) tomorrow: Start of next day (used as a cutoff) This ensures only emails from today are analyzed, keeping the summary focused and current. Get All Messages (Gmail Node – Fetch Filtered Emails) Pulls in all Gmail messages with internalDate between today and tomorrow. Outputs structured data: from, subject, and body text of each email. This forms the raw data for the daily business pulse. Aggregator (Function or Item Lists Node – Combine Message Fields) Aggregates each message into a readable format: From: John@example.com Subject: Demo Follow-up Body: Let’s schedule a time this week... All messages are stitched together into a single combinedText string for analysis. This gives the AI model full context for the day in one unified document. Email Cleanup (Function Node – Remove Noise & Normalize Text) Cleans the combinedText blob to remove: HTML tags Marketing footers (e.g., unsubscribe links) Redundant whitespace or formatting artifacts Ensures GPT gets clean, relevant message content with no distractions. Agent (OpenAI Node – Generate Structured Summary) Uses a System Prompt to define its role as an AI Chief of Staff. Uses a User Prompt that instructs it to categorize messages into sections: 📝 Open Loops / Pending Follow-Up 🚀 Next Steps You’ve Committed To 🧲 Leads Worth Following Up On 🛑 Conversations That Aren’t Leading Anywhere 🧠 Strategy Notes ✅ Top 3 Tasks for Tomorrow Built-in guardrails ensure the model only uses real content (no hallucination). Sections with no relevant data are omitted to keep it concise. HTML Formatter (Function Node – Wrap Markdown in Email-Ready HTML) Wraps the GPT-generated markdown summary in a simple <html><body> structure. Applies white-space: pre-wrap to preserve formatting and spacing. The result is a clean, readable email that renders well across all inboxes (especially Gmail). Email Send (Email Node – Deliver the Final Pulse) Sends the formatted summary to your email inbox. Subject: Your Daily Business Pulse – {{today}} HTML body: Uses the formatted output from the previous step. Final output: a well-organized, scannable summary of the day’s communication — focused on what matters. Why It Works Automates the end-of-day review ritual without effort Prioritizes follow-ups, action items, and time-sensitive leads Filters out noise and low-value conversations Leverages GPT without risk of hallucination or irrelevant output Delivers clarity, helping you focus on tomorrow’s most important tasks

AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template 2. Configure Triggers and Connections 🔁 GitHub Trigger Node**: PR Trigger Repository**: Select the GitHub repo(s) to monitor Events**: Set to pull_request Auth**: Use GitHub OAuth2 credentials 📥 HTTP Request Node: Get file's Diffs from PR No authentication needed; it uses dynamic path from trigger 🧠 OpenAI Model Node**: OpenAI Chat Model Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo Credential**: Provide your OpenAI API Key 🧑‍💻 Code Review Agent Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets 💬 GitHub Comment Poster Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository 🏷️ PR Labeler (optional) Adds label ReviewedByAI after successful comment Node: Add Label to PR Label : you ca customize the label text of your owen tag. 📊 Google Sheet Best Practices config (optional) Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base First prepare your best practices list with the clear description and the code bad/good examples Add al the best practices in your Google Sheet Configure* the Code *Best Practices node** in the template : Credential : Use your Google Sheet account by OAuth2 URL : Add your Google Sheet document URL Sheet : Add the name of the best practices sheet

This n8n workflow automates website security audits. It combines direct website scanning, threat intelligence from AlienVault OTX, and advanced analysis from an OpenAI large language model (LLM) to generate and email a comprehensive security report. How it Works (Workflow Flow): Input: A user provides a website URL via a simple web form. Data Collection: An HTTP Request node visits the provided URL to gather initial data (status code, headers). An AlienVault HTTP Request node queries AlienVault OTX for known threats associated with the website's hostname. Data Preparation (Prepare Data for AI): A custom code node consolidates the collected website data and AlienVault intelligence, performing initial checks for common issues (e.g., error codes, missing security headers, AlienVault warnings). AI Analysis (Security Configuration Audit): The prepared data is sent to an OpenAI Chat Model, which acts as a cybersecurity expert. The AI analyzes the data to identify vulnerabilities, explain their impact, suggest exploitation methods, and outline mitigation steps. Report Formatting (Format Report for Email): Another custom code node takes the AI's plain-text report and converts it into a structured HTML format suitable for email. Delivery (Send Security Report): The final HTML report is sent via Gmail to a specified email address. Setup Steps: To use this workflow, you'll need an n8n instance and the following credentials: n8n Instance: Ensure your n8n environment is running. OpenAI API Key: Generate a key from OpenAI. Add an "OpenAI API" credential in n8n (e.g., "OpenAI account"). AlienVault OTX API Key: Obtain a key from your AlienVault OTX profile. Add an "AlienVault OTX API" credential in n8n (e.g., "AlienVault account"). Gmail Account: Set up a "Gmail OAuth2" credential in n8n for sending emails (recommended for security; involves Google Cloud setup). Import Workflow: Copy the workflow's JSON code. In n8n, import the workflow via "Workflows" > "New" > "Import from JSON". Configure Recipient: In the "Send Security Report" node, specify the email address where reports should be sent. Activate: Enable the workflow to start processing submissions. Once activated, access the "On form submission" webhook URL to input a URL and trigger an audit.

Define Your ICP from Customer LinkedIn Profiles Use Case This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it generates a clear ICP definition and scoring methodology for future targeting. What This Automation Does This automation analyzes LinkedIn profiles of your existing customers and produces: A structured ICP definition A scoring model to evaluate future prospects A Google Boolean search string to find similar prospects Input: LinkedIn profile URLs of existing high-fit customers (e.g., https://www.linkedin.com/in/amirashkenazi/) Output: A Google Doc containing the ICP analysis and scoring methodology How It Works Trigger: Waits for a chat message containing one or more LinkedIn profile URLs. AI Agent: Parses and processes the URLs. Airtop Data Enrichment: Uses Airtop to extract structured information from each LinkedIn profile (e.g., job title, company, experience, skills). Memory: Maintains state between inputs for consistent analysis. LLM Analysis: Uses Claude 3.7 Sonnet to synthesize enriched data into a meaningful ICP. Google Docs: Automatically creates a new doc with a timestamped title and appends the ICP definition. Setup Requirements Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool Airtop API credentials. Get it free here If you choose to activate saving the profiles in Google Docs you will need OAuth2 credentials (or just copy the ICP definition from the chat) Next Steps Use the ICP for Scoring**: Feed new LinkedIn profiles through the same Airtop enrichment and use the scoring function to evaluate fit. Automate Target Discovery**: Plug the Boolean search output into LinkedIn, Google, or People Data Labs for ICP-matching lead generation. Refine Continuously**: Repeat the workflow as your customer base grows or segments evolve. Read more about how to Define ICP from Customer Examples

Convert PDF documents to AI-generated podcasts with Google Gemini and Text-to-Speech Transform any PDF document into an engaging, natural-sounding podcast using Google's Gemini AI and advanced Text-to-Speech technology. This automated workflow extracts text content, generates conversational scripts, and produces high-quality audio files. Who is this for? This workflow template is perfect for content creators, educators, researchers, and marketing professionals who want to repurpose written content into audio format. Ideal for creating podcast episodes, educational content, or making documents more accessible. What problem does this solve? Converting written documents to engaging audio content manually is time-consuming and requires scriptwriting skills. This workflow automates the entire process, turning static PDFs into dynamic, conversational podcasts that sound natural and engaging. What this workflow does Extracts text from uploaded PDF documents Generates podcast script using Google Gemini AI with conversational tone Converts script to speech using Google's advanced TTS with customizable voices Processes audio into properly formatted WAV files Saves final podcast ready for distribution Setup Obtain API credentials: Get Google Gemini API key from AI Studio Configure credentials in n8n as "Google Gemini(PaLM) Api account" Configure voice settings: Choose from available voices: Kore (professional), Aoede (conversational), Laomedeia (energetic) Customize script generation prompts if needed Test the workflow: Upload a sample PDF file Verify audio output quality Adjust voice settings as preferred How to customize this workflow Modify script style:** Edit the prompt in the "Generate Podcast Script" node to change tone, length, or format Change voice:** Update the voice name in "Prepare TTS Request" node Add preprocessing:** Insert text cleaning nodes before script generation Integrate with storage:** Connect to Google Drive, Dropbox, or other storage services Add notifications:** Include Slack or email notifications when podcasts are ready Note: This template requires Google Gemini API access and works best with text-based PDF files under 10MB.

🧾 What this workflow does This workflow turns YouTube video links into ready-to-edit newsletter drafts using Dumpling AI and GPT-4o. It reads new video URLs from a Google Sheet, extracts their transcripts, summarizes them into email-friendly content, and logs the finished draft back into the same sheet. An email notification is also sent to alert the user once each draft is created. 👤 Who is this for Newsletter writers or marketers repurposing video content YouTube creators building email follow-ups from videos Agencies or VAs batching social → email content Automation users streamlining content workflows ⚙️ How to set up ✅ Requirements Google Sheet** with the following columns: link — YouTube video URL blog post — for saving the generated newsletter draft Active accounts for: Dumpling AI (API for YouTube transcripts) OpenAI GPT-4 or GPT-4o Google Sheets Gmail (OAuth2 credential) 🔧 Setup steps Connect all credentials using n8n's Credential Manager: Google Sheets (OAuth2) Dumpling AI (via HTTP Header Auth) OpenAI Gmail Update the sheet ID and tab name in both Google Sheets nodes. Customize the GPT-4o prompt (optional): Located in the “GPT-4o: Write Newsletter Draft from Transcript” node You can edit tone, structure, and audience targeting in the system message Verify email recipient in the Gmail node and update if needed. 🧠 How it works The workflow is triggered manually or on schedule. It pulls YouTube links without drafts from the sheet. Each video’s transcript is fetched using Dumpling AI. GPT-4o summarizes the transcript into a clean, friendly newsletter format. The draft is written back to the same row in Google Sheets. An email is sent to notify the user that the draft is ready. 🛠️ Customization ideas Send finished drafts to Notion or Airtable instead of Sheets Generate social media posts from the same transcript Add automatic review steps using GPT scoring or editing Trigger this on new form submissions or YouTube uploads instead This is a fast, AI-powered way to turn long-form video content into clean, polished newsletters — ready to share or schedule with minimal editing.

The Problem 😫 Tired of manually logging every coffee and cab ride? Stop wrestling with spreadsheets! This template automates your expense tracking so you can manage your finances effortlessly. It's perfect for freelancers, small business owners, and anyone who wants a simple, chat-based way to track spending. How It Works ✨ Just send a message to your personal Telegram bot like "5 usd for coffee with my card" and this workflow will automatically: 📲 Get your message from Telegram. 🤖 Use AI to understand the amount, category, currency, and payment method. 💱 Convert currencies automatically using live exchange rates. ✍️ Log everything neatly into a new row in your Google Sheet. 🛠️ Quick Setup Guide Google Sheets 📝 Create a new Google Sheet. Make sure your first row has these exact column names: date, amount, category, description, user_id, payment_method, currency, exchange_rate, amount_converted Copy the Sheet ID from the browser's URL bar. Telegram Bot 🤖 Chat with @BotFather on Telegram, use the /newbot command, and get your API Token. Chat with @userinfobot to get your personal Chat ID. n8n Workflow 🔗 Add your credentials for Google Sheets, Telegram, and your AI model. Paste your Chat ID into the Telegram Trigger node. Paste your Sheet ID into the Append row in sheet node. Activate the workflow and start tracking! ✅

Who is this template for? Anyone who is drowning in emails Busy parents who has alot of school emails Busy executives with too many emails Case Study I get too many emails from my kid's school about soccer practice, lunch orders and parent events. I use this workflow to read all the emails and tell me what is important and what requires actioning. Read more -> How I used A.I. to read all my emails What this workflow does It uses IMAP to read the emails from your email account (i.e. Gmail). It then passes the email to Openrouter.ai and uses a free A.I. model to read and summarize the email. It then sends the summary as a message to your messenger (i.e. Line). Setup You need to find your email server IMAP credentials. Input your openrouter.ai API credentials or replace the HTTP request node with an A.I. node such as OpenAI. Input your messenger credentials. I use Line but you can change the node to another messenger line Telegram. You need to change the message ID to your ID inside the http request. You can find your user ID inside the https://developers.line.biz/console/. Change the "to": {insert your user ID}. How to adjust it to your needs You can change the A.I. prompt to fit your needs by telling it to mark emails from a certain address as important. You can change the A.I. model from the current meta-llama/llama-3.1-70b-instruct:free to a paid model or other free models. You can change the messenger node to telegram or any other messenger app you like.

Description This n8n workflow enables users to send an image to a Telegram bot and receive the extracted text using Tesseract OCR (via the n8n-nodes-tesseractjs Community Node). It's a quick and straightforward way to convert images into readable text directly through chat. How it Works The workflow listens for new image messages coming in via the Telegram bot. Once an image is received, it downloads the image file from Telegram (which initially arrives as application/octet-stream). The image data, now properly identified, is then sent to the Tesseract OCR node to extract the text. Finally, the recognized text is sent back as a reply to the Telegram user. Setup Steps Install Community Node: Ensure you have installed n8n-nodes-tesseractjs in your n8n instance. Connect Telegram Bot: Configure the Telegram Trigger node with your Telegram bot. Bot Token: Add your Telegram bot token to the Send Message node to send replies. Deploy & Test: Activate (deploy) the workflow and send an image to your Telegram bot to test.