Agents & Automations

Easily create engaging content ideas from Airtable records using Eden AI's text generation capabilities. Enhance your writing experience, boost productivity, and improve search rankings.

Functionality This workflow automates the handling of incoming emails by summarizing their content, generating appropriate responses, and validating the responses through a "Human-in-the-Loop" system. It integrates with IMAP email services (e.g., Gmail, Outlook) and uses AI models to streamline the email response process. The workflow ensures that all AI-generated responses are reviewed by a human before being sent, maintaining a high level of professionalism and accuracy. This approach is particularly useful for businesses that receive a high volume of emails and need to respond quickly while ensuring quality control. How It WorksEmail 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 (OpenAI) to generate a concise summary of the email. The summary is limited to 100 words and is written in a professional tone. 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. Human-in-the-Loop Approval: The Set Email text node prepares the drafted response for approval. The Approve Email node sends the drafted response to a human approver (e.g., an internal email address) for review. The email includes: - The original message. - The AI-generated response. The Approved? node checks if the response has been approved by the human reviewer. If approved, the workflow proceeds to send the response; otherwise, it stops. Sending the Response: The Send Email node sends the approved response back to the original sender. Key Features - Automated Email Summarization: Summarizes incoming emails to provide a quick overview of the content. - AI-Powered Response Generation: Drafts professional responses to emails using AI. - Human-in-the-Loop Approval: Ensures all AI-generated responses are reviewed and approved by a human before being sent. - IMAP Integration: Works with IMAP email services like Gmail and Outlook. - Efficient Email Management: Reduces the time and effort required to handle incoming emails while maintaining high-quality responses. This workflow is ideal for businesses looking to automate their email response process while maintaining control over the quality of outgoing communications. It leverages AI to handle repetitive tasks and ensures that all responses are reviewed by a human, providing a balance between automation and human oversight.

Generate and Publish AI Content to LinkedIn and X (Twitter) with n8n This n8n workflow automates the generation and publishing of AI-powered social media content across LinkedIn and X (formerly Twitter). By leveraging AI, this workflow helps social media managers, marketers, and content creators streamline their posting process. Who is this for? - Social media managers - Content creators - Digital marketers - Businesses looking to automate content generation Features - AI-powered content creation tailored for LinkedIn and X (Twitter) - Automated publishing to both platforms - Structured output parsing to ensure consistency - OAuth2 authentication for secure posting - Merge and confirmation steps to track successful postings Setup InstructionsPrerequisites Before using this workflow, ensure you have: - An n8n instance set up - API credentials for: - Google Gemini AI (for content generation) - X Developer Account with OAuth2 authentication - LinkedIn Developer Account with OAuth2 authentication - A form submission service integrated with n8n Workflow Breakdown 1. Trigger: Form Submission A user submits a form containing the post title. The form is secured with Basic Authentication. The submitted title is passed to the AI Agent. 2. AI Content Generation The Google Gemini Chat Model processes the title and generates: - LinkedIn post content - Twitter (X) post content - Hashtags - Call-to-action (LinkedIn) - Character limit check (Twitter) 3. Parsing AI Output A structured output parser converts the AI-generated content into a JSON format. Ensures correct formatting for LinkedIn and Twitter (X). 4. Publishing to Social Media - X (Twitter) Posting: Extracts the Twitter post from the AI output. Publishes it via an OAuth2-authenticated X (Twitter) account. - LinkedIn Posting: Extracts the LinkedIn post from the AI output. Publishes it via an OAuth2-authenticated LinkedIn account. 5. Merging Post Results Merges the response data from both LinkedIn and Twitter after publishing. 6. Confirmation Step Displays a final confirmation form once the posts are successfully published. Benefits - Save time by automating content creation and publishing. - Ensure consistency across platforms with structured AI-generated posts. - Secure authentication using OAuth2 for LinkedIn and Twitter. - Increase engagement with AI-optimized hashtags and CTAs. This workflow enables seamless social media automation, helping professionals post engaging AI-powered content effortlessly. 🚀

If you have a shared or personal drive location with a high frequency of files created by humans, it can become difficult to organise. This may not matter... until you need to search for something! This n8n workflow works with the local filesystem to target the messy folder and categorise as well as organise its files into subdirectories automatically. Disclaimer Unfortunately, due to the intended use-case, this workflow will not work on n8n Cloud and a self-hosted version of n8n is required. How it works Uses the local file trigger to activate once a new file is introduced to the directory. The new file's filename and filetype are analysed using AI to determine the best location to move this file. The AI assesses the current subdirectories to avoid creating duplicates. If a relevant subdirectory is not found, a new subdirectory is suggested. Finally, an Execute Command node uses the AI's suggestions to move the new file into the correct location. Requirements Self-hosted version of n8n. The nodes used in this workflow only work in the self-hosted version. If you are using Docker, you must create a bind mount to a host directory. Mistral.ai account for LLM model. Customise this workflow If the frequency of files created is high enough, you may not want the trigger to activate on every new file created event. Switch to a timer to avoid concurrency issues. Want to go fully local? A version of this workflow is available which uses Ollama instead.

UTM Link Creator & QR Code Generator with Scheduled Google Analytics Reports This workflow enables marketers to generate UTM-tagged links, convert them into QR codes, and automate performance tracking in Google Analytics with scheduled reports every 7 days. This solution helps monitor traffic sources from different marketing channels and optimize campaign performance based on analytics data. Prerequisites Before implementing this workflow, ensure you have the following: - Google Analytics 4 (GA4) Account & Access Ensure you have a GA4 property set up. - Access to the GA4 Data API Schedule performance tracking. Refer to the Google Analytics Data API Overview for more information. - Airtable Account & API Key Create an Airtable base to store UTM links, QR codes, and analytics data. Obtain an Airtable API key from your Account Settings. Detailed instructions are available in the Airtable API Authentication Guide.Step-by-Step Guide to Setting Up the Workflow 1. Generate UTM Links Create a form or interface to input: - Base URL (e.g., https://example.com) - Campaign Name (utm_campaign) - Source (utm_source) - Medium (utm_medium) - Term (Optional: utm_term) - Content (Optional: utm_content) Append UTM parameters to generate a trackable URL. 2. Store UTM Links & QR Codes in Airtable Set up an Airtable base with the following columns: - UTM Link - QR Code - Campaign Name - Source - Medium - Date Created Adjust as needed based on your tracking requirements. For guidance on setting up your Airtable base and using the API, refer to the Airtable Web API Documentation. 3. Convert UTM Links to QR Codes Use a QR code generator API (e.g., goqr.me, qrserver.com) to generate QR codes for each UTM link and store them in Airtable. 4. Schedule Google Analytics Performance Reports (Every 7 Days) Use the Google Analytics Data API to pull weekly performance reports based on UTM parameters. Extract key metrics such as: - Sessions - Users - Bounce Rate - Conversions - Revenue (if applicable) Store the data in Airtable for tracking and analysis. Adjust timeframe as needed. For more details on accessing and using the Google Analytics Data API, consult the Google Analytics Data API Overview.Benefits of This Workflow ✅ Track Marketing Campaigns: Easily monitor which channels drive traffic. ✅ Automate QR Code Creation: Seamless integration of UTM links with QR codes. ✅ Scheduled Google Analytics Reports: No manual reporting—everything runs automatically. ✅ Improve Data-Driven Decisions: Optimize ad spend and marketing strategies based on performance insights.

This workflow is an experiment to build HTML pages from user input using the new Structured Output from OpenAI. How it works: Users add what they want to build as a query parameter. The OpenAI node generates an interface following a structured output defined in the body. The JSON output is then converted to HTML along with a title. The HTML is encapsulated in an HTML node (where the Tailwind CSS script is added). The HTML is rendered to the user via the Webhook response. Set up steps: 1. Create an OpenAI API Key. 2. Create the OpenAI credentials. 3. Use the credentials for both nodes HTTP Request (as Predefined Credential type) and OpenAI. 4. Activate your workflow. Once active, go to the production URL and add what you'd like to build as the parameter "query". Example: https://production_url.com?query=a%20signup%20form

This n8n automation is designed to extract, process, and store content from Notion pages into a Pinecone vector store. Here's a breakdown of the workflow: Notion - Page Added Trigger: The automation starts by monitoring for newly added pages in a specific Notion database. It triggers whenever a new page is created, capturing the page's metadata. Notion - Retrieve Page Content: Once triggered, the automation fetches the full content of the newly added Notion page, including blocks like text, images, and videos. Filter Non-Text Content: The next step filters out non-text content (such as images and videos), ensuring only textual content is processed. Summarize - Concatenate Notion's blocks content: The remaining text content is concatenated into a single block of text for easier processing. Token Splitter: The concatenated text is then split into manageable tokens, which are chunks of text that can be used for embedding. Create metadata and load content: Metadata such as the page ID, creation time, and title are added to the content, making it easy to reference and track. Embeddings Google Gemini: The processed text is passed through a Google Gemini model to generate embeddings, which are numerical representations of the text that capture its semantic meaning. Pinecone Vector Store: Finally, the embeddings, along with the content and metadata, are stored in a Pinecone vector store, making it searchable and ready for use in applications like document retrieval or natural language processing tasks. This workflow ensures that every new page added to the Notion database is processed into a format that can be easily searched and used in machine learning applications. The automation runs every minute to capture new data in real-time, providing an up-to-date and searchable vector database of Notion content. Use Case: This automation converts Notion pages into vector embeddings and stores them in Pinecone for enhanced search and AI-driven insights. It’s ideal for teams using Notion for knowledge management, enabling semantic search and context-based content retrieval. For example, employees can easily find relevant information across documents, and data scientists can use AI models to analyze and summarize the content stored in Notion.

Job Search Automation Workflow Transform Your Job Search with API-Powered Automation Are you tired of manually searching through countless job boards, trying to find the perfect position that matches your skills and experience? The Job Search Automation Workflow revolutionizes this tedious process by leveraging the power of APIs and artificial intelligence to streamline your job search like never before. Unlike traditional job search tools that rely on RSS feeds with limited information and frequent delays, this workflow connects directly to the Adzuna API to fetch real-time job listings with comprehensive details. This direct API integration ensures you're always seeing the most current opportunities available in the market, giving you a competitive edge in your job search journey. Who This Workflow Is For This powerful automation tool is designed for multiple user groups who can benefit from streamlined job search processes: • For job seekers, this workflow eliminates hours of manual searching by automatically finding and evaluating positions that match your profile. Instead of spending countless hours scrolling through job boards, you can focus your energy on preparing for interviews and networking with potential employers. • For recruiters, this tool helps identify quality positions to match with your candidate pool, enabling you to provide better service to both job seekers and employers. You'll be able to quickly assess job opportunities and match them with appropriate candidates in your database. • For career coaches, this workflow provides a systematic approach to help your clients find suitable positions, allowing you to offer more value and demonstrate concrete results. You can use the AI-powered scoring system to objectively evaluate how well certain positions align with your clients' skills and experience. • For AI automation builders, this workflow serves as an excellent template to understand how to integrate job APIs with AI evaluation systems, providing inspiration for your own custom solutions. You can study the architecture and modify it to create even more sophisticated job-related automation tools. Key Features That Make This Workflow Special The workflow incorporates several powerful features that set it apart from conventional job search methods: • The direct integration with Adzuna's comprehensive job API provides access to thousands of current job listings across multiple industries and locations. Unlike RSS-based solutions that often contain outdated information, this API connection ensures you're always working with the most recent job postings. • The intelligent job summarization feature uses AI to extract the most relevant information from lengthy job descriptions, saving you valuable time while ensuring you don't miss critical details. The system condenses verbose job listings into concise summaries that highlight the most important aspects of each position. • The automated job-candidate matching system employs advanced AI to score how well each position aligns with a candidate's profile, helping prioritize applications for positions with the highest potential fit. This objective scoring system helps eliminate bias and focuses attention on opportunities where success is most likely. • The workflow is built on n8n, a powerful and flexible automation platform that allows for easy customization to meet your specific needs. You can modify search parameters, adjust AI prompts, or integrate with other tools in your workflow with minimal effort. Easy Setup and Immediate Results Getting started with the Job Search Automation Workflow is straightforward. You'll need an API key from Adzuna.com. Once these prerequisites are in place, simply import the workflow and you're ready to begin transforming your job search process. The workflow is designed to be user-friendly even for those with limited technical experience. Clear documentation guides you through each step of the setup process, and the intuitive interface makes it easy to customize search parameters to match your specific requirements. Affordable Investment in Your Career Future At just $1 (or pay what you think is fair), this workflow represents an incredible value for the time and effort it will save you in your job search. Consider how much your time is worth—even saving just one hour of searching per day translates to significant value over the course of a job search. This isn't just a tool; it's an investment in your career development and job search efficiency. By automating the most time-consuming aspects of finding new opportunities, you can focus your energy on the activities that truly matter: preparing compelling applications, developing your skills, and acing interviews. What Users Are Saying Early adopters of the Job Search Automation Workflow have reported significant improvements in their job search efficiency and outcomes. Many have noted how the AI-powered scoring system helped them identify promising opportunities they might otherwise have overlooked, while others appreciate the time saved through automated summarization of lengthy job descriptions. The workflow has proven particularly valuable for career coaches working with multiple clients simultaneously, allowing them to quickly identify relevant opportunities for each individual based on their unique skills and experience profiles. Get Started Today Transform your job search experience today with this innovative, API-powered automation workflow. Say goodbye to endless scrolling through job boards and hello to a streamlined, efficient process that connects you with the opportunities that matter most. Your future career success is worth the investment!

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL; the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input, and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: - Yes: the AI Agent passes the SQL query to the next MySQL node for execution. - No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: - Would you please list me all customers from Germany? - What are the music genres in the database? - What tables are available in the database? - Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL queries outside the agent. It contains the database schema, user questions, and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

Find OAuth URIs with AI Llama. Overview: The AI agent identifies: - Authorization URI - Token URI - AudienceMethodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: - Score Range: 0 < x ≤ 1 - Score Granularity: 0.01 increments Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model. How it works: This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience. Set up instructions: 1. Configuration Nodes Structured Output Node: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing. Code Node: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios. 2. AI Model Prompt The prompt for the AI model includes: A detailed structure and objectives of the query. Flexibility for the model to improvise when accurate results cannot be determined. 3. Confidence Scoring The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity. Adaptability: Customize this template: Update the AI model prompt with details specific to your API or OAuth2 setup. Adjust the JSON schema in the Structured Output node to match the data format. Modify the Code logic to suit the application's requirements.

What this workflow does? This workflow primarily uses the GPT-4o API from PiAPI and automatically creates front, side, and top views of 3D models from commands. Who is this for?3D Designers: Quickly generate standardized orthographic views for design review. E-commerce Operators: Create multi-angle product display images. 3D Modeling Beginners: Instantly produce basic reference views. Step-by-step Instruction 1. Fill in the X-API-Key of your PiAPI account and the image prompt based on your inspiration. 2. Click Test workflow. 3. Get the image URL in the final node.

🔍 Scrape Glassdoor with Bright Data Designed for sales teams, recruiters, and marketers aiming to automate job discovery and prospecting. This workflow scrapes Glassdoor job listings using Bright Data and automatically generates targeted pitches using AI, streamlining lead identification and outreach. 🧩 How It Works This automation leverages n8n, Bright Data, Google Sheets, and OpenAI: 1. Trigger Starts with a custom form input (Location, Keyword, Country). 2. Bright Data Job Scrape Triggers a Bright Data dataset snapshot via HTTP Request. Polls snapshot progress using a Wait node, ensuring data readiness. Retrieves full job listings dataset once ready. 3. Google Sheets Integration Writes detailed job data (company, role, location, overview, metrics) into a Google Sheet. Uses a pre-built template for organized data storage. 4. Automated Pitch Generation (AI) Splits listings into actionable parts: company name, title, and description. Sends data to OpenAI (via LangChain) to generate relevant pitches or icebreakers. Saves generated content back into the same sheet for easy access. ✅ Requirements Ensure you have the following: - Google Sheets - Google account - Template Sheet with columns for job details and AI-generated pitches - Bright Data - Active account with Dataset API access - API key and dataset ID - OpenAI - Valid OpenAI API key for GPT models - n8n Environment - Nodes: HTTP Request, Wait, If, Google Sheets, Split Out, LangChain (OpenAI) - Credentials: - Google Sheets OAuth2 - Bright Data API credentials - OpenAI API key ⚙️ Setup InstructionsStep 1: Prepare Google Sheets Copy the provided Google Sheets template. Do not change headers. Step 2: Import & Configure Workflow in n8n Import the workflow JSON file. Set Google Sheets node: Link to your copied sheet. Confirm correct tab name. Step 3: Configure Bright Data Replace <YOUR_BRIGHT_DATA_API_KEY> with your real key. Set your dataset ID in all HTTP Request nodes. Step 4: Configure OpenAI (LangChain) Connect OpenAI API key to the LangChain node. Customize prompt to match tone and outreach style. Step 5: Testing & Scheduling Test via manual form trigger. Schedule runs or leave form enabled for on-demand use. 🧠 Tips & Best Practices - Use specific keywords and locations for better results. - Adjust polling intervals based on dataset size. - Refine AI prompts regularly to improve pitch quality. - Clean unused columns from your sheet to boost performance. 💬 Support & Feedback For help or customization: 📧 Email: [email protected] 📺 YouTube: @YaronBeen 🔗 LinkedIn: linkedin.com/in/yaronbeen 📚 Bright Data Docs: docs.brightdata.com/introduction

This template is a simple AI Agent that acts as a Google Calendar Assistant. It is designed for beginners to have their "first AI Agent" performing common tasks and to help them understand how it works. For new users of n8n, AI Agents, and OpenAI: This template involves using an OpenAI API Key. If you are new to AI Agents, make sure to research and understand key concepts such as: - "Tokens" (used for API requests) - "Tool calling" (how the AI interacts with external tools) - OpenAI's usage costs (how you will be billed for API usage). Functionality It has two main functionalities: - Create events in a calendar - Retrieve events from a calendar How you can use it Everything is explained with sticky notes in the workflow. It is ready-to-use: all you need to do is connect your OpenAI credentials, and you can start using the workflow.

With this workflow, you get a fully automated AI-powered Support-Agent for your WooCommerce webshop. It allows customers to request information about things like: - The status of their order - The ordered products - Shipping and billing address - Current DHL shipping status How it works: The workflow receives chat messages from a website integrated chat. For security and data privacy reasons, the website transmits the email address of the user encrypted with the requests. This ensures that users can only request information about their own orders. An AI agent with a custom tool supplies the needed information. The tool calls a sub-workflow (in this case, in the same workflow for convenience) to retrieve the required information. This includes the full information of past orders plus the shipping information from DHL. If other shipping providers are used, it should be simple to adjust the workflow to query information from other APIs like UPS, FedEx, or others.

How it works: Send notes from Obsidian via Webhook to start the audio conversion. OpenAI converts your text to natural-sounding audio and generates episode descriptions. Audio files are stored in Cloudinary and automatically attached to your notes in Obsidian. A professional podcast feed is generated, compatible with all major podcast platforms (Apple, Spotify, Google). Set up steps: 1. Install and configure the Post Webhook Plugin in Obsidian. 2. Set up Custom Auth credentials in n8n for Cloudinary using the following JSON: ```json { "name": "Cloudinary API", "type": "httpHeaderAuth", "authParameter": { "type": "header", "key": "Authorization", "value": "Basic {{Buffer.from('your_api_key:your_api_secret').toString('base64')}}" } } ``` 3. Configure podcast feed metadata (title, author, cover image, etc.). Note: The second flow is a generic Podcast Feed module that can be reused in any '[...]-to-Podcast' workflow. It generates a standard RSS feed from Google Sheets data and podcast metadata, making it compatible with all major podcast platforms.

Automatically update Airtable records with AI-generated content. Trigger via webhook when new form responses are received, fetch records, generate ChatGPT completions, and update Airtable seamlessly.

Your Landing Page is Leaking Sales—Here’s How to Fix It in Seconds Visitors land on your page. But instead of converting, they bounce. Why? Something’s broken. Something’s missing. But what? ❌ Is your CTA too weak? ❌ Is your messaging unclear? ❌ Is your design creating friction? You know something is off, but don’t know what. What if you could get an instant, expert-level report on exactly what to fix? This workflow will do an AI Analysis of your landing page, providing a CRO Audit, so you can optimize your landing page. ### Who is This For? - SaaS Founders & Startups: Stop leaving money on the table. Make every visitor count. - Marketers & Growth Experts: Turn landing pages into high-converting assets. - E-commerce & Lead Gen Businesses: More conversions = more revenue. ### How It Works 1. Paste your URL 2. Get an instant roast + fix list 3. Implement changes & watch conversions jump The workflow scrapes the URL you input, gets the HTML source code of the landing page, and sends it to the OpenAI AI Agent. The Agent makes a deep analysis, roasts the landing page, and provides 10 Conversion Rate Optimization Tips to improve your landing page. ### Setup Guide You will need OpenAI Credentials with an API Key to run the workflow. The workflow is using the OpenAI-o1 model to deliver the best results. It costs between $0.20/0.30 per run. You can adjust the prompt to your wish in the AI Agent parameters. Once the workflow has been completed, select Logs to get a readable version. Below is an example.

This workflow has multiple functionalities. It starts with a manual trigger, "When clicking 'Execute Workflow'", that activates two separate paths. The first path takes a preset string "Tell me a joke" and processes it through a custom Language Learning Model (LLM) chain node. This node interacts with an OpenAI node for query processing. The second path takes another preset string "What year was Einstein born?" and passes it to an "Agent" node. This agent further interacts with a Chat OpenAI node and a custom Wikipedia node to produce the required information. The workflow uses both built-in and custom nodes and integrates with OpenAI for both paths. It's built for experimenting with language models, specifically in the context of conversational agents and information retrieval. Note that to use this template, you need to be on n8n version 1.19.4 or later.