OpenAI Automation Workflows
899 ready-made OpenAI workflows for n8n, Make, Zapier, Activepieces, and Pipedream. GPT-powered enrichment, generation, and classification inside your automations.
Automate Deep Research with n8n, Apify, and OpenAI
Leverage n8n, Apify, and OpenAI to automate complex research tasks, synthesizing online information into comprehensive reports.
n8n$19.99Automate Personalized Instagram DMs Using Apify, OpenAI, and Google Sheets
Streamline your Instagram outreach by automating the creation of personalized direct messages using Apify, OpenAI, and Google Sheets. Ideal for creator outreach, customer interviews, and partnership prospecting.
n8n$9.99Automate Student Course Scheduling with Google Sheets and OpenAI
Efficiently generate personalized Fall 2025 course schedules for students by leveraging Google Sheets and OpenAI. This workflow selects courses based on completed prerequisites and term availability, ensuring a balanced schedule.
n8n$9.99Automate Sales Outreach with OpenAI, Airtable & Google Sheets for Prospect Research
### Sales Assistant Build: Automate Prospect Research and Personalized Outreach for Sales Calls ### Steps to Connect: 1. **Google Sheets Setup** - Connect your Google account via OAuth2 in the Review Calls, Product List, Testimonials Tool, Update Sheet, and Update Sheets 2 nodes. - Duplicate the mock Google Sheet (ID: `1u3WMJwYGwZewW1IztY8dfbEf5yBQxVh8oH7LQp4rAk4`) to your drive and update the `documentId` in all Google Sheets nodes to match your copy's ID. - Ensure the sheet has tabs for Meeting Data, Products, and Success Stories populated with your data. - Setup time: ~5 minutes. 2. **OpenAI API Key** - Go to [OpenAI](https://platform.openai.com/) and generate your API key. - Paste this key into the credentials for both OpenAI Chat Model and OpenAI Chat Model1 nodes. - Setup time: ~2 minutes. 3. **Avily API Key** - Sign up at [Avily](https://avily.com/) and get your API key. - In the Avily node, replace the placeholder `api_key` in the JSON body with your key (e.g., `api_key: your-avily-key-here`). - Setup time: ~3 minutes. --- ### How it Works Triggers on a new sales call booking (manual for testing). Pulls prospect details from Google Sheets and researches their company, tech stack, and updates using Avily. Matches relevant products/solutions from your product list and updates the sheet. Generates personalized email confirmation (subject + body) and SMS using testimonials for relevance. Updates the sheet with the outreach content for easy follow-up. Setup takes ~10-15 minutes total. All nodes are pre-configured—edit only the fields above. Detailed notes (e.g., prompt tweaks) are in sticky notes within the workflow. ---
n8n$9.99AI Voice Chatbot with ElevenLabs & OpenAI for Customer Service and Restaurants
The Voice RAG Chatbot with ElevenLabs and OpenAI workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities, and restaurants. ### How it works: Here's how it operates: 1. **Webhook Activation**: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. 2. **AI Agent Processing**: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. 3. **Knowledge Base Retrieval**: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user's query. 4. **Text Generation**: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user's question. 5. **Response Delivery**: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. 6. **Continuous Interaction**: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. ### Set up steps: To configure this workflow effectively, follow these detailed setup instructions: 1. **ElevenLabs Agent Creation**: - Begin by creating an agent on ElevenLabs (e.g., named test_n8n). - Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at Pizzeria da Michele. - Add a Webhook tool labeled test_chatbot_elevenlabs configured to receive questions via POST requests. 2. **Qdrant Collection Initialization**: - Utilize the HTTP Request nodes (Create collection and Refresh collection) to initialize and clear existing collections in Qdrant. Ensure you update placeholders `QDRANTURL` and `COLLECTION` accordingly. 3. **Document Vectorization**: - Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. - Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. 4. **AI Agent Configuration**: - Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). - Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. 5. **Testing Workflow**: - Execute test runs of the entire workflow by clicking Test workflow in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. - Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. 6. **Integration with Website**: - Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.
n8n$14.99Automate Website Content Chatbot with Pinecone, Airtable, and OpenAI
This n8n workflow extracts website content, indexes it in Pinecone, and uses Airtable to power a chat agent for customer Q&A. Ideal for creating a knowledge base and chatbots that utilize your site's content.
n8n$14.99Automate News Monitoring and Categorization with SerpAPI and OpenAI
This workflow leverages SerpAPI and OpenAI within n8n to automate the process of fetching, categorizing, and summarizing Google News articles. Ideal for market researchers and business leaders, it provides structured insights from news headlines, saving time and enhancing decision-making.
n8n$9.99Automate Lead Qualification and Call Scheduling with RetellAI, OpenAI, and Google Sheets
Streamline your lead qualification and call scheduling process by integrating RetellAI, OpenAI, and Google Sheets. This workflow automates outbound and inbound call handling, ensuring efficient lead management and follow-up.
n8n$14.99Create a Financial Q&A Chatbot with Real-Time Data Using Google Finance, SerpAPI, and OpenAI
This workflow integrates Google Finance, SerpAPI, and OpenAI to create a chatbot that answers financial questions in real-time. It fetches market data and uses AI to generate natural language responses.
n8n$9.99AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI
## **Who is this for?** This workflow is perfect for: * Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. * Researchers who want to quickly analyze and query large collections of research papers, reports, or datasets. * Customer support teams looking to streamline access to product documentation and support resources. * Legal and compliance professionals needing to reference and query legal documents with confidence. * AI enthusiasts and developers wanting to implement Retrieval-Augmented Generation (RAG) systems without starting from scratch. ## **What problem is this workflow solving?** Manually organizing, processing, and searching through documents can be time-consuming, error-prone, and inefficient. This workflow solves that by: * **Automating document processing** from Google Drive, supporting multiple formats like PDFs, CSVs, and Google Docs. * **Extracting, chunking, and enhancing document text**, preserving context and improving AI comprehension. * **Storing vector embeddings** in a secure, scalable Supabase vector database, enabling semantic search and retrieval. * **Providing an interactive AI chat interface** that allows users to ask natural language questions and get precise, document-based answers. This means teams can quickly access relevant insights from their document repositories, boosting productivity and ensuring accurate information retrieval. ## **Key Features** * **End-to-End Document Processing**: From Google Drive upload detection to vector embedding and storage. * **Semantic Search & Retrieval**: Users can ask complex, natural-language questions and receive contextually relevant answers. * **AI-Powered Summaries & Metadata**: Automatically generates document titles and summaries using Google Gemini AI. * **Smart Chunking & Contextual Enhancement**: Breaks documents into smart chunks with overlap, preserving context and table integrity. * **Secure & Scalable Vector Database**: Stores and retrieves embeddings in a Supabase vector store for fast, reliable searches. * **Conversational AI Interface**: Uses OpenAI to power natural, accurate, and cost-effective AI chat interactions. ## **How does this workflow work?** * Monitors Google Drive for new files. * Extracts text from PDFs and CSVs (or Google Docs auto-converted). * Splits text into context-preserving chunks. * Enhances chunk quality and stores embeddings in Supabase. * Enables natural language search and AI-powered chat interactions with the stored documents. ## **Typical Use Cases** * Corporate Knowledge Base * Research Paper Analysis * Customer Support Document Query * Legal Document Review and Analysis * Internal Team Documentation Search ## **Why You'll Love It** This workflow lets you build a scalable, searchable, and AI-powered document system without needing to write complex code or manage multiple systems. With this, you can: * Stay organized with automated document processing. * Deliver faster, more accurate answers to user queries. * Reduce manual work and improve productivity. * Gain a competitive edge with cutting-edge AI search capabilities. ## **Setup Requirements** * An n8n instance with Google Drive, Supabase, OpenAI, and Gemini credentials configured. * Access to a Supabase vector store for storing document embeddings. * Configurable chunk size, overlap, and processing limits (default: 1000 characters per chunk, 20 chunks max). **Contact me for consulting and support:** **billychartanto@gmail.com**
n8n$24.99Build a Knowledge Base Chatbot with OpenAI, RAG, and MongoDB Vector Embeddings
### Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. ### What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. ### What these workflows do **Workflow 1**: Document Ingestion & Indexing - Manually triggered to import product documentation from Google Docs. - Automatically splits large documents into chunks for efficient searching. - Generates vector embeddings for each chunk using OpenAI embeddings. - Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. **Workflow 2**: AI-Powered Query & Response - Listens for incoming user questions (can be extended to webhook). - Converts questions to vector embeddings and performs similarity search on MongoDB vector store. - Uses OpenAI's GPT-4-mini model with retrieval-augmented generation to produce direct, context-aware answers. - Maintains short-term conversation context using a memory buffer node. ### Setup **Setting up vector embeddings** - Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. - Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. - Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. **Setting up chat** - Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company's tone, answering style, and any business rules. - Update the workflow description and instructions to help users understand the chat's purpose and capabilities. - Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. ### Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: - An embedding field storing vector data, - Relevant metadata fields (e.g., document ID, source), and - The same vector index name configured (e.g., data_index). **Search Index Example:** ```json { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } } ```
n8n$9.99Automate PDF Knowledge Base Creation with S3, OpenAI, and Qdrant
This workflow automates the creation of a knowledge base from PDFs stored in AWS S3, utilizing OpenAI for text embeddings and Qdrant for vector storage and retrieval.
n8n$9.99Create an AI Chatbot with PDF-Based RAG System Using OpenAI, Pinecone, and Cohere
Build an AI chatbot that answers questions using content from PDF documents with a Retrieval-Augmented Generation (RAG) system. Integrate OpenAI, Pinecone, and Cohere for enhanced performance.
n8n$9.99Automate Multilingual Voice Calls with OpenAI, ElevenLabs, and Twilio
Leverage AI to automate voice calls in multiple languages for scheduling appointments and placing service orders using OpenAI, ElevenLabs, and Twilio.
n8n$9.99Automate AI-Driven Sales Pitches from Website URLs Using Google Sheets and OpenAI
This n8n workflow transforms website URLs listed in Google Sheets into personalized AI-generated sales pitches using OpenAI. It automates the process of scraping website content, generating context-rich pitches, and updating the Google Sheets with the results, streamlining prospect research and messaging.
n8n$9.99Automate YouTube Video Analysis and Content Ideation with Apify, OpenAI, and Google Sheets
This workflow automates the process of scraping trending YouTube videos, analyzing them with AI for optimized titles and outlines, and updating a Google Sheet for content planning.
n8n$9.99Automate Email Classification and Response with Gmail, OpenAI, and Google Sheets
Streamline your email management by automatically classifying, labeling, and responding to emails using Gmail, OpenAI, and Google Sheets. This workflow enhances efficiency in customer support, sales, and general inquiries.
n8n$24.99Automate Google Calendar Management with OpenAI AI Agent
This workflow enables an AI agent to manage Google Calendar events using OpenAI, allowing users to create and retrieve events through chat interactions.
n8n$9.99Automate Daily Cybersecurity Briefs with RSS Feeds and OpenAI
This workflow aggregates cybersecurity news from multiple RSS feeds, summarizes the information using OpenAI, and sends a concise daily brief via Gmail.
n8n$14.99Automate Website Crawling and Query Responses Using Google Sheets and OpenAI
This workflow enables SEO analysts, content creators, and developers to automate website indexing and interact with its content using Google Sheets and OpenAI. It efficiently crawls websites, extracts key data, and allows for dynamic querying as if interacting with a chatbot.
n8n$24.99Automate Email Summarization and Sharing via Slack with OpenAI
Automatically summarize new emails using OpenAI and share concise overviews with your team on Slack, enhancing communication and productivity.
Make$3.99Automate AI News Delivery to Telegram with Perplexity and OpenAI
This workflow automates the collection, filtering, formatting, and distribution of daily AI news summaries to a Telegram channel using Perplexity and OpenAI. It ensures only the most relevant updates are shared, avoiding duplicates with a Google Sheets archive.
n8n$9.99Automate PDF Q&A System with LlamaIndex, OpenAI, and Pinecone
This workflow automates the process of parsing, normalizing, and storing PDF content for Retrieval-Augmented Generation (RAG) using LlamaIndex, OpenAI embeddings, and Pinecone. Ideal for developers and teams handling structured documents like insurance policies.
n8n$14.99OX & OpenAI Web Security Check
# This n8n workflow automates website security audits. It combines direct website scanning, threat intelligence from AlienVault OX, 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 OX 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 OX API Key:** - Obtain a key from your AlienVault OX profile. - Add an AlienVault OX 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.
n8n$4.99
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