AI Automation Workflows — Page 22 | Neura Market

Neura Market/Categories

Home/Categories/AI

AI Workflows

Artificial Intelligence tools and workflows

Automate RAG System with Source Citations Using Qdrant, Google Gemini, and OpenAI
This workflow automates a Retrieval-Augmented Generation (RAG) system by storing vectorized documents in Qdrant, retrieving relevant content, generating AI responses with Google Gemini, and citing document sources from Google Drive.
n8nFree
Automate Passport Photo Validation Using AI Vision
This n8n workflow automates the validation of passport photos using AI vision models, ensuring compliance with UK government standards. It streamlines the process by leveraging multimodal LLMs to handle complex image validation tasks efficiently.
n8nFree
Automated Real-Time Meeting Transcription and Insights
Leverage AI to automate real-time transcription and generate insights during virtual meetings, enhancing productivity and communication clarity.

Marketplace

Prompts
Workflows
Agents Store
Workflow Packs
Categories
Marketplace

Directories

AI Tools Directory
ChatGPT
Claude
Gemini
Cursor
Grok
DeepSeek
Perplexity
CoPilot
Midjourney
Stable Diffusion
MCP Servers
.md Directory
All Directories

Free Tools

AI Text Humanizer
AI Content Detector
Workflow Generator
Model Comparison
AI Pricing Calculator
AI Benchmarks
ROI Calculator
All Free Tools

Resources

AI News
Blog
AI Models
Integrations
Alternatives
n8n vs Zapier
Make vs Zapier
n8n vs Make
Resource Library
Documentation

Community

AI Jobs
AI Events
AI Companies
Start Selling
Sell n8n Workflows
Sell AI Agents
Sell Prompts
Creator Guide
Advertise
Affiliates

Company

About
Contact
Help
Careers
Pricing
Terms
Privacy
License
DMCA

Stay Updated

Get the latest AI tools and insights delivered to your inbox.

© 2026 Neura Market. All rights reserved.

n8nFree

Notion AI Summary & Tags

**What This Workflow Does:** This n8n workflow automatically generates an AI-powered summary and relevant tags whenever a new row is added to your Notion database. Simply save any URL to your Notion database using the [Notion Web Clipper] Chrome extension or [Save to Notion] on both desktop and mobile. This keeps all your saved content organized in one place instead of scattered across different platforms. **How it works:** 1. The workflow is triggered when a new row is added to your Notion database (it checks for updates every minute). 2. It retrieves the content from the saved URL. 3. An AI agent analyzes the content to generate a summary and relevant tags. 4. The AI output is then formatted properly. 5. Finally, the formatted summary and tags are saved into the appropriate columns in your Notion database. **Notes:** Make sure your Notion database includes the following columns: - URL - Stores the content URL you want to summarize. - AI Summary - Where the AI-generated summary will be added. - Tags - Where the AI-generated tags will be saved.

Automate LINE Chatbot Responses with Google Sheets and Google Gemini AI

This workflow automates AI-assisted replies for LINE Official Account messages, using Google Sheets to store and reference chat history, ensuring contextual and personalized interactions.

Create a Versatile AI Chatbot with GPT-4 and Pipedream Integration

This n8n workflow integrates Pipedream's MCP server with AI capabilities to build a dynamic chatbot that can interact with thousands of APIs and tools, enhancing productivity and communication.

Automate SEO Article Creation and Publishing to WordPress with GPT-4 and PostgreSQL

This workflow automates the generation and publication of SEO-optimized articles on WordPress using GPT-4 and PostgreSQL for category management. It selects the least-used category, generates content, creates a placeholder image, and publishes the post with the correct category and featured image.

Automate Image Hosting and Optimization with ImgBB and ReSmush.it

Effortlessly convert and optimize images for web use by uploading them to ImgBB for URL generation and reducing their file size with ReSmush.it. Optionally, generate images using OpenAI.

Automate Document QA with RAG Using Milvus, Cohere, and OpenAI for Google Drive

This workflow automates the creation of a Retrieval Augmented Generation (RAG) system by monitoring Google Drive for new PDFs, extracting content, generating embeddings with Cohere, and storing them in Milvus. It enables a RAG agent to provide context-aware responses using OpenAI.

AI-Powered Auto-translate Google Slides Presentation to Any Language with Gemini

This workflow automates the **translation of Google Slides presentations** from **any languages**, while preserving the original formatting and slide structure. It leverages **Google APIs**, **AI translation (Gemini/PaLM)**, and modular execution for high flexibility and accuracy. **DISCLAIMER**: Texts are split by Google Slides APIs into small blocks, so the translation will not always be contextualized. --- ### **Key Benefits** * **Time-Saving**: Automates a typically manual and error-prone task of translating slides. * **AI-Powered Accuracy**: Uses Google Gemini to provide context-aware translations while respecting defined rules. * **Safe & Non-Destructive**: The original presentation is never modified - a new copy is always created. * **Precision**: Skips irrelevant text (e.g., emails, URLs, names) to avoid mistranslation. * **Modular & Scalable**: Uses subworkflows and batching, ideal for presentations with many slides. * **Layout Preservation**: Keeps the original design and formatting intact. --- ### **How it Works** 1. **Initialization**: - The workflow starts with a manual trigger (When clicking "Execute workflow"). - Set the language to translate (IMPORTANT: **format ISO-639**). - It duplicates a specified Google Slides presentation (Duplicate presentation) to create a new copy for translation, preserving the original. 2. **Slide Processing**: - The workflow retrieves slides from the copied presentation (Get slides from a presentation) and processes them in batches (Loop Over Items). - For each slide, text content is extracted (Extract text) using a custom JavaScript snippet, which identifies and collects text elements while retaining the slides `objectId`. 3. **Translation**: - The extracted texts are passed to a LangChain agent (Translation expert), which translates the content from Italian to English. The agent follows strict guidelines (e.g., skipping URLs, brand names, etc.). - The translated text is sent to the Translate Google Slides node, which replaces the original text in the presentation using the slides `objectId` for targeting. 4. **Execution Flow**: - The workflow includes delays (Wait 10 sec and Wait 3 sec) to manage API rate limits and ensure smooth execution. - The process repeats for each batch of slides until all content is translated. --- ### **Set Up Steps** 1. **Prerequisites**: - Ensure access to the source Google Slides presentation (specified by `fileId` in Duplicate presentation). - Set up Google OAuth2 credentials for Google Drive and Slides (nodes reference credentials like Google Slides account). - Configure the Google Gemini (PaLM) API credentials for the translation agent. 2. **Configuration**: - Update the `fileId` in the Duplicate presentation node to point to your source presentation. - Adjust the translation guidelines in the Translation expert node if needed (e.g., language pairs or exclusion rules). - Modify batch sizes or wait times (e.g., Wait 10 sec) based on API constraints. 3. **Execution**: - Run the workflow manually or trigger it via the Execute Workflow node from another workflow. - Monitor progress in n8n's execution log, as each slide is processed and translated sequentially. 4. **Output**: - The translated presentation is saved as a new file in Google Drive, with the filename including a timestamp (e.g., `NAME_PRESENTATION_{lang}_{timestamp}`). *Note*: The workflow is currently inactive (`active: false`); enable it after configuration. --- ### **Need help customizing?** [Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/).

Automate AI News Summaries to Telegram with Gemini and n8n

Streamline your AI news updates by automatically generating and sending concise summaries to Telegram using n8n, Jina AI, and Google Gemini.

Self-Learning AI Assistant with Permanent Memory | GPT, Telegram & Pinecone RAG

## Your AI secretary that self-learns every day and remembers everything you said (text, audio, image). **Imagine having a personal AI secretary accessible right from your Telegram, ready to assist you with information and remember everything you discuss.** This n8n workflow transforms Telegram into your intelligent assistant, capable of understanding text, audio, and images, and continuously learning from your interactions. **It integrates RAG's offline data ingestion and online querying functionalities, letting you save inspiration and key information permanently in real-time, and giving you an AI assistant that remembers all your dialogues and information.** It builds and queries a powerful vector database in real-time, ensuring relevant and accurate responses. **Video guidance on how to set up Telegram integration is also included.** **Who is this for?** This template is ideal for: - Individuals seeking a personal AI assistant for quick information retrieval and note-taking. - Professionals who need to keep track of important conversations and insights. - Anyone interested in leveraging the power of Retrieval-Augmented Generation (RAG) and vector databases for personal knowledge management. - Users who want a self-learning AI that improves over time based on their interactions. **What problem is this workflow solving?** **This workflow integrates RAG's offline data ingestion and online querying functionalities, letting you save inspiration and key information permanently in real-time, and giving you an AI assistant that remembers all your dialogues and information.** This workflow addresses the challenge of information overload and the need for an easily accessible, personalized knowledge base. It eliminates the need to manually organize notes and search through past conversations. By automatically storing and retrieving information from a vector database, this workflow makes it effortless to access the knowledge you need, when you need it. It also provides a way to retain information from various media types like voice notes and images. **What this workflow does:** This workflow automates the following steps: - **Instant Information Capture:** Receives text messages, audio notes (transcribed), and images (with content analysis) directly from your Telegram. - **Intelligent Question Answering:** When you ask a question, the AI searches its knowledge base (Pinecone vector store) for relevant information and provides a comprehensive answer. It even considers your recent conversations for context. - **Automatic Knowledge Storage:** When you make a statement or provide information, the AI extracts key details and saves them in a Google Docs memory palace. - **Daily Self-Learning:** Every day, the workflow automatically takes all the information stored in the Google Docs, converts it into a vector representation, and adds it to its knowledge base (Pinecone vector store). This ensures the AI continuously learns and remembers everything you've shared. - **Image Understanding:** Extracts text and information from images you send. - **Audio Transcription:** Automatically transcribes your voice notes into text for processing and storage. - **Short-term Memory:** Remembers recent interactions within a session for more context-aware conversations. **Setup:** To get started, you'll need to connect the following services to your n8n instance: 1. **Telegram:** Connect your Telegram bot API credentials. A video guidance is included for Telegram integration setup. 2. **OpenAI:** Provide your OpenAI API key for audio transcription and image analysis. 3. **Pinecone:** Set up a Pinecone account and provide your API key and environment. Create a namespace in Pinecone. 4. **Google Docs:** Connect your Google account with access to Google Docs. You'll need to create a Google Doc that will serve as the daily memory palace and provide its ID in the workflow. **How to customize this workflow:** - **Adjust the AI Agent's Personality:** Modify the system prompt in the AI Agent node to tailor the AI's tone and behavior. - **Expand Knowledge Sources:** Integrate other data sources into the daily learning process, such as emails or other documents, by adding more nodes to the scheduled trigger workflow. - **Add More Tools for the AI Agent:** Integrate additional tools into the AI Agent, such as web search or other APIs, to further enhance its capabilities. - **Modify the Daily Schedule:** Adjust the schedule trigger to run at a different time or interval.

Convert Text to Speech with Local KOKORO SDK

## Disclaimer The **Execute Command** node is only supported on **self-hosted** (local) instances of n8n. ## Introduction ![1.jpg](fileId:1114) **KOKORO S** - Kokoro S is a compact yet powerful text-to-speech model, currently available on Hugging Face and GitHub. Despite its modest size—trained on less than 100 hours of audio—it delivers impressive results, consistently topping the S leaderboard on Hugging Face. Unlike larger systems, Kokoro S offers the advantage of running locally, even on devices without GPUs, making it accessible for a wide range of users. **Who will benefit from this integration?** This will be useful for video bloggers, TikTokers, and it will also enable the creation of a free voice chat bot. Currently, S models are mostly paid, but this integration will allow for fully free voice generation. The possibilities are limited only by your imagination. #### Note Unfortunately, we can't interact with the KOKORO API via browser URL (GET/POST), **but** we can run a Python script through n8n and pass any variables to it. In the tutorial, the D drive is used, but you can rewrite this for any paths, including the C drive. ## Step 1 You need to have Python installed. [link](https://github.com/PierrunoY/Kokoro-S-Local) Also, download and extract the portable version of KOKORO from GitHub. Create a file named voicegen.py with the following code in the KOKORO folder: (C:\KOKORO). As you can see, the output path is: (D:\output.mp3). ``` import sys import shutil from gradio_client import Client # Set UTF-8 encoding for stdout sys.stdout.reconfigure(encoding='utf-8') # Get arguments from command line text = sys.argv[1] # First argument: input text voice = sys.argv[2] # Second argument: voice speed = float(sys.argv[3]) # Third argument: speed (converted to float) print(f"Received text: {text}") print(f"Voice: {voice}") print(f"Speed: {speed}") # Connect to local Gradio server client = Client("http://localhost:7860/") # Generate speech using the API result = client.predict( text=text, voice=voice, speed=speed, api_name="/generate_speech" ) # Define output path output_path = r"D:\output.mp3" # Move the generated file shutil.move(result[1], output_path) # Print output path print(output_path) ``` ## Step 2 Go to n8n and create the following workflow. ![2.jpg](fileId:1111) ## Step 3 Edit Field Module. ``` { voice: "af_sarah", text: "Hello world!" } ``` ![33.jpg](fileId:1110) ## Step 4 We'll need an Execute Command module with the command: python ``` C:\KOKORO\voicegen.py "{{ $json.text }}" "{{ $json.voice }}" 1 ``` ![44.jpg](fileId:1112) ## Step 5 The script is already working, but to listen to it, you can connect a Binary module with the path to the generated MP3 file ``` D:/output.mp3 ``` ![55.jpg](fileId:1113) ## Step 6 Click “Next workflow” and enjoy the result. There are more voices and accents than in ChatGPT, plus it's free. ### P.S. If you want, there is a [detailed tutorial](https://blog.bswlife.site/2025/04/14/n8n-kokoro-tts-integration-setup/) on my blog.

Automate AI Image Creation and Storage with OpenAI and Azure Blob

This workflow automates the generation of AI images using OpenAI's DALL-E and stores them in Azure Blob Storage, providing a seamless no-code solution for managing cloud-based image storage.

AI-Powered LinkedIn Content Engine (n8n + OpenAI + Perplexity + Replicate)

A fully automated content engine that **researches, writes, scores, and visualizes LinkedIn posts** - built with n8n, OpenAI, Perplexity, and Replicate. What it does: - Researches any topic using real-time data - Writes a personalized post in your voice - Refines tone and structure - Generates abstract, high-quality visual assets - Scores the output and saves it to Google Sheets How it works: - Triggered when you change a row status in Google Sheets - Uses Perplexity to research - GPT-4 (OpenAI) to create and polish content - Replicate (FLUX Pro) to generate images - Scores the post using heuristics - Appends everything back to your sheet

Automate AI Video Creation with HeyGen API in n8n

Effortlessly generate AI-driven videos using digital avatars and voiceovers with HeyGen API through n8n. Streamline your content creation process for marketing or personalized messaging.

Create a Knowledge-Based Chatbot Using Google Drive, OpenAI, and Supabase

Develop a chatbot that leverages your Google Drive PDFs to provide intelligent responses, ideal for support, internal documentation, education, or research.

Automate Web Data Scraping and Screenshot Capture with Firecrawl and OpenAI

This workflow automates the process of scraping structured web data and capturing full-page screenshots using Firecrawl and OpenAI. Ideal for researchers and content creators, it enhances competitive research and content curation.

MCP Server with AI Agent as a Tool Context Reducer

## Overview Transform your LLM into a powerful GitHub automation specialist with this n8n workflow template. In a world where multiple MCP servers can overwhelm LLMs with context, this streamlined solution provides a dedicated GitHub Agent that handles all GitHub API operations through a single, specialized tool. When you need GitHub operations like creating repositories, managing issues, or handling pull requests, your LLM can make one simple call to the GitHub Agent. This agent specializes exclusively in GitHub MCP server operations, offloading all contextual complexity and providing clean, efficient GitHub automation. ## Features - **Single MCP Server Trigger** - One tool and one parameter to handle all GitHub API interactions - **Specialized GitHub Agent** - Dedicated AI agent with direct GitHub MCP Server connection - **Self-Executing Workflow** - When executed by another workflow trigger enables seamless workflow chaining - **Scalable Architecture** - Ready to integrate with unlimited GitHub tools and operations - **Context Optimization** - Reduces LLM token usage by delegating GitHub complexity to a specialized agent - **Flexible Request Processing** - Handles any GitHub operation through natural language requests ## Use Cases - **Repository Management** - Create, clone, and manage repositories programmatically - **Issue Tracking** - Automate issue creation, updates, and management workflows - **Pull Request Automation** - Streamline code review and merge processes - **GitHub Actions Integration** - Trigger and monitor CI/CD workflows - **Team Collaboration** - Automate notifications and team management tasks - **Documentation Updates** - Automatically update README files and documentation ## Workflow Architecture ### Node Breakdown: 1. **MCP Server Trigger** - Receives requests with GitHub operation parameters 2. **Set GitHub Username** - Configures GitHub user context for API calls 3. **OpenAI Chat Model** - Powers the intelligent GitHub agent with contextual understanding 4. **Simple Memory** - Maintains conversation context and operation history 5. **GitHub AI Agent** - Specialized Tools Agent with direct GitHub MCP Server access ``` [MCP Server Trigger] → [Set GitHub Username] → [GitHub AI Agent] [OpenAI Chat Model] ← [Simple Memory] ← [GitHub API Operations] ``` ## Requirements ### Essential Prerequisites: - **OpenAI API Key** - For AI Agent and Chat Model functionality - **GitHub Username Configuration** - Edit the Set GitHub Username node with your GitHub username for API calls - **n8n Version** - Compatible with n8n 2024+ releases - **MCP Server Setup** - Existing GitHub MCP server configuration ### Recommended Setup: - GitHub Personal Access Token with appropriate permissions - Basic understanding of n8n workflow configuration - Familiarity with GitHub API operations ## Setup Instructions ### Step 1: Import and Configure 1. Import the workflow template into your n8n instance 2. Navigate to the **Set GitHub Username** node 3. Replace the placeholder with your actual GitHub username ### Step 2: API Keys Setup 1. Configure your **OpenAI API key** in the Chat Model node 2. Ensure your GitHub credentials are properly configured in n8n 3. Test the connection to verify API access ### Step 3: MCP Server Integration 1. Connect your existing GitHub MCP server to the workflow 2. Verify the MCP Server Trigger is properly configured 3. Test with a simple GitHub operation (e.g., List my repositories) ### Step 4: Deploy and Test 1. Activate the workflow in your n8n instance 2. Test with various GitHub operations to ensure functionality 3. Monitor execution logs for any configuration issues ## Customization Options ### Agent Behavior - **Modify the Chat Model prompt** to adjust agent personality and response style - **Configure memory settings** to control conversation context retention - **Adjust timeout settings** for long-running GitHub operations ### GitHub Operations - **Extend supported operations** by adding new GitHub API endpoints - **Configure repository filters** to limit the scope of operations - **Set up notification preferences** for important GitHub events ### Integration Points - **Webhook triggers** for real-time GitHub event processing - **Scheduled operations** for regular repository maintenance - **Cross-workflow triggers** for complex automation chains ## Pro Tips - **Start Simple**: Begin with basic operations like repository listing before attempting complex workflows - **Monitor Token Usage**: The specialized agent approach significantly reduces OpenAI API costs - **Batch Operations**: Group related GitHub operations in single requests for efficiency - **Error Handling**: The agent provides detailed error messages for troubleshooting ## Support and Community - **Documentation**: [Official n8n Documentation](https://docs.n8n.io) - **Community Forum**: [n8n Community](https://community.n8n.io)

Create a Conversational AI Chatbot with Real-Time Web Search and Advanced Reasoning

This workflow builds a sophisticated AI chatbot using the Claude 3.7 Sonnet model, enhanced with real-time web search and advanced reasoning capabilities. It maintains conversation history and adheres to ethical guidelines for safe interactions.

Dynamic AI Agent with Self-Updating Behavioral Rules

This workflow enables an AI agent to dynamically update its own rules based on user interactions, allowing for customizable behavior modifications. It integrates with databases or Google Sheets to manage rules efficiently.

Enhance AI Agent System Prompts with GPT-4o Feedback and Email Notifications

This workflow refines AI agent system prompts using user feedback and GPT-4o analysis, delivering improved prompts via email.

Automate YouTube Video Uploads with Content Generation via ChatGPT

Streamline your video upload process to YouTube by automatically generating engaging content with ChatGPT and retrieving files from Dropbox.

Automate Image Background Removal and Log Results in Google Sheets

Streamline your image processing workflow by automatically removing backgrounds using AI and logging the results in Google Sheets for easy tracking.

← PreviousPage 22 of 81Next →

Related categories

Communication (2,463)Business Operations & ERPs (1,540)Other (1,425)Productivity (1,202)Marketing (1,145)Data & Analytics (995)File & Document Management (802)CRM - Sales (604)Notifications (580)Social Media (562)

Need a custom ai workflow?

Our automation experts build tailored workflows for your exact stack and process.

Request a Custom Workflow