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    • Automate Your Tweet Scheduling with Google Sheets Integration

      This workflow enables users to automate the posting of tweets based on data stored in a Google Sheet. By leveraging a scheduled trigger, it fetches tweets from a designated Google Sheets document at defined intervals, ensuring a steady flow of con...

      n8n$11.47

    • Create an AI Chatbot with InfraNodus for Enhanced Knowledge-Based Responses

      Develop an AI chatbot that leverages InfraNodus to access a comprehensive knowledge base, enhancing response accuracy and relevance.

      n8nFree

    • Automate SEO Article Creation and Publishing with ChatGPT and WordPress

      This workflow automates the creation of long SEO articles using ChatGPT and publishes them on WordPress. It generates articles of approximately 1,500 - 2,000 words by leveraging multiple OpenAI calls.

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  1. Automate AI Video Creation from Images and Upload to Google Drive

    Streamline the process of creating AI-generated videos from images and automatically upload them to Google Drive, enhancing efficiency and productivity.

    n8nFree

  2. Process Voice, Images & Documents with GP-40, MongoDB & Gmail Tools

    ## What it does This n8n workflow creates a cutting-edge, multi-modal AI Memory Assistant designed to capture, understand, and intelligently recall your personal or business information from diverse sources. It automatically processes voice notes, images, documents (like PDFs), and text messages sent via Telegram. Leveraging GPT-4 for advanced AI processing (including visual analysis, document parsing, transcription, and semantic understanding) and MongoDB Atlas Vector Search for persistent and lightning-fast recall, this assistant acts as an external brain. Furthermore, it integrates with Gmail, allowing the AI to send and search emails as part of its memory and response capabilities. This end-to-end solution blueprint provides a powerful starting point for personal knowledge management and intelligent automation. ## How it works #### 1. Multi-Modal Input Ingestion Your memories begin when you send a voice note, an image, a document (e.g., PDF), or a text message to your Telegram bot. The workflow immediately identifies the input type. #### 2. Advanced AI Content Processing Each input type undergoes specialized AI processing by GPT-4: 1. Voice notes are transcribed into text using OpenAI Whisper. 2. Images are visually analyzed by GPT-4 Vision, generating detailed textual descriptions. 3. Documents (PDFs) are processed for text extraction, leveraging GPT-4 for robust parsing and understanding of content and structure. Unsupported document types are gracefully handled with a user notification. 4. Text messages are directly forwarded for further processing. This phase transforms all disparate input formats into a unified, rich textual representation. #### 3. Intelligent Memory Chunking & Vectorization The processed content (transcriptions, image descriptions, extracted document text, or direct text) is then fed back into GPT-4. The AI intelligently chunks the information into smaller, semantically coherent pieces, extracts relevant keywords and tags, and generates concise summaries. Each of these enhanced memory chunks is then converted into a high-dimensional vector embedding using OpenAI Embeddings. #### 4. Persistent Storage & Recall (MongoDB Atlas Vector Search) These vector embeddings, along with their original content, metadata, and tags, are stored in your MongoDB Atlas cluster, which is configured with Atlas Vector Search. This allows for highly efficient and semantically relevant retrieval of memories based on user queries, forming the core of your smart recall system. #### 5. AI Agent & External Tools (Gmail Integration) When you ask a question, the AI Agent (powered by GPT-4) acts as the central intelligence. It uses the MongoDB Chat Memory to maintain conversational context and, crucially, queries the MongoDB Atlas Vector Search store to retrieve relevant past memories. The agent also has access to Gmail tools, enabling it to send emails on your behalf or search your past emails to find information or context that might not be in your personal memory store. #### 6. Smart Response Generation & Delivery Finally, using the retrieved context from MongoDB and the conversational history, GPT-4 synthesizes a concise, accurate, and contextually aware answer. This response is then delivered back to you via your Telegram bot. ## How to set it up (~20 Minutes) Getting this powerful workflow running requires a few key configurations and external service dependencies. **Telegram Bot Setup:** 1. Use BotFather in Telegram to create a new bot and obtain its API Token. 2. In your n8n instance, add a new Telegram API credential. Give it a clear name (e.g., My AI Memory Bot) and paste your API Token. **OpenAI API Key Setup:** 1. Log in to your OpenAI account and generate a new API key. 2. Within n8n, create a new OpenAI API credential. Name it appropriately (e.g., My OpenAI Key for GPT-4) and paste your API key. This credential will be used by the OpenAI Chat Model (GPT-4 for processing, chunking, and RAG), Analyze Image, and Transcribe Audio nodes. **MongoDB Atlas Setup:** 1. If you don't have one, create a free-tier or paid cluster on MongoDB Atlas. 2. Create a database and a collection within your cluster to store your memory chunks and their vector embeddings. 3. Crucially, configure an Atlas Vector Search index on your chosen collection. This index will be on the field containing your embeddings (e.g., embedding field, type knnVector). Refer to MongoDB Atlas documentation for detailed instructions on creating vector search indexes. 4. In n8n, add a new MongoDB credential. Provide your MongoDB Atlas connection string (ensure it includes your username, password, and database name), and give it a clear name (e.g., My Atlas DB). This credential will be used by the MongoDB Chat Memory node and for any custom HTTP requests you might use for Atlas Vector Search insertion/querying. **Gmail Account Setup:** 1. Go to Google Cloud

    n8nFree

  3. Transcribing Bank Statements to Markdown Using Gemini Vision AI

    This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document, especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditional OCR because: - It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. - It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. - It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! ## How it works You can use the example bank statement created specifically for this workflow here: [https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing](https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing) - A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. - Because multimodal LLMs do not accept PDFs directly, we'll have to convert the PDF to a series of images. We can achieve this by using a tool such as [Stirling PDF](https://github.com/Stirling-Tools/Stirling-PDF/). Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. - Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. - Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. - Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a user message of type binary (data) which is how we add our image data as an input. - Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLM's ability to accurately read the page. - Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. ## Requirements - Google Gemini API for Multimodal LLM. - Google Drive access for document storage. - [Stirling PDF](https://github.com/Stirling-Tools/Stirling-PDF) instance for PDF to Image conversion ## Customizing the workflow - At the time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini, however, you can switch to other multimodal LLMs such as OpenAI GPT or Anthropic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. - Not parsing any bank statements any time soon? This template also works for invoices, inventory lists, contracts, legal documents, etc.

    n8nFree

  4. Evaluate RAG Response Accuracy with OpenAI: Document Groundedness Metric

    ### This n8n template demonstrates how to calculate the evaluation metric RAG document groundedness, which in this scenario, measures the ability to provide or reference information included only in retrieved vector store documents. The scoring approach is adapted from [https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_groundedness](https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_groundedness) ### How it works - This evaluation works best for an agent that requires document retrieval from a vector store or similar source. - For our scoring, we need to collect the agent's response and the documents retrieved and use an LLM to assess if the former is based off the latter. - A key factor is to look out for information in the response which is not mentioned in the documents. - A high score indicates LLM adherence and alignment, whereas a low score could signal inadequate prompt or model hallucination. ### Requirements - n8n version 1.94+ - Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)

    n8nFree

  5. Convert Reddit Threads into Short Vertical Videos with AI

    # Convert Reddit threads into short vertical videos with AI ## Who is this for? This workflow is ideal for: - **Content creators** and **video editors** automating short-form content production - **Reddit storytellers** converting text posts into engaging TikTok, YouTube Shorts, or Reels - **Social media managers** repurposing community discussions into visual narratives ## What problem is this solving? Manually converting Reddit posts into vertical video content is time-consuming: - You have to read, summarize, write a script - Generate S - Find stock footage - Edit everything in a timeline This workflow automates the full pipeline. It converts any Reddit thread into a polished video with: - S narration - Subtitle overlays - B-roll from Pexels - Automatic rendering via Shotstack ## What this workflow does This workflow: 1. **Extracts Reddit post and comments** via Reddit API 2. **Summarizes the thread into structured clips** using OpenAI 3. **Generates search queries** for each clip for stock footage 4. **Queries Pexels API** for relevant vertical videos 5. **Generates S audio** for each clip using OpenAI Whisper 6. **Creates subtitles** matching the audio 7. **Uploads footage/audio to Shotstack** 8. **Renders a full vertical video (720x1280)** with synced S, subtitles, and b-roll 9. **Returns a final video URL** ## Setup - Create accounts and API keys for: - [Reddit Developer App](https://www.reddit.com/prefs/apps) - [OpenAI](https://platform.openai.com/) - [Pexels](https://www.pexels.com/api/) - [Shotstack](https://shotstack.io/) - Add credentials in n8n: - Reddit (HTTP Basic Auth) - OpenAI (API Key) - Shotstack (HTTP Header Auth) - Pexels (HTTP Header Auth) - Trigger via webhook or manual node. The input must include: ```json { "voice": "nova", "ttsSpeed": 1, "videoLength": 60, "redditLink": "https://www.reddit.com/r/example/comments/example_id/example_title" } ``` ## How to customize this workflow - **Tweak OpenAI prompts** to change tone or clip granularity - **Change stock source** by swapping Pexels for another API - **Adjust S voices** or languages by modifying the `voice` field - **Modify video styling** (fonts, colors, fit modes) in the timeline construction code node - **Control duration** by editing the character length formula in the `Limit comments length` node ## Additional Notes - All stock videos are selected to match clip themes using generalized keywords to avoid API misses - Includes `wait` nodes to ensure Shotstack's async upload/render processes complete before proceeding - Annotated with **sticky notes** explaining major sections like S, Reddit input, and media timeline - Avoids community nodes to ensure cloud compatibility ## Template Category **AI**, **Marketing**, **Building Blocks**, **Other (Content Creation)**

    n8nFree

  6. Evaluation Metric Example: String Similarity

    ## AI Evaluation in n8n This is a template for n8n's [evaluation feature](https://docs.n8n.io/advanced-ai/evaluations/overview). Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. ## How it Works This template shows how to calculate a workflow evaluation metric: **text similarity, measured character-by-character**. The workflow takes images of hand-written codes, extracts the code, and compares it with the expected answer from the dataset. The images look like this: ![image](https://storage.googleapis.com/n8n_template_data/handwriting_scans/doc20250302_08223946_001.jpg) The workflow works as follows: - We use an evaluation trigger to read in our dataset. - It is wired up in parallel with the regular trigger so that the workflow can be started from either one. [More info](https://docs.n8n.io/advanced-ai/evaluations/tips-and-common-issues/#combining-multiple-triggers) - We download the image and use AI to extract the code. - If we're evaluating (i.e., the execution started from the evaluation trigger), we calculate the string distance metric. - We pass this information back to n8n as a metric.

    n8nFree

  7. Evaluate AI Agent Response Correctness with OpenAI and RAGAS Methodology

    ### This n8n template demonstrates how to calculate the evaluation metric Correctness which in this scenario, measures, compares, and classifies the agent's response against a set of ground truths. The scoring approach is adapted from the open-source evaluations project [RAGAS](https://docs.ragas.io/) and you can see the source here [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py) ### How it works - This evaluation works best where the agent's response is allowed to be more verbose and conversational. - For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not in ground truth), and False Negative (not in answer but in ground truth). - We also calculate an average similarity score on the agent's response against all ground truths. - The classification and the similarity score are then averaged to give the final score. - A high score indicates the agent is accurate, whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer. ### Requirements - n8n version 1.94+ - Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)

    n8nFree

  8. Advanced WordPress Auto-Blogging with Deep Research and AI Content Generation

    Automate the creation of in-depth WordPress blog content using multi-level research and AI-driven content generation. This workflow enhances content depth and authority by integrating PerplexityAI for research and OpenAI for content creation.

    n8nFree

  9. Automate AI-Generated Summaries for WordPress Posts

    This n8n workflow automates the creation of AI-generated summaries for WordPress posts, enhancing content with concise summaries without the need for heavy plugins.

    n8nFree

  10. Implement Role-Based Access Control for AI Agents with Airtable and Telegram

    This workflow enables precise control over AI agent tool access using Role-Based Access Control (RBAC) with Airtable and Telegram. It ensures users can only access tools they are authorized for, enhancing security and management of AI interactions.

    n8nFree

  11. Automate Job Analysis with GPT-4 and Telegram

    This workflow uses GPT-4 and Telegram to evaluate job roles and determine automation potential across four distinct zones based on the Human Agency Scale (HAS).

    n8nFree

  12. Automate GitLab Merge Request Reviews with AI

    Streamline your code review process by automatically analyzing GitLab merge requests using AI. Trigger reviews with a simple comment and receive detailed feedback directly in your GitLab discussions.

    n8nFree

  13. Automate Image Generation with Lumi AI and Replicate API

    This workflow automates image generation using the Lumi AI model via the Replicate API, streamlining the process by managing API authentication, parameter configuration, and result retrieval.

    n8nFree

  14. Explore Sequential, Agent-Based, and Parallel LLM Processing with Claude 3.7

    This workflow showcases three methods for chaining LLM operations using Claude 3.7: sequential, agent-based, and parallel processing. Each method offers unique benefits in terms of implementation, performance, and context management.

    n8nFree

  15. Automate HTML Document Creation and Hosting with AI and AWS S3

    This workflow automates the creation of HTML documents using AI and hosts them on AWS S3. It leverages AI to dynamically generate content and style, offering a flexible and efficient solution for web content delivery.

    n8nFree

  16. Batch Process Text Prompts with Anthropic Claude API

    Efficiently send multiple text prompts to Anthropic's Claude models in a single batch request and retrieve results, optimizing processing time and scalability.

    n8nFree

  17. Automate Supabase Management with AI-Powered Natural Language Commands

    This workflow allows you to manage your Supabase database using natural language commands, facilitated by an AI agent. It translates chat messages into database actions, enabling you to create, update, delete, or search records without writing SQL.

    n8nFree

  18. Automate Data Extraction via Telegram with AI and Bright Data MCP

    Leverage AI to automate data extraction tasks through a Telegram bot using Bright Data MCP tools. This workflow simplifies complex operations by allowing natural language commands.

    n8nFree

  19. Automate AI Image Editing and Delivery via Google Drive and Telegram

    Streamline your image editing workflow by automatically processing images with OpenAI's DALL-E 2, storing them in Google Drive, and delivering the results via Telegram.

    n8nFree

  20. Automate High-Quality Audio Generation with Voxtral Model via Replicate API

    This n8n workflow automates the generation of high-quality audio using the Voxtral Small 24B 2507 model from Replicate. It manages API authentication, prediction creation, status polling, and result processing to deliver structured audio outputs.

    n8nFree

  21. Automate WordPress & WooCommerce Content with AI: Reviews, Comments, and Enhancements

    This workflow leverages AI to automate the generation of reviews, comments, and content enhancements for WordPress and WooCommerce. It includes five independent paths that can be executed manually or scheduled for automatic operation.

    n8nFree
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