Recent Workflows

For Google Sheets, this workflow automates the import of multiple CSV files, ensuring data is cleaned by removing duplicates and filtering for active subscribers. It sorts the data by subscription date and uploads it efficiently, streamlining data management and enhancing productivity.

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For n8n, this manual workflow identifies parameters in active workflows that use expressions affected by recent version changes. It helps ensure workflows function correctly after upgrades by pinpointing potential issues, allowing for timely adjustments and maintaining operational integrity.

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For the HR & IT Helpdesk Chatbot, automate employee support by integrating a chatbot with audio transcription capabilities. This workflow extracts internal policy documents, creates a vector store for accurate responses, and processes text and voice messages via Telegram. It enhances employee interaction by providing quick, accurate answers to HR and IT queries, ensuring a seamless support experience.

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Travel AssistantAgent automates trip planning by integrating chat interactions with a MongoDB database for memory and vector search. It efficiently retrieves and stores points of interest, enabling users to ask questions and receive tailored travel recommendations. This streamlined process enhances user experience, making travel planning faster and more personalized.

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For the Vision-Based AI Agent Scraper, automate data extraction from webpages using screenshots and HTML. This workflow integrates Google Sheets for managing URLs and storing results, ScrapingBee for capturing full-page screenshots, and the Gemini-1.5-Pro AI model for accurate data parsing. It efficiently converts HTML to Markdown, optimizing processing costs, and is designed for e-commerce scraping, ensuring structured data is easily accessible and customizable for various needs.

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FLUX-fill standalone automates image processing by integrating webhooks and API calls, enabling users to generate and edit images efficiently. It streamlines the workflow from image selection to final output, ensuring quick responses and seamless user interaction. With a focus on enhancing creativity, it allows for customizable prompts and settings, delivering high-quality results in a user-friendly interface.

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For DeepSeek, this automated workflow enables quick and efficient chat interactions by integrating AI reasoning capabilities. It processes incoming messages, utilizes advanced language models, and provides seamless access to DeepSeek's API, enhancing user engagement and response accuracy.

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For Telegram, this workflow automates user interaction by validating messages, routing them to an AI agent for personalized responses, and saving long-term memories for enhanced future conversations. It efficiently handles various message types, ensuring users receive timely and relevant replies while maintaining a seamless communication experience.

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Docsify example automates documentation management by integrating multiple nodes to streamline file conversion, extraction, and editing. It allows users to create, view, and edit documentation in real-time, enhancing collaboration and efficiency. This workflow supports Markdown files, ensuring easy access and updates while providing a user-friendly interface for seamless interaction.

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Ollama Chat automates chat message processing by integrating the Llama 3.2 model, providing structured JSON responses. It efficiently handles incoming messages, processes them through a basic language model chain, and ensures robust error handling, delivering consistent feedback to users. This workflow enhances communication by transforming user prompts into clear, actionable responses.

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For n8n, this workflow automates the retrieval and analysis of community creator and workflow statistics, generating a detailed Markdown report. It identifies the top 25 creators and 300 workflows based on unique weekly and monthly visitors and inserters. By filtering data based on a specified username, it provides tailored insights into individual contributions, showcasing their impact on the community. The final report is saved locally, making it easy to access and review valuable performance metrics and trends.

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For LM Studio, this automated workflow tests multiple local LLMs by integrating with Sticky Note, LangChain, and Google Sheets. It captures response metrics, analyzes readability, and tracks performance over time, allowing users to evaluate model outputs effectively. With 21 nodes, it streamlines the process of comparing LLMs, ensuring concise and clear responses while providing optional data logging for comprehensive analysis.

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