This workflow enables simultaneous conversations between users and multiple AI agents, supporting personalized configurations for each agent's name, instructions, and language model. Users can mention specific agents using @, allowing the system to dynamically invoke multiple agents, avoiding the creation of duplicate nodes, and supporting multi-turn dialogue memory to enhance the coherence of interactions. It is suitable for scenarios such as intelligent Q&A, decision support, and education and training, meeting complex and diverse interaction needs.

This workflow achieves efficient information retrieval and intelligent Q&A by automatically downloading specified files from Google Drive and splitting their content into manageable text blocks. Users can ask questions through a chat interface, and the system quickly searches for relevant content using a vector database and OpenAI models, generating accurate answers along with citations. This process significantly enhances the efficiency of document information acquisition and the credibility of answers, making it suitable for various scenarios such as academic research, enterprise knowledge management, and customer support.

This workflow automatically retrieves "Calvin and Hobbes" comics daily, extracts image links, and uses AI to translate the comic dialogues into English and Korean. Finally, the comics, complete with original text and translations, are automatically pushed to a Discord channel, allowing users to access the latest content in real time. This process eliminates the hassle of manually visiting websites and enables intelligent sharing of multilingual comics, making it suitable for comic enthusiasts, content operators, and language learners.

This workflow automatically downloads images from Google Drive, extracts color information and semantic keywords, and combines them with advanced multimodal AI models to generate embedded documents stored in a memory vector database. It supports text-based image vector searches. This solution addresses the inefficiencies and inaccuracies of traditional image search methods and is suitable for scenarios such as digital asset management, e-commerce recommendations, and media classification, enhancing the intelligence of image management and retrieval.

This workflow can automatically retrieve the transcription text of YouTube videos and utilize artificial intelligence technology to extract key points, generating a concise text summary. Through this process, users can quickly grasp the essential information from the video, saving time on watching lengthy videos. It is suitable for content creators, researchers, and professionals, helping them efficiently acquire and manage valuable information, enabling rapid conversion and application of knowledge.

This workflow demonstrates how to analyze and process web content step by step through multiple chained calls to a large language model. Users can choose sequential, iterative, or parallel processing methods to meet different scenario requirements. It supports context memory management to enhance conversational continuity and integrates with external systems via a Webhook interface. It is suitable for automatic web content analysis, intelligent assistants, and complex question-answering systems, catering to both beginners and advanced users' expansion needs.

This workflow utilizes artificial intelligence technology to automatically assign primary categories to WordPress blog posts, significantly enhancing content management efficiency. It addresses the time-consuming and error-prone issues of traditional manual categorization, making it suitable for content operators and website administrators, especially when managing a large number of articles. Users only need to manually trigger the process to retrieve all articles, which are then categorized through intelligent AI analysis. Finally, the categories are updated back to WordPress, streamlining the content organization process and improving the quality of the website's content and user experience.

This workflow integrates an intelligent assistant specifically designed to query the capitals of fictional countries. Users can obtain capital information for specific countries through simple natural language requests, or receive a list of all supported country names when they request "list." It combines language understanding and data mapping technologies, enabling quick and accurate responses to user inquiries, significantly enhancing the interactive experience. This is suitable for various scenarios, including game development, educational training, and role-playing.

This workflow aims to enhance the intelligence and accuracy of online searches. After the user inputs a research question, the system automatically generates the optimal search query and retrieves results through the Brave Web Search API. By leveraging advanced large language models, it conducts multi-dimensional semantic analysis and result re-ranking, ultimately outputting the top ten high-quality links and key information that closely match the user's needs. This process is suitable for scenarios such as academic research, market analysis, and media editing, effectively addressing the issues of imprecise traditional search queries and difficulties in information extraction.

This workflow is designed to automate the processing of YouTube videos by calling an API to extract video subtitles and using an AI language model to generate concise and clear content summaries. Users only need to provide the video link to quickly obtain the core information of the video, significantly enhancing information retrieval efficiency and saving time on watching and organizing. It is suitable for content creators, researchers, and professionals, helping them efficiently distill and utilize video materials to optimize their learning and work processes.

This workflow utilizes the OpenAI GPT-4 model to achieve understanding and generation of natural language. It can generate structured information based on user input and ensures the accuracy of output format and content through an automatic correction mechanism. It addresses the shortcomings of traditional language models in terms of data formatting and information accuracy, making it suitable for scenarios such as data organization, report generation, and content creation. It helps users efficiently extract and verify structured data, thereby enhancing work efficiency and reliability.

This workflow automatically detects and summarizes the AI model information used by all workflows in the current instance. It extracts the model IDs and names associated with each node and exports the results to Google Sheets. Through batch processing, users can quickly understand the model invocation status in a multi-workflow environment, avoiding the tediousness of manual checks and enhancing project management transparency and operational efficiency. It is suitable for automation engineers, team managers, and data analysts.

This workflow integrates the OpenAI intelligent assistant with custom tools, providing flexible intelligent interaction capabilities. Users can easily inquire about the capital information of fictional countries, supporting input of country names or retrieval of country lists, enhancing the practicality of the conversation. Additionally, the built-in time retrieval tool adds temporal context to the dialogue, making it suitable for various scenarios such as smart customer service and educational entertainment, thereby optimizing the efficiency and accuracy of data queries.

This workflow combines OpenAI assistants with vector storage technology to implement a document retrieval and question-answering function. It can accurately extract relevant content from a document library and generate text with citations. It supports Markdown formatting and HTML conversion, enhancing the readability and professionalism of the output content while ensuring the reliability of the generated information. This makes it suitable for various scenarios such as intelligent Q&A, content creation, enterprise knowledge management, and educational research.

This workflow is designed to automate the scraping of the latest articles from Paul Graham's official website, extracting article links and obtaining titles and body content. It utilizes the OpenAI GPT-4 model to intelligently generate article summaries, ultimately integrating structured data that includes titles, summaries, and links. Through this process, users can efficiently acquire and understand Paul Graham's core insights, making it applicable to various scenarios such as content planning, research, and media editing, significantly enhancing information processing efficiency.

This workflow automatically receives YouTube video links through an interface, extracts video information and subtitles, and utilizes a large language model to perform structured summarization and analysis of the subtitles, generating clear technical summaries. At the same time, it provides real-time feedback of the analysis results to the caller and pushes the video title and link via Telegram, significantly enhancing the efficiency of video content processing and helping users quickly understand the core information of the video. It is applicable in various fields such as education, content creation, research, and enterprise knowledge management.

This workflow implements automatic monitoring and processing of PDF files in a specific folder on Google Drive, including file downloading, content extraction, and cleaning. The processed document content is converted into vector embeddings and stored in a Pinecone database, while also supporting users in intelligent Q&A through a chat interface, providing accurate answers by incorporating contextual information. This process enhances document management efficiency and simplifies information retrieval, making it suitable for businesses and teams to quickly access the required document information.

This workflow automatically monitors the status of Epic tasks in Jira. Once marked as "Done," it retrieves the relevant issues and comments, and uses AI analysis to generate a detailed agile retrospective report. Finally, the report is automatically updated in a structured Markdown format to a designated Google Docs document, ensuring that the content is clear and standardized, making it easy for the team to share and archive. This significantly improves the team's efficiency and quality in project summarization and experience sharing.

This workflow utilizes intelligent methods to automatically analyze quarterly financial reports of publicly listed companies, extract key information, and generate structured financial analysis reports. It combines vector databases and AI technology to quickly identify financial trends and anomalies, improving analysis efficiency and reducing human errors. The final report is automatically saved to Google Docs for easy viewing and sharing, making it suitable for financial analysts, investors, and corporate finance teams, thereby supporting informed decision-making and in-depth insights.

This workflow seamlessly integrates an AI smart assistant with Google Drive, providing intelligent Q&A services based on document content. Users can upload important documents, and the system automatically updates the assistant's knowledge base, enabling it to respond to customer inquiries in a professional and friendly manner. This automated process significantly enhances the speed and accuracy of customer consultations, making it particularly suitable for travel agencies and business scenarios that require document support, helping companies reduce labor costs and improve service quality.

This workflow can automatically receive research topics submitted by users and utilize Perplexity AI for in-depth information retrieval and content generation. Through a multi-step AI model processing, the workflow structures the research results and converts them into modern, responsive HTML webpages, beautified with Tailwind CSS. This process achieves full automation from topic research to webpage presentation, making it particularly suitable for content creators and researchers to quickly generate professional webpages, enhance work efficiency, and simplify the information integration and design process.

This intelligent dialogue agent workflow combines advanced language models with information retrieval tools, featuring contextual memory capabilities that allow it to respond to user chat messages in real time. By retaining recent conversation records and accessing external data sources, the workflow effectively addresses the issues of inaccurate responses and outdated information commonly found in traditional chatbots. It is suitable for various scenarios such as customer service, intelligent Q&A systems, and educational tutoring, enhancing the coherence and richness of conversations while providing users with a high-quality intelligent interaction experience.

This workflow implements an intelligent email Q&A bot that allows users to interact with it via Telegram for quick inquiries about their personal emails. It combines semantic search with structured SQL queries, enabling it to understand natural language questions and accurately locate email content and time information, thereby providing precise answers. This system is particularly suitable for individuals and businesses that require efficient email management, enhancing the intelligence and convenience of email queries.

This workflow utilizes RAG technology to automatically process and analyze the quarterly financial reports of publicly listed companies in PDF format, generating structured financial analysis reports. It accurately extracts key information through semantic retrieval and large language models, intelligently generating detailed reports that include content such as revenue, costs, and profits, which are then automatically saved to Google Docs. This process significantly enhances the efficiency and accuracy of financial data insights, helping investment analysts, financial advisors, and others quickly obtain in-depth analysis results.