This workflow utilizes Perplexity AI's Sonar Pro model to provide intelligent Q&A functionality. Users can customize system prompts and questions, as well as flexibly set query domains. Through API integration, it automatically extracts and cleans the returned answers, enhancing the efficiency and accuracy of information retrieval. It is suitable for various scenarios such as customer service responses, market research, and internal training, helping users quickly obtain structured authoritative answers and reducing the cumbersome steps of manual searching.

This workflow is designed to automate the generation of research reports based on user-defined topics, integrating various information sources such as OpenAI, Wikipedia, news APIs, Google Search, and Google Scholar. Through intelligent analysis and integration, it produces structured PDF reports that include an introduction to the topic, key findings, and academic insights, which are automatically sent to designated users via Gmail and Telegram. Additionally, all data is recorded in Google Sheets for easy management and tracking, significantly enhancing research efficiency and the accuracy of information integration.

This workflow utilizes RAG technology, combined with the Pinecone vector database and OpenAI intelligent models, to build an intelligent Q&A chatbot for the GitHub API. It can real-time scrape and index GitHub's API documentation, quickly answering users' technical queries through vector search and semantic understanding, significantly improving the efficiency and accuracy of developers in obtaining interface information. It is suitable for scenarios such as technical support, documentation maintenance, and training.

This workflow builds an intelligent chatbot that combines the GPT-4o language model with MCP Brave Search, enabling it to process user chat messages in real-time and perform web searches. The chatbot not only generates high-quality intelligent responses but also supports short-term memory, enhancing the coherence of conversations and the user experience. It is suitable for various scenarios such as automated customer service, knowledge retrieval, and information inquiry, helping users quickly obtain the information they need and improving interaction efficiency.

This workflow creates a multilingual No-Code tool query bot. When users input the tool name in Telegram, the bot automatically retrieves detailed information from a remote database and translates it into the user's native language, subsequently sending it as a multimedia message. Through this process, users can easily access introductions to No-Code tools, overcoming language barriers and achieving instant information retrieval. This greatly enhances the convenience and user-friendliness of inquiries, making it suitable for technical support and educational training in multilingual environments.

This workflow combines AI language models with the Open-Meteo weather forecast API to provide intelligent weather inquiry and forecasting services. Users can simply enter the city name and their requirements through a chat interface, and the AI will automatically obtain the geographic coordinates and retrieve weather information, generating accurate weather forecast responses. This process significantly simplifies the traditional weather inquiry operations, enhances interaction efficiency, and is suitable for various scenarios such as smart customer service, travel planning, and education and training, meeting users' needs for real-time weather information.

This in-depth research workflow helps users efficiently conduct research on complex topics through automated searches and content scraping, combined with advanced language models. After the user inputs the research topic, the system generates multiple search queries and filters relevant information, supporting dynamic adjustments to the depth and breadth of the research. Ultimately, the gathered information is compiled into a detailed report and automatically uploaded to a cloud management platform, achieving systematic organization and sharing of materials, significantly enhancing research efficiency and quality.

This workflow is designed to automate fact-checking in text by utilizing natural language processing technology to split the input text into sentences and verify the authenticity of each one. By invoking a locally running customized language model, it efficiently identifies false information, reduces the workload of manual proofreading, and enhances the accuracy and efficiency of content review. It is suitable for fields such as media, research, and content creation, helping users ensure the authenticity and authority of information, and enabling rapid fact screening and error correction.

This workflow automatically generates optimized web search queries through intelligent semantic analysis and multi-chain thinking, and calls the Brave Search API to obtain relevant results. It is capable of deeply reordering search results and extracting information based on the user's true intent, filtering out the top 10 most relevant high-value links to help users quickly locate the answers they need. It supports Webhook triggers and is applicable in various scenarios such as scientific research, market research, and corporate decision-making, significantly enhancing the relevance and effectiveness of information retrieval.

This workflow provides real-time consultation and assistance through intelligent dialogue and multi-tool collaboration, aiming to enhance users' learning and usage efficiency on the automation platform. It intelligently receives user inquiries, analyzes issues, and automatically retrieves relevant tools and content to generate clear, actionable responses. This helps solve users' challenges in understanding functions and operational guidance, making it suitable for beginners, advanced users, corporate support teams, and training scenarios.

This workflow automates the processing and analysis of financing pitch materials for startups. It detects and downloads PDF files from the Airtable database, uses an AI vision model to transcribe the content into a structured Markdown format, and extracts key information to generate reports. Finally, the data is written back to Airtable and a vector database is constructed, enabling team members to perform natural language queries, significantly enhancing the efficiency of processing financing materials and the convenience of information retrieval.

This workflow automatically generates and edits knowledge base articles by combining multiple AI models. Users only need to input a topic, and the system can conduct in-depth research to produce a structured and content-rich draft, followed by multiple rounds of intelligent editing and review. Ultimately, high-quality articles are automatically published to the content management system, ensuring professionalism and practicality. This process significantly enhances content production efficiency, addressing the time and quality issues associated with traditional manual writing, making it suitable for enterprises and content teams.

This workflow combines intelligent web scraping and API data interaction, allowing it to automatically retrieve relevant information and provide smart recommendations based on users' natural language input. By efficiently utilizing the Firecrawl API to scrape web content and flexibly calling external APIs, it simplifies traditional data processing workflows. The integrated AI Agent and chat model enhance the intelligence of automated responses, significantly reducing development difficulty and time costs, making it suitable for various scenarios such as automated development, customer service systems, and information recommendation.

This workflow automatically filters relevant "Ask HN" posts and comments from HackerNews based on the learning topics submitted by users. It utilizes advanced language models for analysis, extracting high-quality learning resource recommendations, and generates a list in structured Markdown format, which is ultimately sent to the user via email. This process effectively addresses the issue of information overload, helping users quickly find practical learning materials and enhancing their learning efficiency and experience.

This workflow automates the process from receiving a Request for Proposal (RFP) document to generating a complete response document. It intelligently extracts questions from the RFP, automatically generates answers using internal company resources, and organizes them into a structured Google Docs document. Additionally, the system supports email and Slack notifications to ensure the team is promptly informed about the response status. This process significantly improves response efficiency, reduces labor costs, and helps the sales team quickly and accurately address customer needs.

This workflow automatically captures the homepage content of the custom website field when a new organization is created in Pipedrive. It utilizes AI for intelligent analysis to generate detailed notes that include the company description, market positioning, and competitor information. This information is synchronized back to Pipedrive and pushed to Slack after format conversion, ensuring that team members can share customer information in real-time, enhancing sales and customer management efficiency while reducing manual data entry work.

This workflow can automatically monitor a specified Google Drive folder, real-time retrieve the content of newly uploaded Google Docs, and generate intelligent summaries using an AI model. The summaries and the information of the document uploaders will be automatically saved to Google Sheets, facilitating later management and quick reference. This process significantly improves document management efficiency, reduces the time spent on manual organization, and minimizes the risk of omissions, making it suitable for businesses, teams, and educational institutions that need to quickly obtain and organize document information.

This workflow builds an intelligent travel assistant that integrates large language models and vector search technology to achieve personalized travel recommendations and intelligent Q&A functions. Through dynamic data reception and chat memory, users can receive real-time updates on travel information, enhancing the interactive experience. At the same time, the system addresses issues such as the isolation of traditional travel information, inaccurate recommendations, and incoherent interactions, making it suitable for online travel platforms, travel agencies, and personal travel planning, significantly improving service intelligence and travel efficiency.

This workflow utilizes advanced artificial intelligence technology to automate the execution of in-depth research tasks. Users only need to input the research topic, and the system can generate precise search queries, conduct multiple rounds of online searches, and integrate information from various authoritative sources through intelligent analysis. Ultimately, the workflow produces a structured research report in Markdown format, significantly enhancing research efficiency and information accuracy. It is suitable for various scenarios such as academic research, market analysis, and product research, helping users quickly obtain comprehensive and valuable research results.

This workflow automates the retrieval of the latest academic papers from Hugging Face, utilizing the advanced GPT-4 model for in-depth analysis and structured extraction of paper abstracts. Ultimately, it intelligently stores key information in a Notion database. It effectively addresses the tediousness of manually searching for papers, avoids redundant information storage, and provides efficient management of academic resources. This is suitable for researchers, academic institutions, and AI practitioners to continuously track the latest research developments, enhancing the efficiency and quality of literature organization.

The DSP Agent is an intelligent learning assistant specifically designed for students in the field of signal processing. It receives text and voice messages through Telegram and utilizes advanced AI models to provide instant knowledge queries, calculation assistance, and personalized learning tracking. This tool helps students quickly understand complex concepts, offers dynamic problem analysis and learning suggestions, addressing the issues of insufficient interactivity and lack of personalized tutoring in traditional learning. It enhances learning efficiency and experience.

This workflow implements a Retrieval-Augmented Generation (RAG) function through real-time data updates, automatically retrieving the latest content from the Notion knowledge base. It performs text chunking and vectorization, storing the results in the Supabase vector database. By integrating OpenAI's GPT-4 model, it provides contextually relevant intelligent Q&A, significantly enhancing the efficiency and accuracy of knowledge base utilization. This is applicable in scenarios such as enterprise knowledge management, customer support, and education and training, ensuring that users receive the most up-to-date information.

This workflow implements a session-based A/B split testing, which can randomly assign different prompts (baseline and alternative) to users in order to evaluate the effectiveness of language model responses. By integrating a database to record sessions and allocation paths, and combining it with the GPT-4o-mini model, it ensures continuous management of conversation memory, enhancing the scientific rigor and accuracy of the tests. It is suitable for AI product development, chatbot optimization, and multi-version effectiveness verification, helping users quickly validate prompt strategies and optimize interaction experiences.

This workflow enables real-time synchronization of data from the Airtable database to Obsidian notes. Users simply need to select the relevant text in Obsidian and send a request. An intelligent AI agent will understand the query intent and invoke the OpenAI model to retrieve the required data. Ultimately, the results will be automatically inserted into the notes, streamlining the process of data retrieval and knowledge management, thereby enhancing work efficiency and user experience. It is suitable for professionals and team collaboration users who need to quickly access structured data.