Workflow Templates

Disclaimer This workflow contains a community node. What It Does Leverage the power of GPT-4o to seamlessly summarize a scientific research PDF of your choosing. By simply downloading a PDF of a scientific research article into a folder on your computer this powerful workflow will automatically read the article and produce a detailed summarization of the article. The workflow will then save this summarization onto your computer for future convenience. Who Is This For? The workflow is the perfect tool for all types of self-learners attempting to improve their knowledge base as efficiently as possible. It is a way to rapidly improve your knowledge base using peer reviewed scientific articles in a quick and efficient way. This workflow will provide a more detailed summary of the scientific research article than a typical abstract, while taking a fraction of the time it would take to read an entire paper. It will provide you with enough information to have a firm grasp on the information provided within the scientific article and will allow you to determine if you would like to dive deeper into the article. This workflow is perfect for professionals who need to stay current on the most recent literature in their field, as well as the self-learners who enjoy diving deep into a specific topic. It can aid anyone who is performing academic research, a literature review, or attempting to increase their knowledge base in a field using peer reviewed sources. How It Works Utilizing the power of GPT-4o, the moment you save a PDF of a scientific research article to a predesignated folder it will being to read the article and produce a summary that will be saved into another designated folder on your computer via the following steps below. Search the internet and your favorite journal databases for a scientific article that interests you. With the n8n workflow activated, download a PDF of the scientific article and save it to a specific designated folder. Saving the scientific article to this folder will trigger the workflow to initiate. The workflow will then extract the contents of the PDF and pass the data along to an AI agent utilizing the power of GPT-4o. This AI agent will produce a detailed summary of the scientific article. This summary will include the following: Introduction heading discussing the importance of the article and the specific aims of the study Methods heading detailing how the study was conducted, what variables they evaluated, what their inclusion and exclusion criteria were, and what their measurement standards were. Results heading providing specific data provided in the study for all variables tested as well as the statistical significance of each result. Summary heading evaluating the importance of the results, how it compares to other scientific articles in the same field, as well as the recommendations of the authors on how to interpret the data provided by the results. Conclusion heading summarizing the strengths and weaknesses of the scientific article as well as providing deficiencies in knowledge on the subject that would be a good topic for future studies. After the AI agent has completed its summary, it will convert the summary to text and save it to a designated folder on your computer for future viewing. Set Up Steps You will need to create a folder on your computer where you would like to save your scientific article PDFs. You will then copy the pathway to this folder into the local file trigger node. You will need to obtain an Open AI API key from platform.openai.com/api-keys After you obtain this Open AI API key you will need to connect it to the Open AI Chat Model connected to the Summarizer Tools Agent. You will now need to fund your Open AI account. GPT-4o costs ~$0.01 to run the workflow. Finally, create a folder on your computer you wish to have the summarizations saved to. Copy the pathway to this folder into the Save to Folder node. Customization This workflow is easy to customize to a specific area of research to provide the best possible summarization. If you have a specific expertise in a field of study, you can customize the output to provide data at a higher level of understanding for that field. For example, if you are a marine biologist, you can change the portion of the text prompt in the summarizer tool from "You are a research expert who is providing data to another researcher." to "You are a marine biologist expert who is providing data to another marine biologist." Disclaimer If the pdf is too large, open AI will not be able to summarize it and will provide the error that you have reached your limit of requests.

🧵 Generate Conversational Twitter/X Threads with GPT-4o AI (n8n Workflow) This workflow uses OpenAI (GPT-4o) and Twitter/X to automatically generate and publish engaging, conversational threads in response to a trigger (e.g., from a chatbot or form). 🚀 What Does It Do? Listens for an incoming message (e.g., via webhook or another n8n input). Uses GPT-4o to craft a narrative-style Twitter thread in a personal, friendly tone. Publishes the first tweet, then automatically posts each following tweet as a reply—building a full thread. 🛠️ What Do You Need to Configure? Before using this template, make sure to set up the following credentials: OpenAI Add your OpenAI API key in the OpenAI Chat Model node. This is used to generate the thread content. Twitter/X Add your Twitter OAuth2 credentials to the First Tweet and Thread Reply nodes. This allows the workflow to publish tweets on your behalf. ✨ Who Is This For? This template is perfect for: Content creators who want to share ideas regularly Personal brands looking to grow their presence Social media managers automating thread creation 🔧 How to Customize It You can easily adjust the tone, structure, or length of the threads by modifying the system prompt in the OpenAI node. For example: To create threads with humor, change the prompt to “Write in a witty and humorous tone.” To tailor it for marketing, prompt it with “Write a persuasive product-focused Twitter thread.” You can also integrate this workflow with: Telegram bots Web forms (e.g., Typeform, Tally) CRM tools or newsletter platforms 📋 Sample Output Prompt sent to the workflow: “Tips for growing on Twitter in 2025” Generated thread: ++Tweet 1:++ Thinking of growing your presence on Twitter/X in 2024? Here's a thread with the most effective strategies that actually work 🧵 ++Reply 1:++ Engage, don’t broadcast Twitter is a conversation platform. Reply to others, quote-tweet, and start discussions instead of just posting links. ++Reply 2:++ Consistency beats virality Tweeting regularly builds trust and visibility. You don't need to go viral — just show up.

Create a Telegram bot that combines advanced AI functionalities with LangChain nodes and new tools. Nodes as tools and the HTTP request tool are a new n8n feature that extend custom workflow tool and simplify your setup. We used the workflow tool in the previous Telegram template to call the Dalle-3 model. In the new version, we've achieved similar results using the HTTP Request tool and the Telegram node tool instead. The main difference is that Telegram bot becomes more flexible. The LangChain Agent node can decide which tool to use and when. In the previous version, all steps inside the custom workflow tool were executed sequentially. ⚠️ Note that you'd need to select the Tools Agent to work with new tools. Before launching the template, make sure to set up your OpenAI and Telegram credentials. Here’s how the new Telegram bot works: Telegram Trigger listens for new messages in a specified Telegram chat. This node activates the rest of the workflow after receiving a message. AI Tool Agent receives input text, processes it using the OpenAI model and replies to a user. It addresses users by name and sends image links when an image is requested. The OpenAI GPT-4o model generates context-aware responses. You can configure the model parameters or swap this node entirely. Window buffer memory helps maintain context across conversations. It stores the last 10 interactions and ensures that the agent can access previous messages within a session. Conversations from different users are stored in different buffers. The HTTP request tool connects with OpenAI's DALL-E-3 API to generate images based on user prompts. The tool is called when the user asks for an image. Telegram node tool sends generated images back to the user in a Telegram chat. It retrieves the image from the URL returned by the DALL-E-3 model. This does not happen directly, however. The response from the HTTP request tool is first stored in the Agent’s scratchpad (think of it as a short-term memory). In the next iteration, the Agent sends the updated response to the GPT model once again. The GPT model will then create a new tool request to send the image back to the user. To pass the image URL, the tool uses the new $fromAI() expression. Send final reply node sends the final response message created by the agent back to the user on Telegram. Even though the image was already passed to the user, the Agent always stops with the final response that comes from dedicated output. ⚠️ Note, that the Agent may not adhere to the same sequence of actions in 100% of situations. For example, sometimes it could skip sending the file via the Telegram node tool and instead just send an URL in the final reply. If you have a longer series of predefined steps, it may be better to use the “old” custom workflow tool. This template is perfect as a starting point for building AI agentic workflow. Take a look at another agentic Telegram AI template that can handle both text and voice messages.

This flow migrates all repositories of a Gitlab group to a Gitea organization by triggering Gitea's integrated migration tool. Set up steps: Copy this workflow Create an empty Gitea-organization you want to migrate to. (The flow will skip all projects which have the same name of possibly already existing repos in the target Gitea organization.) Create an access token in your Gitea (https://gitea.example.com/user/settings/applications), set it up as a Header Auth with it's name being "Authorization" and value being "token [your-gitea-token]" and select it for the "Gitea:"-named nodes. Create a Personal access token in Gitlab (https://gitlab.com/-/user_settings/personal_access_tokens), create a Header Auth with name "PRIVATE-TOKEN" and value "[your-gitlab-token]" and select it for the "Gitlab:"-named node. Also keep the value of your Gitlab-token available for step 5. Edit the Set node right after the trigger node and set paste your personal access token in there as well as the names of the Gitlab source group and the Gitea target organization. Use the url-friendly version of their names by simply copy&pasting them from their URLs. Run the flow and enjoy the show :)

This workflow demonstrates how to automate live information gathering, fact-checking, and trend analysis in response to any chat message - using a powerful AI agent, memory, and a real-time search tool. Use cases are many: This is perfect for researchers needing instant, up-to-date data; support teams providing live, accurate answers; content creators looking to verify facts or find hot topics; and analysts automating regular reports with the freshest information. How It Works The workflow is triggered whenever a chat message is received (e.g., a user question, research prompt, or data request). The message is sent to the AI Agent, which follows the following steps: First, it queries SerpAPI – Research to gather the latest real-time information and data from the web. Next, it checks the Window Buffer Memory for any related past interactions or contextual information that may be useful. Finally, it sends all collected data and context to the Google Gemini Chat Model, which analyzes the information and generates a comprehensive, intelligent response. Then, the AI Agent delivers the analyzed, up-to-date answer directly in the chat, combining live data, context, and expert analysis. How To Set Up Download and import the workflow into your n8n workspace. Set up API credentials and tool access for the AI Agent: Google Gemini (for chat-based intelligence) → connected to Node Google Gemini Chat Model. SerpAPI (for real-time web and search results) → connected to Node SerpAPI - Research. Window Buffer Memory (for richer, context-aware conversations) → connected to Node Window Buffer Memory. Open the chat in n8n and type the topic or trend you want to research. Send the message and wait for the process to complete. Receive the AI-powered research reply in the chat box. Requirements An n8n instance (self-hosted or cloud). SerpAPI** credentials for live web search and data gathering. Window Buffer Memory** configured to provide relevant conversation context in history. Google Gemini API** access to analyze collected data and generate responses. How To Customize Choose your preferred AI model: Replace **Google Gemini with OpenAI ChatGPT, or any other chat model as preferred. Add or change memory: Replace **Window Buffer Memory with more advanced memory options for deeper recall. Connect your preferred chat platform**: Easily swap out the default chat integration for Telegram, Slack, or any other compatible messaging platform to trigger and interact with the workflow. Need Help? If you’d like this workflow customized, or if you’re looking to build a tailored AI Agent for your own business - please feel free to reach out to Agent Circle. We’re always here to support and help you to bring automation ideas to life. Join our community on different platforms for assistance, inspiration and tips from others. Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle

🧨 VIP Radar: Instantly Spot & Summarize High-Value Shopify Orders with AI + Slack Alerts Automatically detect when a new Shopify order exceeds $200, fetch the customer’s purchase history, generate an AI-powered summary, and alert your team in Slack—so no VIP goes unnoticed. 🛠️ Workflow Overview | Feature | Description | |------------------------|-----------------------------------------------------------------------------| | Trigger | Shopify “New Order” webhook | | Conditional Check | Filters for orders > $200 | | Data Enrichment | Pulls full order history for the customer from Shopify | | AI Summary | Uses OpenAI to summarize buying behavior | | Notification | Sends detailed alert to Slack with name, order total, and customer insights | | Fallback | Ignores low-value orders and terminates flow | 📘 What This Workflow Does This automation monitors your Shopify store and reacts to any high-value order (over $200). When triggered: It fetches all past orders of that customer, Summarizes the history using OpenAI, Sends a full alert with context to your Slack channel. No more guessing who’s worth a closer look. Your team gets instant insights, and your VIPs get the attention they deserve. 🧩 Node-by-Node Breakdown 🔔 1. Trigger: New Shopify Order Type**: Shopify Trigger Event**: orders/create Purpose**: Starts workflow on new order Pulls**: Order total, customer ID, name, etc. 🔣 2. Set: Convert Order Total to Number Ensures the total_price is treated as a number for comparison. ❓ 3. If: Is Order > $200? Condition**: $json.total_price > 200 Yes** → Continue No** → End workflow 🔗 4. HTTP: Fetch Customer Order History Uses the Shopify Admin API to retrieve all orders from this customer. Requires your Shopify access token. 🧾 5. Set: Convert Orders Array to String Formats the order data so it's prompt-friendly for OpenAI. 🧠 6. LangChain Agent: Summarize Order History Prompt**: "Summarize the customer's order history for Slack. Here is their order data: {{ $json.orders }}" Model**: GPT-4o Mini (customizable) 📨 7. Slack: Send VIP Alert Sends a rich message to a Slack channel. Includes: Customer name Order value Summary of past behavior 🧱 8. No-Op (Optional) Used to safely end workflow if the order is not high-value. 🔧 How to Customize | What | How | |--------------------------|----------------------------------------------------------------------| | Order threshold | Change 200 in the If node | | Slack channel | Update channelId in the Slack node | | AI prompt style | Edit text in LangChain Agent node | | Shopify auth token | Replace shpat_abc123xyz... with your actual private token | 🚀 Setup Instructions Open n8n editor. Go to Workflows → Import → Paste JSON. Paste this workflow JSON. Replace your Shopify token and Slack credentials. Save and activate. Place a test order in Shopify to watch it work. 💡 Real-World Use Cases 🎯 Notify sales team when a potential VIP buys 🛎️ Prep support reps with customer history 📈 Detect repeat buyers and upsell opportunities 🔗 Resources & Support 👨‍💻 Creator: Yaron Been 📺 YouTube: NoFluff with Yaron Been 🌐 Website: https://nofluff.online 📩 Contact: Yaron@nofluff.online 🏷️ Tags #shopify, #openai, #slack, #vip-customers, #automation, #n8n, #workflow, #ecommerce, #customer-insights, #ai-summaries, #gpt4o

Automate LinkedIn engagement without sounding like a bot. This workflow: 🌍 Detects language & tone (German / English) 👍 Chooses the right reaction (like / celebrate / support …) 🗣 Generates a personalised comment in your voice and mentions the author 📲 Optional Telegram review – approve ✅ or regenerate ❌ before posting 💸 Runs on cost-efficient GPT-4o mini or Claude 3.5 Haiku ☁️ Publishes comment + reaction via the Unipile API Setup (≈ 15-30 min) Unipile – connect LinkedIn → copy account_id, dsn, then create an Access-Token (X-API-KEY). Telegram (optional) – create a bot, add a credential named YOUR TELEGRAM ACCOUNT. OpenAI / Anthropic – add your API key and keep one LLM node (delete the other). Open the “Defining guardrails” node and replace the credential placeholders. (Optional) Tweak role, comment_length and openers_example_1-3 for your brand voice. Security: no live keys included – all secrets are placeholders. Best for: solopreneurs, marketing teams, personal-branding consultants.

AI evaluation in n8n This is a template for n8n's evaluation feature. 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: whether a specific tool was called by an agent. 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 We make sure that the agent outputs the list of tools that it used We then check whether the expected tool (from the dataset) is in that list Finally we pass this information back to n8n as a metric

This guide will show you how to use a workflow as a reusable tool in n8n, such as integrating an AI Agent or other specialized processes into your workflows. By the end of this example, you'll have a simple, reusable workflow that can be easily plugged into larger projects, making your automations more efficient and scalable. With this approach, you can create reusable workflows like "Scrape a Page," "Search Brave," or "Generate an Image," which you can then call whenever needed. While n8n makes it easy to build these workflows from scratch, setting them up as reusable components saves time as your automations grow in complexity. Setup Add the "Execute Workflow Trigger" node Add the node(s) to perform the desired tasks in the workflow Add a final "Set" or "Edit Fields" node at the end to ensure all external workflows return a consistent output format Details In this example, the "Execute Workflow Trigger" expects input in the following JSON format: [ { "query": { "url": "https://en.wikipedia.org/wiki/some_info" } } ] Once your external workflow is ready, you can instruct the AI Agent to use this tool by connecting it to the external workflow. Set up the schema type to "Generate from JSON Example" using this structure: { "url": "URL_TO_GET" } Finally, ensure your external workflow includes a "Set" or "Edit Fields" node at the end to define the response format. This helps keep the outputs of your reusable workflows consistent and predictable.

Who is this for? This workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance. What problem is this workflow solving? / Use case Designing a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness. What this workflow does The workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design. Setup Prepare your unique services and target audience profile for customization later. Gather the competitor’s landing page URL you wish to analyze. Run the workflow, inputting your competitor’s URL when prompted. How to customize this workflow to your needs After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging. Add or remove sections based on your objectives and audience insights. Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.

This workflow automatically transcribes audio files, translates the content between languages, and generates natural-sounding speech from the translated text - all in one seamless process. Who's it for Content creators, educators, and businesses needing to make their audio content accessible across language barriers. Perfect for translating podcasts, voice messages, lectures, or any audio content while preserving the spoken format. How it works The workflow receives an audio file through a webhook, transcribes it using OpenAI's Whisper, translates and structures the text with GPT-4, generates new audio in the target language, and stores it in S3 for easy access. The entire process takes seconds and returns both the transcribed/translated text and a URL to the translated audio file. How to set up Configure OpenAI credentials - Add your OpenAI API key for Whisper transcription and GPT-4 translation Set up AWS S3 - Create a bucket with public read permissions for audio storage Update configuration - Replace 'YOUR-BUCKET-NAME' with your actual S3 bucket name Activate webhook - Deploy and copy your webhook URL for receiving audio files Send a POST request with: Binary audio file (as 'audiofile') Languages parameter (e.g., "English, Spanish") Requirements OpenAI API account with access to Whisper and GPT-4 AWS account with S3 bucket configured Basic understanding of webhooks and API requests How to customize Add language detection** - Automatically detect source language if not specified Customize voice settings** - Adjust speech speed, pitch, or select different voices Add file validation** - Implement size limits and format checks Enhance security** - Add webhook authentication and rate limiting Extend functionality** - Add subtitle generation or multiple output formats

Make OpenAI Citation for File Retrieval RAG Use case In this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags. This is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example. What this workflow does In this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval. The assistant will perform the file search within the OpenAI infrastructure and will return the content with citations. We will make an HTTP request to retrieve all the details we need to format the text output. Setup Insert an OpenAI Key How to adjust it to your needs At the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.