AI research assistant via Telegram (GPT-4o mini + DeepSeek R1 + SerpAPI)
AI Research Assistant via Telegram (GPT-4o mini + DeepSeek R1 + SerpAPI) 👥 Who’s it for This workflow is perfect for anyone who wants to receive AI-powered research summaries directly on Telegram. Ideal for people asking frequent product, tech, or decision-making questions and want up-to-date answers sourced from the web. 🤖 What it does Users send a question via Telegram. An AI agent (DeepSeek R1) reformulates and understands the intent, while a second agent (GPT-4o mini) performs live research using SerpAPI. The most relevant answers, including links and images, are delivered back via Telegram. ⚙️ How it works 📲 Telegram Trigger – Starts when a user sends a message to your Telegram bot. 🧠 DeepSeek R1 Agent – Understands, clarifies, or reformulates the user query. 🧠 Research AI Agent (GPT-4o mini + SerpAPI) – Searches the web and summarizes the best results. 📤 Send Telegram Message – Sends the response back to the same user. 📋 Requirements Telegram bot (via BotFather) with API token set in n8n credentials OpenAI account with API key and balance for GPT-4o mini SerpAPI account (100 free searches/month) with API key DeepSeek account with API key and balance 🛠️ How to set up Create your Telegram bot using BotFather and connect it using the Telegram Trigger node Set up DeepSeek credentials and add a Chat Model AI Agent node using DeepSeek R1 to reformulate the user’s question Set up OpenAI credentials and add a second ChatGPT AI Agent node using GPT-4o mini In the GPT-4o node, enable the SerpAPI Tool and add your SerpAPI API key Pass the reformulated question from DeepSeek to the GPT-4o agent for live search and summarization Format the response (text, links, optional images) Send the final reply to the user using the Telegram Send Message node Ensure your n8n instance is publicly accessible Test the workflow by sending a message to your Telegram bot ✅
By Arlin PerezGet comments from Facebook page
This workflow automates the collection of comments from posts on a Facebook Page. Providing clean, structured data for analysis or further automation. What this workflow does Fetches recent posts from a Facebook Page. Retrieves comments for each post. Outputs structured data of Comments and Posts for further use. Setup Facebook Graph API: Connect your Access Token with the required permissions (pagesreadengagement, pagesreaduser_content). Workflow: Set the Page ID and the number of posts to fetch in the "Set Number of Latest Posts to Fetch" node.
By Sira EkabutExport new deals from HubSpot to Slack and Airtable
This workflow is triggered when a new deal is created in HubSpot. Then, it processes the deal based on its value and stage. The first branching follows three cases: If the deal is closed and won, a message is sent in a Slack channel, so that the whole team can celebrate the success. If a presentation has been scheduled for the deal, then a Google Slides presentation template is created. If the deal is closed and lost, the deal’s details are added to an Airtable table. From here, you can analyze the data to get insights into what and why certain deals don’t get closed. The second branching follows two cases: If the deal is for a new business and has a value above 500, a high-priority ticket assigned to an experienced team member is created in HubSpot If the deal is for an existing business and has a value below 500, a low-priority ticket is created.
By LorenaReceive updates on a new invoice via Invoice Ninja
Companion workflow for Invoice Ninja Trigger node docs
By amudhanEvaluation metric example: categorization
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 category matches the expected one. The workflow takes support tickets and generates a category and priority, which is then compared with the correct answers in the dataset. 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 Once the category is generated by the agent, we check whether it matches the expected one in the dataset Finally we pass this information back to n8n as a metric
By David RobertsEnrich new accounts in Pipedrive using Datagma API
This workflow enriches new accounts in Pipedrive using Datagma API by adding data about ICP (ideal customer profile). Instead of Pipedrive, you can use any other CRM. In this example, ideal buyers are heads of sales/business development. Prerequisites Pipedrive account and Pipedrive credentials How it works Pipedrive trigger node starts the workflow when a new company is created. HTTP Request node queries data from Datagma. Pipedrive node updates Pipedrive contact with new data from Datagma. The Item Lists node simplifies returned data from Datagma that contain lists (arrays), enabling you to easily modify the structure for further processing without the need to use Function nodes and write custom JavaScript. IF node identifies if the lead corresponds ICP. HTTP Request node searches for emails in Datagma. Set node prepares data for further merging. Merge node combines data from multiple streams. Pipedrive node adds a new person in Pipedrive.
By Lucas Perret