Integrate LangChain AI Agent in your LLM apps and 422+ apps and services

This workflow implements a custom tool via JavaScript code which returns a random color to users and excludes the given colors. Note that to use this template, you need to be on n8n version 1.19.4 or later.

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.

This AI agent can access data provided by another n8n workflow. Since that workflow can be used to retrieve any data from any service, this template can be used give an agent access to any data. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow connects Telegram bots with LangChain nodes in n8n. The main AI Agent Node is configured as a Conversation Agent. It has a custom System Prompt which explains the reply formatting and provides some additional instructions. The AI Agent has several connections: OpenAI GPT-4 model is called to generate the replies Window Buffer Memory stores the history of conversation with each user separately There is an additional Custom n8n Workflow tool (Dall-E 3 Tool). AI Agent uses this tool when the user requests an image generation. In the lower part of the workflow, there is a series of nodes that call Dall-E 3 model with the user Telegram ID and a prompt for a new image. Once image is ready, it is sent back to the user. Finally, there is an extra Telegram node that masks HTML syntax for improved stability in case the AI Agent replies using the unsupported format.

This workflow allows you to ask questions about the data in a Google Sheet over a chat interface. It uses n8n's built-in chat, but could be modified to work with Slack, Teams, WhatsApp, etc. Behind the scenes, the workflow uses GPT4, so you'll need to have an OpenAI API key that supports it. How it works The workflow uses an AI agent with custom tools that call a sub-workflow. That sub-workflow reads the Google Sheet and returns information from it. Because models have a context window (and therefore a maximum number of characters they can accept), we can't pass the whole Google Sheet to GPT - at least not for big sheets. So we provide three ways of querying less data, that can be used in combination to answer questions. Those three functions are: List all the columns in the sheet Get all values of a single column Get all values of a single row Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow allows you to ask questions about data stored in a database using AI. To use it, you'll need an OpenAI API key (although you could also swap in a model from another service). Supported databases: Postgres MySQL SQLite The workflow uses n8n's embedded chat, but you could also modify it to work with a chat service such as Slack, MS Teams or WhatsApp. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Use n8n to bring data from any API to your AI. This workflow uses the Chat Trigger to provide the chat interface, and the Custom n8n Workflow Tool to call a second workflow that calls the API. The second workflow uses AI functionality to refine the API request based on the user's query. It then makes an API call, and returns the response to the main workflow. This workflow is used in Advanced AI examples | Call an API to fetch data in the documentation. To use this workflow: Load it into your n8n instance. Add your credentials as prompted by the notes. Requires n8n 1.28.0 or above

This is a workflow that tries to answer user queries using the standard GPT-4 model. If it can't answer, it sends a message to Slack to ask for human help. It prompts the user to supply an email address. This workflow is used in Advanced AI examples | Ask a human in the documentation. To use this workflow: Load it into your n8n instance. Add your credentials as prompted by the notes. Configure the Slack node to use your Slack details, or swap out Slack for a different service.

Use case LLMs have provided a lot of value for several use cases. Especially some OpenAI models are proving to be quite valuable. However, it's sometimes not super accessible to chat with these models. This workflow enables you to chate directly with OpenAI's GPT-3.5 via Telegram. How it works A simple telegram bot that connects to your botfather bot to give AI responses, using OpenAI's GPT 3.5 model, to a user's messages with emojis. What to do Add your telegram API key and your OpenAI api key and have fun!

With this workflow you get a fully automated AI powered Support-Agent for your WooCommerce webshop. It allows customers to request information about things like: the status of their order the ordered products shipping and billing address current DHL shipping status How it works The workflow receives chat messages from an in a website integrated chat. For security and data-privacy reasons, does the website transmit the email address of the user encrypted with the requests. That ensures that user can just request the information about their own orders. An AI agent with a custom tool supplies the needed information. The tool calls a sub-workflow (in this case, in the same workflow for convenience) to retrieve the required information. This includes the full information of past orders plus the shipping information from DHL. If otherr shipping providers are used it should be simple to adjust the workflow to query information from other APIs like UPS, Fedex or others.

This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. 💪 🚀 Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db). 🗣️ Chatting with Your Data Send a message in a chat window. Locally saved SQLite database loads automatically. User's chat input is combined with the binary data. The LangChain Agend node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️ 🌟 Example Queries Try these sample queries to see the AI Agent in action: "Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed. "What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬 Read the full article: 👉 https://blog.n8n.io/ai-agents/

This workflow with AI agent is designed to navigate through the page to retrieve specific type of information (in this example: social media profile links). The agent is equipped with 2 tools: text tool:** to retrieve all the text from the page, URLs tool:** to extract all possible links from the page. 💡 You can edit prompt and JSON schema connected to the agent in order to return other data then social media profile links. 👉 This workflow uses Supabase as storage (input/output). Feel free to change it to any other database of your choice. 🎬 See this workflow in action in my YouTube video. How it works? The workflow uses the input URL (website) as a starting point to retrieve the data (e.g. example.com). Using the "URLs tool", the agent is able to retrieve all links from the page and navigate to them. For example, if you want to retrieve contact information, agent will try to find a subpage that might contain this information (e.g. example.com/contact) and extract the information using the text tool. Set up steps Connect database with input data (website addresses) or pin sample data to trigger node. Configure the crawling agent to retrieve the desired data (e.g. modify prompt and/or parsing schema). Set credentials for OpenAI. Optionally: split agent tools to separate workflows. If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

Who is this for? This workflow is for all sales reps and lead generation manager who need to prepare their prospecting activities, and find relevant information to personalize their outreach. Use Case This workflow allows you to do account research with the web using AI. It has the potential to replace manual work done by sales rep when preparing their prospecting activities by searching complex information available online. What this workflow does The advanced AI module has 2 capabilities: Research Google using SerpAPI Visit and get website content using a sub-workflow From an unstructured input like a domain or a company name. It will return the following properties: domain company Linkedin Url cheapest plan has free trial has entreprise plan has API market (B2B or B2C) The strength of n8n here is that you can adapt this workflow to research whatever information you need. You just have to precise it in the prompt and to precise the output format in the "Strutured Output Parser" module. Detailed instructions + video guide can be found by following this link.

Who is this for? This workflow is for everyone who wants to have easier access to their Odoo sales data without complex queries. Use Case To have a clear overview of your sales data in Odoo you typically needs to extract data from it manually to analyse it. This workflow uses OpenAI's language models to create an intelligent chatbot that provides conversational access to your Odoo sales opportunity data. How it works Creates a summary of all Odoo sales opportunities using OpenAI Uses that summary as context for the OpenAI chat model Keeps the summary up to date using a schedule trigger Set up steps: Configure the Odoo credentials Configure OpenAI credentials Toggle "Make Chat Publicly Available" from the Chat Trigger node.

This n8n workflow takes Slack conversations and turns them into Calendar events complete with accurate date and times and location information. Adding and removing attendees are also managed automatically. How it works Workflow monitors a Slack channel for invite messages with a "📅" reaction and sends this to the AI agent. AI agent parses the message determining the time, date and location. Using its Location tool, the AI agent searches for the precise location address from Google Maps. Using its Calendar tool, the AI agent creates a Google Calendar invite with the title, description and location address for the user. Back in the Slack channel, others can RSVP to the invite by reacting with the "✅" emjoi. The workflow polls the message after a while and adds the users who have reacted to the Calendar Invite as attendees. Conversely, removing any attendees who have since removed their reaction. Examples Jill: "Hey team, I'm organising a round of Laser Tag (Bunker 51) next Thursday around 6pm. Please RSVP with a ✅" AI: "I've helped you create an event in your calendar https://cal.google.com/..." Jack: "✅" AI: "I've added Jack to the event as an attendee". Requirements Slack channel to attach the workflow OpenAI account to use a GPT model Google Calendar to create and update events Customising the Workflow This workflow can work with other messaging platforms that support reactions or tagging like features such as discord. Don't use Google Calendar? Swap it out for Outlook or your own. Use any combinations of emjoi reactions and add new rules like "RSVP maybe" which could send reminder updates nearer the event date.

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites. How it works This workflow scans for the calendar for client or team meetings which were held online. * Attempts will be made to fetch any recorded transcripts which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to to create the event for the time, date and place for the next meeting as well as add known attendees. Requirements Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) OpenAI account for access to the LLM. Customising the workflow This example only books follow-on meetings but could be extended to generate reports or send emails.

This n8n workflow assists property managers and surveyors by reducing the time and effort it takes to complete property inventory surveys. In such surveys, articles and goods within a property may need to be captured and reported as a matter of record. This can take a sizable amount of time if the property or number of items is big enough. Our solution is to delegate this task to a capable AI Agent who can identify and fill out the details of each item automatically. How it works An AirTable Base is used to capture just the image of an item within the property Our workflow monitoring this AirTable Base sends the photo to an AI image recognition model to describe the item for purpose of identification. Our AI agent uses this description and the help of Google's reverse image search in an attempt to find an online product page for the item. If found, the product page is scraped for the item's specifications which are then used to fill out the rest of the details of the item in our Airtable. Requirements Airtable for capturing photos and product information OpenAI account to for image recognition service and AI for agent SerpAPI account for google reverse image search. Firecrawl.dev account for webspacing. Customising this workflow Try building an internal inventory database to query and integrate into the workflow. This could save on costs by avoiding fetching new each time for common items.

This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API. Use this example to build recommendation features in your AI Agents for your users. How it works For our recipes, we'll use HelloFresh's weekly course and recipes for data. We'll scrape the website for this data. Each recipe is split, vectorised and inserted into a Qdrant Collection using Mistral Embeddings Additionally the whole recipe is stored in a SQLite database for later retrieval. Our AI Agent is setup to recommend recipes from our Qdrant vector store. However, instead of the default similarity search, we'll use the Recommendation API instead. Qdrant's Recommendation API allows you to provide a negative prompt; in our case, the user can specify recipes or ingredients to avoid. The AI Agent is now able to suggest a recipe recommendation better suited for the user and increase customer satisfaction. Requirements Qdrant vector store instance to save the recipes Mistral.ai account for embeddings and LLM agent Customising the workflow This workflow can work for a variety of different audiences. Try different sets of data such as clothes, sports shoes, vehicles or even holidays.

This n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts. This assistant can then generate a report to quick flag attention to ensure remedial action is taken. How it works The workflow monitors a local network drive to watch for new bank statements that are added. This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent. The AI Agent analyses the line items to identify the dates and any incoming payments from tenants. The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration. Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts. The final report is generated and logged in the same XLSX for a human to check and action. Requirements A self-hosted version of n8n is required. OpenAI account for the AI model Customising this workflow If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1YRKjfakpInm23F_g8AHupKPBN-fphWgK/view?usp=sharing

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot. In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user. Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries. Example Human: "Tell me about what the tax code says about cargo for intentional commerce?" AI: "Section 11.25 of the Texas Property Tax Code pertains to "MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE." In this section, a person who is a citizen of a foreign country or an en..." How it works The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters. Each chapter is then parsed and split into its sections using data manipulation expressions. Each section is then inserted into our Qdrant vector store tagged with its source, chapter and section numbers as metadata. When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant. Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node. When the AI Agent, needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document. Requirements A Qdrant instance is required for the vector store and specifically for it's filtering functionality. Mistral.ai account for Embeddings and AI models. Customising this workflow Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust. Not using Mistral? You are able to replace but note to match the distance and dimension size of Qdrant collection to your chosen embedding model.

This n8n workflow builds an appointment scheduling AI agent which can Take enquiries from prospective customers and help them book an appointment by checking appointment availability Where no appointment is booked, the Agent is able to send follow-up messages to re-engage leads. After an appointment is booked, the agent is able reschedule or even cancel the booking for the user without human intervention. For small outfits, this workflow could contribute the necessary "man-power" required to increase business sales. The sample Airtable can be found here: https://airtable.com/appO2nHiT9XPuGrjN/shroSFT2yjf87XAox 2024-10-22 Updated to Cal.com API v2. How it works The customer sends an enquiry via SMS to trigger our workflow. For this trigger, we'll use a Twilio webhook. The prospective or existing customer's number is logged in an Airtable Base which we'll be using to track all our enquries. Next, the message is sent to our AI Agent who can reply to the user and decide if an appointment booking can be made. The reply is made via SMS using Twilio. A scheduled trigger which runs every day, checks our chat logs for a list of prospective customers who have yet to book an appointment but still show interest. This list is sent to our AI Agent to formulate a personalised follow-up message to each lead and ask them if they want to continue with the booking. The follow-up interaction is logged so as to not to send too many messages to the customer. Requirements A Twilio account to receive customer messages. An Airtable account and Base to use as our datastore for enquiries. Cal.com account to use as our scheduling service. OpenAI account for our AI model. Customising this workflow Not using Airtable? Swap this out for your CRM of choice such as hubspot or your own service. Not using Cal.com? Swap this out for API-enabled services such as Acuity Scheduling or your own service.

This n8n template showcases the new HTTP tool released in version 1.47.0. Overall, the tool helps simplify AI Agent workflows where custom sub-workflows were performing the same simple http requests. Comparisons 1. AI agent that can scrape webpages Remake of https://n8n.io/workflows/2006-ai-agent-that-can-scrape-webpages/ Changes: Replaces Execute Workflow Tool and Subworkflow Replaces Response Formatting 2. Allow your AI to call an API to fetch data Remake of https://n8n.io/workflows/2094-allow-your-ai-to-call-an-api-to-fetch-data/ Changes: Replaces Execute Workflow Tool and Subworkflow Replaces Manual Query Params Definitions Replaces Response Formatting

This n8n workflow demonstrates a simple approach to improve chat UX by staggering an AI Agent's reply for users who send in a sequence of partial messages and in short bursts. How it works Twilio webhook receives user's messages which are recorded in a message stack powered by Redis. The execution is immediately paused for 5 seconds and then another check is done against the message stack for the latest message. The purpose of this check lets use know if the user is sending more messages or if they are waiting for a reply. The execution is aborted if the latest message on the stack differs from the incoming message and continues if they are the same. For the latter, the agent receives the buffered messages up to that point and is able to respond to them in a single reply. Requirements A Twilio account and SMS-enabled phone number to receive messages. Redis instance for the messages stack. OpenAI account for the language model. Customising the workflow This workflow should work for other common messaging platforms such as Whatsapp and Telegram. 5 seconds too long or too short? Adjust the wait threshold to suit your customers.

This n8n workflow is a fun way to query and search over your credentials on your n8n instance. Good to know Your credentials should remain safe as this workflow does not decrypt or use any decrypted data. Example Usage "Which workflows are using Slack and Google Calendar?" "Which workflows have AI in their name but are not using openAI?" How it works Using the n8n API, it fetches all workflow data on the instance. Workflow data contains references to credentials used so this will be extracted. With some necessary reformatting, the workflows and their credentials metadata are stored to a SQLite database. Next, an AI agent is used with a custom SQL tool that reads the SQLite database created in the previous step. The AI agent is instructed to perform SQL queries against our workflow credential table when asked about credentials by the user. Requirements You'll need an n8n API key. Please note that only workflows will be scoped to your API key. Customising the workflow Add extra table fields to the SQLite database to answer even more complex queries such as: workflow status to differentiate between active and inactive workflows.

This n8n workflow demonstrates a simple multi-agent setup to perform the task of competitor research. It showcases how using the HTTP request tool could reduce the number of nodes needed to achieve a workflow like this. How it works For this template, a source company is defined by the user which is sent to Exa.ai to find competitors. Each competitor is then funnelled through 3 AI agents that will go out onto the internet and retrieve specific datapoints about the competitor; company overview, product offering and customer reviews. Once the agents are finished, the results are compiled into a report which is then inserted in a notion database. Check out an example output here: https://jimleuk.notion.site/2d1c3c726e8e42f3aecec6338fd24333?v=de020fa196f34cdeb676daaeae44e110&pvs=4 Requirements An OpenAI account for the LLM. Exa.ai account for access to their AI search engine. SerpAPI account for Google search. Firecrawl.dev account for webscraping. Notion.com account for database to save final reports. Customising the workflow Add additional agents to gather more datapoints such as SEO keywords and metrics. Not using notion? Feel free to swap this out for your own database.

This workflow was presented at the AI Developers meet up in San Fransico on 24 July, 2024. AI workflows Categorize incoming Gmail emails and assign custom Gmail labels. This example uses the Text Classifier node, simplifying this usecase. Ingest a PDF into a Pinecone vector store and chat with it (RAG example) AI Agent example showcasing the HTTP Request tool. We teach the agent how to check availability on a Google Calendar and book an appointment.

This is an example of how we can build a slack bot in a few easy steps Before you can start, you need to o a few things Create a copy of this workflow Create a slack bot Create a slash command on slack and paste the webhook url to the slack command Note Make sure to configure this webhook using a https:// wrapper and don't use the default http://localhost:5678 as that will not be recognized by your slack webhook. Once the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent. To have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history. The final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create a new message with reference to the input we passed. We can advance this using the tools or data sources for it to be more custom tailored for your company. Usage To use the slackbot, go to slack and click on your set slash command eg /Bob and send your desired message. This will send the message to your endpoint and get return the processed results as the message. If you would like help setting this up, feel free to reach out to zacharia@effibotics.com

Purpose This workflow automatically creates Tasks from forwarded Emails, similar to Asana, but better. Emails are processed by AI and converted to rather actionable task. In addition this workflow is build in a way, that multiple users can share this single process by setting up their individual configuration through a user friendly portal (internal tool) instead of the need to manage their own workflows. Demo How it works One Gmail account is used to process inbound mails from different users. A custom web portal enables users to define “routes”. Thats where the mapping between an automatically generated Gmail Alias and a Notion Database URL, including the personal API Token, happens. Using a Gmail Trigger, new entries are split by the Email Alias, so the corresponding route can be retrieved from the Database connected to the portal. Every Email then gets processed by AI to get generate an actionable task and get a short summary of the original Email as well as some metadata. Based on a predefined structure a new Page is created in the corresponding Notion Database. Finally the Email is marked as “processed” in Gmail. If an error happens, the route gets paused for a possible overflow and the user gets notified by Email. Setup Create a new Google account (alternatively you can use an existing one and set up rules to keep your inbox organized) Create two Labels in Gmail: “Processed” and “Error” Clone this Softr template including the Airtable dataset and publish the application Clone this workflow and choose credentials (Gmail, Airtable) Follow the additional instructions provided within the workflow notes Enable the workflow, so it runs automatically in the background How to use Open published Softr application Register as a new user Create a new route containing the Notion API key and the Notion Database URL Expand the new entry to copy the Email address Save the address as a new contact in your Email provider of choice Forward an Email to it and watch how it gets converted to an actionable task Disclamer Airtable was chosen, so you can setup this template fairly quickly. It is advised to replace the persistence by something you own, like a self hosted SQL server, since we are dealing with sensitive information of multiple users This solution is only meant for building internal tools, unless you own an embed license for n8n.

Video Demo: Click here to see a video of this workflow in action. Summary Description: The "IT Department Q&A Workflow" is designed to streamline and automate the process of handling IT-related inquiries from employees through Slack. When an employee sends a direct message (DM) to the IT department's Slack channel, the workflow is triggered. The initial step involves the "Receive DMs" node, which listens for new messages. Upon receiving a message, the workflow verifies the webhook by responding to Slack's challenge request, ensuring that the communication channel is active and secure. Once the webhook is verified, the workflow checks if the message sender is a bot using the "Check if Bot" node. If the sender is identified as a bot, the workflow terminates the process to avoid unnecessary actions. If the sender is a human, the workflow sends an acknowledgment message back to the user, confirming that their query is being processed. This is achieved through the "Send Initial Message" node, which posts a simple message like "On it!" to the user's Slack channel. The core functionality of the workflow is powered by the "AI Agent" node, which utilizes the OpenAI GPT-4 model to interpret and respond to the user's query. This AI-driven node processes the text of the received message, generating an appropriate response based on the context and information available. To maintain conversation context, the "Window Buffer Memory" node stores the last five messages from each user, ensuring that the AI agent can provide coherent and contextually relevant answers. Additionally, the workflow includes a custom Knowledge Base (KB) tool (see that tool template here) that integrates with the AI agent, allowing it to search the company's internal KB for relevant information. After generating the response, the workflow cleans up the initial acknowledgment message using the "Delete Initial Message" node to keep the conversation thread clean. Finally, the generated response is sent back to the user via the "Send Message" node, providing them with the information or assistance they requested. This workflow effectively automates the IT support process, reducing response times and improving efficiency. To quickly deploy the Knowledge Ninja app in Slack, use the app manifest below and don't forget to replace the two sample urls: { "display_information": { "name": "Knowledge Ninja", "description": "IT Department Q&A Workflow", "background_color": "#005e5e" }, "features": { "bot_user": { "display_name": "IT Ops AI SlackBot Workflow", "always_online": true } }, "oauth_config": { "redirect_urls": [ "Replace everything inside the double quotes with your slack redirect oauth url, for example: https://n8n.domain.com/rest/oauth2-credential/callback" ], "scopes": { "user": [ "search:read" ], "bot": [ "chat:write", "chat:write.customize", "groups:history", "groups:read", "groups:write", "groups:write.invites", "groups:write.topic", "im:history", "im:read", "im:write", "mpim:history", "mpim:read", "mpim:write", "mpim:write.topic", "usergroups:read", "usergroups:write", "users:write", "channels:history" ] } }, "settings": { "event_subscriptions": { "request_url": "Replace everything inside the double quotes with your workflow webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad", "bot_events": [ "message.im" ] }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } }

This workflow is an experiment to integrate charts in AI Agents, using the new Structured Output from OpenAI and Quickchart.io. How it works Users chat with an AI Agent. Anytime the AI Agent considers a chart is needed, it calls a tool to generate a chart OpenAI generates a chart using the Quickchart definition This object is added at the end of a Quickchart.io URL (see documentation) The url is added in the conversation via the AI Agent as markdown. Set up steps Create an OpenAI API Key Create the OpenAI credentials Use the credentials for the HTTP Request node (as Predefined Credential type) Activate your workflow Start chatting For example, you can ask the AI Agent to generate a chart about the top 5 movies at the box office Start exploring the limits Shout-out Quickchart.io is an amazing open source project that provides a free API to test. Go check them out! Example of chart

Who is this for This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup. Check out the Notion template this Assistant is set up to use: https://www.notion.so/templates/knowledge-base-ai-assistant-with-n8n How it works The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or question and retrieves content from inside individual Notion pages for additional context. Key features include: Querying the database with flexible filters. Searching within individual Notion pages and extracting relevant blocks. Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification. This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets. Set up Find basic set up instructions inside the workflow itself or watch a quickstart video 👇

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

This template demonstrates how to trigger an AI Agent with Siri and Apple Shortcuts, showing a simple pattern for voice-activated workflows in n8n. It's easy to customize—add app nodes before the AI Agent step to pass additional context, or modify the Apple Shortcut to send inputs like text, geolocation, images, or files. Set Up Basic instructions in template itself. Requirements n8n account** (cloud or self-hosted) Apple Shortcuts app** on iOS or macOS. Dictation ("Siri") must be activated. Download the Shortcuts template here. Key Features: Voice-Controlled AI:** Trigger AI Agent via Siri for real-time voice replies. Customizable Inputs:** Modify Apple Shortcut to send text, images, geolocation, and more. Flexible Outputs:** Siri can return the AI’s response as text, files, or customize it to trigger CRUD actions in connected apps. Context-Aware:** Automatically feeds the current date and time to the AI Agent, with easy options to pass in more data. How It Works: Activate Siri and speak your request. Siri sends the transcribed text to the n8n workflow via Apple Shortcuts. AI Agent processes the request and generates a response. Siri reads the response, or the workflow can return geolocation, files, or even perform CRUD actions in apps. Inspiration: Custom Use Cases Tweak this template and make it your own. Capture Business Cards:** Snap a photo of a business card and record a voice note. Have the AI Agent draft a follow-up email in Gmail, ready to send. Voice-to-Task Automation:** Speak a new to-do item, and the workflow will add it to a Notion task board. Business English on the Fly:** Convert casual speech into polished business language, and save the refined text directly to your pasteboard, ready to be pasted into any app. "It's late because of you" -> "There has been a delay, and I believe your input may have contributed to it."

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

Purpose Use a lightweight Voice Interface, for you and your entire organization, to interact with an AI Supervisor, a personal AI Assistant, which has access to your custom workflows. You can also connect the supervisor to your already existing Agents. Demo & Explanation How it works After recording a message in the Vagent App, it gets transcribed and sent in combination with a session ID to the registered webhook The Main Agent acts as a router. I interprets the message while using the stored chat history (bound to the session ID) and chooses which tool to use to perform the required action and. Tools on this level are workflows, which contain subordinated Agents. Since the Main Agent interprets the original message, the raw input is passed to the Tools/Sub-Agents as a separate parameter Within the Sub-Agents the actual processing takes place. Each of those has it’s separate chat memory (with a suffix to the main session ID), to achieve a clear separation of concerns Depending on the required action an HTTP Request Tool is called. The result is being formatted in Markdown and returned to the Main Agent with an additional short prompt, so it does not get interpreted by the Main Agent. Drafts are separated from a short message by added indentation (angle brackets). If some information is missing, no tool is called just yet, instead a message is returned back to the user The Main Agent then outputs the result from the called Sub-Agent. If a draft is included, it gets separated from the spoken output Finally the formatted output is returned as response to the webhook. The message is split into a spoken and a text version, which enables the App to read out loud unnecessary information like drafts in this example See the full documentation of Vagent: https://vagent.io/docs Setup Import this workflow into your n8n instance Follow the instructions given in the sticky notes on the canvas Setup your credentials. OpenAI can be replaced by another LLM in the workflow, but is required for the App to work. Google Calendar and Notion are required for all scenarios to work Copy the Webhook URL from the Webhook node of the main workflow Download the Vagent App from https://vagent.io In the settings paste your OpenAI API Token, the Webhook URL and the password defined for Header Auth Now you can use the App to interact with the Multi-Agent using your Voice by tapping the Mic symbol in the App to record your message. To use the chat trigger (for testing) properly, temporarily disable the nodes after the Tools Agent.

Automate your email management with this workflow, designed for freelancers and business professionals who receive high volumes of emails. By leveraging AI-powered categorisation and dynamic email processing, this template helps you organise your inbox and streamline communication for better efficiency and productivity. Check out the YouTube video for step-by-step set up instructions! How it works: Fetch & Filter Emails: The workflow retrieves emails from your Microsoft Outlook account, filtering out flagged emails and those already categorised. Content Preparation: Each email is cleaned up and converted to a structured format using Markdown, making it easier for AI processing. AI Categorization: The content is analysed using an AI model, which categorises the emails into predefined categories (e.g., Action, Junk, Business, SaaS) based on the context and content. Email Categorization & Folder Management: The categorised emails are updated in Microsoft Outlook and moved to respective folders such as "Junk Email" or "Receipts" based on the AI's classification. Conditional Processing & Final Checks: Additional checks and conditions ensure that only unread emails are processed, and errors are gracefully managed to maintain workflow stability. Set up steps: Connect Microsoft Outlook: Link your Microsoft Outlook account using the built-in credentials node to enable email fetching, updating, and folder management. Configure AI Model (Ollama API): Set up the AI model by connecting to the Ollama API and choosing your desired language model for categorisation. Modify Email Categories (Optional): Customize the categories and subcategories within the workflow to suit your unique email management needs. Set Up Error Handling: Review the error handling node settings to ensure smooth workflow execution. This template offers a robust solution for managing and organising your inbox, helping you save time and keep your focus on important emails.

Purpose of workflow: The purpose of this workflow is to automatically transform complex, lengthy PDFs into concise, insightful blog posts. This process aims to make dense information more accessible and understandable to a broader audience. How it works: Multi-agent team: Working together to transform PDF to insightful blog Content Analyst: Extracts key information and structure from the PDF. Project Planner: Organizes the workflow and ensures efficient processing. Writers: Multiple agents that craft engaging content based on the analyzed information. Editor: Polishes the final output for accuracy, reliability, and readability. Step by step User inputs parameters via a n8n form (tone, word count, sections, PDF file). System extracts text from the PDF. Content Analyst AI agent analyzes the extracted text, identifying main topics and key points. Project Planner AI agent organizes the workflow, creating a structure for the blog post. Multiple Writer AI agents (based on the number of sections specified) each write a portion of the blog post. Editor AI agent combines all sections and polishes the final draft. Generates a title for the blog post. Final content is uploaded to a publishing Ghost platform

Purpose of workflow: The purpose of this workflow is to create a news reporter AI agent that automatically monitors specific news events and sends summary emails to the user. This tool aims to keep users up-to-date with the latest happenings on topics of interest without the need to constantly check multiple news sources manually. It's particularly useful for investors or researchers who must stay informed about specific events that may impact their work or investments. How it works: It leverages the power of large language models, specifically Perplexity's (perplexity.ai) online models accessed through Open Router (openrouter.ai), to understand and summarize up-to-date news. The workflow is scheduled to run at predetermined intervals (e.g., daily at 7 AM), automatically scanning for news on the specified topic, generating a summary, and sending it via email to the user. Setup: Sign up and generate an API key from Openrouter.ai This provides access to Perplexity's online language models Update Perplexity node with Openrouter.ai API key Specify the event/topic to monitor in the News Reporter node Activate workflow to turn on scheduling feature

How it works: This project creates a personal AI assistant named Angie that operates through Telegram. Angie can summarize daily emails, look up calendar entries, remind users of upcoming tasks, and retrieve contact information. The assistant can interact with users via both voice and text inputs. Step-by-step: Telegram Trigger: The workflow starts with a Telegram trigger that listens for incoming message events. The system determines if the incoming message is voice or text. If voice, the voice file is retrieved and transcribed to text using OpenAI's API Speech to Text AI Assistant: The telegram request is passed to the AI assistant (Angie). Tools Integration: The AI assistant is equipped with several tools: Get Email: Uses Gmail API to fetch recent emails, filtering by date. Get Calendar: Retrieves calendar entries for specified dates. Get Tasks: Connects to a Baserow (open-source Airtable alternative) database to fetch to-do list items. Get Contacts: Also uses Baserow to retrieve contact information. Response Generation: The AI formulates a response based on the gathered information and sends back to the user on Telegram

This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations**: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens**: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company**: The name of the company Website**: The website URL of the company These two fields are required at this step. Business Area**: The business area deduced by OpenAI from the scraped data Offer**: The offer deduced by OpenAI from the scraped data Value Proposition**: The value proposition deduced by OpenAI from the scraped data Business Model**: The business model deduced by OpenAI from the scraped data ICP**: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information**: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

This n8n template demonstrates the beginnings of building your own n8n-powered WhatsApp chatbot! Under the hood, utilise n8n's powerful AI features to handle different message types and use an AI agent to respond to the user. A powerful tool for any use-case! How it works Incoming WhatsApp Trigger provides a way to get messages into the workflow. The message received is extracted and sent through 1 of 4 branches for processing. Each processing branch uses AI to analyse, summarize or transcribe the message so that the AI agent can understand it. The supported types are text, image, audio (voice notes) and video. The AI Agent is used to generate a response generally and uses a wikipedia tool for more complex queries. Finally, the response message is sent back to the WhatsApp user using the WhatsApp node. How to use Once you have setup and configured your WhatsApp account, you'll need to activate your workflow to start processing messages. Good to know: Large media files may negatively impact workflow performance. Requirements WhatsApp Buisness account Google Gemini for LLM. Gemini is used specifically because it can accept audio and video files whereas at time of writing, many other providers like OpenAI's GPT, do not. Customising this workflow For performance reasons, consider detecting large audio and video before sending to the LLM. Pre-processing such files may allow your agent to perform better. Go beyond and create rich and engagement customer experiences by responding using images, audio and video instead of just text!

This n8n template is designed to assist and improve customer support team member capacity by automating the resolution of long-lived and forgotten JIRA issues. How it works Schedule Trigger runs daily to check for long-lived unresolved issues and imports them into the workflow. Each Issue is handled as a separate subworkflow by using an execute workflow node. This allows parallel processing. A report is generated from the issue using its comment history allowing the issue to be classified by AI - determining the state and progress of the issue. If determined to be resolved, sentiment analysis is performed to track customer satisfaction. If negative, a slack message is sent to escalate, otherwise the issue is closed automatically. If no response has been initiated, an AI agent will attempt to search and resolve the issue itself using similar resolved issues or from the notion database. If a solution is found, it is posted to the issue and closed. If the issue is blocked and waiting for responses, then a reminder message is added. How to use This template searches for JIRA issues which are older than 7 days which are not in the "Done" status. Ensure there are some issues that meet this criteria otherwise adjust the search query to suit. Works best if you frequently have long-lived issues that need resolving. Ensure the notion tool is configured as to not read documents you didn't intend it to ie. private and/or internal documentation. Requirements JIRA for issues management OpenAI for LLM Slack for notifications Customising this workflow Why not try classifying issues as they are created? One use-case may be for quality control such as ensuring reporting criteria is adhered to, summarising and rephrasing issue for easier reading or adjusting priority.

What this workflow does: This flow uses an AI node to generate Seed Keywords to focus SEO efforts on based on your ideal customer profile. You can use these keywords to form part of your SEO strategy. Outputs: List of 20 Seed Keywords Setup Fill the Set Ideal Customer Profile (ICP) Connect with your credentials Replace the Connect to your own database with your own database Pre-requisites / Dependencies You know your ideal customer profile (ICP) An AI API account (either OpenAI or Anthropic recommended) More templates and n8n workflows >>> @simonscrapes

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: Yes: the AI Agent passes the SQL query to the next MySQL node for execution. No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: Would you please list me all customers from Germany? What are the music genres in the database? What tables are available in the database? Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

📈 Daily Financial News - Description This workflow automates the process of collecting, organizing, and delivering a daily summary of financial news by following these key steps: Scheduled Activation The workflow starts at 7:00 AM each day, triggered by the Schedule Trigger node. News Retrieval The HTTP Request node fetches the latest financial news from FT.com. Content Extraction The Extract Specific Content node scrapes targeted sections of the HTML (headlines, editor's picks, top stories, etc.) using CSS selectors to locate and capture relevant content. News Aggregation The Set Node gathers and organizes the extracted news data, preparing it for summarization. Categories like "Headline #1," "Editor's Picks," and "Europe News" are all structured into a single data block. Summarization An AI Agent (Google Gemini) takes the aggregated news data and creates a concise, HTML-formatted summary tailored to give investors an insightful market snapshot. Email Delivery Finally, the Microsoft Outlook node sends the summary via email to the designated recipient with the subject "Financial news from today." This process ensures that financial news is efficiently curated, summarized, and delivered without manual intervention.

From PDF to Powerful Blog Posts: AI-Powered Content Transformation Turn complex documents into engaging digital content that drives results. This n8n Workflow uses AI to transforms lengthy PDFs into compelling blog posts that attract and retain readers while you focus on strategic initiatives. Time-Saving Innovation 🚅Lightning-Fast Processing Transform lengthy documents into polished blog content in under 1 minute, eliminating hours of manual work. Our system handles the heavy lifting, delivering up to a 95% reduction in content production time. 📱Intelligent Analysis The AI engine identifies and extracts key insights, organizing information for maximum impact. Each document undergoes comprehensive analysis to ensure no valuable content is overlooked. Advanced Content Optimization ✍️Dynamic Writing Styles Possible Adjust the prompt for multiple tone options: Professional for corporate communications Conversational for engaging blogs Thought leadership for industry authority 📊SEO-Ready Content Potential Adjust the prompt to automatically optimized for search engines, incorporating relevant keywords and semantic structure to improve visibility and drive organic traffic. Ideal Applications 🤼Content Marketing Teams Scale content production without sacrificing quality or consistency. Perfect for teams looking to maintain a robust publishing schedule while maximizing resource efficiency. 🏫Academic Communication Help researchers and institutions share complex findings with broader audiences through accessible, engaging content that maintains academic integrity. 🧑‍💻Digital Publishers Streamline the content transformation process while ensuring each piece meets modern digital standards and reader expectations. Transform your content strategy with an intelligent system that delivers consistent, high-quality results while dramatically reducing production time.

Free template for voice & text messages with short-term memory This n8n workflow template is a blueprint for an AI Telegram bot that processes both voice and text messages. Ready to use with minimal setup. The bot remembers the last several messages (10 by default), understands commands and provides responses in HTML. You can easily swap GPT-4 and Whisper for other language and speech-to-text models to suit your needs. Core Features Text: send or forward messages Voice: transcription via Whisper Extend this template by adding LangChain tools. Requirements Telegram Bot API OpenAI API (for GPT-4 and Whisper) 💡 New to Telegram bots? Check our step-by-step guide on creating your first bot and setting up OpenAI access. Use Cases Personal AI assistant Customer support automation Knowledge base interface Integration hub for services that you use: Connect to any API via HTTP Request Tool Trigger other n8n workflows with Workflow Tool

Edit 19/11/2024: As explained on the workflow, the AI Agent with the original system prompt was not effective when using gpt4-o-mini. To address this, I optimized the prompt to work better with this model. You can find the prompts I’ve tested on this Notion Page. And yes, there is one that works well with gpt4-o-mini. AI Agent to chat with you Search Console Data, using OpenAI and Postgres This AI Agent enables you to interact with your Search Console data through a chat interface. Each node is documented within the template, providing sufficient information for setup and usage. You will also need to configure Search Console OAuth credentials. Follow this n8n documentation to set up the OAuth credentials. Important Notes Correctly Configure Scopes for Search Console API Calls It’s essential to configure the scopes correctly in your Google Search Console API OAuth2 credentials. Incorrect configuration can cause issues with the refresh token, requiring frequent reconnections. Below is the configuration I use to avoid constant re-authentication: Of course, you'll need to add your client_id and client_secret from the Google Cloud Platform app you created to access your Search Console data. Configure Authentication for the Webhook Since the webhook will be publicly accessible, don’t forget to set up authentication. I’ve used Basic Auth, but feel free to choose the method that best meets your security requirements. 🤩💖 Example of awesome things you can do with this AI Agent

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

Hacker News to Video Content Overview This workflow converts trending articles from Hacker News into engaging video content. It integrates AI-based tools to analyze, summarize, and generate multimedia content, making it ideal for content creators, educators, and marketers. Features Article Retrieval: Pulls trending articles from Hacker News. Limits the number of articles to process (configurable). Content Analysis: Uses OpenAI's GPT model to: Summarize articles. Assess their relevance to specific topics like automation or AI. Extract key image URLs. Image and Video Generation: Leonardo.ai: Creates stunning AI-generated images based on extracted prompts. RunwayML: Converts images into high-quality videos. Structured Content Creation: Parses content into structured formats for easy reuse. Generates newsletter-friendly blurbs and social media-ready captions. Cloud Integration: Uploads generated assets to: Dropbox Google Drive Microsoft OneDrive MinIO Social Media Posting (Optional): Supports posting to YouTube, X (Twitter), LinkedIn, and Instagram. Workflow Steps 1. Trigger Initiated manually via the "Test Workflow" button. 2. Fetch Articles Retrieves articles from Hacker News. Limits the results to avoid processing overload. 3. Content Filtering Evaluates if articles are related to AI/Automation using OpenAI's language model. 4. Image and Video Generation Generates: AI-driven image prompts via Leonardo.ai. Videos using RunwayML. 5. Asset Management Saves the output to cloud storage services or uploads directly to social media platforms. Prerequisites API Keys**: Hacker News OpenAI Leonardo.ai RunwayML Creatomate n8n Installation**: Ensure n8n is installed and configured locally or on a server. Credentials**: Set up credentials in n8n for all external services used in the workflow. Customization Replace Hacker News with any other data source node if needed. Configure the "Article Analysis" node for different topics. Adjust the cloud storage services or add custom storage options. Usage Import this workflow into your n8n instance. Configure your API credentials. Trigger the workflow manually or schedule it as needed. Check the outputs in your preferred cloud storage or social media platform. Notes Extend this workflow further by automating social media posting or newsletter integration. For any questions, refer to the official documentation or reach out to the creator. About the Creator This workflow was built by AlexK1919, an AI-native workflow automation architect. Check out the overview video for a quick demo. Tools Used Leonardo.ai** RunwayML** Creatomate** Hacker News API** OpenAI GPT** Feel free to adapt and extend this workflow to meet your specific needs! 🎉

Overview This workflow aims to provide data visualization capabilities to a native SQL Agent. Together, they can help foster data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds visualization capabilities thanks to OpenAI’s Structured Output and Quickchart.io. How it works Information Extraction: The Information Extractor identifies and extracts the user's question. If the question includes a visualization aspect, the SQL Agent alone may not respond accurately. SQL Querying: It leverages a regular SQL Agent: it connects to a database, queries it, and translates the response into a human-readable format. Chart Decision: The Text Classifier determines whether the user would benefit from a chart to support the SQL Agent's response. Chart Generation: If a chart is needed, the sub-workflow dynamically generates a chart and appends it to the SQL Agent’s response. If not, the SQL Agent’s response is output as is. Calling OpenAI for Chart Definition: The sub-workflow calls OpenAI via the HTTP Request node to retrieve a chart definition. Building and Returning the Chart: In the "Set Response" node, the chart definition is appended to a Quickchart.io URL, generating the final chart image. The AI Agent returns the response along with the chart. How to use it Use an existing database or create a new one. For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. Add the PostgreSQL or MySQL credentials. Alternatively, you can use SQLite binary files (check this template). Activate the workflow. Start chatting with the AI SQL Agent. If the Text Classifier determines a chart would be useful, it will generate one in addition to the SQL Agent's response. Notes The full Quickchart.io specifications have not been fully integrated, so there may be some glitches (e.g., radar graphs may not display properly due to size limitations).

Important Notes: Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! Workflow Description: 😮‍💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping ? This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured. This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy. Key Features: Google Sheets Integration**: Manage URLs to scrape and store structured results. ScrapingBee**: Capture full-page screenshots and retrieve HTML data for fallback extraction. AI-Powered Data Parsing**: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON. Token Efficiency**: HTML is converted to Markdown to optimize processing costs. This template is designed for e-commerce scraping but can be customized for various use cases.

This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.

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.

Time Logging on Clockify Using Slack How it works This workflow simplifies time tracking for teams and agencies by integrating Slack with Clockify. It enables users to log, update, or delete time entries directly within Slack, leveraging an AI-powered assistant for seamless and conversational interactions. Key features include: Effortless Time Logging**: Create and manage time entries in Clockify without leaving Slack. AI-Powered Assistant**: Get step-by-step guidance to ensure accurate and efficient time logging. Project and Client Management**: Retrieve project and client information from Clockify effortlessly. Overlap Prevention**: Avoid overlapping entries with built-in time validation. Automated Descriptions**: Generate ethical, grammatically correct descriptions for time logs. Set up steps 1. Prepare your integrations Ensure you have active accounts for both Slack and Clockify. Generate your Clockify API credentials for integration. 2. Import the workflow Download and import the workflow template into your n8n instance. Configure the workflow to connect with your Slack and Clockify accounts. 3. Configure the workflow Add your Clockify API credentials in the workflow settings. Set up the Slack Trigger to listen for app mentions or specific commands. 4. Test the workflow Use Slack to create a time entry and verify it in Clockify. Test updating and deleting existing entries to ensure smooth functionality. Check for any overlapping time logs or incorrect data entries. Why use this workflow? Efficiency**: Eliminate the need to switch between tools for time tracking. Accuracy**: AI-driven validation ensures error-free entries. Automation**: Simplify repetitive tasks like updating or deleting time logs. Proactive Guidance**: Conversational assistant ensures smooth operations.

Youtube Video This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates: Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one. AI Agent (Language Model): The central node utilizes a GPT-based AI agent to: Understand the user's niche or content preferences. Generate tailored search terms related to the niche. Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments). YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates. Video Details Retrieval: The workflow fetches additional details for each video: Video Snippet: Metadata like title, description, and tags. Video Statistics: Metrics such as views, likes, and comments. Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed. Data Processing: Video metadata is cleaned, sanitized, and stored in memory. Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos. Output: The workflow compiles insights and presents them to the user, highlighting: The most common themes or patterns within the niche. URLs to trending videos and their respective channels. Engagement statistics, helping the user understand the popularity of the content. Key Notes for Setup: API Keys**: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes. Memory Buffer**: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights. Search Term Customization**: The AI agent dynamically creates search terms based on the user’s niche to improve search precision. Use Case: This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience. Example Output: For the niche "digital marketing": Trending Topic: Videos about "mental triggers" and "psychological marketing." Tags: "SEO," "Conversion Rates," "Social Proof." Engagement: Videos with over 200K views and high likes/comment ratios are leading trends. Video links: https://www.youtube.com/watch?v=video_id1 https://www.youtube.com/watch?v=video_id2

Purpose of workflow: This AI-powered workflow is designed to automatically generate comprehensive, well-researched articles on any given topic. It utilizes a team of AI agents to streamline the research and writing process, producing high-quality content with proper citations and credible sources. How it works Multi-agent team: Research Leader: Plans and conducts initial research, creating a table of contents. Project Planner: Breaks down the table of contents into manageable sections. Research Assistants: Multiple agents that conduct in-depth research on assigned sections. Editor: Compiles and refines the final article, ensuring coherence and proper citations. Key features: Utilizes Perplexity AI for internet search and citation capabilities Produces well-structured articles with proper citations Customizable parameters (topic, tone, word count, number of sections) Step by step setup: Get account from OpenRouter.ai to access Perplexity API Set API key in the Perplexity API node Credential key name : Authorization and key value Bearer <api-key value>

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for developers, data analysts, and business owners who want to enable conversational interactions with their database. It’s particularly useful for cases where users need to extract, analyze, or aggregate data without writing SQL queries manually. What problem does this workflow solve? Accessing and analyzing database data often requires SQL expertise or dedicated reports, which can be time-consuming. This workflow empowers users to interact with a database conversationally through an AI-powered agent. It dynamically generates SQL queries based on user requests, streamlining data retrieval and analysis. What this workflow does This workflow integrates OpenAI with a Supabase database, enabling users to interact with their data via an AI agent. The agent can: Retrieve records from the database. Extract and analyze JSON data stored in tables. Provide summaries, aggregations, or specific data points based on user queries. Dynamic SQL Querying: The agent uses user prompts to create and execute SQL queries on the database. Understand JSON Structure: The workflow identifies JSON schema from sample records, enabling the agent to parse and analyze JSON fields effectively. Database Schema Exploration: It provides the agent with tools to retrieve table structures, column details, and relationships for precise query generation. Setup Preparation Create Accounts: N8N: For workflow automation. Supabase: For database hosting and management. OpenAI: For building the conversational AI agent. Configure Database Connection: Set up a PostgreSQL database in Supabase. Use appropriate credentials (username, password, host, and database name) in your workflow. N8N Workflow AI agent with tools: Code Tool: Execute SQL queries based on user input. Database Schema Tool: Retrieve a list of all tables in the database. Use a predefined SQL query to fetch table definitions, including column names, types, and references. Table Definition: Retrieve a list of columns with types for one table.

I am submitting this workflow for the Obsidian community to showcase the potential of integrating Obsidian with n8n. While straightforward, it serves as a compelling demonstration of the potential unlocked by integrating Obsidian with n8n. How it works This workflow lets you retrieve specific Airtable data you need in seconds, directly within your Obsidian note, using n8n. By highlighting a question in Obsidian and sending it to a webhook via the Post Webhook Plugin, you can fetch specific data from your Airtable base and instantly insert the response back into your note. The workflow leverages OpenAI’s GPT model to interpret your query, extract relevant data from Airtable, and format the result for seamless integration into your note. Set up steps Install the Post Webhook Plugin: Add this plugin to your Obsidian vault from the plugin store or GitHub. Set up the n8n Webhook: Copy the webhook URL generated in this workflow and insert it into the Post Webhook Plugin's settings in Obsidian. Configure Airtable Access: Link your Airtable account and specify the desired base and table to pull data from. Test the Workflow: Highlight a question in your Obsidian note, use the “Send Selection to Webhook” command, and verify that data is returned as expected.

This n8n template shows how anyone can build a simple newsletter-like subscription service where users can enrol themselves to receive messages/content on a regular basis. It uses n8n forms for data capture, Airtable for database, AI for content generation and Gmail for email sending. How it works An n8n form is setup up to allow users to subscribe with a desired topic and interval of which to recieve messages via n8n forms which is then added to the Airtable. A scheduled trigger is executed every morning and searches for subscribers to send messages for based on their desired intervals. Once found, Subscribers are sent to a subworkflow which performs the text content generation via an AI agent and also uses a vision model to generate an image. Both are attached to an email which is sent to the subscriber. This email also includes an unsubscribe link. The unsubscribe flow works similarly via n8n form interface which when submitted disables further scheduled emails to the user. How to use Make a copy of sample Airtable here: https://airtable.com/appL3dptT6ZTSzY9v/shrLukHafy5bwDRfD Make sure the workflow is "activated" and the forms are available and reachable by your audience. Requirements Airtable for Database OpenAI for LLM (but compatible with others) Gmail for Email (but can be replaced with others) Customising this workflow This simple use can be extended to deliver any types of content such as your company newsletter, promotions, social media posts etc. Doesn't have to be limited to just email - try social messaging, Whatsapp, Telegram and others.

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.

Video Guide I prepared a detailed guide to help you set up your workflow effectively, enabling you to extract insights from YouTube for content generation using an AI agent. Youtube Link Who is this for? This workflow is ideal for content creators, marketers, and analysts looking to enhance their YouTube strategies through data-driven insights. It’s particularly beneficial for individuals wanting to understand audience preferences and improve their video content. What problem does this workflow solve? Navigating the content generation and optimization process can be complex, especially without significant audience insight. This workflow automates insights extraction from YouTube videos and comments, empowering users to create more engaging and relevant content effectively. What this workflow does The workflow integrates various APIs to gather insights from YouTube videos, enabling automated commentary analysis, video transcription, and thumbnail evaluation. The main functionalities include: Extracting user preferences from comments. Transcribing video content for enhanced understanding. Analyzing thumbnails via AI for maximum viewer engagement insights. AI Insights Extraction: Automatically pulls comments and metrics from selected YouTube creators to evaluate trends and gaps. Dynamic Video Planning: Uses transcriptions to help creators outline video scripts and topics based on audience interest. Thumbnail Assessment: Provides analysis on thumbnail designs to improve click-through rates and viewer attraction. Setup N8N Workflow API Setup: Create a Google Cloud project and enable the YouTube Data API. Generate an API key to be included in your workflow requests. YouTube Creator and Video Selection: Start by defining a request to identify top creators based on their video views. Capture the YouTube video IDs for further analysis of comments and other video metrics. Comment Analysis: Gather comments associated with the selected videos and analyze them for user insights. Video Transcription: Utilize the insights from transcriptions to formulate content plans. Thumbnail Analysis: Evaluate your video thumbnails by submitting the URL through the OpenAI API to gain insights into their effectiveness.

Instructions This automation enables you to just upload any Image (via Form) of a Logo Sheet, containing multiple Images of Product Logos (most likely) which brings them in some context to one another. After submitting an AI-Agent eats that Logo Sheet, turning it into an List of "Productname" and "Attributes", also checks if Tools are kind of similar to another, given the Context of the Image. We utilize AI Vision capabilities for that. NOTE: It might not be able to extract all informations. For a "upload and forget it" Workflow it works for me. You can even run it multiple times, to be sure. But if you need to make sure it extracts everything you might need to think about an Multi-Agent Setup with Validation-Agent Steps. Once the Agent finishes the extraction, it will traditionally and deterministicly add those Attributes to Airtable (Creates those, if not already existing.) and also Upserts the Tool Informations. It uses MD5 Hashes for turning Product Names into.. something fancy really, you could also use it without that, but I wanted to have something that looks atleast like an ID. Setup Set Up the Airtable like shown below. Update and set Credentials for all Airtable Nodes. Check or Adjust the Prompt of the Agent matching your use-case. Activate the Workflow. Open the Form (default: https://your-n8n.io/form/logo-sheet-feeder) Enjoy growing your Airtable. Enjoy the workflow! ❤️ let the work flow — Workflow Automation & Development

Purpose of workflow: This workflow transforms spreadsheet data into an interactive, AI-powered knowledge base that enables users to gain deep insights through natural language queries, searchability, and comparative analysis. How it works: Data Storage & Integration: Spreadsheet data is imported into a no-code database (NocoDB) System connects with an AI data analyst agent Agent accesses table metadata and column information Query Processing: Users input natural language questions AI agent interprets queries and converts them to database filters System retrieves relevant data using filter formulas AI synthesizes responses with analysis and insights Advanced Capabilities: Performs comparative analysis across multiple data points Handles complex multi-part queries Automatically creates visualizations: Visualization AI Agent figures out the data and the chart type and generates professional visualization using Quickchart Step by step setup: Create account on nocodb.com Create table by importing csv, copy table id Create API token https://app.nocodb.com/#/account/tokens In workflow, settings node, update with table id In NocoDB tool node, setup authentication with API token created in step 3 Specify the workspace and base fields after connecting to NocoDB

Check Online Version ! [https://n8n-tools.streamlit.app/](https://n8n-tools.streamlit.app/ ) Who is it for? This workflow is perfect for n8n users who want to maintain clean and organized workflows without manually repositioning nodes. Whether you're building complex workflows or sharing them with a team, maintaining visual clarity is essential for efficiency and collaboration. This template automates the positioning process, saving time and ensuring consistent layout standards. How does it work? The template is divided into two parts: Positioning Engine: A webhook node kicks off the process by receiving a workflow ID. Using the provided workflow ID, an n8n API node fetches the workflow details. The fetched workflow is sent to a processing webhook that calculates optimized positions for the nodes. Finally, an n8n API node updates the workflow with the newly positioned nodes, ensuring a clean and professional layout. Reusable Positioning Block: This is an HTTP Request node that can be seamlessly integrated into any workflow you create. When triggered, it sends the current workflow for automatic positioning via the first part of this template. How to set it up? Enable n8n API Access: Ensure that your n8n instance has API access enabled with the appropriate credentials. Input Your n8n API URL and Credentials: Open the template, locate the n8n API nodes, and update them with your instance API key. Update the URL of the 'Magic Positioning' Http Request node to point to your n8n instance webhook URL. Embed the Reusable Block: Add the provided HTTP Request node to any of your workflows to instantly connect to the auto-positioning engine.

Who is this for? This workflow is for small business owners, personal assistants, or project managers who rely on multiple platforms for communication and scheduling. Ideal for users managing customer support, personal scheduling, or group event coordination via LINE, Google Calendar, and Gmail. What problem is this workflow solving? Reduces the manual effort needed to manage conversations, schedule events, and handle email communications. Provides an intelligent system for replying to user messages and fetching relevant calendar or email information in real time. Bridges the gap between messaging platforms and productivity tools, improving efficiency. What this workflow does LINE Chatbot Automation**: Automatically processes and responds to messages received via LINE. Google Calendar Management**: Retrieves upcoming events or schedules new events dynamically based on user queries. Email Retrieval**: Fetches recent emails using Gmail and filters them based on user instructions. AI-Powered Replies**: Uses OpenAI GPT to interpret user queries and provide tailored responses. Setup Prerequisites: LINE Developer account and API access. Google Calendar and Gmail accounts with OAuth credentials. An n8n instance with access to environment variables. Steps: Set up environment variables (LINE_API_TOKEN and DYNAMIC_EMAIL). Configure API credentials for Google Calendar and Gmail in n8n. Test the workflow by sending a sample message via LINE. Enhancements: Use sticky notes to provide inline instructions for each node. Include a video walkthrough or a step-by-step document for first-time users. How to customize this workflow to your needs Localization**: Modify responses in the AI Agent node to match the language and tone of your audience. Integration**: Add more integrations like Slack or Microsoft Teams for additional notifications. Advanced Filters**: Add specific conditions to Gmail or Google Calendar nodes to fetch only relevant data, such as events with specific keywords or emails from certain senders. Advanced Use Cases Customer Support**: Automatically schedule meetings with clients based on their messages in LINE. Event Management**: Handle RSVP confirmations, event reminders, and email follow-ups for planned events. Personalized Assistant**: Use the workflow to act as a personal virtual assistant that syncs your schedule, replies to messages, and summarizes emails. Tips for Optimization Edit Fields Node**: Add a centralized node to configure dynamic inputs (e.g., tokens, emails, or thresholds) for easy updates. Fallback Responses**: Use a switch node to handle unrecognized input gracefully and provide clear feedback to users. Logs and Monitoring**: Add nodes to log interactions and track message flows for debugging or analytics. Let me know if you'd like me to expand on any specific section or add more customization ideas!

Transform simple queries into comprehensive, well-structured content with this n8n workflow that leverages Perplexity AI for research and GPT-4 for content transformation. Create professional blog posts and HTML content automatically while maintaining accuracy and depth. Intelligent Research & Analysis 🚀 Automated Research Pipeline Harnesses Perplexity AI's advanced research capabilities Processes complex topics into structured insights Delivers comprehensive analysis in minutes instead of hours 🧠 Smart Content Organization Automatically structures content with clear hierarchies Identifies and highlights key concepts Maintains technical accuracy while improving readability Creates SEO-friendly content structure Content Transformation Features 📝 Dynamic Content Generation Converts research into professional blog articles Generates clean, responsive HTML output Implements proper semantic structure Includes metadata and categorization 🎨 Professional Formatting Responsive Tailwind CSS styling Clean, modern HTML structure Proper heading hierarchy Mobile-friendly layouts Blockquote highlighting for key insights Perfect For 📚 Content Researchers Save hours of manual research by automating the information gathering and structuring process. ✍️ Content Writers Focus on creativity while the workflow handles research and technical formatting. 🌐 Web Publishers Generate publication-ready HTML content with modern styling and proper structure. Technical Implementation ⚡ Workflow Components Webhook endpoint for query submission Perplexity AI integration for research GPT-4 powered content structuring HTML transformation engine Telegram notification system (optional) Transform your content creation process with an intelligent system that handles research, writing, and formatting while you focus on strategy and creativity.

Video Guide I prepared a comprehensive guide detailing how to create a Smart Agent that automates meeting task management by analyzing transcripts, generating tasks in Airtable, and scheduling follow-ups when necessary. Youtube Link Who is this for? This workflow is ideal for project managers, team leaders, and business owners looking to enhance productivity during meetings. It is particularly helpful for those who need to convert discussions into actionable items swiftly and effectively. What problem does this workflow solve? Managing action items from meetings can often lead to missed tasks and poor follow-up. This automation alleviates that issue by automatically generating tasks from meeting transcripts, keeping everyone informed about their responsibilities and streamlining communication. What this workflow does The workflow leverages n8n to create a Smart Agent that listens for completed meeting transcripts, processes them using AI, and generates tasks in Airtable. Key functionalities include: Capturing completed meeting events through webhooks. Extracting relevant meeting details such as transcripts and participants using API calls. Generating structured tasks from meeting discussions and sending notifications to clients. Webhook Integration: Listens for meeting completion events to trigger subsequent actions. API Requests for Data: Pulls necessary details like transcripts and participant information from Fireflies. Task and Notification Generation: Automatically creates tasks in Airtable and notifies clients of their responsibilities. Setup N8N Workflow Configure the Webhook: Set up a webhook to capture meeting completion events and integrate it with Fireflies. Retrieve Meeting Content: Use GraphQL API requests to extract meeting details and transcripts, ensuring appropriate authentication through Bearer tokens. AI Processing Setup: Define system messages for AI tasks and configure connections to the AI chat model (e.g., OpenAI's GPT) to process transcripts. Task Creation Logic: Create structured tasks based on AI output, ensuring necessary details are captured and records are created in Airtable. Client Notifications: Use an email node to notify clients about their tasks, ensuring communications are client-specific. Scheduling Follow-Up Calls: Set up Google Calendar events if follow-up meetings are required, populating details from the original meeting context.

Use case Workshop We are using this workflow in our workshops to teach how to use Tools a.k.a functions with artificial intelligence. In this specific case, we will use a generic "AI Agent" node to illustrate that it could use other models from different data providers. Enhanced Weather Forecasting In this small example, it's easy to demonstrate how to obtain weather forecast results from the Open-Meteo site to accurately display the upcoming days. This can be used to plan travel decisions, for example. What this workflow does We will make an HTTP request to find out the geographic coordinates of a city. Then, we will make other HTTP requests to discover the weather for the upcoming days. In this workshop, we demonstrate that the AI will be able to determine which tool to call first—it will first call the geolocation tool and then the weather forecast tool. All of this within a single client conversation call. Setup Insert an OpenAI Key and activate the workflow. by Davi Saranszky Mesquita https://www.linkedin.com/in/mesquitadavi/

Video Guide I prepared a detailed guide that shows the entire process of building an AI agent that integrates with Airtable data in n8n. This template covers everything from data preparation to advanced configurations. Youtube Link Who is this for? This workflow is designed for developers, data analysts, and business owners who want to create an AI-powered conversational agent integrated with Airtable datasets. It is particularly useful for users looking to enhance data interaction through chat interfaces. What problem does this workflow solve? Engaging with data stored in Airtable often requires manual navigation and time-consuming searches. This workflow allows users to interact conversationally with their datasets, retrieving essential information quickly while minimizing the need for complex queries. What this workflow does This workflow enables an AI agent to facilitate chat interactions over Airtable data. The agent can: Retrieve order records, product details, and other relevant data. Execute mathematical functions to analyze data such as calculating averages and totals. Optionally generate maps for geographic data visualization. Dynamic Data Retrieval: The agent uses user prompts to dynamically query the dataset. Memory Management: It retains context during conversations, allowing users to engage in a more natural dialogue. Search and Filter Capabilities: Users can perform tailored searches with specific parameters or filters to refine their results. Set up steps Separate workflows: Create additional workflow and move there Workflow 2. Replace credentials: Replace connections and credentials in all nodes. Start chat: Ask questions and don't forget to mention required base name.

This template is a simple AI Agent that acts as a Google Calendar Assistant. It is designed for beginners to have their "first AI Agent" performing common tasks and to help them understand how it works. For new users of n8n, AI Agents, and OpenAI: This template involves using an OpenAI API Key. If you are new to AI Agents, make sure to research and understand key concepts such as: "Tokens"** (used for API requests), "Tool calling"** (how the AI interacts with external tools), OpenAI's usage costs** (how you will be billed for API usage). Functionality It has two main functionalities: Create events** in a calendar Retrieve events** from a calendar How you can use it Everything is explained with sticky notes in the workflow. It is ready-to-use: all you need to do is connect your OpenAI credentials, and you can start using the workflow.

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API. You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbout that anyone in your company can use. How it works: Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository. Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning. Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database. Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model. Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding. Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable. Set up steps: Create accounts: You'll need accounts with OpenAI and Pinecone. API keys: Obtain API keys for both services. Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys. Import the workflow: Import this workflow into your n8n instance. Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box. Setup Time: Approximately 15-20 minutes. Why use this workflow? Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot. Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases. n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch youtube tutorial 🎯 Purpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. 🔄 What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutes ⚙️ Setup Requirements WordPress site with admin access Predefined categories in WordPress OpenAI API credentials (or your preferred AI provider) n8n with WordPress credentials 🛠️ Configuration Steps Add your WordPress categories (manually in WordPress) Note down category IDs Update the AI prompt with your category IDs Configure WordPress credentials in n8n Set up AI API connection 🔧 Customization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updates ⚠️ Important Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually first 🎁 Bonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently. #n8n #WordPress #ContentManagement #Automation #AI Created by rumjahn

Automate Instagram DMs with OpenAI GPT and ManyChat How It Works: Once connected, GPT will automatically initiate conversations with messages from new recipients in Intagram. Who Is This For? This workflow is ideal for marketers, business owners content creators who want to automatically respond to Instagram direct messages using OpenAI GPT. By integrating ManyChat, you can manage conversations, nurture leads, and provide instant replies at scale. What This Workflow Does Captures** incoming Instagram DMs through ManyChat’s integration. Processes** messages with GPT to generate a relevant response. Delivers** instant replies back to Instagram users, creating efficient, AI-driven communication. Setup Import the Template: Copy the n8n workflow into your workspace. OpenAI Credentials: Add your OpenAI API key in n8n so GPT can generate responses. ManyChat Account: Create (or log in to) your ManyChat account. Connect Instagram: Link your Instagram profile as a channel in ManyChat. ManyChat Custom Field: Create a custom field for storing user input or conversation context. Configure Default Reply: In ManyChat, set up the default Instagram reply flow to point to your n8n webhook. Add External Request: Create an external request step in ManyChat to send messages to n8n. Test the Flow: Send yourself a DM on Instagram to confirm the workflow triggers and GPT responds correctly. Instructions and links: Notion instruction Register in ManyChat

The purpose of this workflow, "Sell: Stock Vision," is to create an AI-powered technical analysis agent capable of analyzing financial charts for equity stocks and cryptocurrencies. This workflow provides users with actionable insights into market trends, price movements, candlestick patterns, and technical indicators to support informed trading decisions. How It Works Integration with TradingView:** The workflow uses the Chart-Img.com API to fetch detailed financial charts for the specified stock or cryptocurrency. AI-Powered Analysis:** The workflow employs advanced AI models, including Google's Gemini Chat Model, to analyze the retrieved charts for candlestick patterns, support/resistance levels, and technical indicators like MACD and RSI. News and Sentiment Analysis:** By integrating with SerpAPI, the workflow gathers relevant news about the stock or cryptocurrency to evaluate its potential impact on market movements. Comprehensive Insights:** It provides detailed trading strategies, including buy/sell recommendations, stop-loss levels, and risk-reward evaluations. Continuous Memory:** The AI agent uses buffer memory to retain context for enhanced analysis and continuity. Use Case This workflow is perfect for traders and analysts who need reliable and AI-powered market analysis to make informed trading decisions efficiently. Tutorial Obtain API keys for Chart-Img.com and SerpAPI. Configure the workflow in your n8n instance by inputting the required API keys and connecting the nodes. Trigger the workflow by providing the stock or cryptocurrency symbol, and let the agent do the rest! https://youtu.be/9fR4qNMT5LM

Description This workflow automates email categorization in Gmail using the Gmail API and OpenAI's language model. It periodically checks for new emails, reads their content, and categorizes them based on existing Gmail labels. If no matching label is found, the workflow creates a new label and assigns it to the email. Key Features Polling for Emails**: The workflow triggers every 5 minutes to check for new emails using the Gmail Trigger node. Reading Labels**: Existing Gmail labels are fetched to determine the most relevant match for email categorization. Dynamic Labeling**: If no existing label matches, a new label is created dynamically based on the email's content. OpenAI Integration**: The workflow uses OpenAI's Chat model to analyze email content and suggest or create appropriate labels. Email Categorization**: Labels are applied to emails, ensuring they are organized in Gmail's structure. The workflow also removes less relevant emails (e.g., ads) from the inbox. Nodes in Use Gmail Trigger: Polls Gmail every 5 minutes for new emails. Gmail - Read Labels: Fetches all existing Gmail labels. Gmail - Get Message: Retrieves the full content of a specific email. Gmail - Add Label to Message: Assigns a chosen label to the email. Gmail - Create Label: Creates a new label if necessary. OpenAI Chat Model: Analyzes email content for categorization. Memory Buffer: Retains context for email analysis across multiple iterations. Wait Node: Adds a buffer period to manage email processing. Prerequisites Gmail API Setup**: Ensure Gmail OAuth2 credentials are configured in n8n. OpenAI API Key**: Configure OpenAI credentials for email analysis. Labeling Standards**: Maintain a consistent Gmail label structure for better organization. Instructions Add your Gmail API credentials to the Gmail nodes. Add your OpenAI API credentials to the OpenAI Chat Model node. Activate the workflow. It will start polling for new emails every 5 minutes. Monitor and refine the categorization logic if necessary to ensure alignment with Gmail's organizational needs. Use Case Ideal for individuals or teams handling high email volumes who want to maintain an organized inbox and automate repetitive categorization tasks. Note: You can improve the prompt to get better results from the agent by giving it more personal rules on how to categorize.

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API key: Obtain a Google AI API key from Google AI Studio. Pinecone account and API key: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-earnings in your Pinecone project. Google Drive - download and save financial documents: Go to a company you want to analize and download their quarterly earnings PDFs Save the PDFs in Google Drive Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive Configure credentials in your n8n environment for: Google Sheets OAuth2 Google Drive OAuth2 Google Docs OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import and configure the workflow: Import this workflow into your n8n instance. Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. Update the Download File From Google Drive to point to the column where the file URLs are Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

Proxmox AI Agent with n8n and Generative AI Integration This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters. Buy My Book: Mastering n8n on Amazon Full Courses & Tutorials: http://lms.syncbricks.com Watch Video on Youtube How It Works Trigger Mechanism The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat). Interact with the AI agent conversationally. AI-Powered Parsing A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation. API Call Generation The AI parses the input and generates structured JSON output, which includes: response_type: The HTTP method (GET, POST, PUT, DELETE). url: The Proxmox API endpoint to execute. details: Any required payload parameters for the API call. Proxmox API Execution The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as: Retrieving cluster or node information. Creating, deleting, starting, or stopping VMs. Migrating VMs between nodes. Updating or resizing VM configurations. Response Formatting The workflow formats API responses into a user-friendly summary. For example: Success messages for operations (e.g., "VM started successfully"). Error messages with missing parameter details. Extensibility You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports: Telegram/Slack integration for real-time notifications. Backup and restore workflows. Cloud monitoring extensions. Key Features Multi-Channel Input**: Use chat, email, or custom triggers to communicate with the AI agent. Low-Code Automation**: Easily customize the workflow to suit your Proxmox environment. Generative AI Integration**: Supports advanced AI models for precise command interpretation. Proxmox API Compatibility**: Fully adheres to Proxmox API specifications for secure and reliable operations. Error Handling**: Detects and informs you of missing or invalid parameters in your requests. Example Use Cases Create a Virtual Machine Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1." Action: Sends a POST request to Proxmox to create the VM with specified configurations. Start a VM Input: "Start VM 105 on node psb2." Action: Executes a POST request to start the specified VM. Retrieve Node Details Input: "Show the memory usage of psb3." Action: Sends a GET request and returns the node's resource utilization. Migrate a VM Input: "Migrate VM 202 from psb1 to psb3." Action: Executes a POST request to move the VM with optional online migration. Pre-Requisites Proxmox API Configuration Enable the Proxmox API and generate API keys in the Proxmox Data Center. Use the Authorization header with the format: PVEAPIToken=<user>@<realm>!<token-id>=<token-value> n8n Setup Add Proxmox API credentials in n8n using Header Auth. Connect a generative AI model (e.g., Google Gemini) via the relevant credential type. Access the Workflow Import this template into your n8n instance. Replace placeholder credentials with your Proxmox and AI service details. Additional Notes This template is designed for Proxmox 7.x and above. For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow. Always test with a non-production Proxmox environment before deploying in live systems.

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It Works Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up Steps Estimated Time**: 20–25 minutes Prerequisites**: Create an account with an AI provider (e.g., OpenAI). Connect WhatsApp or Telegram credentials in n8n. Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. Import the workflow template into n8n and update environment variables for your credentials.

This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated documents in a Pinecone vector database, allowing the chatbot to provide accurate and up-to-date information. The workflow uses Google's Gemini AI for both embeddings and response generation. How it works The workflow uses two Google Drive Trigger nodes: one for detecting new files added to a specified Google Drive folder, and another for detecting file updates in that same folder. Automated Indexing: When a new or updated document is detected The Google Drive node downloads the file. The Default Data Loader node loads the document content. The Recursive Character Text Splitter node breaks the document into smaller text chunks. The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model. The Pinecone Vector Store node indexes the text chunks and their embeddings in a specified Pinecone index. 7.The Chat Trigger node receives user questions through a chat interface. The user's question is passed to an AI Agent node. The AI Agent node uses a Vector Store Tool node, linked to a Pinecone Vector Store node in query mode, to retrieve relevant text chunks from Pinecone based on the user's question. The AI Agent sends the retrieved information and the user's question to the Google Gemini Chat Model (gemini-pro). The Google Gemini Chat Model generates a comprehensive and informative answer based on the retrieved documents. A Window Buffer Memory node connected to the AI Agent provides short-term memory, allowing for more natural and context-aware conversations. Set up steps Google Cloud Project and Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API Key: Obtain a Google AI API key from Google AI Studio. Pinecone Account: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-files in your Pinecone project. Google Drive: Create a dedicated folder in your Google Drive where company documents will be stored. Credentials in n8n: Configure credentials in your n8n environment for: Google Drive OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import the Workflow: Import this workflow into your n8n instance. Configure the Workflow: Update both Google Drive Trigger nodes to watch the specific folder you created in your Google Drive. Configure the Pinecone Vector Store nodes to use your company-files index.

Template Guide for Employee Shortlisting AI Agent Automation Overview This template automates the process of shortlisting job applicants using ERPNext, n8n, and AI-powered decision-making tools like Google Gemini and OpenAI. It reduces manual effort, ensures fast evaluations, and provides justifiable decisions about applicants. This is ideal for businesses aiming to streamline their recruitment process while maintaining accuracy and professionalism. YouTube Tutorial:** For a full walkthrough of this template, visit: Integrate AI in ERPNext: Automate Recruitment Job Applicant Shortlisting in Seconds! What Does This Template Do? Webhook Integration with ERPNext: Automatically triggers the workflow when a job application is created in ERPNext. Resume Validation: Ensures resumes are attached and correctly processes various file formats like PDF and DOC. AI-Powered Evaluation: Uses AI to compare resumes against job descriptions and provides a: Fit Level (Strong, Moderate, or Weak) Score (0–100) Justification for the decision. Automated Decision Making: Based on AI-generated scores: Candidates with a score of 80 or higher are Accepted. Candidates below 80 are Rejected. Applications missing required fields or attachments are put On Hold. ERPNext Integration: Updates applicant records in ERPNext, including custom fields such as justification, fit level, and scores. Notifications: Notifies candidates via email, WhatsApp, or SMS about their application status. Step-by-Step Guide Step 1: Set Up ERPNext Webhook Go to Webhooks in ERPNext. Create a webhook for the Job Applicant DocType. Set the trigger to Insert. Pin and test the webhook to ensure proper data flow. Step 2: Import the Template into n8n Open your n8n instance. Import the provided workflow template. Check all nodes for proper configuration. Step 3: Configure Credentials Add your ERPNext API credentials to the ERPNext nodes. Add credentials for AI services like OpenAI or Google Gemini. Configure additional services like WhatsApp or email if you plan to use them for notifications. Step 4: Test Resume Validation Test how the workflow handles different file types (e.g., PDF, DOC, JPG). Ensure resumes without the proper format or attachment are flagged and rejected. Step 5: AI Evaluation The AI model (Google Gemini or OpenAI) will evaluate resumes against job descriptions. Customize the AI prompt to suit your job evaluation needs. The output will include a Fit Level, Score, Rating, and Justification. Step 6: Decision Automation The workflow automatically categorizes applicants: Accepted for scores ≥ 80. Rejected for scores < 80. On Hold if essential fields or attachments are missing. Step 7: Update ERPNext Records The workflow updates the Job Applicant record in ERPNext with: Status (Accepted, Rejected, On Hold) AI-generated Fit Level, Score, Rating, and Justification. Step 8: Notify Candidates Configure notification nodes (email, WhatsApp, or SMS). Inform candidates about their application status and include feedback if required. How It Works Trigger: The workflow starts when a job application is submitted in ERPNext. Validation: Checks if the resume is attached and in the correct format. AI Evaluation: Compares the resume with the job description and generates a decision. ERPNext Update: Updates the applicant's record with the decision and justification. Notification: Sends a personalized notification to the candidate. Dos and Don’ts Dos: Customize Prompts:** Tailor the AI prompt to match your specific job evaluation requirements. Test the Workflow:** Run sample data to ensure the process works as intended. Secure Your Credentials:** Keep your API credentials safe and do not share them publicly. Optimize for Different Formats:** Ensure the workflow can handle all types of resumes you expect. Don’ts: Avoid Manual Intervention:** Let the workflow handle most of the tasks to ensure efficiency. Do Not Skip Testing:** Always test the workflow with various scenarios to avoid errors. Do Not Overlook Notifications:** Ensure candidates are notified promptly to maintain professionalism. Customization Options Add logic for more file types (e.g., scanned images using OCR). Enhance the AI prompts to analyze more complex resume data. Integrate additional tools like Slack or Trello for recruitment tracking. Resources YouTube Tutorial:** For a full walkthrough of this template, visit: SyncBricks YouTube Channel Detailed Guides and Courses:** Learn more about ERPNext and AI-driven automation at: SyncBricks LMS Support If you encounter issues or want to explore more possibilities with AI-driven automation, feel free to reach out: Email:** amjid@amjidali.com Website:** ERPNext and Other Courses LinkedIn:** Amjid Ali Let me know if you'd like further details or modifications to the guide!

Overview This workflow template automates lead management and customer inquiry processing by integrating ERPNext, AI agents, and email notifications. It streamlines the process of capturing leads, analyzing inquiries, and generating actionable responses. The workflow uses ERPNext to capture inquiries, analyzes them with AI, and notifies the appropriate team or individual, all while maintaining a professional approach. What This Template Does ERPNext Webhook Integration: Captures leads and inquiries through ERPNext webhooks. Triggers the workflow when a new lead is created. AI-Powered Inquiry Analysis: Uses AI to extract key details from lead notes (e.g., customer name, organization, inquiry summary). Classifies inquiries as valid or invalid based on relevance to products, services, or solutions. Contact Assignment: Matches inquiries to the appropriate contact(s) using a Google Sheets database or ERPNext contact information. Handles multiple contacts if required. Email Notifications: Generates professional email notifications for valid inquiries. Sends emails to the appropriate contact(s) with inquiry details and action steps. Invalid Lead Handling: Identifies invalid inquiries (e.g., unrelated to products or services) and flags them for follow-up or dismissal. Custom Email Formatting: Converts plain text into professionally formatted HTML emails. Ensures that communication is clear, concise, and visually appealing. How It Works Step 1: Capture Lead Data Webhook in ERPNext:** Create a webhook in ERPNext for the "Lead" DocType. Set the trigger to on_insert to capture new leads in real-time. Lead Details:** The workflow fetches lead details, including notes, contact information, and the source of the lead. Step 2: Validate and Analyze Inquiry AI Agent for Analysis:** An AI agent analyzes the lead notes to extract key details and classify the inquiry as valid or invalid. The analysis includes checking the relevance of the inquiry to products, services, or solutions offered by the company. Invalid Leads:** If the inquiry is invalid, the workflow flags it and stops further processing. Step 3: Assign Contact(s) Google Sheets Integration:** Uses a Google Sheets database to map products, services, or solutions to responsible contacts. Ensures that inquiries are directed to the right person or team. Multiple Contacts:** Handles cases where multiple contacts are responsible for a particular product or service. Step 4: Generate and Send Email Notifications AI-Generated Emails:** The workflow generates a professional email summarizing the inquiry. Emails include details like customer name, organization, inquiry summary, and action steps. Custom HTML Formatting:** Emails are converted to HTML for a polished and professional appearance. Send Notifications:** Sends email notifications through Microsoft Outlook or another configured email client. Optionally, notifies via WhatsApp or SMS for urgent inquiries. Step 5: Post-Inquiry Actions ERPNext Record Updates:** Updates the lead record in ERPNext with relevant details, including inquiry status and contact information. Setup Instructions Prerequisites ERPNext: A configured ERPNext instance with lead data and a webhook for the "Lead" DocType. Google Sheets: A sheet mapping products, services, or solutions to responsible contacts. AI Integration: Credentials for OpenAI or other supported AI platforms. Email Client: Credentials for Microsoft Outlook or another email client. Step-by-Step Setup ERPNext Configuration: Create a webhook for the "Lead" DocType in ERPNext. Test the webhook with sample data to ensure proper integration. Workflow Import: Import the workflow template into n8n. Configure nodes with your API credentials for ERPNext, Google Sheets, and AI tools. Google Sheets Integration: Prepare a Google Sheet with columns for product, service, or solution and the responsible contact(s). Link the sheet to the workflow. AI Agent Configuration: Customize the AI agent’s prompts to align with your business’s products and services. Adjust criteria for valid and invalid inquiries as needed. Email Setup: Configure the email client node with your email service credentials. Customize the email template for your organization. Testing: Run the workflow with sample leads to validate the entire process. Check email notifications, contact assignments, and record updates in ERPNext. Dos and Don’ts Dos: Test Thoroughly:** Test the workflow with various scenarios before deploying in production. Secure Credentials:** Keep API and email credentials secure to avoid unauthorized access. Customize Prompts:** Tailor AI prompts to match your business needs and language style. Use Professional Email Templates:** Ensure emails are clear and well-formatted. Don’ts: Skip Validation:** Always validate inquiry data to avoid sending irrelevant notifications. Overload the Workflow:** Avoid adding unnecessary nodes that can slow down processing. Ignore Errors:** Monitor logs and address errors promptly for a smooth workflow. Resources YouTube Tutorial:** Watch the full step-by-step tutorial on setting up this workflow: SyncBricks YouTube Channel Courses and Training:** Learn more about ERPNext and AI automation through my comprehensive courses: SyncBricks LMS Support and Contact:** Email: amjid@amjidali.com Website: SyncBricks LinkedIn: Amjid Ali

Workflow Documentation: HR Job Posting and Evaluation with AI Detailed Description The HR Job Posting and Evaluation with AI workflow is designed to streamline and enhance recruitment for technical roles, such as Automation Specialists. By automating key stages in the hiring process, this workflow ensures a seamless experience for both candidates and HR teams. From collecting applications to evaluating candidates using AI and scheduling interviews, this workflow provides an end-to-end solution for recruitment challenges. Who is this for? This workflow is ideal for: HR Professionals**: Managing multiple job postings and candidates efficiently. Recruitment Teams**: Handling large volumes of applications for technical positions. Hiring Managers**: Ensuring structured and objective candidate evaluations. What problem does this workflow solve? Time-Consuming Processes**: Automates repetitive tasks like data entry, CV management, and scheduling. Fair Candidate Evaluation**: Leverages AI to provide objective insights based on resumes and job descriptions. Streamlined Communication**: Ensures timely and personalized candidate interactions, improving their experience. What this workflow does This workflow automates the following steps: Form Submission: Collects candidate information via a structured application form. Data Storage: Stores applicant details in Airtable for centralized tracking. CV Management: Automatically uploads resumes to Google Drive for easy access and organization. AI-Powered Candidate Evaluation: Scores candidates based on their resumes and job descriptions using OpenAI, providing actionable insights. Interview Scheduling: Automates scheduling based on candidate and interviewer availability. Communication: Sends customized emails to candidates for interview invitations and feedback. Setup Prerequisites To use this workflow, you’ll need: n8n Account**: To create and run the workflow. Airtable Account**: For managing applicant data. Google Drive Account**: For storing candidate CVs. OpenAI API Key**: For AI-powered candidate scoring. SMTP Email Account**: For sending candidate communications. Setup Process Airtable Configuration: Create a base in Airtable with tables for Applicants and Job Positions. Google Drive Setup: Create a folder for CV storage and ensure you have write permissions. Integrate Airtable in n8n: Use the Airtable API key to connect Airtable to n8n. Integrate Google Drive in n8n: Authorize Google Drive to enable CV storage automation. OpenAI Integration: Add your OpenAI API key to n8n for candidate scoring. Email Configuration: Set up your SMTP email account in n8n for sending notifications and invitations. How to customize this workflow Tailor the workflow to fit your unique recruitment needs: Edit Job Descriptions: Adjust the form parameters to match the specific role and qualifications. Refine AI Evaluation Criteria: Modify OpenAI prompts to reflect the skills and competencies for the desired position. Personalize Email Templates: Update email content to match your organization’s tone and branding. Add New Features: Incorporate additional steps like feedback collection or integration with other HR tools. Conclusion The HR Job Posting and Evaluation with AI workflow simplifies and automates the recruitment process, enabling HR teams to focus on engaging with candidates rather than handling administrative tasks. With its powerful integrations and customization options, this workflow helps organizations hire efficiently while improving the candidate experience.

This n8n workflow demonstrates multiple ways to harness DeepSeek's AI models in your automation pipeline! 🌟 Core Features Multiple Integration Methods 🔌 Local deployment using Ollama for DeepSeek-R1 Direct API integration with DeepSeek Chat V3 Conversational agent with memory buffer HTTP request implementation with both raw and JSON formats Model Options 🧠 DeepSeek Chat V3 for general conversation DeepSeek-R1 for advanced reasoning Memory-enabled agent for persistent context Quick Setup 🛠️ API Configuration Base URL: https://api.deepseek.com Get your API key from platform.deepseek.com/api_keys Local Setup 💻 Install Ollama for local deployment Set up DeepSeek-R1 via Ollama Configure local credentials in n8n Implementation Details 🔧 Conversational Agent Window Buffer Memory for context Customizable system messages Built-in error handling with retries API Endpoints 🌐 Chat completions for V3 and R1 models OpenAI API format compatibles

Purpose of workflow: This AI-powered workflow automatically generates detailed, well-researched newsletters by monitoring and analyzing specified news topics (like Bitcoin, Nvidia, etc.). It uses a team of AI research agents to gather, analyze, and compile information with automatic citations, saving significant time in newsletter creation. How it works: Multi-agent system: Research Leader: Analyzes topics and creates content outline Project Planner: Breaks down research into specific tasks Research Assistants: Conduct detailed research on assigned subtopics Editor: Combines research and polishes final output Key features: Automated daily monitoring of specified news topics Real-time information gathering using Perplexity AI Auto-citation functionality for source verification Flexible time window filtering (day/week/month) Options for detailed or simple reports Direct email delivery of completed newsletters Step-by-step setup: Perplexity API Setup: Create account at perplexity.ai Navigate to API tab Generate API key Set up credentials with 'Bearer' authentication Workflow Configuration: Connect Google Sheets containing news monitoring topics Configure schedule trigger for daily execution Set up email delivery settings Define report type preferences (detailed/simple) Specify time window for news gathering Integration: Connect with newsletter tools like Kit Import generated content as starting point Edit and customize as needed before publishing

1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger**: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive Upload to OpenAI**: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job**: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model**: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...). Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model). Start the Workflow: Use the manual trigger ("Test workflow") to begin the upload and training process. Test the model via the "Chat Trigger" (chat messages). Integrated Documentation: Follow the instructions in the Sticky Notes to: Properly format the .jsonl (Step 1). Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/). Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

What this workflow does This workflow retrieves Online Marketing data (Google Analytics for several domains, Google Ads, Meta Ads) from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary. The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account. This workflow has the following sequence: time trigger (e.g. every Monday at 7 a.m.) retrieval of Online Marketing data from the last 7 days (via sub workflows) assignment and summary of the data retrieval of Online Marketing data from the same time period of the previous year allocation and summary of the data preparation in tabular form and brief analysis by AI. sending the report as an email preparation in short form by AI for Telegram (optional) sending as Telegram message. Requirements The following accesses are required for the workflow: Google Analytics (via Google Analytics API): Documentation Google Ads (via HTTP Request -> Google Ads API):Documentation Meta Ads (via Facebook Graph API): Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) Telegram access data (optional for sending as Telegram message): Documentation You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools. You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5). Feel free to contact me via LinkedIn, if you have any questions!

This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. How It Works 1. Chat Interaction & Intent Detection Chat Trigger**: Starts when a user sends a message ("When chat message received"). Information Extractor**: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. Extracts: search (true/false). keyword, priceRange, SKU, category (if product-related). Example: { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } 2. Product Search (WooCommerce Integration) AI Agent**: If search: true, routes the request to the personal_shopper tool. WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). Filters products in stock (stockStatus: "instock"). Returns matching products (e.g., "red handbags under €100"). 3. General Inquiries (RAG System) RAG Tool**: If search: false, uses the Qdrant Vector Store to retrieve store information from documents. Google Drive Integration: Documents (e.g., store policies, FAQs) are stored in Google Drive. Downloaded, split into chunks, and embedded into Qdrant for semantic search. OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM"). Set Up Steps 1. Configure the RAG System Google Drive Setup**: Upload store documents . Update the Google Drive2 node with your folder ID. Qdrant Vector Database**: Clean the collection (update Qdrant Vector Store node with your URL). Use Embeddings OpenAI to convert documents into vectors. 2. Configure OpenAI & WooCommerce OpenAI Credentials**: Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.). WooCommerce Integration**: Connect your WooCommerce store (credentials in the personal_shopper node). Ensure product data is synced and accessible. 3. Customize the AI Agent Intent Detection**: Modify the Information Extractor’s system prompt to align with your store’s terminology. RAG Responses**: Update the tool description to reflect your store’s documents. Notes This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support.

Create a Branded AI Website Chatbot Engage website visitors with an intelligent chat widget powered by OpenAI. This template includes: 💬 Natural conversation handling 📅 Microsoft Outlook calendar integration 📝 Lead capture and information gathering 🔄 Human handoff capabilities Simply add a JavaScript snippet to your website and configure the workflow to match your needs. Follow our detailed setup guide to get started in minutes. > Note: Widget includes a "Powered By" affiliate link

This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose). It automatically retrieves your Google Calendar events and feeds them to the model. The workflow uses Google’s Gemini AI for response generation. How it works The workflow uses a Scheduled Trigger Node as the main trigger. The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data. The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash). The Google Gemini Chat Model generates a summary and informative response based on today’s meetings. ++Setup Steps++ Google Cloud Project and Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API Key: Obtain a Google AI API key from Google AI Studio. Credentials in n8n: Configure credentials in your n8n environment for: Google Gemini (PaLM) API (using your Google AI API key). Import the Workflow: Import this workflow into your n8n instance. Configure the Workflow: Update both Slack and Gemini nodes with your credentials.

Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n" Description This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp. Workflow Details Trigger: Strava Activity Updates Node:** Strava Trigger Purpose:** Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more. Integration:** Uses Strava API for real-time synchronization. Step 1: Data Preprocessing Node:** Code Purpose:** Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes. Logic:** A recursive function flattens JSON input to create a clean and readable structure. Step 2: AI Analysis with Google Gemini Node:** Google Gemini Chat Model Purpose:** Leverages Google Gemini's advanced language model to analyze the activity data. Functionality:** Identifies key performance metrics. Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). Offers tailored recommendations and motivational advice. Step 3: Generate Structured Output Node:** Structure Output Purpose:** Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists. Output:** Formats the response for clear communication. Step 4: Convert to HTML Node:** Convert to HTML Purpose:** Converts the structured output into an HTML format suitable for email or other presentation methods. Output:** Ensures the response is visually appealing and easy to understand. Step 5: Send Email with Training Insights Node:** Send Email Purpose:** Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages. Integration:** Utilizes Gmail or SMTP for secure and efficient email delivery. Optional Step: WhatsApp Notifications Node:** WhatsApp Business Cloud Purpose:** Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access. Integration:** Connects to WhatsApp Business Cloud for automated messaging. Additional Notes Customization: You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels. The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats. Scalability: Ideal for individual athletes or coaches managing multiple athletes. Can be expanded to include additional metrics or insights based on user preferences. Performance Metrics Handled: Swimming: SWOLF, stroke count, pace. Cycling: Cadence, power zones, elevation. Running: Pacing, stride length, heart rate zones. Implementation Steps Set Up Strava API Key: Log in to Strava Developers to generate your API key. Integrate the API key into the Strava Trigger node. Configure Google Gemini Integration: Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node. Customize Email and WhatsApp Messaging: Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details. Automate Execution: Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution. Developer Notes Author:** Amjid Ali improvements. Resources:** See in Action: Syncbricks Youtube PayPal: Support the Developer Courses : SyncBricks LMS By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.

This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization. Setup Requirements Create a designated folder in Google Drive (Desktop). Create a symbolic link between this folder and a new target folder in your Obsidian Vault. Configure Google Drive n8n node settings. Send the output of any workflow to the trigger, and the notes will appear in your Vault folder. Optional Features You can use AI agents to: Write notes in your preferred format (e.g., Zettelkasten). Compose YAML front matter. Suggest tags. Use Cases Convert RSS feed items to notes. Create notes from YouTube video transcripts. Transform tasks in Slack messages into Obsidian tasks. (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)

What this workflow does This workflow retrieves the Zoom meeting data from the last 24 hours. The transcript of the last meeting is then retrieved, processed, a summary is created using AI and sent to all participants by email. AI is then used to create tasks and follow-up appointments based on the content of the meeting. Important: You need a Zoom Workspace Pro account and must have activated Cloud Recording/Transcripts! This workflow has the following sequence: manual trigger (Can be replaced by a scheduled trigger or a webhook) retrieval of of Zoom meeting data filter the events of the last 24 hours retrieval of transcripts and extract of the text creating a meeting summary, format to html and send per mail create tasks and follow-up call (if discussed in the meeting) in ClickUp/Outlook (can be replaced by Gmail, Airtable, and so forth) via sub workflow Requirements: Zoom Workspace (via API and HTTP Request): Documentation Microsoft Outlook: Documentation ClickUp: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools. You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5). Feel free to contact me via LinkedIn, if you have any questions!

Microsoft Outlook AI Email Assistant Prerequisites 1. Microsoft 365 Login Credentials Provide your Office 365 credentials to connect Outlook. 2. Monday.com Generate an API token and have a board with your contact details. 3. Airtable Obtain an API key (or personal access token) and set up a base to store: Contacts (populated by the Monday.com sync). Rules & Categories (used by the AI Email Assistant). Use this Airtable base as the template: Airtable AI Email Assistant Template. Define your own rules, categories, and delete rules. 4. OpenAI API Key Sign up for OpenAI if you don’t already have an account. Generate a new API key at OpenAI API Keys. What the System Does 1. Daily Contact Sync (Monday.com → Airtable) Runs once a day to pull the latest contacts from Monday.com and store or update them in Airtable. 2. AI Email Categorisation & Prioritisation Fetches Outlook emails with filters. Cleans and processes email content. Matches emails with known contacts from Airtable. Uses an AI agent to classify, categorise, and prioritise emails. Updates Outlook categories and importance based on AI results. Runs in parallel with Airtable rules & categories retrieval for real-time decision-making. Workflow 1: Daily Contact Sync (Monday.com → Airtable) Purpose Keep Airtable’s Contacts table up to date by pulling new or updated contact data from Monday.com daily. Steps Schedule Trigger Runs at a set interval (daily) to initiate contact syncing. Monday.com: Get Contacts Reads the specified board/columns from Monday.com where you store contact details. Airtable - Contacts Upserts (adds or updates) the fetched Monday.com data into Airtable’s Contacts table. Ensures daily updates reflect changes from Monday.com. Result A consolidated contact list in Airtable, ready for AI email categorisation. Workflow 2: Categorise & Prioritise Outlook Emails Purpose Fetches Outlook emails, cleans and processes their content, matches senders with known contacts, and uses AI to categorise and prioritise them. Steps 1. Get Outlook Emails with Filters Trigger**: Either scheduled (Check Mail Schedule Trigger) or manual (Test Workflow). Outlook Filters**: Not flagged (flag/flagStatus == 'notFlagged'). Not categorised (not categories/any()). 🔹 Result: A batch of fresh, unprocessed emails ready for processing. 2. Sanitise Email Convert to Markdown: Strips **HTML tags and normalises formatting. Email Messages Processing: Allows manual removal of **signatures, disclaimers, or extra content. 🔹 Result: A clean, AI-friendly email for categorisation. 3. Match Contact Loop Over Emails**: Iterates over each email. Contact Lookup: Checks Airtable’s **Contacts table (updated daily). Merge Data: Enriches emails with known **client, supplier, or internal team info. 🔹 Result: Enhanced email context for AI processing. 4. AI Agent to Categorise & Prioritise Retrieve Rules & Categories** Reads Rules, Categories, and Delete Rules from Airtable in parallel. AI: Analyse Email (Tools Agent)** Uses email text, sender info, and rules to build a structured AI prompt. OpenAI Chat Model** Processes the AI prompt and outputs: Category Subcategory (optional) Priority level Short rationale Structured Output Parser** Ensures AI response is valid JSON format. 🔹 Result: Each email is labelled, categorised, and prioritised with AI-driven logic. 5. Set Outlook Category & Importance Set Category: Updates Outlook with the assigned **category. Check Priority Conditions** (If Node): If Action Required or from a VIP, mark as High Priority. Set Importance: Updates the email's **importance flag in Outlook. 🔹 Result: Outlook is updated with categories & importance based on AI recommendations. Parallel Processing: Retrieve Rules & Categories Runs alongside the email categorisation workflow. Ensures Airtable-based rules are available before AI processing. Steps Airtable: Get Rules & Categories Fetches Rules, Categories, and Delete Rules from Airtable. Delete Rules (Optional) If a delete rule matches, the email is removed. 🔹 Result: A dynamic, updatable rule system ensuring emails are handled properly. Final Outcome Daily Contact Sync** keeps contacts up to date. AI-driven email workflow** ensures smart categorisation. Outlook automatically updated** with AI-generated categories and importance. This automated system saves time, ensures efficient inbox management, and allows for customisable rules via Airtable.

Welcome to my AI Social Media Caption Creator Workflow! What this workflow does This workflow automatically creates a social media post caption in an editorial plan in Airtable. It also uses background information on the target group, tonality, etc. stored in Airtable. This workflow has the following sequence: Airtable trigger (scan for new records every minute) Wait 1 Minute so the Airtable record creator has time to write the Briefing field retrieval of Airtable record data AI Agent to write a caption for a social media post. The agent is instructed to use background information stored in Airtable (such as target group, tonality, etc.) to create the post. Format the output and assign it to the correct field in Airtable. Post the caption into Airtable record. Requirements Airtable Database: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) Example of an editorial plan in Airtable: Editorial Plan example in Airtable For this workflow you need the Airtable fields "created_at", "Briefing" and "SoMe_Text_AI" Feel free to contact me via LinkedIn, if you have any questions!

Use Case It is very convenient to add expenses via simple chat message. This workflow attempts to do exactly this using AI-powered n8n magic! Send message to a chat, something like "car wash; 59.3 usd; 25 jan 2024" And get a response: Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"} LLM will smartly parse your message to structured JSON and save the expense as a new row into Google Sheet! Installation 1. Set up Google Sheets: Clone this Sheet: https://docs.google.com/spreadsheets/d/1D0r3tun7LF7Ypb21CmbTKEtn76WE-kaHvBCM5NdgiPU/edit?gid=0#gid=0 (File -> Make a copy) Choose this sheet into "Save expense into Google Sheets" node. 2. Fix sub-workflow dropdown: open "Parse msg and save to Sheets" node (which is an n8n sub-workflow executor tool) and make sure the SAME workflow is chosen in the dropdown. it will allow n8n to locate and call "Workflow Input Trigger" properly when needed. 3. Activate the workflow to make chat work properly. Sent message to chat, something like "car wash; 59.3 usd; 25 jan 2024" you should get a response: Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"} and new row in Google sheets should be inserted!

n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant Who is this for? This workflow is ideal for: Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**. IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system. Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**. Anyone using n8n and Qdrant* to build *AI-powered security workflows**. What problem does this workflow solve? Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by: ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs. ✔ Tagging & classifying alerts based on known attack techniques. ✔ Providing remediation steps to guide the response team. ✔ Enhancing security tickets in Zendesk with relevant threat intelligence. What this workflow does 1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk). 2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques. 3️⃣ Extracts relevant TTPs (Tactics, Techniques, & Procedures) from the alert. 4️⃣ Generates remediation steps using AI-powered enrichment. 5️⃣ Updates Zendesk tickets with threat intelligence & recommended actions. 6️⃣ Provides structured alert data for further automation or reporting. Setup Guide Prerequisites n8n instance** (Cloud or Self-hosted). Qdrant vector store** with MITRE ATT&CK data embedded. OpenAI API key** (for AI-based threat processing). Zendesk account** (for ticket enrichment, if applicable). Clean Mitre Data Python Script Cleaned Mitre Data Full Mitre Data Steps to Set Up 1️⃣ Embed MITRE ATT&CK data into Qdrant This workflow pulls MITRE ATT&CK data from Google Drive and loads it into Qdrant. The data is vectorized using OpenAI embeddings for fast retrieval. 2️⃣ Deploy the n8n Chatbot The chatbot listens for SIEM alerts and sends them to the AI processing pipeline. Alerts are analyzed using an AI agent trained on MITRE ATT&CK. 3️⃣ Enrich Zendesk Tickets The workflow extracts MITRE ATT&CK techniques from alerts. It updates Zendesk tickets with contextual threat intelligence. The remediation steps are included as internal notes for SOC teams. How to Customize This Workflow 🔧 Modify the chatbot trigger: Adapt the chatbot node to receive alerts from Slack, Microsoft Teams, or any other tool. 🔧 Change the SIEM input source: Connect your workflow to Splunk, Elastic SIEM, or Chronicle Security. 🔧 Customize remediation steps: Use a custom AI model to tailor remediation responses based on organization-specific security policies. 🔧 Extend ticketing integration: Modify the Zendesk node to also work with Jira, ServiceNow, or another ITSM platform. Why This Workflow is Powerful ✅ Saves time: Automates alert triage & classification. ✅ Improves security posture: Helps SOC teams act faster on threats. ✅ Leverages AI & vector search: Uses LLM-powered enrichment for real-time context. ✅ Works across platforms: Supports n8n Cloud, Self-hosted, and Qdrant. 🚀 Get Started Now! 📖 Watch the Setup Video 💬 Have Questions? Join the Discussion in the YouTube Comments!

Entrust customer service to AI using n8n and Kommo! Using this workflow, you can make the AI agent answer customer questions for your managers. See how it works in the video. Advantages of integration Works with any message channel that is connected to Kommo (telegram, whatsapp, facebook) Understands voice and text messages You can stop for a specific transaction or contact if you need a person's help. It is possible to supplement the AI agent with additional tools to suit your needs Where it can be useful In customer support In the qualification of clients When invoicing How it works 1) Any incoming message to the Kommo chats is sent by the webhook to n8n 2) n8n processes the webhook according to the specified logic 3) n8n sends a reply message to the Kommo chat Installation Steps 1) Install workflow 2) Follow the instructions to connect the kommo to the n8n 3) Set up Credentials for OpenAI 4) Fill in the Credentials as shown in the workflow notes 5) Activate Workflow 6) Write your first message as client

The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp. Set up steps: Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables in the workflow. Use the Create collection HTTP request node to initialize the collection in Qdrant. Vectorize Documents: Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. Use the Embeddings OpenAI node to generate embeddings for the downloaded files. Store the vectorized documents in Qdrant using the Qdrant Vector Store node. Configure Webhooks: Ensure both Verify and Respond webhooks have the same URL. Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. Set Up AI Agent: Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. Test Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp.

The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. Set up steps: To configure this workflow effectively, follow these detailed setup instructions: ElevenLabs Agent Creation: Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n'). Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele". Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests. Qdrant Collection Initialization: Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly. Document Vectorization: Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. AI Agent Configuration: Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. Testing Workflow: Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. Integration with Website: Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

UPDATE This is an updated version of the previous Ultimate Content Generator for WordPress v2. https://n8n.io/workflows/2737-ultimate-content-generator-for-wordpress/ It includes an additional workflow to generate content using Anthropic Claude in addition to OpenAI 4o. I also mofidied it to use the AI Agent Tool node with structured output for more consistent outputs. Lastly, the workflow updates a new table in AirTable with the published blog post url and featured image url. Overview- This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress. It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly. Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy. Features Content Creation: AI-Powered Content: Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions. Custom Prompts: Tailor the AI-generated content to match your brand’s tone and voice. SEO Optimization: RankMath Plugin Integration: Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized. Content Management: Airtable Integration: Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators. Visuals: Branded Featured Images: Automatically generates on-brand images for every post. Publishing: Effortless Formatting: Adapts content to fit your WordPress theme and schedules it for publication. Workflow Steps Trigger: Initiated manually or on a schedule. Content Management: Retrieves and organizes ideas from Airtable. Content Generation: Generates AI-driven blog content tailored to your audience. SEO Optimization: Automatically updates RankMath with SEO details. Featured Image Creation: Produces on-brand images for the post. Publishing: Formats and schedules the post on WordPress. Prerequisites API Keys: OpenAI Claude Airtable WordPress REST API Custom Code: Add a small update to your WordPress theme’s functions.php file to enable seamless automation. Customization Replace Airtable with another content management system if preferred. Adjust AI prompts to reflect different tones, styles, or industries. Add integrations for additional plugins, analytics, or storage services. Usage Import the workflow into your n8n instance. Configure API credentials for WordPress, Airtable, OpenAI, Claude and Slack. Update your functions.php file with the provided code snippet. Customize prompts and Airtable structure for your content needs. Trigger the workflow manually or set it on a schedule. Notes Experiment with Airtable views or add filters for more granular control over your content pipeline. Extend the workflow to include social media posting or analytics tracking. Feel free to adapt and extend this workflow to meet your specific needs! 🎉

How it works This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits: Retrieves company policies, benefits, and HR documents from BambooHR. Uses AI to analyze and answer employee questions based on company records. Identifies the most relevant contact person for escalations. Seamlessly integrates with company systems to provide real-time HR assistance. Set up steps: Estimated time: ~20 minutes Connect your BambooHR account to allow policy retrieval. Configure AI parameters and access control settings. (Optional) Set up the employee lookup tool for personalized responses. Test the chatbot to ensure accurate responses and seamless integration. Benefits This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries. Outperform BambooHR's "Ask BambooHR" Chatbot #1. Superior specificity of replies to general inquiries #2. More appropriate escalations when responding to sensitive employee concerns

This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted response. It ensures the response is professional and formatted in HTML (e.g., using `, , , ` tags where necessary). Email Sending: The Send Email node sends the reviewed and formatted response back to the original sender. Vector Database Integration: The Qdrant Vector Store node retrieves relevant information from a vector database (Qdrant) to assist in generating accurate responses. This is particularly useful for emails classified as "Company info request". The Embeddings OpenAI node generates embeddings for the email content, which are used to query the vector database. Document Vectorization: The workflow includes steps to create and refresh a Qdrant collection (Create collection and Refresh collection nodes). Documents from Google Drive are downloaded (Get folder and Download Files nodes), processed into embeddings (Embeddings OpenAI1 node), and stored in the Qdrant vector store (Qdrant Vector Store1 node). Set Up Steps Configure Email Trigger: Set up the Email Trigger (IMAP) node with the appropriate IMAP credentials to monitor the email inbox. Set Up AI Models: Configure the DeepSeek R1, OpenAI, and DeepSeek nodes with the appropriate API credentials for text summarization, response generation, and review. Set Up Email Classification: Define the categories in the Email Classifier node (e.g., "Company info request", "Other"). Ensure the OpenAI 4-o-mini node is configured to assist in classification. Set Up Vector Database: Configure the Qdrant Vector Store and Qdrant Vector Store1 nodes with the appropriate Qdrant API credentials and collection details. Set up the Embeddings OpenAI and Embeddings OpenAI1 nodes to generate embeddings for the email content and documents. Set Up Document Processing: Configure the Get folder and Download Files nodes to access and download documents from Google Drive. Use the Token Splitter and Default Data Loader nodes to process and split the documents into manageable chunks for vectorization. Set Up Email Sending: Configure the Send Email node with the appropriate SMTP credentials to send responses. Test the Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all steps execute correctly. Verify that emails are summarized, classified, and responded to accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling incoming emails. Key Features Automated Email Handling**: Automatically processes incoming emails, summarizes them, and generates professional responses. AI-Powered Classification**: Uses AI to classify emails into relevant categories for targeted responses. Vector Database Integration**: Retrieves relevant information from a vector database to enhance response accuracy. Document Vectorization**: Processes and stores documents from Google Drive in a vector database for quick retrieval. Professional Email Formatting**: Ensures responses are professionally formatted and concise.

Who is this template for? This workflow template is designed for any professionals seeking relevent data from database using natural language. How it works Each time user ask's question using the n8n chat interface, the workflow runs. Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions. Once AI Agent has the data, it uses it to form answer and returns it to the user. Set up instructions Complete the Set up credentials step when you first open the workflow. You'll need a Postgresql Credentials, and OpenAI api key. Template was created in n8n v1.77.0

How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials like IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials section since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems like Google Sheets or similar platforms where records might need tracking post-interaction completion.

How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Additionally, there's a Text Classifier node designed to categorize feedback from humans as either "Approved" or "Declined", guiding whether the email should proceed directly to being sent or require additional editing. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials such as IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems where records might need tracking post-interaction completion, such as Google Sheets or similar platforms.

This n8n workflow template is designed to integrate a DeepSeek AI agent with Telegram, incorporating long-term memory capabilities for personalized and context-aware responses. Here's a detailed breakdown: Core Features Telegram Integration Uses a webhook to receive messages from Telegram users. Validates user identity and message content before processing. AI-Powered Responses Employs DeepSeek's AI models for conversational interactions. Includes memory capabilities to personalize responses based on past interactions. Error Handling Sends an error message if the input cannot be processed. Model Options 🧠 DeepSeek-V3 Chat**: Handles general conversational tasks. DeepSeek-R1 Reasoning**: Provides advanced reasoning capabilities for complex queries. Memory Buffer Window**: Maintains session context for ongoing conversations. Quick Setup 🛠️ Telegram Webhook Configuration Set up a webhook using the Telegram Bot API: https://api.telegram.org/bot{my_bot_token}/setWebhook?url={url_to_send_updates_to} Replace {my_bot_token} with your bot's token and {url_to_send_updates_to} with your n8n webhook URL. Verify the webhook setup using: https://api.telegram.org/bot{my_bot_token}/getWebhookInfo DeepSeek API Configuration Base URL: https://api.deepseek.com Obtain your API key from the DeepSeek platform. Implementation Details 🔧 User Validation The workflow validates the user's first name, last name, and ID using data from incoming Telegram messages. Only authorized users proceed to the next steps. Message Routing Routes messages based on their type (text, audio, or image) using a switch node. Ensures appropriate handling for each message format. AI Agent Interaction Processes text input using DeepSeek-V3 or DeepSeek-R1 models. Customizable system prompts define the AI's behavior and rules, ensuring user-centric and context-aware responses. Memory Management Retrieves long-term memories stored in Google Docs to enhance personalization. Saves new memories based on user interactions, ensuring continuity across sessions.

Automate Pinterest Analysis & AI-Powered Content Suggestions With Pinterest API This workflow automates the collection, analysis, and summarization of Pinterest Pin data to help marketers optimize content strategy. It gathers Pinterest Pin performance data, analyzes trends using an AI agent, and delivers actionable insights to the Marketing Manager via email. This setup is ideal for content creators and marketing teams who need weekly insights on Pinterest trends to refine their content calendar and audience engagement strategy. Prerequisites Before setting up this workflow, ensure you have the following: Pinterest API Access & Developer Account Sign up at Pinterest Developers and obtain API credentials. Ensure you have access to both Organic and Paid Pin data. Airtable Account & API Key Create an account at Airtable and set up a database. Obtain an API key from Account Settings. AI Agent for Trend Analysis An AI-powered agent (such as OpenAI's GPT or a custom ML model) is required to analyze Pinterest trends. Ensure integration with your workflow automation tool (e.g., Zapier, Make, or a custom Python script). Email Automation Setup Configure an SMTP email service (e.g., Gmail, Outlook, SendGrid) to send the summarized results to the Marketing Manager. Step-by-Step Guide to Automating Pinterest Pin Analysis 1. Scheduled Trigger for Data Collection At 8:00 AM (or your preferred time), an automated trigger starts the workflow. Adjust the timing based on your marketing schedule to optimize trend tracking. 2. Fetch Data from Pinterest API Retrieve recent Pinterest Pin performance data, including impressions, clicks, saves, and engagement rate. Ensure both Organic and Paid Ads data are labeled correctly for clarity. 3. Store Data in Airtable Pins are logged and categorized in an Airtable database for further analysis. Sample Airtable Template for Pinterest Pins | Column Name | Description | |---------------|---------------------------------------| | pin_id | Unique identifier for each Pin | | created_at | Timestamp of when the Pin was created | | title | Title of the Pin | | description| Short description of the Pin | | link | URL linking to the Pin | | type | Type of Pin (e.g., organic, ad) | 4. AI Agent Analyzes Pinterest Trends The AI model reviews the latest Pinterest data and identifies: Trending Topics & Keywords** Engagement Patterns** Audience Interests & Behavior Changes** Optimal Posting Times & Formats** 5. Generate Content Suggestions with AI The AI Agent recommends new Pin ideas and content calendar updates to maximize engagement. Suggestions include creative formats, hashtags, and timing adjustments for better performance. 6. Summary & Insights Generated by AI A concise report is created, summarizing Pinterest trends and actionable insights for content strategy. 7. Email Report Sent to the Marketing Manager The summary is emailed to the Marketing Manager to assist with content planning and execution. The report includes: Performance Overview of Recent Pins Trending Content Ideas Best Performing Pin Formats AI-Generated Recommendations This workflow enables marketing teams to automate Pinterest analysis and optimize their content strategy through AI-driven insights. 🚀

Chat with an AI and ask questions about your Meta Ads metrics. Get key metrics like impressions, spend, reach, conversions, CTR, CPC… you name it. Who is this template for? Marketing professionals who want an AI assistant to analyze their Meta Ads data. You can also replace the Telegram node with another chat app, like WhatsApp, Slack, or Discord. Which languages are supported? Any of the 85+ languages supported by OpenAI. How to set up Instructions are included within the workflow itself. Check out my other templates 👉 https://n8n.io/creators/solomon/

Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document context. 🔹 Agent Node - Generate Context for Each Chunk: We use an Agent node powered by OpenAI’s GPT-4.0-mini via OpenRouter to generate contextual metadata for each chunk, ensuring better retrieval accuracy. 🔹 Prepend Context to Chunks & Create Embeddings - The generated context is prepended to the original chunk, creating context-rich embeddings that improve searchability. 🔹 Google Gemini - Text Embeddings: The processed text is passed through Google Gemini text-embedding-004, which converts the text into semantic vector representations. 🔹 Pinecone Vector Store - Store Embeddings: The final embeddings, along with the enriched chunk content and metadata, are stored in Pinecone, making them easily retrievable for RAG-based AI applications. Use Case: This automation enhances RAG retrieval by ensuring each chunk is contextually aware of the entire document, leading to more accurate AI responses. It’s perfect for applications that require semantic search, AI-powered knowledge management, or intelligent document retrieval. By implementing context-based chunking, this workflow ensures that LLMs retrieve the most relevant data, improving response quality and accuracy in AI-driven applications.

This workflow template creates an AI agent chatbot with long-term memory and note storage using Google Docs and Telegram integration. Google Docs Integration 📄 n8n Google Docs Node Setup Google Credentials Telegram Integration 💬 Telegram Setup Core Features 🌟 AI Agent Integration 🤖 Implements a sophisticated AI agent with memory management capabilities Uses GPT-4o-mini and DeepSeek models for intelligent conversation handling Maintains context awareness through session management Memory System 🧠 Long-term memory storage using Google Docs Separate note storage system for specific information Window buffer memory for maintaining conversation context Intelligent memory retrieval and storage mechanisms Communication Interface 💬 Telegram integration for message handling Real-time message processing and response generation Technical Components 🔧 Memory Architecture 📚 Dual storage system separating memories from notes Automated memory retrieval before each interaction Structured memory saving with timestamps AI Models 🤖 Primary GPT-4o-mini mini model for general interactions DeepSeek-V3 Chat for specialized processing Custom agent system with tool integration Storage Integration 💾 Google Docs integration for persistent storage Separate document management for memories and notes Automated document updates and retrievals

Open Deep Research - AI-Powered Autonomous Research Workflow Description This workflow automates deep research by leveraging AI-driven search queries, web scraping, content analysis, and structured reporting. It enables autonomous research with iterative refinement, allowing users to collect, analyze, and summarize high-quality information efficiently. How it works 🔹 User Input The user submits a research topic via a chat message. 🧠 AI Query Generation A Basic LLM generates up to four refined search queries to retrieve relevant information. 🔎 SERPAPI Google Search The workflow loops through each generated query and retrieves top search results using the SerpAPI API. 📄 Jina AI Web Scraping Extracts and summarizes webpage content from the URLs obtained via SerpAPI. 📊 AI-Powered Content Evaluation An AI Agent evaluates the relevance and credibility of the extracted content. 🔁 Iterative Search Refinement If the AI finds insufficient or low-quality information, it generates new search queries to improve results. 📜 Final Report Generation The AI compiles a structured markdown report, including sources with citations. Set Up Instructions 🚀 Estimated setup time: ~10-15 minutes ✅ Required API Keys:** SerpAPI → For Google Search results Jina AI → For text extraction OpenRouter → For AI-driven query generation and summarization ⚙️ n8n Components Used:** AI Agents with memory buffering for iterative research Loops to process multiple search queries efficiently HTTP Requests for direct API interactions with SerpAPI and Jina AI 📝 Recommended Enhancements:** Add sticky notes in n8n to explain each step for new users Implement Google Drive or Notion Integration to save reports automatically 🎯 Ideal for: ✔️ Researchers & Analysts - Automate background research ✔️ Journalists - Quickly gather reliable sources ✔️ Developers - Learn how to integrate multiple AI APIs into n8n ✔️ Students - Speed up literature reviews 🔗 Completely free and open-source! 🚀

Who Is This For? This workflow is designed for AI engineers, automation specialists, and content creators who need a scalable system to dynamically manage prompts stored in GitHub. It eliminates manual updates, enforces required variable checks, and ensures that AI interactions always receive fully processed prompts. 🚀 What Problem Does This Solve? Manually managing AI prompts can be inefficient and error-prone. This workflow: ✅ Fetches dynamic prompts from GitHub ✅ Auto-populates placeholders with values from the setVars node ✅ Ensures all required variables are present before execution ✅ Processes the formatted prompt through an AI agent 🛠 How This Workflow Works This workflow consists of three key branches, ensuring smooth prompt retrieval, variable validation, and AI processing. 1️⃣ Retrieve the Prompt from GitHub (HTTP Request → Extract from File → SetPrompt) The workflow starts manually or via an external trigger. It fetches a text-based prompt stored in a GitHub repository. The Extract from File Node retrieves the content from the GitHub file. The SetPrompt Node stores the prompt, making it accessible for processing. 📌 Note: The prompt must contain n8n expression format variables (e.g., {{ $json.company }}) so they can be dynamically replaced. 2️⃣ Extract & Auto-Populate Variables (Check All Prompt Vars → Replace Variables) A Code Node scans the prompt for placeholders in the n8n expression format ({{ $json.variableName }}). The workflow compares required variables against the setVars node: ✅ If all variables are present, it proceeds to variable replacement. ❌ If any variables are missing, the workflow stops and returns an error listing them. The Replace Variables Node replaces all placeholders with values from setVars. 📌 Example of a properly formatted GitHub prompt: Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. This ensures seamless replacement when processed in n8n. 3️⃣ AI Processing & Output (AI Agent → Prompt Output) The Set Completed Prompt Node stores the final, processed prompt. The AI Agent Node (Ollama Chat Model) processes the prompt. The Prompt Output Node returns the fully formatted response. 📌 Optional: Modify this to use OpenAI, Claude, or other AI models. ⚠️ Error Handling: Missing Variables If a required variable is missing, the workflow stops execution and provides an error message: ⚠️ Missing Required Variables: ["launch_date"] This ensures no incomplete prompts are sent to AI agents. ✅ Example Use Case 📜 GitHub Prompt File (Using n8n Expressions) Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. 🔹 Variables in setVars Node { "company": "PropTechPro", "features": "AI-powered Property Management", "launch_date": "March 15, 2025" } ✅ Successful Output Hello PropTechPro, your product AI-powered Property Management launches on March 15, 2025. 🚨 Error Output (If Missing launch_date) ⚠️ Missing Required Variables: ["launch_date"] 🔧 Setup Instructions 1️⃣ Connect Your GitHub Repository Store your prompt in a public or private GitHub repo. The workflow will fetch the raw file using the GitHub API. 2️⃣ Configure the SetVars Node Define the required variables in the SetVars Node. Make sure the variable names match those used in the prompt. 3️⃣ Test & Run Click Test Workflow to execute. If variables are missing, it will show an error. If everything is correct, it will output the fully formatted prompt. ⚡ How to Customize This Workflow 💡 Need CRM or Database Integration? Connect the setVars node to an Airtable, Google Sheets, or HubSpot API to pull variables dynamically. 💡 Want to Modify the AI Model? Replace the Ollama Chat Model with OpenAI, Claude, or a custom LLM endpoint. 📌 Why Use This Workflow? ✅ No Manual Updates Required – Fetches prompts dynamically from GitHub. ✅ Prevents Broken Prompts – Ensures required variables exist before execution. ✅ Works for Any Use Case – Handles AI chat prompts, marketing messages, and chatbot scripts. ✅ Compatible with All n8n Deployments – Works on Cloud, Self-Hosted, and Desktop versions.

What it is: In version 1.78, n8n introduced a dedicated node to use the OpenRouter service, which lets you to use a lot of different LLM models and providers and change models on the fly in an agentic workflow. For prior n8n versions, there's a workaround to make OpenRouter accessible, by using the OpenAI node with a OpenRouter-specific BaseURL. This trivial workflow demonstrates this for version before 1.78, so that you can use different LLM model dynamically with the available n8n nodes for OpenAI LLM and OpenAI credentials. What you can do: Use any of the OpenRouter models Have the model even dynamically configured or changing (by some external config, some rule, or some specific chat message) Setup steps: Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Configure the "OpenRouter" credentials with your own credentials, using an OpenAI type credential, but making sure in the credential's config form its "Base URL" is set to https://openrouter.ai/api/v1 so OpenRouter is used instead of OpenAI. Open the "Settings" node and change the model value to any valid model id from the OpenRouter models list or even have the model property set dynamically

Automate LinkedIn Prospecting with AI Agent and Get Results Straight to Google Sheets! A practical AI-powered workflow that helps you find relevant LinkedIn profiles using natural language queries and saves the results automatically to Google Sheets. 🎯 What It Actually Does Accepts natural language requests (e.g., "Find marketing managers in Paris") Uses AI to extract search parameters: Job titles Industry Location Searches for matching LinkedIn profiles Saves profile URLs and titles to Google Sheets 🛠️ Technical Components AI Query Processor Converts natural language to structured search parameters Handles various input formats and languages Maintains context for accurate parameter extraction Search Engine Integration Uses Google Custom Search API Handles pagination for multiple results Filters for actual LinkedIn profile URLs Data Storage Automatically saves to Google Sheets Stores profile titles and URLs Easy to access and export 📋 Prerequisites OpenAI API key Google Custom Search API credentials Google Sheets access 💡 Adaptation Possibilities This template can be modified to scrape other websites by: Changing the search domain in the HTTP Request node Adjusting the URL filter in the Code node Modifying the column structure in Google Sheets Updating the AI prompt for different parameter extraction Example adaptations: Twitter profile finder GitHub repository search Company website finder Professional blog discovery 🔧 Setup Instructions API Setup Configure Google Custom Search API Set up OpenAI API Prepare Google Sheets Workflow Configuration Import both workflows Connect your API credentials Set up your Google Sheet Test with a sample query 📝 Usage Example Input: "Find software developers in London working in fintech" Output: A Google Sheet containing: LinkedIn profile URLs of software developers Profile titles/headlines Ready for your review and outreach #AIAgent #WebScraping #Automation #n8n #Workflow #LinkedInProspecting

This n8n automation is designed to analyze cryptocurrency trends by extracting, processing, and interpreting candlestick charts using AI-powered agents. The workflow enhances technical analysis by integrating real-time market data, ensuring traders receive accurate and actionable insights. Workflow Breakdown: 🔹 1. Chat Node – Provide Crypto Information Users enter a crypto symbol in the required format (EXCHANGE:SYMBOL), such as BINANCE:BTCUSDT. This ensures the workflow retrieves the correct market data. 🔹 Retrieve Daily Candlestick Chart Once the input is received, the workflow fetches the full-day candlestick chart for the selected crypto, providing a macro-level market trend. 🔹 AI Agent – Analyze Daily Chart The first AI agent, powered by Google Gemini 2.0 Flash via OpenRouter, analyzes the daily candlestick pattern to detect trends and potential market signals. 🔹 Fetch 5-Minute Candlestick Chart To refine the analysis, the workflow retrieves a 5-minute interval candlestick chart, allowing for real-time market movement evaluation. 🔹 AI Agent – Advanced Candlestick Analysis This AI agent combines the 5-minute chart with the daily analysis to provide an in-depth market prediction. Here’s where the real magic happens—AI interprets short-term trends in the context of long-term movements. 🔹 Shared Windows Buffer – Store Intermediate Results The Windows Buffer temporarily stores analysis results, ensuring seamless data flow between AI agents for a more structured interpretation. 🔹 Serp API – Retrieve Crypto News To add fundamental analysis, the Serp API tool fetches the latest crypto-related news from the web, providing additional market context. 🔹 Chat Window – Deliver Final Insights Once all data points are processed, the final market analysis is displayed in the chat window, combining technical and fundamental analysis for a more comprehensive trading strategy. Use Case: This automation simplifies crypto market analysis by integrating AI-driven technical and fundamental insights. It’s ideal for: ✅ Traders looking for automated market insights ✅ Analysts seeking structured candlestick interpretations ✅ Developers wanting to integrate AI-powered trading analysis into applications By automating candlestick chart analysis, this workflow enhances decision-making and reduces manual effort, making it a valuable tool for anyone involved in cryptocurrency trading. Setup Instructions: 1️⃣ Import the workflow to your n8n instance 2️⃣ Prepare & add credentials: OpenRouter (Google Gemini 2.0 Flash) Get a free API key from https://openrouter.ai/ Serp API (for news retrieval) Get a free API key from https://serpapi.com/ Chart Img (For candlestick chart) Get a free API key from https://chart-img.com/ 3️⃣ Run the workflow and get AI-powered crypto insights! NOTE Remember: Not all LLM models are capable of analyzing image data, so choose your model wisely. Limitations: All free services come with usage limits. For example, OpenRouter has a daily limit, and once it's consumed, the workflow will stop processing further requests. Disclaimer This workflow is designed purely for educational and research purposes. It does not provide financial advice. 🚀

Functionality This workflow automates the handling of incoming emails by summarizing their content, generating appropriate responses, and validating the responses through a "Human-in-the-Loop" system. It integrates with IMAP email services (e.g., Gmail, Outlook) and uses AI models to streamline the email response process. The workflow ensures that all AI-generated responses are reviewed by a human before being sent, maintaining a high level of professionalism and accuracy. This approach is particularly useful for businesses that receive a high volume of emails and need to respond quickly while ensuring quality control. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (OpenAI) to generate a concise summary of the email. The summary is limited to 100 words and is written in a professional tone. Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. Human-in-the-Loop Approval: The Set Email text node prepares the drafted response for approval. The Approve Email node sends the drafted response to a human approver (e.g., an internal email address) for review. The email includes: The original message. The AI-generated response. The Approved? node checks if the response has been approved by the human reviewer. If approved, the workflow proceeds to send the response; otherwise, it stops. Sending the Response: The Send Email node sends the approved response back to the original sender. Key Features Automated Email Summarization**: Summarizes incoming emails to provide a quick overview of the content. AI-Powered Response Generation**: Drafts professional responses to emails using AI. Human-in-the-Loop Approval**: Ensures all AI-generated responses are reviewed and approved by a human before being sent. IMAP Integration**: Works with IMAP email services like Gmail and Outlook. Efficient Email Management**: Reduces the time and effort required to handle incoming emails while maintaining high-quality responses. This workflow is ideal for businesses looking to automate their email response process while maintaining control over the quality of outgoing communications. It leverages AI to handle repetitive tasks and ensures that all responses are reviewed by a human, providing a balance between automation and human oversight.

Use Case When creating chatbots that interface through applications such as Telegram and WhatsApp, users can often sends multiple shorter messages in quick succession, in place of a single, longer message. This workflow accounts for this behaviour. What it Does This workflow allows users to send several messages in quick succession, treating them as one coherent conversation instead of separate messages requiring individual responses. How it Works When messages arrive, they are stored in a Supabase PostgreSQL table The system waits briefly to see if additional messages arrive If no new messages arrive within the waiting period, all queued messages are: Combined and processed as a single conversation Responded to with one unified reply Deleted from the queue Setup Create a table in Supabase called message_queue. It needs to have the following columns: user_id (uint8), message (text), and message_id (uint8) Add your Telegram, Supabase, OpenAI, and PostgreSQL credentials Activate the workflow and test by sending multiple messages the Telegram bot in one go Wait ten seconds after which you will receive a single reply to all of your messages How to Modify it to Your Needs Change the value of Wait Amount in the Wait 10 Seconds node in order to to modify the buffering window Add a System Message to the AI Agent to tailor it to your specific use case Replace the OpenAI sub-node to use a different language model

UTM Link Creator & QR Code Generator with Scheduled Google Analytics Reports This workflow enables marketers to generate UTM-tagged links, convert them into QR codes, and automate performance tracking in Google Analytics with scheduled reports every 7 days. This solution helps monitor traffic sources from different marketing channels and optimize campaign performance based on analytics data. Prerequisites Before implementing this workflow, ensure you have the following: Google Analytics 4 (GA4) Account & Access Ensure you have a GA4 property set up. Access to the GA4 Data API to schedule performance tracking. Refer to the Google Analytics Data API Overview for more information. Airtable Account & API Key Create an Airtable base to store UTM links, QR codes, and analytics data. Obtain an Airtable API key from your Account Settings. Detailed instructions are available in the Airtable API Authentication Guide. Step-by-Step Guide to Setting Up the Workflow 1. Generate UTM Links Create a form or interface to input: Base URL** (e.g., https://example.com) Campaign Name** (utm_campaign) Source** (utm_source) Medium** (utm_medium) Term** (Optional: utm_term) Content** (Optional: utm_content) Append UTM parameters to generate a trackable URL. 2. Store UTM Links & QR Codes in Airtable Set up an Airtable base with the following columns: UTM Link** QR Code** Campaign Name** Source** Medium** Date Created** Adjust as needed based on your tracking requirements. For guidance on setting up your Airtable base and using the API, refer to the Airtable Web API Documentation. 3. Convert UTM Links to QR Codes Use a QR code generator API (e.g., goqr.me, qrserver.com) to generate QR codes for each UTM link and store them in Airtable. 4. Schedule Google Analytics Performance Reports (Every 7 Days) Use the Google Analytics Data API to pull weekly performance reports based on UTM parameters. Extract key metrics such as: Sessions Users Bounce Rate Conversions Revenue (if applicable) Store the data in Airtable for tracking and analysis. Adjust timeframe as needed For more details on accessing and using the Google Analytics Data API, consult the Google Analytics Data API Overview. Benefits of This Workflow ✅ Track Marketing Campaigns: Easily monitor which channels drive traffic. ✅ Automate QR Code Creation: Seamless integration of UTM links with QR codes. ✅ Scheduled Google Analytics Reports: No manual reporting—everything runs automatically. ✅ Improve Data-Driven Decisions: Optimize ad spend and marketing strategies based on performance insights. This version ensures proper Markdown structure, includes relevant documentation links, and improves readability. Let me know if you need any further refinements! 🚀

Intelligent Email Support System with Vector Database Overview This n8n workflow automates email support using AI and vector database technology to provide smart, context-aware responses. It seamlessly integrates email automation and document management, ensuring efficient customer support. 📌 System Components ✉️ Email Support System Email Monitoring & Classification Gmail trigger node monitoring inbox AI-powered email classification Intelligent routing (support vs non-support inquiries) AI Response Generation LangChain agent for response automation OpenAI integration for NLP-driven replies Vector-based knowledge retrieval Automated draft creation in Gmail Vector Database System Supabase vector store for document management OpenAI embeddings for vector conversion Fast and efficient similarity search 📂 Document Management System Google Drive Integration Monitors specific folders for new/updated files Automatic document processing Supports various file formats Document Processing Pipeline Auto file download & text extraction Smart text chunking for better indexing Embedding generation via OpenAI Storage in Supabase vector database 🔄 Workflow Processes 📧 Email Support Flow Monitor Gmail inbox for new emails AI classification of incoming messages Route support emails to AI response generator Perform vector similarity search for knowledge retrieval Generate personalized AI-driven response Create email drafts in Gmail 📁 Document Management Flow Monitor Google Drive for new/updated files Auto-download and process documents Clean up outdated vector entries for updated files Extract and split document text efficiently Generate OpenAI embeddings Store processed data in Supabase vector DB ⚙️ Setup Instructions 1️⃣ Prerequisites Supabase** account & project OpenAI API key** Gmail account** with OAuth2 setup Google Drive API** access n8n installation** 2️⃣ Supabase Database Setup -- Create the vector extension create extension if not exists vector; -- Create the documents table create table documents ( id bigserial primary key, content text, metadata jsonb, embedding vector(1536) ); -- Create an index for similarity search create index on documents using ivfflat (embedding vector_cosine_ops) with (lists = 100); 3️⃣ Google Drive Setup Create & configure two monitored folders: RAG folder for new documents documents Assign correct folder permissions Add folder IDs to the workflow 4️⃣ Document Processing Configuration Set up triggers for file creation and file updates Configure text extraction: Define chunk size & overlap settings Set document metadata processing 🔍 Maintenance & Optimization 📌 Regular Tasks Monitor system performance Update the knowledge base regularly Review AI response quality Optimize vector search parameters Clean up outdated document embeddings ✅ Best Practices Document Organization Maintain structured folders & naming conventions Keep knowledge base content updated System Optimization Track AI classification accuracy Tune response times & chunk sizes Perform regular database maintenance 🛠️ Troubleshooting Email Issues Verify Gmail API credentials Check AI service uptime Monitor classification performance Document Processing Issues Ensure correct file permissions Validate extraction & embedding processes Debug vector database insertions

This workflow template demonstrates how to create an AI-powered agent that provides users with current weather information and Wikipedia summaries. By integrating n8n with Ollama's local Large Language Models (LLMs), this template offers a seamless and privacy-conscious solution for real-time data retrieval and summarization. Who is this for? Developers and Enthusiasts: Individuals interested in building AI-driven workflows without relying on external APIs. Privacy-Conscious Users: Those who prefer processing data locally to maintain control over their information. Educators and Students: Learners seeking hands-on experience with AI integrations and workflow automation. What problem does this workflow solve? Accessing up-to-date weather information and concise Wikipedia summaries typically requires multiple API calls to external services, which can raise privacy concerns and incur costs. This workflow addresses these issues by utilizing Ollama's self-hosted LLMs within n8n, enabling users to retrieve and process information locally. What this workflow does: User Input Capture: Begins with a chat interface where users can input queries. AI Processing: The input is sent to an AI Agent node configured with Ollama's LLMs, which interprets the query and determines the required actions. Weather Retrieval: For weather-related queries, the workflow fetches current weather data from a specified source. Wikipedia Summarization: For queries seeking information, it retrieves relevant Wikipedia content and generates concise summaries. Setup: Install Required Tools: Ollama: Install and run Ollama to manage local LLMs. Configure n8n Workflow: Import the provided workflow template into your n8n instance. Set up the AI Agent node to connect with Ollama's API. Ensure nodes responsible for fetching weather data and Wikipedia content are correctly configured. Run the Workflow: Start the workflow and interact with the chat interface to test various queries. How to customize this workflow to your needs: Automate Triggers: Set up scheduled triggers to provide users with regular updates, such as daily weather forecasts or featured Wikipedia articles.

AppSheet Intelligent Query Orchestrator A friendly, practical tool that makes working with AppSheet data simpler and more efficient. This workflow is your go-to helper for building precise queries without getting lost in a sea of different tables. Background Previously, I built a community node to enable this functionality: Appsheet n8n Community node How It Works This workflow fetches the most up-to-date schema and taxonomy from your Google Sheet mirror and constructs a custom query using key components: TableName**: Specifies exactly which table to query. Selector**: Uses powerful functions like SELECT(), FILTER(), and CONTAINS() to filter data with precision. Columns Required: Extracts only the essential fields, keeping the payload **lean and focused. Natural Language Search Query: Provides a **clear, descriptive context that helps refine and re-rank results. Real-World Use Cases This orchestrator is designed for various industries, making data retrieval effortless: 📦 Supply Chain & Manufacturing Find the right product based on specific attributes. Locate suppliers that meet certain quality or pricing criteria. Obtain details about the lowest-priced raw materials. 🛍 Retail & E-commerce Match customer queries to the most relevant product listings. Identify inventory levels and stock variations. Compare pricing and product features across vendors. 🏥 Healthcare Retrieve patient records based on specific attributes. Track inventory of medical supplies. Schedule and manage appointments dynamically. 🎓 Education Monitor student attendance or performance metrics. Allocate resources and track equipment usage. Manage events and class schedules efficiently. 🔧 Field Services & Maintenance Schedule maintenance tasks by matching service requirements. Track asset conditions and inventory for field equipment. Monitor work orders and dispatch field teams based on real-time data. Examples: Iterative Refinement This workflow operates iteratively, refining the query until it finds the best match—even if it takes multiple rounds. This makes it incredibly versatile for complex inventory management, procurement, and precise data retrieval. In a Nutshell The AppSheet Intelligent Query Orchestrator is like having a smart assistant that: ✅ Understands your data structure ✅ Builds the perfect query every time ✅ Handles a variety of real-world scenarios with ease 🚀 Practical, adaptable, and ready to tackle your toughest data challenges!

🎯 Who is this automation for? Perfect for content creators, marketers, and any X (Twitter) user looking to grow their account quickly without manual effort. ⚙️ How does this workflow work? ✔️ Auto-publishing: Whenever a new article appears in your RSS feed, a tweet is generated and posted on X. ✔️ Optimized content: A unique, engaging tweet with text and an image is created in under 280 characters. (Verified users can bypass this limit for longer tweets). ✔️ Free X API: No extra subscription costs for automation. ✔️ AI-powered: Use models like OpenAI, gemini and Perplexity, but you can configure your preferred model, whether free or paid. 🛠️ Easy Setup 📂 The downloaded file includes a step-by-step video tutorial. If you need help, I’m here for you! 💡 Save time and grow your X audience efficiently with this n8n automation.

What it is Chat with your event schedule from Google Sheets in Telegram: "When is the next meetup?" "How many events are there next month?" "Who presented most often?" "Which future meetups have no presenters yet?" This workflow lets you chat with a telegram bot about past, present and future events that are scheduled in a Google Spreadsheet. (Info: This proof-of-concept was created as a demo for a hackathon of an AI & Developer Meetup in Da Nang (Vietnam) that uses a telegram group to organize) Who it is for If you want an easy way for your audience to get information about your events, you can us this workflow for the same purpose, or easily adapt it to your needs and different use-cases where you want to query smaller amounts of tabular data in natural language. How it works Upon getting triggered by a chat message to a telegram bot, the schedule of meetups is retrieved from Google Spreadsheets, converted into a markdown table syntax and fed into the system prompt of an LLM (we're using OpenRouter in this example), whose output is posted back as answer into the same telegram chat. Setup steps TO REVIEWING IN ACTION As the reviewer of this workflow, you can temporarily use it via an existing telegram bot, simply point your telegram client to https://t.me/AiDaNangBot and start to ask questions like: "When is the next meetup?" "What future meetings do not have presenters?" "Who presented on Future of Human Relationships?" To build upon this workflow: Import the workflow Customize the Google Docs credentials for your individual access Create a telegram bot and connect it to the workflow by entering its API token into the credentials used in the telegram trigger node In the "Settings" node, replace the "scheduleURL" with the URL of your own copy of the Google Spreadsheet or a copy of the Event Schedule Template Sheet to spin off your own – whereby the structure of the spreadsheet doesn't matter, it's just important that you semantically structure your information in dedicated columns clearly labeled in the header row.

Automated Workflow for Google Form Submissions, GitHub Issues, and Discord Notifications This workflow streamlines how new Google Form submissions are processed by automatically creating GitHub issues and sending real-time notifications to a Discord channel through a webhook. Who Is This Template For? Developers** looking to centralize bug reporting and issue tracking. Project Managers** seeking an efficient way to log tasks and updates. QA Teams** that need a fast, automated process for reporting and resolving issues. Workflow Overview Google Form Trigger The workflow begins with a Google Sheets Trigger node that checks for new form submissions every minute. Add New Form Submissions to Google Sheets Each new submission is recorded in a dedicated Google Sheet for easy reference and archiving. Filter Out Already Posted Issues An If node checks if the issue is already posted on GitHub by looking for an existing GitHub link in the sheet. Format Message / Output Parsing If the issue is new, an OpenAI Chat Model node generates a structured output, including a title, description, and suggested fix. Add Issue to GitHub The structured output is used to create a new issue in the specified GitHub repository. Send Notification to Discord A Discord webhook is triggered to send a notification to your chosen channel, including a link to the newly created GitHub issue. Add GitHub Link to the Sheet The GitHub issue link is added back to the Google Sheet for easy cross-referencing. Setup Steps 1. Google Sheets Setup Create/Designate a Google Sheet:** Ensure it’s set to receive form submissions. Configure the Trigger Node:** Provide the correct document ID and sheet name in the Google Sheets Trigger node. 2. OpenAI Configuration API Credentials:** Set up the OpenAI Chat Model node with valid OpenAI API credentials. Prompt Definition:** Create a prompt that formats the form submission data into a structured output (e.g., title, description, suggested fix). 3. GitHub Configuration OAuth Credentials:** Configure the GitHub node with your GitHub OAuth credentials. Target Repository:** Specify the repository where new issues should be created. 4. Discord Webhook Webhook URL:** Obtain a webhook URL from your Discord server. Notification Setup:** Use the HTTP Request node to send notifications to your chosen Discord channel. 5. Google Sheets Update Document & Sheet Name:** Provide the same Google Sheet details used in the first step. Add GitHub Link:** Update the corresponding row with the newly created GitHub issue link. Recommended Google Sheet Columns Timestamp**: Automatically recorded when the form is submitted. Issue Title**: Generated by the OpenAI Chat Model. Issue Description**: Detailed breakdown of the issue. GitHub Link**: Automatically populated once the issue is created. Discord Notification Status** (optional): Indicates whether the notification was sent successfully. Additional Notes Duplicate Prevention**: The conditional logic ensures existing issues aren’t recreated on GitHub. AI-Powered Formatting**: OpenAI helps structure the issue details, providing clarity for developers. Real-Time Alerts**: Discord notifications keep your entire team updated on new issues as they arise.

n8n Creators Leaderboard Workflow Why Use This Workflow? The n8n Creators Leaderboard Workflow is a powerful tool for analyzing and presenting detailed statistics about workflow creators and their contributions within the n8n community. It provides users with actionable insights into popular workflows, community trends, and top contributors, all while automating the process of data retrieval and report generation. Benefits Discover Popular Workflows**: Identify workflows with the most unique visitors and inserters (weekly and monthly). Understand Community Trends**: Gain insights into what workflows are resonating with the community. Recognize Top Contributors**: Highlight impactful creators to foster collaboration and inspiration. Save Time with Automation**: Automates data fetching, processing, and reporting for efficiency. Use Cases For Workflow Creators**: Track performance metrics of your workflows to optimize them for better engagement. For Community Managers**: Identify trends and recognize top contributors to improve community resources. For New Users**: Explore popular workflows as inspiration for building your own automations. How It Works This workflow aggregates data from GitHub repositories containing statistics about workflow creators and their templates. It processes this data, filters it based on user input, and generates a detailed Markdown report using an AI agent. Key Features Data Aggregation: Fetches creator and workflow statistics from GitHub JSON files. Custom Filtering: Focuses on specific creators based on a username provided via chat. AI-Powered Reports: Generates comprehensive Markdown reports with summaries, tables, and insights. Output Flexibility: Saves reports locally with timestamps for easy access. Data Retrieval & Processing Creators Data**: Retrieved via an HTTP Request node from a JSON file containing aggregated statistics about creators. Workflows Data**: Pulled from another JSON file with workflow metrics like visitor counts and inserter statistics. Data Merging**: Combines creator and workflow data by matching usernames to provide enriched statistics. Report Generation The AI agent generates a Markdown report that includes: A summary of the creator’s contributions. A table of workflows with key metrics (e.g., unique visitors, inserters). Insights into trends or community feedback. The report is saved locally as a file with a timestamp for tracking purposes. Quick Start Guide Prerequisites Ensure your n8n instance is running. Verify that the GitHub base URL and file variables are correctly set in the Global Variables node. Confirm that your OpenAI credentials are configured for the AI Agent node. How to Start Activate the Workflow: Make sure the workflow is active in your n8n environment. Trigger via Chat: Use the Chat Trigger node to initiate the workflow by sending a message like: show me stats for username [desired_username] Replace [desired_username] with the username you want to analyze. Processing & Report Generation: The workflow fetches data, processes it, and generates a Markdown report. View Output: The final report is saved locally as a file (with a timestamp), which you can review to explore leaderboard insights.

This n8n workflow is designed to automate the aggregation, processing, and reporting of community statistics related to n8n creators and workflows. Its primary purpose is to generate insightful reports that highlight top contributors, popular workflows, and key trends within the n8n ecosystem. Here's how it works and why it's important: How It Works Data Retrieval: The workflow fetches JSON data files from a GitHub repository containing statistics about creators and workflows. It uses HTTP requests to access these files dynamically based on pre-defined global variables. Data Processing: The data is parsed into separate streams for creators and workflows. It processes the data to identify key metrics such as unique weekly and monthly inserters/visitors. Ranking and Filtering: The workflow sorts creators by their weekly inserts and workflows by their popularity. It selects the top 10 creators and top 50 workflows for detailed analysis. Report Generation: Using AI tools like GPT-4 or Google Gemini, the workflow generates a Markdown report summarizing trends, contributors, and workflow statistics. The report includes tables with detailed metrics (e.g., unique visitors, inserters) and insights into why certain workflows are popular. Distribution: The report is saved locally or uploaded to Google Drive. It can also be shared via email or Telegram for broader accessibility. Automation: A schedule trigger ensures the workflow runs daily or as needed, keeping the reports up-to-date. Why It's Important Community Insights**: This workflow provides actionable insights into the n8n community by identifying impactful contributors and popular workflows. This fosters collaboration and innovation within the ecosystem. Time Efficiency**: By automating data collection, processing, and reporting, it saves significant time and effort for community managers or administrators. Recognition of Contributors**: Highlighting top creators encourages engagement and recognizes individuals driving value in the community. Trend Analysis**: The workflow helps uncover patterns in usage, enabling better decision-making for platform improvements or feature prioritization. Scalability**: With its modular design, this workflow can be easily adapted to include additional metrics or integrate with other tools.

The 🌐🤖 AI Agent Chatbot with Jina.ai Webpage Scraper workflow is a powerful automation designed to integrate real-time web scraping capabilities into an AI-driven chatbot. Here's how it works and why it's important: How It Works 💬 Chat Trigger: The workflow begins when a user sends a chat message, triggering the "When chat message received" node. 🧠 AI Agent Processing: The input is passed to the "Jina.ai Web Scraping Agent," which uses advanced AI logic to interpret the user’s query and determine the information needed. 🌐 Web Scraping: The agent utilizes the "HTTP Request" node to scrape real-time data from a user-provided URL, enabling the chatbot to fetch and analyze live website content. 🗂️ Memory Management: The "Window Buffer Memory" node ensures context retention by storing and managing conversational history, allowing for seamless interactions. 🤖 Language Model Integration: The scraped data is processed using the "gpt-4o-mini" language model, which generates clear, accurate, and contextually relevant responses for the user. Why It's Cool ⏱️ Real-Time Information Retrieval**: This workflow empowers users to access up-to-date web content directly through a chatbot, eliminating manual web searches. ✨ Enhanced User Experience**: By combining web scraping with conversational AI, it delivers precise answers tailored to user queries in real time. 🔄 Versatility**: It can be applied across various domains, such as customer support, research, or data analysis, making it a valuable tool for businesses and individuals alike. ⚙️ Automation Efficiency**: Automating web scraping and response generation saves time and effort while ensuring accuracy.

Who is this template for This template is for marketers, SEO specialists, or content managers who need to analyze keywords to identify which ones contain references to a specific area or topic, in this case – IT software, services, tools, or apps. Use case Automating the process of scanning a large list of keywords to determine if they reference known IT products or services (like ServiceNow, Salesforce, etc.), and updating a Google Sheet with this classification. This helps in categorizing keywords for targeted SEO campaigns, content creation, or market analysis. How this workflow works Fetches keyword data from a Google Sheet Processes keywords in batches to prevent rate limiting Uses an AI agent (OpenAI) to analyze each keyword and determine if it contains a reference to an IT service/software Updates the original Google Sheet with the results in a "Service?" column Continues processing until all keywords are analyzed Set up steps Connect your Google Sheets account credentials Set the Google Sheet document ID (currently using "Copy of Sheet1 1") Configure the OpenAI API credentials for the AI agent Adjust the batch size (currently 6) if needed based on your API rate limits Ensure the Google Sheet has the required columns: "Number", "Keyword", and "Service?" The AI agent's prompt is highly customizable to match different identification needs. For example, instead of looking for IT software/services, you could modify the prompt to identify: Industry-specific terms (healthcare, finance, education) Geographic references (cities, countries, regions) Product categories (electronics, clothing, food) Competitor brand mentions Here's how you could modify the prompt for different use cases: Copy // For identifying educational content keywords "Check the keyword I provided and define if this keyword relates to educational content, courses, or learning materials and return yes or no." // For identifying local service keywords "Check the keyword I provided and determine if it contains location-specific terms (city names, neighborhoods, regions) that suggest local service intent and return yes or no." // For identifying competitor mentions "Check the keyword I provided and determine if it mentions any of our competitors (CompetitorA, CompetitorB, CompetitorC) and return yes or no." `

AI-Powered Social Media Content Generator & Publisher 🚀 This AI-driven n8n workflow automates social media content creation and publishing across LinkedIn, Instagram, Facebook, and Twitter (X). It generates engaging, platform-optimized posts using Google Gemini AI, based on user inputs such as a post title, keywords, and an uploaded image. The workflow ensures seamless content generation and publishing, making it a perfect tool for marketers, business owners, influencers, and content creators. 🌟 Features & Benefits ✅ AI-Generated Social Media Posts – Uses Google Gemini AI to create high-quality, optimized content. ✅ Multi-Platform Support – Automatically generates posts for LinkedIn, Instagram, Facebook, and Twitter (X). ✅ Hashtag & SEO Optimization – Includes trending hashtags to enhance visibility and engagement. ✅ Image Upload & Processing – Allows image uploads for Instagram and Facebook using imgbb and Facebook Graph API. ✅ Automated Publishing – Posts are automatically published on all selected platforms. ✅ Custom Call-to-Action – Each platform's post is optimized with CTAs for better engagement. ✅ User-Friendly Form Submission – Easy-to-use form where users can enter post titles, keywords, links, and images. ✅ Performance Tracking – Provides confirmation and tracking links for published posts. 📌 How It Works 1️⃣ User Submission Form Fill out the form with Post Title, Keywords, and an Optional Link. Upload an image for Instagram & Facebook posts. 2️⃣ AI Content Generation Google Gemini AI generates optimized content for each platform. The AI ensures professional, engaging, and audience-specific content. 3️⃣ Content Review Users review and approve the AI-generated content before publishing. 4️⃣ Automated Publishing Posts are automatically published on LinkedIn, Facebook, Instagram, and Twitter (X). Uses Facebook Graph API, LinkedIn API, Twitter API, and Instagram API. 5️⃣ Post Confirmation & Tracking Get links to track published posts on each platform. 🛠️ Prerequisites Before using this workflow, ensure you have: ✅ n8n Instance (Cloud or Self-Hosted) ✅ Social Media API Credentials (Facebook, Instagram, LinkedIn, Twitter API) ✅ Google Gemini AI API Key ✅ imgbb API Key (for image hosting) Buy My Book: Mastering n8n on Amazon Full Courses & Tutorials: http://lms.syncbricks.com 📺 YouTube Video Tutorial 🎥 Watch the step-by-step tutorial on how to set up and use this n8n workflow template: 🔗 YouTube Tutorial - AI-Powered Social Media Posting in n8n 🎯 Use Cases 📌 Marketing Agencies – Automate client content scheduling. 📌 Businesses & Brands – Maintain a consistent brand presence on social media. 📌 Content Creators & Influencers – Generate high-quality posts quickly. 📌 E-commerce & Startups – Promote products and services effortlessly. 📌 Corporate & Enterprise Teams – Streamline internal and external communications. 👨‍💻 Creator Information 👤 Developed by: Amjid Ali 🌐 Website: SyncBricks 📧 Email: info@syncbricks.com 💼 LinkedIn: Amjid Ali 📺 YouTube: SyncBricks 💡 Support & Contributions If you find this workflow helpful, consider supporting my work: 👉 Donate via PayPal For full courses on * AI Automation*, visit: 📚 SyncBricks LMS 📚 AI and Auotmation Course 👉 Get Started with N8N

🎥 YouTube Video AI Agent Workflow This n8n workflow template allows you to interact with an AI agent that extracts details and the transcript of a YouTube video using a provided video ID. Once the details and transcript are retrieved, you can chat with the AI agent to explore or analyze the video's content in a conversational and insightful manner. 🌟 How the Workflow Works 🔗 Input Video ID: The user provides a YouTube video ID as input to the workflow. 📄 Data Retrieval: The workflow fetches essential details about the video (e.g., title, description, upload date) and retrieves its transcript using YouTube's Data API and additional tools for transcript extraction. 🤖 AI Agent Interaction: The extracted details and transcript are processed by an AI-powered agent. Users can then ask questions or engage in a conversation with the agent about the video's content, such as: Summarizing the transcript. Analyzing key points. Clarifying specific sections. 💬 Dynamic Responses: The AI agent uses natural language processing (NLP) to generate contextual and accurate responses based on the video data, ensuring a smooth and intuitive interaction. 🚀 Use Cases 📊 Content Analysis**: Quickly analyze long YouTube videos by querying specific sections or extracting summaries. 📚 Research and Learning**: Gain insights from educational videos or tutorials without watching them entirely. ✍️ Content Creation**: Repurpose transcripts into blogs, social media posts, or other formats efficiently. ♿ Accessibility**: Provide an alternative, text-based way to interact with video content for users who prefer reading over watching. 🛠️ Resources for Getting Started Google Cloud Console** (for API setup): Visit Google Cloud's Get Started Guide to configure your API access. YouTube Data API Key Setup**: Follow this guide to create and manage your YouTube Data API key. Install n8n Locally**: Refer to this installation guide for setting up n8n on your local machine. ✨ Sample Prompts "Tell me about this YouTube video with id: JWfNLF_g_V0" "Can you provide a list of key takeaways from this video with id: [youtube-video-id]?"

🎦🚀 YouTube Video Comment Analysis Agent This n8n workflow is designed to help YouTube creators analyze video details and comments to generate a comprehensive and actionable report. The workflow provides insights into video performance, audience engagement, and viewer feedback, helping creators identify trends, interests, and opportunities for future content creation. ✨ Key Features Video Performance Analysis: Extracts metrics like views, likes, and comments to evaluate the video's success. Comment Sentiment Analysis: Determines the tone of comments (positive, neutral, or negative) to understand audience sentiment. Recurring Themes Detection: Identifies common topics or questions in comments to highlight viewer interests. Engagement Drivers: Pinpoints what aspects of the video resonated most with viewers. Actionable Recommendations: Offers strategies for creating follow-up content or improving future videos. Keyword Suggestions: Extracts frequently mentioned terms for better discoverability on YouTube. Collaboration Opportunities: Suggests potential partnerships based on viewer feedback or related channels. 🛠️ How to Use Set Up Workflow Variables: Add your GOOGLE_API_KEY and the VIDEO_ID of the YouTube video you want to analyze in the "Workflow Variables" node. Ensure your Google API key has access to the YouTube Data API. Run the Workflow: Trigger the workflow manually or through another workflow using the "Execute Workflow Trigger" node. The workflow will fetch video details and comments using pagination to ensure all data is captured. Generate Insights: The workflow processes video details and comments to create a detailed report with actionable insights. Outputs include sentiment analysis, engagement drivers, content opportunities, and audience profiling. View or Share Results: The report is converted into Markdown and can be emailed via Gmail or saved to Google Drive as a document. 🌟 Value from This Workflow Gain a deeper understanding of your audience's preferences and feedback. Identify trends and engagement drivers to replicate success in future videos. Discover new content opportunities based on viewer questions and suggestions. Improve discoverability by leveraging keyword suggestions extracted from comments. Build stronger connections with your audience by addressing their needs effectively.

The AI-Powered Financial Chart Analyzer is a cutting-edge automation tool that streamlines financial analysis using n8n workflows, AI agents, and MacOS Shortcuts. It enables users to capture, process, and analyze candlestick charts for both stocks and cryptocurrencies. By integrating powerful tools like ChatGPT-4o-mini (via OpenRouter), MarketStack, and SerpAPI, this automation provides real-time market insights, technical analysis, and AI-driven stock trend predictions. Workflow The Webhook node will receive image data from a macOS shortcut in Base64 format. The Convert to File node will convert the Base64 image into a binary file. The AI Agent node will process the binary image. It utilizes OpenRouter, Windows buffer memory, MarketStack, and the SerpAPI tool. Remember to use a model capable of processing images; otherwise, the workflow will throw an error. We use the MarketStack tool to fetch the latest stock data; however, it is rarely used. SerpAPI is used for market research, such as news and insights about stocks. Finally, the Markdown node converts Markdown to HTML. The response is then sent to the Webhook. Use Case Traders & Investors: Quickly analyze candlestick charts and identify trading opportunities. Financial Analysts: Automate chart interpretation and data aggregation for in-depth reports. AI & Automation Enthusiasts: Experiment with AI-driven financial workflows using n8n. Business Owners: Gain real-time stock insights to make informed investment decisions. Setup Instructions Install MacOS Shortcut Download the MacOS Shortcut provided with this product and double-click on it to install. If you don’t have the Shortcuts app (parent app) installed, first download it from the App Store, then follow Step 1. Set Up Workflow Import the n8n workflow provided with this product. Set Up Credentials 🔹Webhook Authentication Create an API key (you can use a key generation tool or simply use a custom string). Add the API key to your n8n Webhook and MacOS Shortcut. If you prefer not to use authentication, remove it from both the n8n Webhook and the MacOS Shortcut. 🔹OpenRouter API Setup Get a free API key from OpenRouter and add it to your workflow. Free API keys have usage limits. OpenRouter provides multiple models—ensure that the selected model supports image processing. 🔹MarketStack API Setup Get a free API key from MarketStack and use it in your workflow. Free API keys have usage limits. 🔹SerpAPI Setup Get a free API key from SerpAPI and use it in your workflow. Free API keys have usage limits. Disclaimer This tool is designed for educational and informational purposes only. The AI-generated insights should not be considered as financial advice. Users should conduct their own research before making investment decisions. AgentsOps is not responsible for any financial losses incurred from using this automation.

Automate Your Video Content Creation: AI-Powered Video Generation This n8n template streamlines the creation of high-quality, faceless videos, automating the entire process from scriptwriting to final video production. Leveraging AI tools like Leonardo AI and Creatomate, this workflow empowers content creators to efficiently produce engaging videos without on-camera presence. Who is this for? This template is ideal for: Content creators looking to scale their presence on YouTube, Instagram, and TikTok. Marketers seeking to automate video marketing campaigns. Anyone wanting to produce professional-looking videos quickly and easily. Individuals wishing to create faceless video content. What problem is this workflow solving? Creating engaging videos can be time-consuming and resource-intensive. This workflow eliminates the manual effort involved in scripting, image sourcing, and video editing, allowing creators to focus on strategy and content ideation. It addresses the challenge of consistent video production by automating repetitive tasks. What this workflow does: This workflow automates the following steps: AI-Driven Scriptwriting:** Uses an LLM (default: DeepSeek V3) to generate a compelling video script based on your system prompt and desired number of scenarios. AI Image Generation:** Integrates with Leonardo AI to create visually appealing images for each scenario in the script. You define the image style and resolution. Automated Video Editing:** Connects to Creatomate to assemble the script and images into a polished video, ready for upload. You’ll use a pre-created “AI generated story template” within Creatomate. Setup: Video Script Generation:** Provide a detailed system prompt describing your video’s topic, target audience, and key message. Specify the number of scenarios. Select your preferred LLM (DeepSeek V3 is the default). Image Generation:** Connect your Leonardo AI account. Choose your preferred image generation model and set the video resolution. Craft a detailed prompt for Leonardo AI, defining the image style (e.g., “cinematic,” “cartoon,” “realistic”). Video Editing:** Link your Creatomate account. Create an empty “AI generated story template” in Creatomate. Copy the cURL from the Creatomate template to the video generation node. Link your ElevenLabs account to Creatomate and choose the desired voice. How to customize this workflow: Script Customization:** Refine the system prompt to guide the AI. Experiment with different LLMs. Image Style:** Adjust the Leonardo AI prompt. Explore different image generation models. Video Editing:** Modify the Creatomate template to customize transitions, text overlays, and other elements. Add voiceover using the ElevenLabs integration in Creatomate. Category: Marketing, Social Media, Content Creation

Are you looking to create a counseling chatbot that provides emotional support and mental health guidance through the LINE messaging platform ? This guide will walk you through connecting LINE with powerful AI language models like GPT-4 to build a chatbot that supports users in navigating their emotions, offering 24/7 conversational therapy and accessible mental health resources . By leveraging LINE's webhook integration and Azure OpenAI , this template allows you to design a chatbot that is both empathetic and efficient, ensuring users receive timely and professional responses. Whether you're a developer, counselor, or business owner, this guide will help you create a customizable counseling chatbot tailored to your audience's needs. Who Is This Template For? Developers who want to integrate AI-powered chatbots into the LINE platform for mental health applications. Counselors & Therapists looking to expand their reach and provide automated emotional support to clients outside of traditional sessions. Businesses & Organizations focused on improving mental health accessibility and offering innovative solutions to their users. Educators & Nonprofits seeking tools to provide free or low-cost counseling services to underserved communities. How this work? Line Webhook to receive new message Send loading animation in Line Check if the input is text or not Send the text as prompt in chat model (GPT 4o) Reply the message to user (you'll need 'edit field' to format it before reply) Pre-Requisites You have access to the LINE Developers Console. An Azure OpenAI account with necessary credentials. Set-up To receive messages from LINE, configure your webhook: Set up a webhook in LINE Developer Console. Copy the Webhook URL from the Line Chatbot node and paste it into the LINE Console. Ensure to remove any 'test' part when moving to production. The loading animation reassures users that the system is processing their request. Authorize using header authorization Message Handling Use the Check Message Type IsText? node to verify if the incoming message is text. If the message type is text, proceed with ChatGPT processing; otherwise, send a reply indicating non-text inputs are not supported. AI Agent Configuration Define the system message within the AI Agent node to guide the conversation based on desired interaction principles. Connect the Azure OpenAI Chat Model to the AI Agent. Formatting Responses Ensure responses are properly formatted before sending them back to the user. Reply Message Use the ReplyMessage - Line node to send the formatted response. Ensure proper header authorization using Bearer tokens.

This n8n workflow automates YouTube video metadata generation using AI. It extracts video transcripts, analyzes content, and produces optimized titles, descriptions, tags, hashtags, and call-to-action elements. Additionally, the workflow integrates affiliate and promotional links to enhance overall video performance. Key Features Automated Metadata Generation Utilizes an AI agent integrated with OpenAI GPT-4 to generate engaging metadata based on the provided video transcript. SEO and Engagement Optimization Creates keyword-rich, well-structured content that boosts search engine visibility and audience engagement. Affiliate and Promotional Integration Retrieves pre-set promotional and affiliate links using a Google Docs integration. Direct YouTube Update Automatically updates video details on YouTube via the YouTube API. Customization Allows you to modify the AI prompt to tailor metadata for your specific niche. Workflow Breakdown User Submission Users supply the YouTube video link, transcript, and optionally, focus keywords. Video ID Extraction The workflow converts the YouTube URL into a video ID to streamline automation. Link Retrieval Affiliate and course links are fetched from a designated Google Docs file. AI-Powered Metadata Generation The AI agent generates the video title, description, tags, hashtags, and call-to-action elements. Metadata Formatting and Update The generated metadata is structured and directly updated on YouTube. Confirmation A success message is displayed upon completion of the update process. Setup and Configuration Deploying the Workflow Deploy the workflow in n8n and ensure all integrations are properly set up. Configuring Integrations Google Docs:** Configure credentials to retrieve affiliate and promotional links. OpenAI (GPT-4):** Set up credentials for AI-powered metadata generation. YouTube API:** Enter your API credentials to enable automatic video updates. User Input Requirements Provide a valid YouTube video link and its corresponding transcript. Optionally, include focus keywords to further enhance metadata accuracy. Ideal For YouTube Content Creators:** Automate video descriptions and boost SEO. Digital Marketers:** Enhance content for improved search rankings and audience engagement. Affiliate Marketers:** Simplify the insertion of promotional and affiliate links. AI & Automation Enthusiasts:** Explore the integration of AI into automated workflows. Additional Resources For further guidance, refer to the tutorial video on this workflow. More courses and resources are available on the SyncBricks website. For support or inquiries, contact Amjid Ali at info@syncbricks.com. You can also support this work via PayPal donations and subscribe for additional AI and automation workflows. Watch the Tutorial:** YouTube Video on This Workflow More Courses & Resources:** SyncBricks LMS Full Course on ERPNext & AI Automation Connect:** Email: info@syncbricks.com Website: SyncBricks YouTube: SyncBricks Channel LinkedIn: Amjid Ali Support & Subscribe:** Donate via PayPal Subscribe for More AI & Automation Workflows

Use case Manually monitoring Reddit for viable business ideas is time-consuming and inconsistent. This workflow automatically analyzes trending Reddit discussions using AI to surface high-potential opportunities, filter irrelevant content, and generate actionable insights - saving entrepreneurs 10+ hours weekly in market research. What this workflow does This AI-powered workflow automatically collects trending Reddit discussions, analyzes posts for viable business opportunities using GPT-4, applies smart filters to exclude low-value content, and generates scored opportunity reports with market insights. It identifies unmet customer needs through sentiment analysis, prioritizes high-potential ideas using custom criteria, and outputs structured data to Google Sheets for actionable decision-making. Setup Add Reddit,Google and OpenAI credentials Configure target subreddits in Subreddit node Test workflow by testing workflow Review generated opportunity report in Google Sheets How to adjust this template Change data sources**: Replace Reddit trigger with Twitter/X or Hacker News API Modify criteria**: Adjust scoring thresholds in Opportunity Calculator node Add integrations**: Create automatic Slack alerts for urgent opportunities Generate draft business plans using AI Document Writer

✍️🌄 WordPress + AI Content Creator This workflow automates the creation and publishing of multi-reading-level content for WordPress blogs. It leverages AI to generate optimized articles, automatically creates featured images, and provides versions of the content at different reading levels (Grade 2, 5, and 9). How It Works Content Generation & Processing 🎯 Starts with a manual trigger and a user-defined blog topic Uses AI to create a structured blog post with proper HTML formatting Separates and validates the title and content components Saves a draft version to Google Drive for backup Multi-Reading Level Versions 📚 Automatically rewrites the content for different reading levels: Grade 9: Sophisticated language with appropriate metaphors Grade 5: Simplified with light humor and age-appropriate examples Grade 2: Basic language with simple metaphors and child-friendly explanations WordPress Integration 🌐 Creates a draft post in WordPress with the Grade 9 version Generates a relevant featured image using Pollinations.ai Automatically uploads and sets the featured image Sends success/error notifications via Telegram Setup Steps Configure API Credentials 🔑 Set up WordPress API connection Configure OpenAI API access Set up Google Drive integration Add Telegram bot credentials for notifications Customize Content Parameters ⚙️ Adjust reading level prompts as needed Modify image generation settings Set WordPress post parameters Test and Deploy 🚀 Run a test with a sample topic Verify all reading level versions Check WordPress draft creation Confirm notification system This workflow is perfect for content creators who need to maintain a consistent blog presence while catering to different audience reading levels. It's especially useful for educational content, news sites, or any platform that needs to communicate complex topics to diverse audiences.

🤖 AI-Powered RAG Chatbot with Google Drive Integration This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI. How It Works Document Processing & Storage 📚 Retrieves documents from a specified Google Drive folder Processes and splits documents into manageable chunks Extracts metadata using AI for enhanced search capabilities Stores document vectors in Qdrant for efficient retrieval Intelligent Chat Interface 💬 Provides a conversational interface powered by Google Gemini Uses RAG to retrieve relevant context from stored documents Maintains chat history in Google Docs for reference Delivers accurate, context-aware responses Vector Store Management 🗄️ Features secure delete operations with human verification Includes Telegram notifications for important operations Maintains data integrity with proper version control Supports batch processing of documents Setup Steps Configure API Credentials: Set up Google Drive & Docs access Configure Gemini AI API Set up Qdrant vector store connection Add Telegram bot for notifications Add OpenAI Api Key to the 'Delete Qdrant Points by File ID' node Configure Document Sources: Set Google Drive folder ID Define Qdrant collection name Set up document processing parameters Test and Deploy: Verify document processing Test chat functionality Confirm vector store operations Check notification system This workflow is ideal for organizations needing to create intelligent chatbots that can access and understand large document repositories while maintaining context and providing accurate responses through RAG technology.

AI-Powered Chatbot Workflow with MySQL Integration This guide shows you how to deploy a chatbot that lets you query your database using natural language. You will build a system that accepts chat messages, retains conversation history, constructs dynamic SQL queries, and returns responses generated by an AI model. By following these instructions, you will have a working solution that integrates n8n’s AI Agent capabilities with MySQL. Prerequisites Before you begin, ensure that you have the following: An active n8n instance (self-hosted or cloud) running version 1.50.0 or later. Valid MySQL credentials configured in n8n. API credentials for the Groq Chat Model (or your preferred AI language model). Basic familiarity with SQL and n8n node concepts such as chat triggers and memory buffers. Access to the n8n Docs on AI Agents for further reference. Workflow Setup 1. Chat Interface & Trigger When Chat Message Received** This node listens for incoming chat messages via a webhook. When a message arrives, it triggers the workflow immediately. 2. Conversation Memory Chat History** This memory buffer node stores the last 10 interactions. It supplies conversation context to the AI Agent, ensuring that responses consider previous messages. 3. AI Agent Core AI Agent (Tools Agent)** The AI Agent node orchestrates the conversation by receiving the chat input and conversation history. It dynamically generates SQL queries based on your requests and coordinates calls to external tools (such as MySQL nodes). 4. Database Interactions MySQL Node** This node executes the SQL query generated by the AI Agent. You reference the query using an expression (e.g., {{$node["AI Agent"].json.sql_query}}), allowing the agent’s output to control data retrieval. MySQL Schema Node** This node retrieves a list of base tables from your MySQL database (excluding system schemas). The agent uses this information to understand the available tables. MySQL Definition Node** This node fetches detailed metadata (such as column names, data types, and relationships) for a specific table. The table and schema names are supplied dynamically by the AI Agent. 5. Language Model Processing Groq Chat Model** This node connects to the Groq Chat API to generate text completions. It processes the combined input (chat message, context, and data fetched from MySQL) and produces the final response. 6. Guidance & Customization Sticky Notes** These nodes provide guidance on: Switching the chat model if you wish to use another provider (e.g., OpenAI or Anthropic). Adjusting the maximum token count per interaction. Customizing the SQL queries and the context window size. They help you modify the workflow to suit your environment and requirements. Workflow Connections The Chat Trigger passes the incoming message to the AI Agent. The Chat History node supplies conversation context to the AI Agent. The AI Agent calls the MySQL nodes as external tools, generating and sending dynamic SQL queries. The Groq Chat Model processes the consolidated input from the agent and outputs the natural language response delivered to the user. Testing the Workflow Send a chat message using the chat interface. Observe how the AI Agent processes the input and generates a corresponding SQL query. Verify that the MySQL nodes execute the query and return data. Confirm that the Groq Chat Model produces a coherent natural language response. Refer to the sticky notes for guidance if you need to fine-tune any node settings. Next Steps and References Customize Your AI Model** Replace the Groq Chat Model with another language model (such as the OpenAI Chat Model) by updating the node credentials and configuration. Enhance Memory Settings** Adjust the Chat History node’s context window to retain more or fewer messages based on your needs. Modify SQL Queries** Update the SQL queries in the MySQL nodes to match your specific database schema and desired data. Further Reading** Consult the n8n Docs on AI Agents for additional details and examples to expand your workflow’s capabilities. Set Up a Website Chatbot** Copy & Paste and replace the placeholders in the following code to embed the chatbot into your personal or company's website: View in CodePen 🡥 By following these steps, you will deploy a robust AI chatbot workflow that integrates with your MySQL database, allowing you to query data using natural language.

Telegram Personal Assistant with Long-Term Memory & Note-Taking This n8n workflow transforms your Telegram bot into a powerful personal assistant that handles voice, photo, and text messages. The assistant uses AI to interpret messages, save important details as long-term memories or notes in a Baserow database, and recall information for future interactions. 🌟 How It Works Message Reception & Routing Telegram Integration: The workflow is triggered by incoming messages on your Telegram bot. Dynamic Routing: A switch node inspects the message to determine whether it's voice, text, or photo (with captions) and routes it for the appropriate processing. Content Processing Voice Messages: Audio files are retrieved and sent to an AI transcription node to convert spoken words into text. Text Messages: Text is directly captured and prepared for analysis. Photos: If an image is received, the bot fetches the file (and caption, if provided) and uses an AI-powered image analysis node to extract relevant details. AI-Powered Agent & Memory Management The core AI agent (powered by GPT-4o-mini) processes the incoming message along with any previous conversation history stored in PostgreSQL memory buffers. Long-Term Memory: When a message contains personal or noteworthy information, the assistant uses a dedicated tool to save this data as a long-term memory in Baserow. Note-Taking: For specific instructions or reminders, the assistant saves concise notes in a separate Baserow table. The AI agent follows defined rules to decide which details are saved as memories and which are saved as notes. Response Generation After processing the message and updating memory/notes as needed, the AI agent crafts a contextual and personalized response. The response is sent back to the user via Telegram, ensuring smooth and natural conversation flow. 🚀 Key Features Multimodal Input:** Seamlessly handles voice, photo (with captions), and text messages. Long-Term Memory & Note-Taking:** Uses a Baserow database to store personal details and notes, enhancing conversational context over time. AI-Driven Contextual Responses:** Leverages an AI agent to generate personalized, context-aware replies based on current input and past interactions. User Security & Validation:** Incorporates validation steps to verify the user's Telegram ID before processing, ensuring secure and personalized interactions. Easy Baserow Setup:** Comes with a clear setup guide and sample configurations to quickly integrate Baserow for managing memories and notes. 🔧 Setup Guide Telegram Bot Setup: Create your bot via BotFather and obtain the Bot Token. Configure the Telegram webhook in n8n with your bot's token and URL. Baserow Database Configuration: Memory Table: Create a workspace titled "Memories and Notes". Set up a table (e.g., "Memory Table") with at least two fields: Memory (long text) Date Added (US date format with time) Notes Table: Duplicate the Memory Table and rename it to "Notes Table". Change the first field's name from "Memory" to "Notes". n8n Workflow Import & Configuration: Import the workflow JSON into your n8n instance. Update credentials for Telegram, Baserow, OpenAI, and PostgreSQL (for memory buffering) as needed. Adjust node settings if you need to customize AI agent prompts or memory management rules. Testing & Deployment: Test your bot by sending various message types (text, voice, photo) to confirm that the workflow processes them correctly, updates Baserow, and returns the appropriate response. Monitor logs to ensure that memory and note entries are correctly stored and retrieved. ✨ Example Interactions Voice Message Processing:** User sends a voice note requesting a reminder. Bot Response: "Thanks for your message! I've noted your reminder and saved it for future reference." Photo with Caption:** User sends a photo with the caption "Save this recipe for dinner ideas." Bot Response: "Got it! I've saved this recipe along with the caption for you." Text Message for Memory Saving:** User: "I love hiking on weekends." Bot Response: "Noted! I’ll remember your interest in hiking." Retrieving Information:** User asks: "What notes do I have?" Bot Response: "Here are your latest notes: [list of saved notes]." 🛠️ Resources & Next Steps Telegram Bot Configuration:** Telegram BotFather Guide n8n Documentation:** n8n Docs Community Forums:** Join discussions and share your customizations! This workflow not only streamlines message processing but also empowers users with a personal AI assistant that remembers details over time. Customize the rules and responses further to fit your unique requirements and enjoy a more engaging, intelligent conversation experience on Telegram!

Personal Portfolio CV Rag Chatbot - with Conversation Store and Email Summary Target Audience This template is perfect for: Individuals looking to create a working professional and interactive personal portfolio chatbot. Developers interested in integrating RAG Chatbot functionality with conversation storage. 1. Description Create a stunning Personal Portfolio CV with integrated RAG Chatbot capabilities, including conversation storage and daily email summaries. 2.Features: Training: Setup Ingestion stage Upload your CV to Google Drive and let the Drive trigger updates to read your resume cv and convert it into your vector database (RAG purpose). Modify any parts as needed. Chat & Track: Use any frontend/backend interface to call the chat API and chat history API. Reporting Daily Chat Conversations: Receive daily automatic summaries of chat conversations. Data stored via NocoDB. 3.Setup Guide: Step-by-Step Instructions: Ensure all credentials are ready. Follow the notes provided. Ingestion: Upload your CV to Google Drive. The Drive triggers RAG update in your vector database. You can change the folder name, files and indexname of the vector database accordingly. Chat: Use any frontend/backend interface to call the chat API (refer to the notes for details) . [optional] Use any frontend/backend interface to call the update chat history API (refer to the notes for details). 3.Tracking Chat: Get daily automatic summaries of chat conversations.Format email conversations report as you like. You are ready to go!

The YogiAI workflow automates sending daily yoga pose reminders and related information via Line Push Messages . This automation leverages data from a Google Sheets database containing yoga pose details such as names, image URLs, and links to ensure users receive personalized and engaging content every day. Purpose Provide users with daily yoga pose suggestions tailored to their practice. Deliver visually appealing and informative content through Line's Flex Messages, including images and clickable links. Log user interactions and preferences back into Google Sheets to refine future recommendations. Key Features Automated Daily Reminders : Sends a curated list of yoga poses at a scheduled time (21:30 Bangkok time). Dynamic Content Generation : Uses AI to rewrite and format messages in a user-friendly manner, complete with emojis and clear instructions. Integration with Google Sheets : Pulls data from a predefined Google Sheet and logs interactions for continuous improvement. Customizable Messaging : Ensures JSON outputs are properly formatted for Line’s Flex Message API, allowing for interactive and visually rich content. Data Source Google Sheets Structure The workflow relies on a Google Sheet structured as follows: PoseName : The name of the yoga pose. uri : The image URL representing the pose. url : A clickable link directing users to more information about the pose. Sample Data Layout Supine Angle https://example.com/SupineAngle-tn146.png https://example.com/pose/SupineAngle Warrior II https://example.com/WarriorII-tn146.png https://example.com/pose/WarriorII *Note : Ensure that you update the Google Sheet with your own data. Refer to this sample sheet for reference. * Scheduled Trigger The workflow is triggered daily at 21:30 (9:30 PM) Bangkok Time (Asia/Bangkok) . This ensures timely delivery of reminders to users, keeping them engaged with their yoga practice. Workflow Process Data Retrieval Node: Get PoseName Fetches yoga pose details from the specified range in the Google Sheet. Content Generation Node: WritePosesToday Utilizes Azure OpenAI to craft user-friendly text, complete with emojis and clear instructions. Node: RewritePosesToday Formats the AI-generated text specifically for Line messaging, ensuring compatibility and visual appeal. JSON Formatting Node: WriteJSONflex Generates JSON structures required for Line’s Flex Messages, enabling carousel displays of yoga pose images and links. Node: Fix JSON Ensures all JSON outputs are correctly formatted before being sent via Line. Message Delivery Node: Line Push with Flex Bubble Sends the final message, including both text and Flex Message carousels, directly to users via Line Push Messages. Logging Interactions Nodes: YogaLog & YogaLog2 Logs each interaction back into Google Sheets to track which poses were sent and how often they appear, refining future recommendations. Setup Prerequisites Google Sheets Account : Set up a Google Sheet with the required structure and populate it with your yoga pose data. Line Developer Account : Create a Line channel to obtain necessary credentials for sending push messages. Azure OpenAI Account : Configure access to Azure OpenAI services for generating and formatting content. Intended Audience This workflow is ideal for: Yoga Instructors : Seeking to engage students with daily pose suggestions. Fitness Enthusiasts : Looking to maintain consistency in their yoga practice. Content Creators : Interested in automating personalized and visually appealing content distribution.

Split Test Agent Prompts with Supabase and OpenAI Use Case Oftentimes, it's useful to test different settings for a large language model in production against various metrics. Split testing is a good method for doing this. What it Does This workflow randomly assigns chat sessions to one of two prompts, the baseline and the alternative. The agent will use the same prompt for all interactions in that chat session. How it Works When messages arrive, a table containing information regarding session ID and which prompt to use is checked to see if the chat already exists If it does not, the session ID is added to the table and a prompt is randomly assigned These values are then used to generate a response Setup Create a table in Supabase called split_test_sessions. It needs to have the following columns: session_id (text) and show_alternative (bool) Add your Supabase, OpenAI, and PostgreSQL credentials Modify the Define Path Values node to set the baseline and alternative prompt values. Activate the workflow and test by sending messages through n8n's inbuilt chat Experiment with different chat sessions to test see both prompts in action Next Steps Modify the workflow to test different LLM settings such as temperature Add a method to measure the efficacy of the two alternative prompts

🐶🤖 AI Agent for Pet Shops – Automate Customer Service & Bookings! 🐾💡 Transform Your Pet Shop with AI-Powered Automation! 🚀 Enhance customer experience and optimize operations with this n8n AI Agent designed for pet shops. 📲🐾 Automate client interactions, appointment scheduling, and service recommendations—saving time and increasing revenue! 🔹 Key Features: ✅ Instant WhatsApp responses – AI-powered chatbot handles customer inquiries. 💬 ✅ Automated appointment scheduling – Clients can book services hassle-free. 📅✂️ ✅ Personalized reminders – Reduce no-shows with automated notifications. 📢🐾 ✅ Customer data & service history management – Track interactions effortlessly. 📊📁 ✅ Product & service recommendations – Improve sales with smart suggestions. 🎁🐶 📌 How It Works 1️⃣ The workflow captures customer inquiries via WhatsApp. 2️⃣ AI processes requests, provides information, and offers booking options. 3️⃣ Clients can schedule grooming, vet visits, or other services in seconds. 4️⃣ Automated reminders ensure appointments are remembered. 5️⃣ Customer data is stored for better service personalization. ⚙️ Setup & Customization 🔧 Connect your WhatsApp API (evolution) for instant messaging. 🔧 Integrate with Google Calendar for appointment booking. 🔧 Customize reminders, services, and pricing rules to fit your business. 💡 Reduce manual work, improve customer satisfaction, and scale your pet shop with AI automation! 🐶🤖 [PT-BR] Agente de IA para Pet Shops – Atendimento e Agendamentos Automatizados! 🐾💡 Transforme Seu Pet Shop com Automação Inteligente! 🚀 Otimize o atendimento ao cliente e agilize processos com este Agente de IA para n8n. 📲🐾 Automatize interações, agendamentos e recomendações de serviços—economizando tempo e aumentando as vendas! 🔹 Principais Funcionalidades: ✅ Atendimento automático no WhatsApp – IA responde clientes instantaneamente. 💬 ✅ Agendamento de serviços automatizado – Clientes marcam banho, tosa ou consultas facilmente. 📅✂️ ✅ Lembretes personalizados – Reduza faltas com notificações automáticas. 📢🐾 ✅ Gestão de clientes e histórico de serviços – Controle dados de forma eficiente. 📊📁 ✅ Sugestão de produtos e serviços – Venda mais com recomendações inteligentes. 🎁🐶 📌 Como Funciona 1️⃣ O fluxo recebe perguntas dos clientes via WhatsApp. 2️⃣ A IA processa os pedidos e fornece opções de agendamento. 3️⃣ O cliente escolhe o serviço desejado e agenda em segundos. 4️⃣ Lembretes automáticos garantem que os clientes não esqueçam os horários. 5️⃣ O histórico do cliente é salvo para oferecer um atendimento mais personalizado. ⚙️ Configuração e Personalização 🔧 Conecte sua API do WhatsApp (evolution) para interação automática. 🔧 Integre ao Google Calendar para gerenciar agendamentos. 🔧 Personalize valores, serviços e regras de envio de lembretes conforme sua necessidade. 💡 Automatize processos, melhore a experiência do cliente e escale seu pet shop com IA! 🚀

🌐 Confluence Page AI Chatbot Workflow This n8n workflow template enables users to interact with an AI-powered chatbot designed to retrieve, process, and analyze content from Confluence pages. By leveraging Confluence's REST API and an AI agent, the workflow facilitates seamless communication and contextual insights based on Confluence page data. 🌟 How the Workflow Works 🔗 Input Chat Message The workflow begins when a user sends a chat message containing a query or request for information about a specific Confluence page. 📄 Data Retrieval The workflow uses the Confluence REST API to fetch page details by ID, including its body in the desired format (e.g., storage, view). The retrieved HTML content is converted into Markdown for easier processing. 🤖 AI Agent Interaction An AI-powered agent processes the Markdown content and provides dynamic responses to user queries. The agent is context-aware, ensuring accurate and relevant answers based on the Confluence page's content. 💬 Dynamic Responses Users can interact with the chatbot to: Summarize the page's content. Extract specific details or sections. Clarify complex information. Analyze key points or insights. 🚀 Use Cases 📚 Knowledge Management**: Quickly access and analyze information stored in Confluence without manually searching through pages. 📊 Team Collaboration**: Facilitate discussions by summarizing or explaining page content during team chats. 🔍 Research and Documentation**: Extract critical insights from large documentation repositories for efficient decision-making. ♿ Accessibility**: Provide an alternative way to interact with Confluence content for users who prefer conversational interfaces. 🛠️ Resources for Getting Started Confluence API Setup: Generate an API token for authentication via Atlassian's account management portal. Refer to Confluence's REST API documentation for endpoint details and usage instructions. n8n Installation: Install n8n locally or on a server using the official installation guide. AI Agent Configuration: Set up OpenAI or other supported language models for natural language processing.

This n8n workflow template is designed for developers, system administrators, and IT professionals who manage Linux VPS environments. It leverages an AI chatbot powered by the OpenAI model to interpret and execute SSH commands on a Linux VPS directly from chat messages. The workflow triggers when a specific chat message is received, which is then processed by the AI SysAdmin ReAct Agent to execute predefined SSH commands securely. How It Works Chat Trigger: The workflow starts when a chat message is received via a supported platform (like Slack, Telegram, etc.). AI Processing: The message is passed to the AI SysAdmin ReAct Agent, which uses an embedded OpenAI model to interpret the command and map it to a corresponding SSH action. Command Execution: The interpreted command is securely executed on the target Linux VPS using SSH, with login credentials managed through a secure method embedded within the workflow. Setup Instructions Import the Workflow: Download and import the workflow into your n8n instance. Configure Chat Integration: Set up the chat trigger node by connecting it to your preferred chat platform and configuring the trigger conditions. Set SSH Credentials: Securely input your SSH credentials in the designated SSH login credentials node. Deploy and Test: Deploy the workflow and perform tests to ensure that commands are executed correctly and securely on your VPS. Embrace the future of VPS management with our AI-driven SysAdmin for Linux VPS template. This innovative solution transforms how system administrators interact with and manage their servers, offering a streamlined, secure, and efficient method to handle routine tasks through simple chat commands. With the power of AI at your fingertips, enhance your operational efficiency, reduce response times, and manage your Linux environments more effectively. Get started today to experience a smarter way to manage your systems directly through your chat tool.

Empower Your AI Chatbot with Long-Term Memory and Dynamic Tool Routing This n8n workflow equips your AI agent with long-term memory and a dynamic tools router, enabling it to provide intelligent, context-aware responses while managing tasks across multiple tools. By combining persistent memory and modular task routing, this workflow makes your AI smarter, more efficient, and highly adaptable. 👥 Who Is This For? AI Developers & Automation Enthusiasts: Integrate advanced AI features like long-term memory and task routing without coding expertise. Businesses & Teams: Automate tasks while maintaining personalized, context-aware interactions. Customer Support Teams: Improve user experience with chatbots that remember past interactions. Marketers & Content Creators: Streamline communication across platforms like Gmail and Telegram. AI Researchers: Experiment with persistent memory and multi-tool integration. 🚀 What Problem Does This Solve? This workflow simplifies the creation of intelligent AI systems that retain memory, manage tasks dynamically, and automate notifications across tools like Gmail and Telegram—saving time and improving efficiency. 🛠️ What This Workflow Does Save & Retrieve Memories**: Uses Google Docs for long-term storage to recall past interactions or user preferences. Dynamic Task Routing**: Routes tasks to the right tools (e.g., saving/retrieving memories or sending notifications). AI-Powered Context Understanding**: Combines OpenAI GPT-based short-term memory with long-term memory for smarter responses. Multi-Channel Notifications**: Sends updates via Gmail or Telegram. 🔧 Setup API Credentials: Connect to OpenAI (AI processing), Google Docs (memory storage), Gmail/Telegram (notifications). Customize Parameters: Adjust the AI agent's system message for your use case. Define task-routing rules in the tools router node. Test & Deploy: Verify memory saving/retrieval, task routing, and notification delivery. 💡 How to Customize Modify the system message in the OpenAI node to tailor your agent’s behavior. Add or adjust routing rules for additional tools. Update notification settings to match your communication preferences.

CallForge - AI Sales Call Processing & Insights Extraction Automate sales call analysis with AI-powered insights for sales, marketing, and product teams. Who is This For? This workflow is designed for: ✅ Sales teams looking to extract structured insights from Gong call transcripts. ✅ Marketing professionals seeking AI-driven customer pain points & content strategy. ✅ Product teams needing feedback from sales calls to prioritize feature development. 🔍 What Problem Does This Workflow Solve? Manually analyzing Gong.io sales call transcripts is slow, inconsistent, and lacks structured insights. With CallForge, you can: ✔ Extract AI-powered insights about use cases, objections, competitors, and next steps. ✔ Provide structured marketing & product intelligence to enhance strategy. ✔ Automatically store call insights in Notion and Salesforce for easy access. ✔ Ensure resilience with automated reruns on failed workflows (handling Notion API limits). ✔ Improve decision-making with AI-powered competitor and sentiment analysis. 📌 Key Workflow Features 🎤 AI-Powered Transcript Analysis Uses AI to identify use cases, objections, competitors, and customer pain points. Categorizes insights for sales, marketing, and product teams. 📌 AI Agent Breakdown 🔹 Sales AI Agent – Extracts customer objections, pain points, competitors, and next steps. 🔹 Marketing AI Agent – Identifies recurring topics, keyword trends, and content opportunities. 🔹 Product AI Agent – Captures feature requests and AI/ML-related references. 📊 Structured Output Processing Sales Data Processor* → Stores insights in *Notion & Salesforce** for sales tracking. Marketing Data Processor* → Extracts *SEO & content strategy insights** for marketing teams. Product AI Data Processor* → Logs *customer feedback* to prioritize *feature development**. 💡 Competitor & Integration Analysis Tracks competing products mentioned in calls**. Identifies integration needs**, flagging workarounds used by prospects. 📢 Real-Time Slack Notifications Alerts teams on workflow progress** and completed call analyses. 🔄 Failure Resilience & Automated Re-Runs If a Notion API limit is reached, the process resumes automatically. 🚀 How This Works 🛠 1. Trigger & Call Data Processing The workflow retrieves Gong call transcripts and metadata. Normalizes data**, correcting common mispronunciations like "n8n." 🤖 2. AI Agents Analyze the Call Sales Agent** – Extracts actionable insights for sales follow-ups. Marketing Agent* – Identifies *recurring themes* and *keyword trends**. Product Agent* – Captures *feature requests and AI/ML usage mentions**. 📡 3. Data is Stored in Notion & Salesforce Logs AI-extracted insights* in *Notion** for structured tracking. Pushes sales-related data* to *Salesforce** for team accessibility. 🔔 4. Slack Alerts for Teams Notifies sales, marketing, and product teams** about extracted insights. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion 📊 Sample Output Data 1️⃣ Sales Insights { "UseCases": [ { "Summary": "A manufacturing company wants to automate inventory tracking and reduce manual entry delays.", "DepartmentTags": ["Operations"], "IndustryTags": ["Manufacturing"], "ImplementationStatus": "Evaluating" } ], "Objection": { "ObjectionTags": ["Feature Limitation"], "Nature": "The prospect wanted a deeper integration with their ERP system, which n8n currently lacks." }, "CallSummary": "The call focused on automation for supply chain processes. The prospect expressed interest but wanted confirmation on ERP integration capabilities.", "NextSteps": ["Schedule a follow-up demo for ERP integration."] } 2️⃣ Marketing Insights { "MarketingInsights": [ { "Tag": "Workflow Template Request", "Summary": "The prospect requested a template for automating CRM lead tracking." } ], "RecurringTopics": [ { "Topic": "CRM Integration", "Mentions": 3, "Context": "Discussed how n8n could sync CRM data automatically." } ], "ActionableInsights": [ { "RecommendationType": "Tutorial", "Title": "How to Automate CRM Lead Tracking with n8n", "Topic": "CRM Integration", "Rationale": "The prospect expressed a need for CRM automation templates." } ] } 3️⃣ Product Feedback { "ProductFeedback": [ { "Sentiment": "Positive", "Feedback": "The external speaker praised the simplicity of n8n's UI, making it easier for non-developers to automate tasks." }, { "Sentiment": "Negative", "Feedback": "The external speaker mentioned frustration over the lack of a dedicated ERP integration node." } ], "AI_ML_References": { "Exist": true, "Context": "The external speaker mentioned using AI for automating customer ticket categorization.", "Details": { "DevelopmentStatus": "Building", "Department": "Support", "RequiresAgents": true, "RequiresRAG": false, "RequiresChat": "Yes: External App (e.g., Slack)" } } } 🔧 How to Customize This Workflow 💡 🔗 Change Data Storage – Swap Notion for Airtable, HubSpot, or another CRM. 💡 📩 Customize Slack Notifications – Send alerts via email, webhook, or another channel. 💡 🛠 Modify AI Processing – Adjust AI models or processing prompts. 💡 📊 Add More Integrations – Sync insights with Pipedrive, HubSpot, or another CRM. 🚀 Why Use This Workflow? ✔ Automates Gong call transcript analysis, eliminating manual work. ✔ Improves collaboration by structuring insights for sales, marketing, and product teams. ✔ Boosts sales conversions by identifying objections and next steps. ✔ Enhances marketing and SEO strategy with AI-driven insights. ✔ Optimizes product roadmap decisions based on customer feedback. This workflow scales AI-powered sales intelligence for better decision-making, content strategy, and sales enablement. 🚀

The best content automation template in the market is now even better—with “deep research” on time-sensitive topics\! Unlike most n8n content automation templates that are mainly for “demo purposes,” this advanced n8n workflow is for the real PROs. It conducts in-depth research on the latest information about a topic, then automatically creates and publishes SEO-optimized blog posts to your WordPress website. In addition, it generates a unique image for each chapter and a featured image for the overall article, and it collects internal website links—seamlessly inserting links throughout each chapter and the entire article. Furthermore, it backs up all content and images to a designated folder in your Google Drive. This integrated approach not only creates high-quality, comprehensive content for readers but also enhances on-page SEO, improves navigation, and streamlines your content creation process—saving you time while ensuring your work is securely stored. This is a complimentary variation of the n8n WordPress Auto-Blogging Pro for SEO topics. That one specializes in auto-blogging content about established topics that do not need up-to-date information while this one takes it further by incorporating online research into the workflow. Both make up the perfect combo to run your blog on autopilot\! How It Works Trigger: It activates upon adding a new row to a Google Sheet. Users can control many customizable parameters, such as key topic, number of subtopics, target audience, length, and style. Initial Research: The workflow initiates preliminary research on the topic to gather insights for the content planning phase. Structure Planning: Based on the initial research, it plans a detailed structure for the entire article and breaks the article into subtopics strongly tied to the main topic. In-Depth Research & Writing: For each subtopic, it conducts further research to gather the most up-to-date, relevant information, then uses that research to write each chapter in depth. Image Generation: It generates and adds images for each chapter, as well as a featured image for the article. Internal Linking: The system gathers internal website links and strategically embeds them within each chapter and throughout the article, boosting SEO and enhancing user navigation. Final Assembly & Publishing: All texts, images, and links are combined into one comprehensive article, which is then published directly to your WordPress website. You can also choose to post it as a draft for final review. Auto Backup to Drive: Get peace of mind knowing all content (blog post and images) is automatically saved to Google Drive, organized in a folder named after the blog post title. Unique Features Deep Research: The workflow leverages advanced, real-time research to gather the latest information about a topic. It breaks down the key topic into subtopics and conducts in-depth research on each one, ensuring the most comprehensive and current knowledge. Full Automation: Designed to be 100% automated, the workflow runs without manual intervention once imported and configured. Simple Activation: It is easily triggered through the Google Sheets interface—simply add a new row to a Google Sheet. Customization Options: Offers a wide array of options, including topic, category, target audience, word count, number of chapters, length of introduction and conclusion, and writing style. It also allows for the inclusion of calls-to-action (CTAs) and company/product introductions. Advanced Loop in Loop: Clever looping is used to write each chapter and generate images, ensuring optimal results. “Wait” nodes are added where appropriate to avoid API call rate limits. Automatic Content Saving: After the blog post is created, all content and images are automatically saved to Google Drive, preventing data loss. The folder is named after the blog post title. SEO-Optimized Content: It is designed to create SEO-optimized content using seed keywords. Internal Link Limit: The workflow limits the number of internal links to 20 by default. Error Catch: “If” nodes are strategically used to ensure that the output from AI nodes strictly meets the JSON schema, ensuring a smooth flow of data without interruption. And many more big and small improvements. Requirements Please make sure you have these requirements ready to ensure smooth deployment of this n8n workflow template: OpenAI API or equivalent for text and image generation PerplexityAI API or equivalent for online research A WordPress website (other website platforms will not work\!) Google Sheets for triggering the workflow, or a trigger of your choice Google Drive and Google Docs for auto backup Set Up Steps Install the Workflow Template: Import the JSON files into your n8n instance. There are 2 files: one for the main workflow and one for the research tool (PerplexityAI). Connect the Workflow with Your Accounts: Link your accounts for OpenAI API, PerplexityAI API, Google Drive, Google Sheets, Google Docs, and WordPress website. Configure the Google Sheet: Ensure your Google Sheet is set up to trigger the workflow upon adding a new row and that the input data is correctly formatted. Customize the Inputs: Adjust parameters like topic, target audience, and writing style to match your specific content needs. Optimize prompts for the best results. Test the Workflow: Use low-cost AI models and image settings initially to ensure everything runs smoothly. Tailor Further as Needed: Modify workflow elements to perfectly align with your needs and content strategy. Tips for PROs Image Generation: The default AI model for image generation is OpenAI’s Dall-E. However, the outputs of this model are not impressive. Consider using FLUX.1 for better image quality. Research: PerplexityAI is the tool of choice for research. The default model used in this workflow is “sonar” due to its fast speed and low cost. Feel free to experiment with its other models, including the “sonar-deep-research” model, which is dedicated to deep research. Triggering: Triggering with a new row on Google Sheets is limited and unreliable. Consider setting an auto trigger with a daily schedule at a specific time. You can even put the whole workflow in a loop to process multiple rows one-by-one from Google Sheets. Human in the Loop: It is possible to incorporate a human review process. For example, after the article is posted to WordPress as a draft, a human can review the draft. Only after approval will the post be published, and a final copy of the post will be saved to the shared Drive folder.

AI-Powered WhatsApp Assistant for Restaurant & Delivery – Automate Orders, Calculate Delivery Fees & Enhance Customer Service with n8n 📌 Optimize your restaurant's delivery process with AI-powered automation! This n8n workflow enables an intelligent WhatsApp assistant for restaurants, burger joints, and delivery businesses. The AI chatbot efficiently handles orders, calculates delivery fees based on distance, manages customer interactions, and seamlessly integrates with your CRM and database for streamlined operations. ⚠️ Important Notice: This template is only compatible with self-hosted n8n instances as it uses community nodes. Ensure secure credential management when configuring APIs. 🔹 Key Features ✅ AI-Powered Order Processing – Automatically receive and confirm orders via WhatsApp. ✅ Real-Time Distance Calculation – Determines the delivery distance and calculates the fee dynamically. ✅ Automated Customer Interaction – Engages customers with intelligent conversation and order updates. ✅ WhatsApp Integration – Processes messages, saves customer details, and retrieves past interactions. ✅ Secure Customer Data Storage – Saves order history, customer preferences, and location details in a database. ✅ Seamless CRM & POS System Integration – Sync orders and customer data with your existing platforms. 📌 How It Works 1️⃣ A customer sends a message via WhatsApp to start an order. 2️⃣ The AI assistant guides them through the menu, recommends items, and confirms their choices. 3️⃣ The system retrieves and saves customer details (name, phone number, and address). 4️⃣ The AI calculates the delivery distance and applies the appropriate delivery fee. 5️⃣ The order details are saved and forwarded to the restaurant’s system for preparation. 6️⃣ The assistant keeps the customer updated about the order status. ⚙️ Setup & Customization 🔧 Webhook for WhatsApp – Capture and process customer messages automatically. 🔧 OpenAI-Powered AI Assistant – Configure conversation flows for natural and engaging interactions. 🔧 Delivery Fee Calculation – Set your delivery price per kilometer and adjust base rates. 🔧 Database & CRM Integration – Store customer details, order history, and location data. 🔧 Customizable Order Flow – Adapt the workflow for different restaurant models. 🔧 Secure Credential Management – Store API keys safely to prevent unauthorized access. 💡 Requirements: This template is recommended for users with basic knowledge of n8n. If you need custom development or setup assistance, contact us via WhatsApp: +55 17 99155-7874. 🚀 Automate order processing, reduce manual tasks, and improve customer satisfaction with AI-powered WhatsApp automation! Assistente de WhatsApp com IA para Restaurantes & Delivery – Automatize Pedidos, Calcule Taxas de Entrega e Melhore o Atendimento ao Cliente com n8n 📌 Otimize o processo de entrega do seu restaurante com automação baseada em IA! Este fluxo para n8n permite criar um atendente virtual inteligente no WhatsApp, ideal para hamburguerias, restaurantes e serviços de delivery. O chatbot baseado em IA recebe pedidos, calcula a taxa de entrega com base na distância, interage com os clientes e integra-se ao seu CRM para operações mais eficientes. ⚠️ Aviso Importante: Este template é compatível apenas com instâncias auto-hospedadas do n8n, pois utiliza nós da comunidade. Certifique-se de gerenciar credenciais de forma segura ao configurar APIs. 🔹 Principais Funcionalidades ✅ Processamento Inteligente de Pedidos – Recebe e confirma pedidos automaticamente via WhatsApp. ✅ Cálculo Dinâmico da Taxa de Entrega – Mede a distância e aplica a taxa correta automaticamente. ✅ Atendimento Automatizado ao Cliente – Interage com clientes de forma natural e profissional. ✅ Integração com WhatsApp – Captura mensagens, armazena dados e recupera histórico de pedidos. ✅ Armazenamento Seguro de Dados do Cliente – Salva pedidos, preferências e endereços de entrega. ✅ Sincronização com CRM e Sistema de PDV – Conecta-se ao seu sistema de gestão para um fluxo contínuo. 📌 Como Funciona 1️⃣ O cliente envia uma mensagem via WhatsApp para iniciar um pedido. 2️⃣ O assistente de IA apresenta o menu, sugere itens e confirma a escolha do cliente. 3️⃣ O sistema captura e salva os dados do cliente (nome, telefone e endereço). 4️⃣ A IA calcula a distância de entrega e aplica a taxa de acordo com a localização. 5️⃣ Os detalhes do pedido são registrados e enviados para a cozinha. 6️⃣ O assistente atualiza o cliente sobre o status do pedido. ⚙️ Configuração e Personalização 🔧 Webhook para WhatsApp – Configuração para capturar e processar pedidos automaticamente. 🔧 Assistente de IA com OpenAI – Configuração de fluxos conversacionais naturais e envolventes. 🔧 Cálculo de Taxa de Entrega – Definição de valores por quilômetro e ajustes de tarifas base. 🔧 Banco de Dados & CRM – Armazena dados de clientes, histórico de pedidos e localização. 🔧 Fluxo de Pedidos Personalizável – Adapte para diferentes tipos de restaurantes e serviços de delivery. 🔧 Gerenciamento Seguro de Credenciais – Proteja suas chaves de API contra acessos não autorizados. 💡 Requisitos: Este template é recomendado para usuários que já possuem conhecimentos básicos em n8n. Se precisar de personalização ou suporte para instalação, entre em contato pelo WhatsApp: +55 17 99155-7874. 🚀 Automatize o processamento de pedidos, reduza tarefas manuais e melhore a satisfação dos clientes com um WhatsApp automatizado e inteligente!

Enhance your data analysis by connecting an AI Agent to your dataset, using n8n tools. This template teaches you how to build an AI Data Analyst Chatbot that is capable of pulling data from your sources, using tools like Google Sheets or databases. It's designed to be easy and efficient, making it a good starting point for AI-driven data analysis. You can easily replace the current Google Sheets tools for databases like Postgres or MySQL. How It Works The core of the workflow is the AI Agent. It's connected to different data retrieval tools, to get data from Google Sheets (or your preferred database) in many different ways. Once the data is retrieved, the Calculator tool allows the AI to perform mathematical operations, making your data analysis precise. Who is this template for Data Analysts & Researchers:** Pull data from different sources and perform quick calculations. Developers & AI Enthusiasts:** Learn to build your first AI Agent with easy dataset access. Business Owners:** Streamline your data analysis with AI insights and automate repetitive tasks. Automation Experts:** Enhance your automation skills by integrating AI with your existing databases. How to Set Up You can find detailed instructions in the workflow itself. Check out my other templates 👉 https://n8n.io/creators/solomon/

Video Guide I prepared a detailed guide that demonstrates the complete process of building a trading agent automation using n8n and Telegram, seamlessly integrating various functions for stock analysis. Youtube Link Who is this for? This workflow is perfect for traders, financial analysts, and developers looking to automate stock analysis interactions via Telegram. It’s especially valuable for those who want to leverage AI tools for technical analysis without needing to write complex code. What problem does this workflow solve? Many traders desire real-time analysis of stock data but lack the technical expertise or tools to perform in-depth analysis. This workflow allows users to easily interact with an AI trading agent through Telegram for seamless stock analysis, chart generation, and technical evaluation, all while eliminating the need for manual interventions. What this workflow does This workflow utilizes n8n to construct an end-to-end automation process for stock analysis through Telegram communication. The setup involves: Receiving messages via a Telegram bot. Processing audio or text messages for trading queries. Transcribing audio using OpenAI API for interpretation. Gathering and displaying charts based on user-specified parameters. Performing technical analysis on generated charts. Sending back the analyzed results through Telegram. Setup Prepare Airtable: Create simple table to store tickers. Prepare Telegram Bot: Ensure your Telegram bot is set up correctly and listening for new messages. Replace Credentials: Update all nodes with the correct credentials and API keys for services involved. Configure API Endpoints: Ensure chart service URLs are correctly set to interact with the corresponding APIs properly. Start Interaction: Message your bot to initiate analysis; specify ticker symbols and desired chart styles as required.

Description: Create Social Media Content from Telegram with AI This n8n workflow empowers you to effortlessly generate social media content and captivating image prompts, all powered by AI. Simply send a topic request through Telegram (as a voice or text message), and watch as the workflow conducts research, crafts engaging social media posts, and creates detailed image prompts ready for use with your preferred AI art generation tool. What does this workflow do? This workflow streamlines the content creation process by automating research, social media content generation, and image prompt creation, triggered by a simple Telegram message. Who is this for? Social Media Managers:** Quickly generate engaging content and image ideas for various platforms. Content Creators:** Overcome writer's block and discover fresh content ideas with AI assistance. Marketing Teams:** Boost productivity by automating social media content research and drafting. Anyone** looking to leverage AI for efficient and creative social media content creation. Benefits Effortless Content and Image Prompt Generation:** Automate the creation of social media posts and detailed image prompts. AI-Powered Creativity:** Leverage the power of LLMs to generate original content ideas and captivating image prompts. Increased Efficiency:** Save time and resources by automating the research and content creation process. Voice-to-Content:** Use voice messages to request content, making content creation even more accessible. Enhanced Engagement:** Create high-quality, attention-grabbing content that resonates with your audience. How it Works Receive Request: The workflow listens for incoming voice or text messages on Telegram containing your content request. Process Voice (if necessary): If the message is a voice message, it's transcribed into text using OpenAI's Whisper API. AI Takes Over: The AI agent, powered by an OpenAI Chat Model and SerpAPI, conducts online research based on your request. Content and Image Prompt Generation: The AI agent generates engaging social media content and a detailed image prompt based on the research. Image Generation (Optional): You can use the generated image prompt with your preferred AI art generation tool (e.g., DALL-E, Stable Diffusion) to create a visual. Output: The workflow provides you with the social media content and the detailed image prompt, ready for you to use or refine. n8n Nodes Used Telegram Trigger Switch Telegram (for fetching voice messages) OpenAI (Whisper API for voice-to-text) Set (for preparing variables) AI Agent (with OpenAI Chat Model and SerpAPI tool) HTTP Request (for optional image generation) Extract from File (for optional image processing) Set (for final output) Prerequisites Active n8n instance Telegram account with a bot OpenAI API key SerpAPI account Hugging Face API key (if you want to generate images within the workflow) Setup Import the workflow JSON into your n8n instance. Configure the Telegram Trigger node with your Telegram bot token. Set up the OpenAI and SerpAPI credentials in the respective nodes. If you want to generate images directly within the workflow, configure the HTTP Request node with your Hugging Face API key. Test the workflow by sending a voice or text message to your Telegram bot with a topic request. This workflow combines the convenience of Telegram with the power of AI to provide a seamless content creation experience. Start generating engaging social media content today!

Automate Multi-Platform Social Media Content Creation with AI Who is this for? Social Media Managers and Digital Marketers seeking to streamline content production across 7+ platforms (X/Twitter, Instagram, LinkedIn, Facebook, TikTok, Threads, YouTube Shorts) using AI-powered automation. What problem does this solve? Creating platform-optimized content at scale while maintaining brand consistency across multiple channels, reducing manual work by 80% through AI generation and automated publishing. What this workflow does AI Content Generation: Uses GPT-4/Gemini to create platform-specific posts Automatically generates hashtags, CTAs, and emoji placement Supports image/video suggestions and image creation using OpenAI or Pollinations.ai Uses SERP api to search for relavent content Approval Workflow: Sends formatted HTML emails for human review Implements double-approval system with Gmail integration Cross-Platform Publishing: One-click deployment to: Instagram/Facebook (via Graph API) X/Twitter (Official API) LinkedIn (Sales Navigator integration) Setup Credentials: OpenAI API key Google Gemini API Social media platform tokens (X, LinkedIn, Facebook) ImgBB for image hosting Gmail SERP API Telegram Configuration: Update all "your-unique-id" placeholders in API nodes Set email recipients in Gmail nodes Customize AI prompts Customization: Adjust character limits per platform Modify approval thresholds Add/remove social platforms as needed How to customize Content Style**: Edit prompt templates in the "Social Media Content Factory" agent node Approval Process**: Modify email templates Analytics**: Connect to Google Sheets for performance tracking Image Generation**: Switch between Pollinations.ai/DALL-E/Midjourney

Meta Ads Analyzer This n8n template builds an automated system to scrape, analyze, and extract insights from Meta advertising content. The workflow uses AI to perform deep analysis of video ads and organize the results in a structured format. How it works The workflow connects to Facebook's Ad Library to scrape video ads based on a specified page ID. Videos are filtered by reach and processed through Google's Gemini AI to analyze their content. Each video is systematically analyzed for its hook, transcript, advertising format, concept, and narrative structure. Results are processed through an AI agent that structures the data into standardized fields. All analysis is saved to a Google Sheet for easy access and further processing. How to use Once you've set up your Credentials and configured the output: Configure the Google Sheets connection for data output. Specify the Meta Ad Library URL you want to analyze. Adjust the maximum number of ads to scrape and analyze based on your needs. Click "Test Workflow" to start the analysis process. Requirements Apify account (for Meta Ad Library scraping) Google Gemini Google Sheets Customizing this workflow Modify the AI prompts in the Settings node to extract different information from the videos. Adjust the output formats in the AI Agent and Structured Output Parser nodes. Change the Google Sheets mapping to match your desired output structure. Increase the number of ads analyzed for more comprehensive research.

Detailed Technical Description This n8n workflow automates Yape payment receipt processing, integrating Telegram bot, AI-powered OCR, and Google Sheets automation. By leveraging ChatGPT Vision Computing, it extracts and structures transaction details, eliminating the need for manual entry. Ideal for freelancers, businesses, and finance teams, this workflow ensures error-free, real-time financial tracking. The AI agent powered by DeepSeek refines and formats the extracted text, storing it in Google Sheets for easy accessibility and reporting. Users can track payments, monitor cash flow, and generate financial reports without any manual work. This seamless integration boosts efficiency, reduces errors, and automates financial record-keeping with 100% accuracy. 🛠️ Technologies Used: ✅n8n – Workflow orchestrator. ✅Telegram – Handles image reception and notifications. ✅Google Drive – Manages file creation and storage. ✅Google Sheets – Automatically logs extracted data into spreadsheets. 🤖Artificial Intelligence: ✅ChatGPT Vision Computing – Performs OCR on payment receipts. ✅DeepSeek AI – Organizes and converts extracted data into a structured format. 📌 pre-conditions: 📩 A Telegram Bot – Must be created to receive images. Setup Guide 🔑 Google Sheets API Key – Required to store extracted data. Setup Guide ⚛️ ChatGPT API Key – Used for OCR and AI text extraction. Get it here 🐋 DeepSeek API Key – Processes extracted text into structured data. Get it here 1️⃣ Image Reception & OCR Processing 📩 The user attaches a Yape payment receipt image to the Telegram bot conversation. 🤳🏻 2️⃣ Analyze Image (OCR) 👁️‍🗨️ A ⚛️ChatGPT Vision Computing model processes the image and extracts all visible text, ensuring high-accuracy OCR for structured data extraction. 3️⃣ Analyze and format text Intelligent Data Processing with AI 🤖 The extracted text is sent to a 🐋DeepSeek-based AI agent that: Identifies and structures key transaction details (amount, date, sender, transaction ID, etc.). Converts the data into a structured JSON format. 4️⃣ Data Storage in Google Sheets 📊 Google Drive integration is established. If the Google Sheets file does not exist, it is automatically created. Extracted data is automatically recorded in the corresponding spreadsheet, enabling effortless tracking and streamlined financial organization. 🚀 Benefits: ✅ Time-saving – Eliminates manual payment processing. ✅ Error-free data entry – Reduces human mistakes in record-keeping. ✅ 100% automation – No manual intervention required. ✅ Seamless integration – Easily connects with other workflows. 💡NOTE: The extracted transaction data will be stored in a Google Sheets file with the following columns: | Column Name | Description | |:---------------------:|-----------------------------------------------------------| | id | Unique identifier for each transaction. | | beneficiaryName | Name of the recipient of the payment. | | amount | Payment amount in the specified currency. | | currency | Currency used for the transaction (e.g., PEN, USD). | | company | The entity or service handling the transaction. | | date | Date of the transaction in a human-readable format. | | hour | Time of the transaction. | | originalDate | The exact date extracted from the receipt. | | dateToISO | Standardized ISO 8601 date format. | | operation | Type of financial operation (e.g., deposit, transfer). | | operationNumber | Unique operation number provided by the payment system. | | beneficiaryNumber | Account or phone number of the recipient. | | commission | Any commission or fee charged for the transaction. | | account | Account number or reference used for the payment. | | channel | Payment method used (e.g., app, POS, bank transfer). | | agentCode | Identifier of the agent or entity processing the payment. | This workflow is perfect for businesses, freelancers, and individuals who need a fully automated solution to process Yape payment receipts efficiently. By leveraging AI-powered OCR and structured data extraction, it eliminates manual effort, reduces errors, and ensures that all transactions are accurately logged in Google Sheets. Whether you're managing personal finances, freelance payments, or business transactions, this system provides a seamless, hands-free approach to financial tracking, allowing you to focus on what truly matters.

How it works Many users have asked in the support forum about different methods to analyze images and PDF documents with Google Gemini AI in n8n. This workflow answers that question by demonstrating five different approaches: Single image with auto binary passthrough - The simplest approach using AI Agent's automatic binary handling Multiple images with predefined prompts - For customized analysis with different instructions per image Native n8n item-by-item processing - For handling multiple items using n8n's standard workflow paradigm PDF analysis via direct API - For document analysis and text extraction Image analysis via direct API - For direct control over API parameters Each method has advantages depending on your specific use case, data volume, and customization needs. Set up steps Setup time: ~5-10 minutes You'll need: A Google Gemini API key n8n with HTTP Request and AI Agent nodes Important: For the HTTP Request nodes making direct API calls to Gemini (Methods 3, 4, and 5), you'll need to set up Query Authentication with your Gemini API key. Add a parameter named "key" with your API key value in the Query Auth section of these nodes. I'll updated this if I find better ways. Also let me know if you know other ways. Eager to learn :)

Want to see it in action? Watch the full breakdown here: 📺 Video Link Template Description This n8n workflow empowers you to query structured financial data from Google Sheets or CSV files using AI-generated SQL. Unlike traditional vector database solutions that falter with numerical queries, this template leverages PostgreSQL for efficient data storage and an AI agent to dynamically create optimized SQL queries from natural language inputs. What It Does Retrieves data from Google Sheets or CSV files Infers the data schema and builds a PostgreSQL table Populates the table with your data Uses an AI agent to translate natural language questions into SQL queries Returns precise numerical results quickly and efficiently Why Use This? No SQL knowledge required—the AI generates queries for you Bypasses the inefficiencies and costs of vector database approaches Scales effortlessly without overwhelming the language model Fully free and open-source Setup Requirements Pre-Conditions PostgreSQL Database**: A running PostgreSQL instance (no specific extensions required beyond standard installation). Google Sheets Access**: A publicly accessible or shared Google Sheet URL with structured data (e.g., financial records). Need a starting point? Use this Sample Google Sheet Template. n8n Instance**: A working n8n setup with access to the Google Drive and PostgreSQL nodes. Step-by-Step Instructions Add Your Google Sheets URL Open the "Google Drive Trigger" node. Replace the placeholder URL with your Google Sheet’s link. Verify the sheet name matches your data source. Configure PostgreSQL Update the "PostgreSQL" nodes with your database credentials (host, database, user, password). The workflow automatically creates and populates the table based on your data schema. Run the Workflow Execute the workflow manually to set up the database. Once initialized, use the AI agent by asking questions like: "How much did I sell last week?" "What were the total sales for Product X in February?" (Optional) Automate Updates Add a "Schedule Trigger" node to sync your Google Sheets data with PostgreSQL on a regular basis. How It Works Schema Detection**: The workflow analyzes your Google Sheets or CSV data to infer its structure and create an appropriate PostgreSQL table. AI-Powered Queries**: An optimized AI agent converts your natural language questions into precise SQL queries, ensuring accurate results. Efficient Retrieval**: By using PostgreSQL instead of vector-based methods, this template avoids common pitfalls like slow performance or inaccurate numerical outputs. Tips for Success Ensure your Google Sheet or CSV has consistent column headers for smooth schema detection. Test with simple questions first to verify the AI agent’s query generation. Check out the n8n Template Submission Guidelines for more best practices.

Generate and Publish AI Content to LinkedIn and X (Twitter) with n8n Overview This n8n workflow automates the generation and publishing of AI-powered social media content across LinkedIn and X (formerly Twitter). By leveraging AI, this workflow helps social media managers, marketers, and content creators streamline their posting process. Who is this for? Social media managers Content creators Digital marketers Businesses looking to automate content generation Features AI-powered content creation** tailored for LinkedIn and X (Twitter) Automated publishing** to both platforms Structured output parsing** to ensure consistency OAuth2 authentication** for secure posting Merge and confirmation steps** to track successful postings Setup Instructions Prerequisites Before using this workflow, ensure you have: An n8n instance set up API credentials for: Google Gemini AI (for content generation) X Developer Account with OAuth2 authentication LinkedIn Developer Account with OAuth2 authentication A form submission service integrated with n8n Workflow Breakdown 1. Trigger: Form Submission A user submits a form containing the post title. The form is secured with Basic Authentication. The submitted title is passed to the AI Agent. 2. AI Content Generation The Google Gemini Chat Model processes the title and generates: LinkedIn post content Twitter (X) post content Hashtags Call-to-action (LinkedIn) Character limit check (Twitter) 3. Parsing AI Output A structured output parser converts the AI-generated content into a JSON format. Ensures correct formatting for LinkedIn and Twitter (X). 4. Publishing to Social Media X (Twitter) Posting Extracts the Twitter post from the AI output. Publishes it via an OAuth2-authenticated X (Twitter) account. LinkedIn Posting Extracts the LinkedIn post from the AI output. Publishes it via an OAuth2-authenticated LinkedIn account. 5. Merging Post Results Merges the response data from both LinkedIn and Twitter after publishing. 6. Confirmation Step Displays a final confirmation form once the posts are successfully published. Benefits Save time** by automating content creation and publishing. Ensure consistency** across platforms with structured AI-generated posts. Secure authentication** using OAuth2 for LinkedIn and Twitter. Increase engagement** with AI-optimized hashtags and CTAs. This workflow enables seamless social media automation, helping professionals post engaging AI-powered content effortlessly. 🚀

Overview An n8n workflow automating business travel planning via Telegram. Uses AI and APIs to find and book flights/hotels efficiently. Prerequisites Telegram Bot** (BotFather) API Keys**: OpenAI (transcription), SerpAPI (flights/hotels), DeepSeek (AI processing) n8n Instance** with API access Setup Instructions Import Workflow: Upload JSON to n8n. Configure API Credentials: Set up Telegram, OpenAI, SerpAPI, and DeepSeek keys. Webhook Activation: Ensure Telegram webhook is active with HTTPS. Test: Send a Telegram message and verify execution. Workflow Operation 1. User Input Processing Telegram bot triggers workflow, extracts text/audio. OpenAI transcribes voice messages. AI (DeepSeek) extracts key travel details (locations, dates, accommodation needs). 2. Travel Search Flights**: Uses SerpAPI for flight options (airlines, times, prices). Hotels**: Fetches accommodations with dynamic check-out date. 3. AI Recommendations & Customization DeepSeek** generates structured travel plans. Users can modify prompts to adjust AI responses for personalized results. Professional, well-structured responses with links. 4. Response Delivery Sends travel recommendations via Telegram with clear details. Use Cases Ideal for business professionals, executive assistants, frequent travelers, and small businesses. Customization & Troubleshooting Adjust memory handling and API calls. Modify prompts to refine AI output. Ensure API keys are active and network is accessible.

Who is this template for? This template is for any Microsoft Outlook user who wants a trained AI agent to reason and reply on their behalf. Teach your agent tone and writing style to replicate your own, or develop a persona for a shared inbox. Requirements Outlook with authentication credentials OpenAI account with authentication credentials A few sample email replies of various lengths and topics How it works: Connect your Outlook account. Select (filter) which email sender(s) your trained AI agent will reply to. [Tip: pick a sender that has some repeatability either with a topic (ie. sales) or an individual (coworker@yourcompany.com)] Connect your OpenAI account. Choose your AI model (ie. gpt-4o-mini) Add Prompt (User Message) and select "system message" from the option below Update the instructions by filling in your name (or persona), response style, and add full email replies from the topic or individual you want the AI agent to emulate. [Tip: Add actual replies from your email sent folder, including your greeting and sign off. Paste each email sample between a set of <example> .... </example> tags] Configure the reply (or reply all) to remain within the original email string Test it! Send an email from the address to which your agent wants to respond. Check your sent (or draft) folder for the result. Enjoy all the free time you now have!! If you have questions or need assistance, email us at: support@teambisonandbird.com ++This template does not include retrieving email addresses out of the message or body of the email.++

Multi-AI Agent Chatbot for Postgres/Supabase Databases and QuickChart Generation Who is this for? This workflow is ideal for data analysts, developers, and business intelligence teams who need an AI-powered chatbot to query Postgres/Supabase databases and generate dynamic charts for data visualization. What problem does this solve? It simplifies data exploration by combining conversational AI with database querying and chart generation. Users can interact with their database using natural language, retrieve insights, and visualize data without manual SQL queries or chart configuration. What this workflow does AI-Powered Chat Interface: Accepts natural language prompts to query databases or generate charts. Routes user requests through a tool agent system to determine the appropriate action (query or chart). Database Querying: Executes SQL queries on Postgres/Supabase databases based on user input. Retrieves schema information, table definitions, and specific data records. Dynamic Chart Generation: Uses QuickChart to create bar charts, line charts, or other visualizations from database records. Outputs a shareable chart URL or JSON configuration for further customization. Memory Integration: Maintains chat history using Postgres memory nodes, enabling context-aware interactions. Workflow diagram showcasing AI agents, database querying, and chart generation paths. Setup Prerequisites: A Postgres-compatible database (e.g., Supabase). API credentials for OpenAI. Configuration Steps: Add your database connection credentials in the Postgres nodes. Set up OpenAI credentials for GPT-4o-mini in the language model nodes. Adjust the QuickChart schema in the "QuickChart Object Schema" node to fit your use case. Testing: Trigger the chat workflow via the "When chat message received" node. Test with prompts like "Generate a bar chart of sales data" or "Show me all users in the database." How to customize this workflow Modify AI Prompts** Add Chart Types** Integrate Other Tools**

Workflow Overview: Extract text from image using AI is worth because you need no code. It incorporates Google Gemini 2.0 Flash model for important text extraction from image. If you code without AI, you have to use multiple condition and may cause a lot of bug but with Google Gemini, you don't need any coding and if the Pay Slip is different, Gemini will extract it automatically. Workflow description: User uses Line Messaging API to send Pay Slip image or message to the chatbot, create Line Business ID from here: Line Business Classify the message which is image or text If the message is Pay Slip image, it will process using Gemini 2.0 Flash EXP and extract important information and response in JSON format without coding by using the following prompt: Analyze image and then return in JSON Response that has the only following value: Status, From, To, Date, Amount To get Google AI Studio API Key, you can find from the following link: Google AI Studio API Key Create Google Sheets which include the fileds (Status, From, To, Date, Amount) that we have created related to the AI prompt Google Sheets as the following example: If the message is text, it will process using Gemini 2.0 Flash EXP model as the AI Assistant else if the message is image, it will extract the important fields then reply to the User and insert into Google Sheets Key Features: Extract text from image with No Code** Without N8N, we have to write code to extract text from image, but with N8N and Google Gemini 2.0 Flash EXP together, we don't need to code and it will process all slip vendors or other document vendors. Multipurpose Chatbot** this chatbot accept both text and image so we don't have to create many chatbot accounts Reduce human error** this workflow let any officer to verify document status when the job ends Note: You can change the information by changing your prompt and also Google Sheets Column names relatively.

Your Landing Page is Leaking Sales—Here’s How to Fix It in Seconds Visitors land on your page. But instead of converting, they bounce. Why? Something’s broken. Something’s missing. But what? ❌ Is your CTA too weak? ❌ Is your messaging unclear? ❌ Is your design creating friction? You know something is off, but don’t know what. What if you could get an instant, expert-level report on exactly what to fix? This workflow will do an AI Analysis of your landing page, provide a CRO Audit, so you can optimize your landing page. Who is This For? SaaS Founders & Startups**: Stop leaving money on the table. Make every visitor count. Marketers & Growth Experts**: Turn landing pages into high-converting assets. E-commerce & Lead Gen Businesses**: More conversions = more revenue. How It Works Paste your URL Get an instant roast + fix list Implement changes & watch conversions jump The workflow scrapes the url you input, gets the htlm source code of the landing page, and sends it to OpenAI AI Agent. The Agent makes a deep analysis, roasts the landing page, and provides 10 Conversion Rate Optimization Tips to improve your landing page. Setup Guide You will need OpenAI Credentials with an API Key to run the workflow. The workflow is using the OpenAI-o1 model to deliver the best results. It costs between $0.20/0.30 per run. You can adjust the prompt to your wish in the AI Agent parameters. Once the workflow has been completed, select Logs to get a readable version. Below is an example.

Who is this template for? This workflow template is designed for people seeking alerts when certain specific changes are made to any web page. Leveraging agentic AI, it analyzes the page every day and autonomously decides whether to send you an e-mail notification. Example use cases Track price changes on [competitor's website]. Notify me when the price drops below €50. Monitor new blog posts on [industry leader's website] and summarize key insights. Check [competitor's job page] for new job postings related to software development. Watch for new product launches on [e-commerce site] and send me a summary. Detect any changes in the terms and conditions of [specific website]. Track customer reviews for [specific product] on [review site] and extract key themes. How it works When clicking 'test workflow' in the editor, a new browser tab will open where you can fill in the details of your espionage assignment Make sure you be as concise as possible when instructing AI. Instruct specific and to the point (see examples at the bottom). After submission, the flow will start off by extracting both the relevant website url and an optimized prompt. OpenAI's structured outputs is utilized, followed by a code node to parse the results for further use. From here on, the endless loop of daily checks will begin: Initial scrape 1 day delay Second scrape AI agent decides whether or not to notify you Back to step 1 You can cancel an espionage assignment at any time in the executions tab Set up steps Insert your OpenAI API key in the structured outputs node (second one) Create a Firecrawl account and connect your Firecrawl API key in both 'Scrape page'-nodes Connect your OpenAI account in the AI agents' model node Connect your Gmail account in the AI agents' Gmail tool node

This n8n template uses a Telegram chatbot to conduct a Product Satisfaction Survey and fetches questions and stores answers in a Google sheet. It augments an AI Agent to ask follow-up questions to engage the user and uncover more insights in their responses. This template is intended to demonstrate how you'd realistically approach a workflow where there is structured conversation (static questions) but you still want to include an free-form element (follow-up questions) which can only be accomplished via AI. Check out an example Survey results: https://docs.google.com/spreadsheets/d/e/2PACX-1vQWcREg75CzbZd8loVI12s-DzSTj3NE_02cOCpAh7umj0urazzYCfzPpYvvh7jqICWZteDTALzBO46i/pubhtml?gid=0&single=true How it works A chat session is started with the user who needs to enter the bot command "/next" to start the survey. Once started, the template pulls in questions from a google sheet to ask the user. Questions are asked in sequence from left column to right column. When the user answers the question, a text classifier node is used to determine if a follow-up question could be asked. If so, a mini conversation is initiated by the AI agent to get more details. If not, the survey proceeds to the next question. All answers and mini-conversations are recorded in the Google Sheet under the respective question. When all questions are answered, the template will stop the survey and give the user a chance to restart. How to use You'll need to setup a Telegram bot (see docs) Create a google sheet with an ID column. Populate the rest of the columns with your survey questions (see sample) Ensure you have a Redis instance to capture state. Either self-host or sign-up to Upstash for a free account. Update the "Set Variable" node with your google sheet ID and survey title. Share your bot to allow others to participate in your survey. Requirements Telegram for Chatbot Google Sheets for Survey questions and answers Redis for State Management and Chat Memory Community+ license and above for Execution data node - you can remove this node if you don't have this licence. Customising this workflow Not using Telegram? This template technically works with other chat apps such as Whatsapp, wechat and even n8n's hosted chat! This state management pattern can also be applied to other use-cases and scenarios. Try it for other types of surveys!

This workflow is for automating and centralizing your bookmarking process using AI-powered tagging and seamless integration between your Android device and a self-hosted Read Deck platform (https://readeck.org/en/). This workflow eliminates manual entry, organizes links with smart AI-generated tags, and ensures your bookmarks are always accessible, searchable, and secure. How It Works 📱 Android Shortcut Integration Use the HTTP Shortcuts app to create a 1-tap trigger that sends URLs and titles from your Android phone directly to n8n. 🤖 AI-Powered Tagging & Processing Leverage ChatGPT-4 to analyze content context and auto-generate relevant tags (e.g., “Tech Tutorials,” “Productivity Tools”). Extract clean titles and URLs from messy shared data (even from apps like Twitter or Reddit). 🔗 Readeck Integration Automatically save processed bookmarks to your self-hosted Readeck-like platform with structured metadata (title, URL, tags). ⚡ Silent Automation It runs in the background—no pop-ups or interruptions. 🔒 Pro Security Optional authentication (API tokens, headers) to protect your data. Use Case Perfect for researchers, content creators, or anyone drowning in tabs who wants to: Save articles, videos, or social posts in one click. Organize bookmarks with AI-generated tags. Build a personal knowledge base that’s always accessible. Tutorial 1️⃣ Set Up Android Shortcut Install "HTTP Shortcuts" and configure it to send data to your n8n webhook. Enable “Share Menu” to trigger bookmarks from any app. 2️⃣ Configure n8n Workflow Import the template and add your Read Deck API token (or similar service). 3️⃣ Test & Scale Share a link from your phone—watch it appear in Read Deck instantly! Add error handling or notifications for advanced use. Note: For self-hosted platforms, ensure your instance is publicly accessible (or use a VPN). Why Choose This Workflow? Zero Manual Entry: Save hours of copying/pasting. AI Organization: Say goodbye to chaotic bookmark folders. Privacy First: Host your data on your terms. Transform your bookmarking chaos into a streamlined system—try “Save: Bookmark” today! 🚀

This n8n template extends the idea of follow-up reminders by having an AI agent suggest and book the next call or message to re-engage prospects which have been ignored. What makes this template particularly interesting and actually usable is that it uses the Human-in-the-loop approach to wait for a user's approval before actually making the booking or otherwise not if the user declined. A twist on a traditional idea where we can reduce the number of actionable tasks a human has to make by delegating them to AI. How it works A scheduled trigger checks your google calendar for sales meetings which happened a few days ago. For each event, gmail search is used to figure out if a follow-up message has been sent or received from the other party since the meeting. If none, it might mean the user needs a reminder to follow-up. For leads applicable for follow-up, we first get an AI Agent to find available meeting slots in the calendar. These slots and reminder are sent to the user via send-and-approval mode of the gmail node. The user replies in natural language either picking a slot, suggesting an entirely new slot or declines the request. When accepted, another AI Agent books the meeting in the calendar with the proposed dates and lead. When declined, no action is taken. How to use Update all calendar nodes (+subnodes) to point to the right calendar. If this is a shared-purpose calendar, you may need to either filter or create a new calendar. Update the gmail nodes to point to the right accounts. Requirements Google OAuth for Email and Calendar OpenAI for LLM Customising the template Not using Google? Swap out for Microsoft Outlook/Calendar or something else. Try swapping out or adding in additional send-for-approval methods such as telegram or whatsapp.

This workflow template is designed for dental assistants and anyone looking to automate appointment scheduling. It integrates Google Calendar for booking appointments and Google Sheets as a database to store patient information. How It Works The user interacts with the chatbot to schedule an appointment. The chatbot collects necessary details and checks availability via Google Calendar. If the requested time is available, the AI books the appointment. If unavailable, the AI suggests alternative time slots. Once booked, the AI logs the appointment details into Google Sheets for record-keeping. Setup Instructions 📌 Watch this 🎥 Setup Video for detailed instructions on running and customizing this workflow. Step 1: Set Up Credentials OpenAI API Key (for chatbot functionality). Google Account (for Google Sheets & Google Calendar integration). Step 2: Choose the Right Tools Select the correct Google Calendar in the Google Calendar tool. Choose the appropriate Google Sheets file in the Google Sheets tool. Step 3: Test Run a test to ensure everything works correctly. Once tested. Example Templates Below are sample Google Sheets template to help you get started.

Social Media Content Publishing Factory for Streamlined Content Creation Across Platforms Who is this for? This workflow is designed for content creators, social media managers, and marketing teams who need to efficiently create and publish content across multiple social media platforms. It's perfect for businesses looking to maintain a consistent presence across X (Twitter), Instagram, Facebook, LinkedIn, Threads, and YouTube Shorts without spending hours crafting platform-specific content manually. What problem is this workflow solving? Creating tailored content for different social media platforms is time-consuming and requires understanding each platform's unique requirements and audience expectations. This workflow solves the challenge of: Maintaining consistent messaging across platforms while respecting platform-specific formats Generating optimized content that follows best practices for each platform Creating accompanying visuals without graphic design skills Streamlining the approval process before publishing Reducing the time spent on repetitive content creation tasks What this workflow does This automated content factory takes your content idea and transforms it into platform-optimized posts for all major social media channels. The workflow: Accepts your content idea or topic through a chat interface Leverages AI to generate tailored content for each platform (X/Twitter, Instagram, Facebook, LinkedIn, Threads, YouTube Shorts) Creates appropriate images using AI image generation Sends content for approval via email before publishing Publishes approved content to selected platforms Maintains chat memory to improve future content generation Setup Connect your social media platform credentials (X/Twitter, Instagram, Facebook, LinkedIn) Set up your email account for the approval process Configure your OpenAI API key for AI content generation Update the image generation service credentials (Pollinations.ai is used by default) Customize the system prompts and schemas in the Google Docs nodes with your brand voice and content requirements How to customize this workflow to your needs Modify the system prompts in the Google Docs nodes to match your brand voice and content strategy Adjust the social media schemas to include specific fields relevant to your content Update the approval process to include additional stakeholders if needed Add or remove social media platforms based on your marketing strategy Customize the image generation prompts to better match your visual branding Integrate with your existing content calendar or CRM systems This workflow eliminates the need to manually craft different versions of the same content for each platform, saving hours of work while ensuring your social media presence remains consistent and optimized for maximum engagement.

Who is this for? This workflow template is designed for AI enthusiasts, developers, and privacy-conscious users who want to leverage the power of local large language models (LLMs) without sending data to external services. It's particularly valuable for those running Ollama locally who want intelligent routing between different specialized models. What problem is this workflow solving? When working with multiple local LLMs, each with different strengths and capabilities, it can be challenging to manually select the right model for each specific task. This workflow automatically analyzes user prompts and routes them to the most appropriate specialized Ollama model, ensuring optimal performance without requiring technical knowledge from the end user. What this workflow does This intelligent router: Analyzes incoming user prompts to determine the nature of the request Automatically selects the optimal Ollama model from your local collection based on task requirements Routes requests between specialized models for different tasks: Text-only models (qwq, llama3.2, phi4) for various reasoning and conversation tasks Code-specific models (qwen2.5-coder) for programming assistance Vision-capable models (granite3.2-vision, llama3.2-vision) for image analysis Maintains conversation memory for consistent interactions Processes everything locally for complete privacy and data security Setup Ensure you have Ollama installed and running locally Pull the required models mentioned in the workflow using Ollama CLI (e.g., ollama pull phi4) Configure the Ollama API credentials in n8n (default: http://127.0.0.1:11434) Activate the workflow and start interacting through the chat interface How to customize this workflow to your needs Add or remove models from the router's decision framework based on your specific Ollama collection Adjust the system prompts in the LLM Router to prioritize different model selection criteria Modify the decision tree logic to better suit your specific use cases Add additional preprocessing steps for specialized inputs This workflow demonstrates how n8n can be used to create sophisticated AI orchestration systems that respect user privacy by keeping everything local while still providing intelligent model selection capabilities.

Transcript Evalu8r – AI-Powered Transcribing and Transcript Analysis Workflow Overview Transcript Evalu8r is an AI-powered transcript analysis workflow that automates the processing, visualization, and evaluation of transcribed conversations. This n8n workflow template is designed to help users quickly analyze sentiment, extract topics and intents, generate key insights, and enhance transcript navigation. It is ideal for customer service teams, legal professionals, researchers, content creators, and businesses seeking actionable intelligence from recorded conversations. Key Features 🔹 Automated Transcript Processing Audio-to-Text Conversion** – Upload an audio file and generate transcripts. File Upload Management** – Track file uploads with real-time progress indicators. Transcript Selection** – Browse and select previously analyzed transcripts from a dropdown. Download Transcripts* – Export transcripts in various formats (Google Docs and JSON*). Error Handling** – Detects and alerts users about upload or processing failures. 🔹 Advanced UI Components Interactive Transcript Viewer** – Displays full transcripts with: Timestamps and speaker identification. Clickable words for easy navigation. Auto-scrolling to highlight the active portion of the transcript. Key Points & Action Items Summary** – AI extracts: Critical insights from conversations. Actionable follow-ups detected in the discussion. Speaker Labeling* – Dynamically assigns *real speaker names** instead of generic placeholders (e.g., "Speaker 1"). Collapsible Sections* – Enables users to *expand/collapse** transcript details, key insights, and analysis sections. 🔹 AI-Powered Conversation Analysis Sentiment Analysis & Visualization** Dynamic sentiment graph tracks emotional shifts. Color-coded speaker sentiment tracking: 🔵 Positive ⚫ Neutral 🔴 Negative Time-based sentiment tracking – Shows sentiment fluctuations across the conversation. Topic & Intent Recognition** Automated Topic Extraction – Identifies key themes discussed. Hierarchical Topic Organization – Groups related topics for structured insights. Intent Classification – Recognizes whether statements reflect: Questioning Commands Decision-making Information seeking Confidence Scoring – Displays confidence levels for topic and intent detection. Speaker Contribution Metrics** Talk-time percentages per speaker. Interruption detection – Highlights moments where speakers cut each other off. Word Frequency Analysis – Identifies commonly used terms and phrases. 🔹 Seamless Workflow Automation API Integration* – Connects with transcription engines like *OpenAI Whisper, AWS Transcribe, and Deepgram**. Webhook Support** – Automates workflows when new transcripts are available. Batch Processing* – Handles *multiple transcript analyses** simultaneously. Export & Reporting** – Sends insights to Google Sheets, Notion, Airtable, or Slack. How It Works Upload an audio file or select a pre-existing transcript. Transcript Processing: Speech-to-text conversion (if required). AI-driven sentiment, topic, and intent analysis. Visualizations & Insights: Review sentiment charts, speaker contributions, and key topics. Extract action items and critical takeaways. Download results or trigger automated follow-ups. Use Cases ✅ Customer Support Review – Detect customer concerns and analyze sentiment trends. ✅ Podcast & Video Content Analysis – Summarize episodes and extract key themes. ✅ Sales Call Insights – Identify common objections and customer pain points. ✅ Market Research – Extract trends from focus groups and discussions. ✅ Legal Compliance Monitoring – Track policy violations in recorded conversations. Setup Instructions Import the Transcript Evalu8r workflow into your n8n instance. Connect DeepGram Speech to Text API. Configure workflow parameters (sentiment thresholds, topic extraction settings). Run the workflow to analyze and visualize transcript data. Review insights and export as needed. Why Use Transcript Evalu8r? 🚀 Automate tedious transcript analysis 💡 Unlock hidden insights in conversations 📊 Visualize conversations with interactive sentiment tracking ⚡ Boost efficiency with AI-powered automation 🔗 Seamlessly integrate with your existing workflows Requirements n8n instance** (cloud or self-hosted) API key for DeegGram API

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: Tech Audit & Governance Leaders:** Those seeking to build a tech landscape AI platform portal. Tech Leaders & Architects:** Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. Product Managers:** Professionals looking to align product innovation with the company's current tech trends. IT & Engineering Teams:** Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. Digital Transformation Experts:** Innovators aiming to leverage AI for actionable insights and strategic recommendations. Data Analysts & Scientists:** Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. Developers:** Those interested in integrating RAG chatbot functionality with conversation storage. 1. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: AI Agent Router:** Analyzes and routes user queries to the most suitable processing agent. SQL Agent:** Executes precise SQL queries on structured data. RAG Agent:** Leverages semantic, vector-based search for in-depth insights. Output Guardrail Agent:** Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. 2. Features Data Ingestion A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. Interactive AI Chat Chat Integration:** An AI-powered chat interface allows users to ask questions about the tech radar. Customizable AI Agents:** AI Agent Router: Determines the query type and routes it to the appropriate agent. SQL Agent: Processes queries using SQL on structured data. RAG Agent: Performs vector-based searches on document-like data. Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples Tell me, is TechnologyABC adopted or on hold, and why? List all the tools that are considered part of the strategic direction for company3 but are not adopted. Project Links & Additional Details GitHub Repository (Frontend Interface Source Code):** github.com/dragonjump/techconstellation Try It:** https://scaler.my

AI Story Generator with GPT-4o and Google Drive Integration Automatically generate complete stories with GPT-4o and seamlessly save them to Google Drive. Who is this for? Creative writers and authors Marketing and sales professionals Educators and content creators Fan fiction enthusiasts Anyone interested in automating storytelling with AI What problem is this workflow solving? Manually creating engaging, structured narratives can be time-consuming. Writers and content creators often struggle to maintain consistency, depth, and engaging storytelling structure. This workflow solves these challenges by automating story creation using advanced AI (GPT-4o) and proven storytelling techniques. What this workflow does This n8n automation generates comprehensive stories through an iterative AI-driven process: Step 1: Provide Your Story Idea Users input a clear description and specify their desired story format (short story, fan fiction, sales email, etc.). Step 2: AI-Driven Analysis GPT-4o analyzes the provided idea, categorizes the story, selects relevant storytelling frameworks inspired by PipDeck Storyteller Tactics, and determines narrative tone and direction. Step 3: Story and Character FoundationEstablishes core themes, emotional hooks, and detailed character backgrounds. Step 4: Initial Story Development Creates a structured plot outline including engaging elements such as hooks, twists, and resolutions. Step 5: Iterative Enhancement Refines the story through multiple automated prompts, improving narrative depth, character development, dialogue, and realism. Step 6: Editorial Feedback Generates automated critiques highlighting clichés, weak dialogues, and areas for improvement. Step 7: Final Polished Version Incorporates editorial feedback to produce a complete, polished, ready-to-use narrative. Step 8: Instant Google Drive OrganizationAutomatically saves the final story directly to your specified Google Drive folder for easy access and management. Setup Instructions Prerequisites: n8n account (cloud or self-hosted) GPT-4o API access via OpenAI Google Drive account Configure OpenAI Node: Add your GPT-4o API key in the OpenAI node settings. Configure Google Drive Node: Connect your Google Drive account by authenticating with n8n. Specify the folder where generated stories should be saved. Test Workflow: Run the workflow with a simple story prompt to ensure proper setup. How to Customize this Workflow Adjust Prompt Details: Modify AI prompt instructions to suit your specific story style and audience. Expand or Narrow Iterations: Change the number of iterations to balance between speed and story complexity. Customize Feedback Level: Adjust the level of editorial feedback provided. Dependencies and Requirements GPT-4o API from OpenAI Google Drive integration enabled in n8n Get Started Download and deploy this template today to streamline your storytelling process and produce consistently engaging, high-quality content effortlessly.

Summarize YouTube Videos & Chat About Content with GPT-4o-mini via Telegram Description This n8n workflow automates the process of summarizing YouTube video transcripts and enables users to interact with the content through AI-powered question answering via Telegram. It leverages the GPT-4o-mini model to generate summaries and provide insights based on the video’s transcript. How It Works Input: The workflow starts by receiving a YouTube video URL. This can be submitted through: A Telegram chat message. A webhook (e.g., triggered by a shortcut on Apple devices). Transcript Extraction: The URL is processed to extract the video transcript using the custom youtubeTranscripter community node (available here). The transcript is concatenated into a single text and stored in a Google Docs document. Summarization: The GPT-4o-mini AI model analyzes the transcript and generates a structured summary, including: A general overview. Key moments. Instructions (if applicable). The summary is then sent back to the user via Telegram. Interactive Q&A: Users can ask questions about the video content via Telegram. The AI retrieves the stored transcript from Google Docs and provides accurate, context-based answers, which are sent back through Telegram. Setup Instructions To configure this workflow, follow these steps: Import the Workflow: Download the provided JSON template and import it into your n8n instance. Install the Community Node: Install the youtubeTranscripter community node via npm: npm install n8n-nodes-youtube-transcription-kasha Important: This node requires a self-hosted n8n instance due to its external dependencies. Configure Nodes: Webhook: Set up the webhook to receive YouTube URLs. Alternatively, configure the Telegram node if using Telegram as the input method. Google Docs: Provide valid credentials to enable writing the transcript to a Google Docs document. AI Model: Set up the GPT-4o-mini model for summarization and Q&A functionality. Test the Workflow: Send a YouTube URL via your chosen input method (Telegram or webhook) and confirm that the summary is generated and delivered correctly. Customization Language**: Adjust the AI prompts to generate summaries and answers in any desired language. Output Format**: Modify the summary structure by editing the prompt in the summarization node. Input Methods**: Replace the Telegram node with another messaging or input node to adapt the workflow to different platforms. Who Can Benefit? This template is perfect for: Content Creators**: Quickly summarize video content for repurposing or review. Students and Researchers**: Extract key insights from educational or informational videos efficiently. General Users**: Interact with video content via AI without needing to watch the full video. Problem Solved This workflow simplifies video content consumption by: Automating the extraction and summarization of key points. Enabling interactive Q&A to address specific questions without rewatching the video. Additional Notes Disclaimer**: The youtubeTranscripter community node is required and only works on self-hosted n8n instances due to its reliance on external services. Apple Users**: Enhance your experience with a custom shortcut to share YouTube videos directly to the workflow. Download the shortcut here.

Get real-time cryptocurrency prices directly in Telegram! This workflow integrates CoinMarketCap API with Telegram, allowing users to request live crypto prices simply by sending a message to the bot. Ideal for crypto traders, analysts, and enthusiasts who need quick and easy access to market data. How It Works A Telegram bot listens for user input (e.g., "BTC" for Bitcoin). The workflow sends a request to the CoinMarketCap API to fetch the latest price. The response is processed using an AI-powered language model (GPT-4o-mini) for structured messaging. The workflow logs session data using a memory buffer for better response tracking. The latest price is sent back to the user via Telegram. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create a bot and obtain an API token. Get a CoinMarketCap API Key Sign up at CoinMarketCap and retrieve your API key. Configure API Credentials in n8n Add the CoinMarketCap API key under HTTP Header Auth. Add your Telegram bot token under Telegram API credentials. Deploy and Test Send a message (e.g., "BTC") to your Telegram bot and receive live price updates instantly! Automate your crypto price tracking today with this powerful Telegram bot!

Leverage the latest AI technology to analyze financial charts and make informed trading decisions with our Technical Analysis AI Agent. This powerful workflow combines Claude Sonnet 3.7 vision capabilities with Perplexity deep reasoning and up-to-date internet information to deliver comprehensive market analysis. Key Capabilities: Visual Chart Analysis** - AI vision technology examines technical charts to identify key price points, volume patterns, and trend indicators Fundamental Research Integration** - Combines technical analysis with real-time fundamental data using DeepSeek R1 reasoning Fully Cited Reports** - Fundamental analysis backed by verifiable sources for confident decision-making Automated Email Delivery** - Receive complete analysis reports directly to your inbox How It Works: This workflow orchestrates multiple AI components to analyze financial instruments: The Technical Analysis Leader coordinates the entire analysis process Chart analysis tool identifies the appropriate exchange and downloads Trading View charts AI Vision examines the chart for technical indicators including RSI, volume patterns, and support/resistance levels Perplexity tool conducts fundamental research using DeepSeek R1 reasoning capabilities All data is synthesized into a comprehensive report with trading recommendations Results can be automatically emailed for reference Setup Instructions: Quick start video included in the template. Get API key from OpenRouter.ai to access the Sonnet 3.7 model Get API key from chart-img.com to access tradingview charts Connect the Gmail node for email delivery functionality IMPORTANT DISCLAIMER: This tool provides technical analysis for informational purposes only and should not be construed as investment advice. This AI-powered technical analysis tool is designed to assist with market analysis but should not be used as the sole basis for any investment decision

Stay ahead with personalized AI news delivered straight to your Telegram! This powerful n8n workflow automates your daily news consumption, leveraging AI to bring you the most relevant trends in AI, (or cryptocurrency, the stock market) and more – all from two free news API sources (GNewsAPI and NewsAPI). What's Included: The n8n Workflow Video Guidance for Telegram Integration: A clear and concise video tutorial demonstrating how to set up the Telegram integration within your n8n instance. Who is this for? This template is ideal for: Individuals wanting to stay updated on the latest AI trends and advancements. Tech enthusiasts eager to track the latest AI breakthroughs and applications. Anyone seeking a free, automated, and highly customizable news briefing delivered directly to their Telegram. Individuals who want to save time and stay informed without the hassle of manual news searching. What problem is this workflow solving? Keeping up with the constant influx of information in fast-paced sectors like AI, crypto, and the stock market can be overwhelming and time-consuming. This n8n workflow solves this problem by automatically gathering news from two free news APIs: News API and GNews API, filtering it with AI, and delivering a concise, personalized briefing directly to your Telegram, ensuring you're always in the know without the manual effort. What this workflow does: This workflow automates the following steps using two free news API sources (GNewsAPI and NewsAPI): Automated News Gathering**: Fetches the latest news on your chosen topics from two distinct free API sources. Intelligent AI Filtering**: Employs an AI agent to identify and extract the most pertinent news articles related to your specified interests (default: AI, but easily customizable). Concise AI Summarization:** The AI agent creates brief summaries of the key information from the top news articles. Direct Telegram Delivery:** Sends a daily digest of the summarized news directly to your Telegram account for convenient access. Effortless Customization**: Allows you to easily tailor the news topics to focus on AI, cryptocurrency, stock market updates, or any other area you need to monitor. Setup: Easy Telegram Integration (Video Guidance Included): Follow our step-by-step video tutorial to seamlessly integrate Telegram with this n8n workflow, enabling automated news delivery to your preferred chat. Free API Keys: This workflow utilizes the free tiers of two popular news APIs: GNewsAPI and NewsAPI. You will need to obtain your own free API keys for these services and input them into the respective HTTP Request nodes within n8n. Clear instructions on how to get these free keys are provided. First Run: Activate the workflow and execute it once to verify that all connections are established and functioning correctly. How to customize this workflow: Tailor Your News Topics**: Easily customize the news you receive by modifying the search queries in the "News Source: GNewsAPI" and "News Source: NewsAPI" nodes. For example, change q=AI to q=Bitcoin for crypto news or q=Tesla stock for stock market updates. Adjust the Language**: Change the lang=en parameter in the API URLs to receive news in your preferred language. Set Your Delivery Schedule**: Modify the trigger time in the "Trigger workflow at 6am everyday" node to have your personalized news briefing delivered at the time that suits you best. Expand Your Sources:** For even more comprehensive news coverage, you can explore and integrate additional free news APIs by adding more HTTP Request nodes to the workflow. Category: News Automation, AI, Cryptocurrency, Stock Market, Information, Personal Productivity, Free Resources

Instantly access real-time decentralized exchange (DEX) insights directly in Telegram! This workflow integrates the DexScreener API with GPT-4o-powered AI and Telegram, allowing users to fetch the latest blockchain token analytics, liquidity pools, and trending tokens effortlessly. Ideal for crypto traders, DeFi analysts, and investors who need actionable market data at their fingertips. How It Works A Telegram bot listens for user queries about tokens or trading pairs. The workflow interacts with the DexScreener API (no API key required) to fetch real-time data, including: Token fundamentals (profiles, images, descriptions, and links) Trending and boosted tokens (hyped projects, potential market movers) Trading pair analytics (liquidity, price action, volumes, volatility) Order and payment activity (transaction insights, investor movements) Liquidity pool depth (market stability, capital flows) Multi-chain pair comparisons (performance tracking across networks) An AI-powered language model (GPT-4o-mini) enhances responses for better insights. The workflow logs session data to improve user interaction tracking. The requested DEX insights are sent back via Telegram in an easy-to-read format. What You Can Do with This Agent This AI-driven Telegram bot enables you to: ✅ Track trending and boosted tokens before they gain mainstream traction. ✅ Monitor real-time liquidity pools to assess token stability. ✅ Analyze active trading pairs across different blockchains. ✅ Identify transaction trends by checking paid orders for tokens. ✅ Compare market activity with detailed trading pair analysis. ✅ Receive instant insights with AI-enhanced responses for deeper understanding. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create a bot and obtain an API token. Configure Telegram API Credentials in n8n Add your Telegram bot token under Telegram API credentials. Deploy and Test Send a query (e.g., "SOL/USDC") to your Telegram bot and receive real-time insights instantly! 🚀 Unlock powerful, real-time DEX insights directly in Telegram—no API key required!

Your AI secretary that self-learning every day and remembers everything you said (text, audio, image). Imagine having a personal AI secretary accessible right from your Telegram, ready to assist you with information and remember everything you discuss. This n8n workflow transforms Telegram into your intelligent assistant, capable of understanding text, audio, and images, and continuously learning from your interactions. It integrates RAG's offline data ingestion and online querying functionalities, letting you save inspiration and key information permanently in real-time, and giving you an AI assistant that remembers all your dialogues and information. It builds and queries a powerful vector database in real-time, ensuring relevant and accurate responses. Video guidance on how to set up Telegram integration is also included. Who is this for? This template is ideal for: Individuals seeking a personal AI assistant for quick information retrieval and note-taking. Professionals who need to keep track of important conversations and insights. Anyone interested in leveraging the power of Retrieval-Augmented Generation (RAG) and vector databases for personal knowledge management. Users who want a self-learning AI that improves over time based on their interactions. What problem is this workflow solving? This workflow integrates RAG's offline data ingestion and online querying functionalities, letting you save inspiration and key information permanently in real-time, and giving you an AI assistant that remembers all your dialogues and information. This workflow addresses the challenge of information overload and the need for an easily accessible, personalized knowledge base. It eliminates the need to manually organize notes and search through past conversations. By automatically storing and retrieving information from a vector database, this workflow makes it effortless to access the knowledge you need, when you need it. It also provides a way to retain information from various media types like voice notes and images. What this workflow does: This workflow automates the following steps: Instant Information Capture:** Receives text messages, audio notes (transcribed), and images (with content analysis) directly from your Telegram. Intelligent Question Answering:** When you ask a question, the AI searches its knowledge base (Pinecone vector store) for relevant information and provides a comprehensive answer. It even considers your recent conversations for context. Automatic Knowledge Storage:** When you make a statement or provide information, the AI extracts key details and saves them in a Google Docs "memory palace." Daily Self-Learning:** Every day, the workflow automatically takes all the information stored in the Google Docs, converts it into a vector representation, and adds it to its knowledge base (Pinecone vector store). This ensures the AI continuously learns and remembers everything you've shared. Image Understanding:** Extracts text and information from images you send. Audio Transcription:** Automatically transcribes your voice notes into text for processing and storage. Short-Term Memory:** Remembers recent interactions within a session for more context-aware conversations. Setup: To get started, you'll need to connect the following services to your n8n instance: Telegram: Connect your Telegram bot API credentials. A video guidance is included for telegram integration setup. OpenAI: Provide your OpenAI API key for audio transcription and image analysis. Pinecone: Set up a Pinecone account and provide your API key and environment. Create a namespace in Pinecone. Google Docs: Connect your Google account with access to Google Docs. You'll need to create a Google Doc that will serve as the daily "memory palace" and provide its ID in the workflow. How to customize this workflow: Adjust the AI Agent's Personality:** Modify the system prompt in the "AI Agent" node to tailor the AI's tone and behavior. Expand Knowledge Sources:** Integrate other data sources into the daily learning process, such as emails or other documents, by adding more nodes to the scheduled trigger workflow. Add More Tools for the AI Agent:** Integrate additional tools into the AI Agent, such as web search or other APIs, to further enhance its capabilities. Modify the Daily Schedule:** Adjust the schedule trigger to run at a different time or interval.

MCP AI Chatbot using Brave Search Disclaimer: This workflow only works with local installations of n8n because it uses a community MCP node Who is this for? This workflow is ideal for developers, automation enthusiasts, and businesses looking to integrate AI-powered chat capabilities into their workflows. It's particularly useful for those leveraging Brave Search and MCP tools to enhance user interactions and streamline data retrieval. What problem is this workflow solving? This workflow addresses the challenge of creating an intelligent chatbot that can process user queries, execute searches using Brave Search, and provide responses enriched by AI. It simplifies the integration of multiple tools into a cohesive system, saving time and effort for users who need a robust conversational AI solution. What this workflow does Listens for incoming chat messages using the Chat Trigger node. Processes user input with an AI Agent powered by GPT-4o. Retrieves relevant tools using the MCP Get Brave Tools node. Executes specific search queries via the MCP Execute Brave Search node. Maintains short-term memory of conversations with the Simple Memory node. Setup Prerequisites: Access to an n8n instance (self-hosted). API credentials for OpenAI and MCP Client Tools. Brave Search API key. Steps: Import the workflow JSON into your n8n instance. Configure the API credentials for OpenAI and MCP Client Tools in their respective nodes. Set up your Brave Search API key in the MCP nodes. https://brave.com/search/api/ Testing: Use the built-in chat interface to send test messages. Verify that the chatbot processes queries and returns results as expected. How to customize this workflow to your needs Modify the AI Agent's prompt settings to tailor responses to your specific use case. Adjust the memory buffer in the Simple Memory node to retain more or less conversational context. Replace or add additional tools in the MCP nodes to expand functionality.

This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using Google Gemini/OpenRouter) to transform static support documents into an interactive, searchable knowledge base. With two interlinked workflows—one for processing support documents and one for handling chat queries—employees receive fast, context-aware answers directly from your support documentation. Overview Document Ingestion Workflow Google Drive Trigger:** Monitors a specified folder for new file uploads (e.g., updated support documents). File Download & Extraction:** Automatically downloads new files and extracts text content. Data Cleaning & Text Splitting:** Utilizes a Code node to remove line breaks, trim extra spaces, and strip special characters, while a text splitter segments the content into manageable chunks. Embedding & Storage:** Generates text embeddings using Google Gemini and stores them in a Pinecone vector store for rapid similarity search. Chat Query Workflow Chat Trigger:** Initiates when an employee sends a support query. Vector Search & Context Retrieval:** Retrieves the top relevant document segments from Pinecone based on similarity scores. Prompt Construction:** A Code node combines the retrieved document snippets with the user’s query into a detailed prompt. AI Agent Response:** The constructed prompt is sent to an AI agent (using OpenRouter Chat Model) to generate a clear, step-by-step solution. Key Benefits & Use Case Imagine a large organization where every IT support document—from troubleshooting guides to system configurations—is stored in a single Google Drive folder. When an employee encounters an issue (e.g., “How do I reset my VPN credentials?”), they simply type the query into a chat interface. Instantly, the workflow retrieves the most relevant context from the ingested documents and provides a detailed, actionable answer. This process reduces resolution times, enhances support consistency, and significantly lightens the load on IT staff. Prerequisites A valid Google Drive account with access to the designated folder. A Pinecone account for storing and retrieving text embeddings. Google Gemini* (or *OpenRouter**) credentials to power the Chat AI agent. An operational n8n instance configured with the necessary nodes and credentials. Workflow Details 1 Document Ingestion Workflow Google Drive Trigger Node:** Listens for file creation events in the specified folder. Google Drive Download Node:** Downloads the newly added file. Extract from File Node:** Extracts text content from the downloaded file. Code Node (Data Cleaning):** Cleans the extracted text by removing line breaks, trimming spaces, and eliminating special characters. Recursive Text Splitter Node:** Segments the cleaned text into manageable chunks. Pinecone Vector Store Node:** Generates embeddings (via Google Gemini) and uploads the chunks to Pinecone. 2 Chat Query Workflow Chat Trigger Node:** Receives incoming user queries. Pinecone Vector Store Node (Query):** Searches for relevant document chunks based on the query. Code Node (Context Builder):** Sorts the retrieved documents by relevance and constructs a prompt merging the context with the query. AI Agent Node:** Sends the prompt to the Chat AI agent, which returns a detailed answer. How to Use Import the Template: Import the template into your n8n instance. Configure the Google Drive Trigger: Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. Set Up Pinecone Nodes: Enter your Pinecone index details and credentials. Configure the Chat AI Agent: Provide your Google Gemini (or OpenRouter) API credentials. Test the Workflows: Validate the document ingestion workflow by uploading a sample support document. Validate the chat query workflow by sending a test query and verifying the returned support information. Additional Notes Ensure all credentials (Google Drive, Pinecone, and Chat AI) are correctly set up and tested before deploying the workflows in production. The template is fully customizable. Adjust the text cleaning, splitting parameters, or the number of document chunks retrieved based on your support documentation's size and structure. This template not only enhances IT support efficiency but also offers a scalable solution for managing and leveraging growing volumes of support content.

Context Hey! I'm Samir, a Supply Chain Data Scientist from Paris who spent six years in China studying and working while struggling to learn Mandarin. I know the challenges of mastering a complex language like Chinese and my greatest support was flash cards. Therefore, I designed this workflow to support fellow Mandarin learners by automating flashcard creation using n8n, so they can focus more on learning and less on manual data entry. Who is this template for? This workflow template is designed for language learners and educators who want to automate the creation of flashcards for Mandarin (or any other language) using Google Translate API, an AI agent for phonetic transcription and generating an illustrative sentence and a free image retrieval API. Why? If you use the open-source application Anki, this workflow will help you automatically generate personalized study materials. How? Let us imagine you want to learn how to say the word Contract in Mandarin. The workflow will automatically Translate the word in Simplified Mandarin (Mandarin: 合同). Provide the phonetic transcription (Pinyin: Hétóng) Generate an example sentence (Example: 我们签订了一份合同.) Download an illustrative picture (For example, a picture of a contract signature) All these fields are automatically recorded in a Google Sheet, making it easy to import into Anki and generate flashcards instantly What do I need to start? This workflow can be used with the free tier plans of the services used. It does not require any advanced programming skills. Prerequisite A Google Drive Account with a folder including a Google Sheet API Credentials: Google Drive API, Google Sheets API and Google Translate API activated with OAuth2 credentials A free API key of pexels.com A google sheet with the columns Next Follow the sticky notes to set up the parameters inside each node and get ready to pump your learning skills. I have detailed the steps in a short tutorial 👇 🎥 Check My Tutorial Notes This workflow can be used for any language. In the AI Agent prompt, you just need to replace the word pinyin with phonetic transcription. You can adapt the trigger to operate the workflow in the way you want. These operations can be performed by batch or triggered by Telegram, email, or webhook. If you want to learn more about how I used Anki flash cards to learn mandarin: 🈷️ Blog Article about Anki Flash Cards This workflow has been created with N8N 1.82.1 Submitted: March 17th, 2025

Overview: Transform Your Trading with AI-Driven Technical Analysis The Stock Market Technical Analysis Bot is an advanced n8n workflow that brings professional-grade stock analysis to Telegram users. This powerful AI agent analyzes stock charts in real-time, providing detailed technical insights that would typically require expensive software or expert knowledge. By combining artificial intelligence with technical analysis indicators, this bot delivers actionable trading insights directly to your Telegram chat, making sophisticated market analysis accessible to traders of all experience levels. Key Features and Capabilities Advanced Technical Analysis Tools Japanese Candlestick Pattern Recognition**: Automatically identifies bullish engulfing, doji, hammer patterns and more MACD Indicator Analysis**: Detects bullish/bearish crossovers and divergence signals Volume Trend Analysis**: Validates price movements with volume confirmation Support & Resistance Level Identification**: Pinpoints key price levels for potential reversals Bollinger Bands & RSI Analysis**: Measures volatility and overbought/oversold conditions Real-Time TradingView Charts**: Professional-quality charts with multiple indicators AI-Powered Trading Assistant Natural Language Interface**: Interact with the bot using simple conversational commands Context-Aware Responses**: The bot remembers your previous interactions for more relevant analysis GPT-4o Integration**: Leverages advanced language models for detailed explanations Actionable Trading Insights**: Receive clear, jargon-free analysis with practical implications Technical Implementation Telegram Bot Integration**: Seamless messaging experience with instant responses n8n Workflow Automation**: No-code solution for complex trading analysis Memory System**: Maintains conversation context for personalized interactions API Connections**: Integrates with TradingView, chart-img.com, and OpenAI Setup Instructions Prerequisites n8n Instance: A running n8n installation with the following nodes: Telegram nodes LangChain nodes HTTP Request nodes Code node Required API Credentials: Telegram Bot API token (obtain from @BotFather) OpenAI API key Chart-img.com API key Installation Process Import the workflow template into your n8n instance Configure your Telegram bot credentials Set up your OpenAI API credentials Enter your chart-img.com API key in the HTTP Request node Activate the workflow to start receiving analysis requests How It Works: The Technical Analysis Process 1. User Interaction Flow When a user sends a message to the Telegram bot: The Telegram Trigger node captures the incoming message The AI Agent processes the request using natural language understanding If a stock symbol is detected, the GetChart tool is invoked 2. Chart Generation System The workflow creates professional TradingView charts with: Candlestick price data Bollinger Bands for volatility measurement Volume indicators for trade validation RSI (Relative Strength Index) for momentum analysis 3. AI Analysis Engine The GPT-4o model analyzes the chart and provides: Detailed breakdown of candlestick patterns MACD trend confirmation signals Volume analysis to validate price movements Support and resistance level identification Overall market sentiment assessment 4. Response Delivery The analysis is formatted and sent back to the user via Telegram, including: The generated chart image Comprehensive technical analysis text Actionable insights based on the indicators Use Cases and Applications For Day Traders Receive quick technical analysis before making trading decisions Validate your own analysis with AI-generated insights Track multiple stocks efficiently throughout the trading day For Swing Traders Analyze medium-term trends and potential entry/exit points Identify key support and resistance levels for stop-loss placement Get objective analysis to complement your trading strategy For Trading Communities Share consistent, AI-generated analysis among group members Create a common analytical framework for discussion Reduce the learning curve for technical analysis concepts For Financial Educators Demonstrate technical analysis concepts with real-time examples Provide students with accessible tools for practice Illustrate the application of various technical indicators Advanced Configuration Options The workflow can be customized to: Add additional technical indicators (Fibonacci retracements, Moving Averages, etc.) Modify the analysis parameters for different trading styles Integrate with other financial data sources for fundamental analysis Connect to different messaging platforms beyond Telegram Create scheduled analysis for watchlist stocks Troubleshooting Common Issues If you encounter problems: Authentication Errors: Verify all API credentials are correctly configured Telegram Connection Issues: Ensure your Telegram bot is properly set up Chart Generation Failures: Check your chart-img.com API key and quota Slow Response Times: Consider upgrading your OpenAI plan for faster processing Missing Indicators: Verify the chart configuration in the HTTP Request node About This Template This n8n workflow template demonstrates the power of combining conversational AI with technical analysis tools to create a sophisticated financial assistant. It showcases advanced workflow automation features including AI integration, external API connections, and complex data processing. By leveraging the latest advancements in AI and technical analysis, this template provides traders with professional-grade insights without requiring expensive software or deep technical expertise. Keywords: stock market analysis bot, AI trading assistant, technical analysis indicators, n8n workflow, Telegram trading bot, candlestick pattern recognition, MACD analysis, trading signals, automated stock analysis, GPT-4o financial analysis, TradingView charts, RSI indicator, volume analysis, support resistance levels, day trading bot, swing trading analysis, AI financial advisor

Overview Mistral OCR is a cutting-edge document understanding API that improves how businesses extract and process information from complex documents. With top scores in benchmarks for accuracy and comprehension capabilities, Mistral OCR handles multi-column text, charts, diagrams, and multiple languages. This workflow uses Mistral's Document understanding OCR API to automatically turns dense PDFs (such as financial reports) into either deep research reports or concise newsletters Key Features Superior Document Understanding**: Processes complex documents with high-fidelity rendering Multi-Format Support**: Handles PDFs containing text, images, charts, and diagrams Multilingual Capabilities**: Accurately processes documents in various languages Seamless API Integration**: Easy implementation through cloud-based API Customizable Research Depth**: Generate comprehensive 8-page reports or concise 1,750-word newsletters How It Works Document Upload: Submit your PDF through an n8n form interface. Output Format Selection: Choose between comprehensive deep research (3,500 words) or Concise newsletter (1,750 words) Custom Instructions: Tailor the analysis by adding specific focus areas (e.g., quantitative data, growth catalysts). AI Processing: The document undergoes multi-stage AI analysis: OCR and text extraction using Mistral AI and Content structuring and summarization using GPT models Agents: Research Leader: Plans and conducts initial research, creating a table of contents. Project Planner: Breaks down the table of contents into manageable sections. Research Assistants: Multiple agents that conduct in-depth research on assigned sections. Editor: Compiles and refines the final article, ensuring coherence and proper citations. Setup API Key Acquisition: Obtain an API key from OpenRouter.ai Get an API key from Mistral.ai n8n Configuration: In your n8n instance, navigate to the credentials section. Create new credentials for OpenRouter and Mistral, inputting the respective API keys. Form Configuration: Customize the input form fields if needed (e.g., adding company-specific options). Output Customization: Adjust the word count parameters in the Project Planner node to change output length.

Instantly Find & Fix What’s Holding Your Page Back You’ve put in the work. Your content is strong. Your design is polished. But… ❌ Your page isn’t ranking where it should. ❌ Your competitors are outranking you—even with weaker content. ❌ You have no idea what’s wrong—or how to fix it. The truth? SEO isn’t just about keywords. Your technical setup, content structure, and on-page elements must work together seamlessly. And if anything is off? Google won’t rank your page. Who Is This For? SaaS Founders & Startups – Get higher rankings & organic traffic that converts. Marketing Teams & Agencies – Audit & optimize pages in seconds. E-commerce & Content Sites – Improve rankings for product pages, blogs, and landing pages. How It Works Paste your URL Get an instant audit + recommendations list Implement changes & watch your rankings jump The workflow scrapes the url you input, gets the htlm source code of the landing page, and sends it to OpenAI AI Agent. The Agent makes a deep analysis, audit the Technical + Content SEO of the page, and provides 10 Recommendations to improve your SEO. Setup Guide You will need OpenAI Credentials with an API Key to run the workflow. The workflow is using the OpenAI-o1 model to deliver the best results. It costs between $0.20/0.30 per run. You can adjust the prompt to your wish in the AI Agent parameters. Once the audit has been completed, it will send an email (don't forget to add your email address here) Below is an example of what you can expect

Get deep insights into NFT market trends, sales data, and collection statistics—all powered by AI and OpenSea! This workflow connects GPT-4o-mini, OpenSea API, and n8n automation to provide real-time analytics on NFT collections, wallet transactions, and market trends. It is ideal for NFT traders, collectors, and investors looking to make informed decisions based on structured data. How It Works Receives user queries via Telegram, webhooks, or another connected interface. Determines the correct API tool based on the request (e.g., collection stats, wallet transactions, event tracking). Retrieves data from OpenSea API (requires API key). Processes the information using an AI-powered analytics agent. Returns structured insights in an easy-to-read format for quick decision-making. What You Can Do with This Agent 🔹 Retrieve NFT Collection Stats → Get floor price, volume, sales data, and market cap. 🔹 Track Wallet Activity → Analyze transactions for a given wallet address. 🔹 Monitor NFT Market Trends → Track historical sales, listings, bids, and transfers. 🔹 Compare Collection Performance → View side-by-side market data for different NFT projects. 🔹 Analyze NFT Transaction History → Check real-time ownership changes for any NFT. 🔹 Identify Market Shifts → Detect sudden spikes in demand, price changes, and whale movements. Example Queries You Can Use ✅ "Get stats for the Bored Ape Yacht Club collection." ✅ "Show me all NFT sales from the last 24 hours." ✅ "Fetch all NFT transfers for wallet 0x123...abc on Ethereum." ✅ "Compare the last 3 months of sales volume for Azuki and CloneX." ✅ "Track the top 10 wallets making the most NFT purchases this week." Available API Tools & Endpoints 1️⃣ Get Collection Stats → /api/v2/collections/{collection_slug}/stats (Retrieve NFT collection-wide market data) 2️⃣ Get Events → /api/v2/events (Fetch global NFT sales, transfers, listings, bids, redemptions) 3️⃣ Get Events by Account → /api/v2/events/accounts/{address} (Track transactions by wallet) 4️⃣ Get Events by Collection → /api/v2/events/collection/{collection_slug} (Get sales activity for a collection) 5️⃣ Get Events by NFT → /api/v2/events/chain/{chain}/contract/{address}/nfts/{identifier} (Retrieve historical transactions for a specific NFT) Set Up Steps Get an OpenSea API Key Sign up at OpenSea API and request an API key. Configure API Credentials in n8n Add your OpenSea API key under HTTP Header Authentication. Connect the Workflow to Telegram, Slack, or Database (Optional) Use n8n integrations to send alerts to Telegram, Slack, or save results to Google Sheets, Notion, etc. Deploy and Test Send a query (e.g., "Azuki latest sales") and receive instant NFT market insights! Stay ahead in the NFT market—get real-time analytics with OpenSea’s AI-powered analytics agent!

Instantly access NFT metadata, collections, traits, contracts, and ownership details from OpenSea! This workflow integrates GPT-4o-mini AI, OpenSea API, and n8n automation to provide structured NFT data for traders, collectors, and investors. How It Works Receives user queries via Telegram, webhooks, or another connected interface. Determines the correct API tool based on the request (e.g., user profile, NFT metadata, contract details). Retrieves data from OpenSea API (requires API key). Processes the information using an AI-powered NFT insights engine. Returns structured insights in an easy-to-read format for quick decision-making. What You Can Do with This Agent 🔹 Retrieve OpenSea User Profiles → Get user bio, links, and profile info. 🔹 Fetch NFT Collection Details → Get collection metadata, traits, fees, and contract info. 🔹 Analyze NFT Metadata → Retrieve ownership, rarity, and trait-based pricing. 🔹 Monitor NFTs Owned by a Wallet → Track all NFTs under a specific account. 🔹 Retrieve Smart Contract Data → Get blockchain contract details for an NFT collection. 🔹 Identify Valuable Traits → Fetch NFT trait insights and rarity scores. Example Queries You Can Use ✅ "Get OpenSea profile for 0xA5f49655E6814d9262fb656d92f17D7874d5Ac7E." ✅ "Retrieve details for the 'Azuki' NFT collection." ✅ "Fetch metadata for NFT #5678 from 'Bored Ape Yacht Club'." ✅ "Show all NFTs owned by 0x123... on Ethereum." ✅ "Get contract details for NFT collection 'CloneX'." Available API Tools & Endpoints 1️⃣ Get OpenSea Account Profile → /api/v2/accounts/{address_or_username} (Retrieve user bio, links, and image) 2️⃣ Get NFT Collection Details → /api/v2/collections/{collection_slug} (Get collection-wide metadata) 3️⃣ Get NFT Metadata → /api/v2/chain/{chain}/contract/{address}/nfts/{identifier} (Retrieve individual NFT details) 4️⃣ Get NFTs Owned by Account → /api/v2/chain/{chain}/account/{address}/nfts (List all NFTs owned by a wallet) 5️⃣ Get NFTs by Collection → /api/v2/collection/{collection_slug}/nfts (Retrieve all NFTs from a specific collection) 6️⃣ Get NFTs by Contract → /api/v2/chain/{chain}/contract/{address}/nfts (Retrieve all NFTs under a contract) 7️⃣ Get Payment Token Details → /api/v2/chain/{chain}/payment_token/{address} (Fetch info on payment tokens used in NFT transactions) 8️⃣ Get NFT Traits → /api/v2/traits/{collection_slug} (Retrieve collection-wide trait data) Set Up Steps Get an OpenSea API Key Sign up at OpenSea API and request an API key. Configure API Credentials in n8n Add your OpenSea API key under HTTP Header Authentication. Connect the Workflow to Telegram, Slack, or Database (Optional) Use n8n integrations to send alerts to Telegram, Slack, or save results to Google Sheets, Notion, etc. Deploy and Test Send a query (e.g., "Azuki latest sales") and receive instant NFT market insights! Unlock powerful NFT analytics with AI-powered OpenSea insights—start now!

Track NFT listings, offers, orders, and trait-based pricing in real time! This workflow integrates OpenSea API, AI-powered analytics (GPT-4o-mini), and n8n automation to provide instant insights into NFT trading activity. Ideal for NFT traders, collectors, and investors looking to monitor the market and identify profitable opportunities. How It Works A user submits a query about NFT listings, offers, or order history. The OpenSea Marketplace Agent determines the correct API tool: Retrieve active NFT listings for a collection. Fetch valid offers for individual NFTs or entire collections. Identify the cheapest NFT listings by collection or token ID. Track the highest offer made for a single NFT. Access detailed order history for a transaction. The OpenSea API (requires API key) is queried to fetch real-time data. The AI engine processes and structures the response, making it easy to interpret. The NFT marketplace insights are delivered via Telegram, Slack, or stored in a database. What You Can Do with This Agent 🔹 Find the Best NFT Listings → Retrieve the cheapest available listings in any collection. 🔹 Track Offers on NFTs → See all active offers, including highest bids. 🔹 Analyze Collection-Wide Market Data → Compare listings, offers, and sales activity. 🔹 Retrieve Order Details → Search by order hash to check buyer, seller, and transaction status. 🔹 Fetch NFT Trait-Based Offers → Identify rare traits that receive premium bids. 🔹 Monitor Multi-Chain Listings → Works across Ethereum, Polygon (Matic), Arbitrum, Optimism, and more. Example Queries You Can Use ✅ "Show me the 10 cheapest listings for Bored Ape Yacht Club." ✅ "Find the highest bid for CryptoPunk #1234." ✅ "Track all open offers for Azuki NFTs." ✅ "Retrieve details for this OpenSea order: 0x123abc... on Ethereum." ✅ "List all NFTs for sale in the 'CloneX' collection." Available API Tools & Endpoints 1️⃣ Get All Listings by Collection → /api/v2/listings/collection/{collection_slug}/all (Fetches active listings for a collection) 2️⃣ Get All Offers by Collection → /api/v2/offers/collection/{collection_slug}/all (Retrieves all offers for a collection) 3️⃣ Get Best Listing by NFT → /api/v2/listings/collection/{collection_slug}/nfts/{identifier}/best (Finds the lowest-priced NFT listing) 4️⃣ Get Best Listings by Collection → /api/v2/listings/collection/{collection_slug}/best (Fetches the cheapest listings per collection) 5️⃣ Get Best Offer by NFT → /api/v2/offers/collection/{collection_slug}/nfts/{identifier}/best (Retrieves the highest offer for an NFT) 6️⃣ Get Collection Offers → /api/v2/offers/collection/{collection_slug} (Shows collection-wide offers) 7️⃣ Get Item Offers → /api/v2/orders/{chain}/{protocol}/offers (Fetches active item-specific offers) 8️⃣ Get Listings by Chain & Protocol → /api/v2/orders/{chain}/{protocol}/listings (Retrieves active listings across blockchains) 9️⃣ Get Order Details by Hash → /api/v2/orders/chain/{chain}/protocol/{protocol_address}/{order_hash} (Checks order status using an order hash) 🔟 Get Trait-Based Offers → /api/v2/offers/collection/{collection_slug}/traits (Fetches offers for specific NFT traits) Set Up Steps Get an OpenSea API Key Sign up at OpenSea API and request an API key. Configure API Credentials in n8n Add your OpenSea API key under HTTP Header Authentication. Connect the Workflow to Telegram, Slack, or Database (Optional) Use n8n integrations to send alerts to Telegram, Slack, or save results to Google Sheets, Notion, etc. Deploy and Test Send a query (e.g., "Get the best listing for BAYC #5678") and receive instant insights! Stay ahead of the NFT market—gain powerful insights with AI-powered OpenSea analytics!

This n8n workflow automates email management by classifying incoming messages, drafting replies, and sending alerts—all powered by AI. Features 🚀 AI-Powered Email Categorization Classifies emails into Spam, Important, Promotion, Notification, Personal, Call Request, Needs Reply. Uses GPT-4o to determine whether an email requires a response. ✉️ Automated Smart Replies Generates context-aware responses using AI. Supports email threading for seamless conversations. 🔔 Real-Time Notifications Telegram Alerts** for important emails. Gmail Drafts** auto-generated for quick replies. 🛠️ Google Calendar Integration Schedules follow-ups based on email content. Setup Instructions Connect Gmail, OpenAI, Telegram, and Google Calendar. Set up classification categories and notification preferences. Customize AI response styles if needed. Run the workflow—watch it organize your inbox effortlessly. Who Should Use This? Busy Professionals**: Focus on high-priority emails. Customer Support**: Manage inquiries with quick replies. Sales Teams**: Respond to leads instantly. Keep your inbox under control with this automation! 🚀