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

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.

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."

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

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 (no sound). 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 OpenAI for LLM Customising this workflow For performance reasons, consider processing audio and video using dedicated services. To handle videos with sound, you have 2 choices: use an LLM like Gemini which fully supports video processing (though video input is not currently supported in LLM node) or split the video into a image track and audio track and process separately. Good luck! Go beyond and create rich and engagement customer experiences by responding using images, audio and video instead of just text!

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.

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.

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 employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.

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.

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

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 👇

📈 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.

The purpose of this n8n workflow is to automate the process of identifying incoming Gmail emails that are requesting an appointment, evaluating their content, checking calendar availability, and then composing and sending a response email. Note that to use this template, you need to be on n8n version 1.19.4 or later.

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.

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!

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.

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 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 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 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.

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 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.

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) Made by Simon @ automake.io

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 [email protected]

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.

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 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 workflow offers an effective way to handle a chatbot's functionality, making use of multiple tools for information retrieval, conversation context storage, and message sending. It's a setup tailored for a Slack environment, aiming to offer an interactive, AI-driven chatbot experience. Note that to use this template, you need to be on n8n version 1.19.4 or later.

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.

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.

The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email. Note that to use this template, you need to be on n8n version 1.19.4 or later.

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 workflow is designed for dynamic and intelligent conversational capabilities. It incorporates OpenAI's GPT-4o model for natural language understanding and generation. Additional tools include SerpAPI and Wikipedia for enriched, data-driven responses. The workflow is triggered manually, and utilizes a 'Window Buffer Memory' to maintain the context of the last 20 interactions for better conversational continuity. All these components are orchestrated through n8n nodes, ensuring seamless interconnectivity. To use this template, you need to be on n8n version 1.50.0 or later.

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.

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! 🎉

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.

Overview This workflow aims at providing data visualization to a native SQL Agent. Together, they can help with fostering data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds some visualization capabilities thanks to OpenAI Structured Output and Quickchart.io. How it works The first part of the workflow is a regular SQL Agent: it connects to a Database and is able to query it and translate the response in a human format. Then, the Text Classifier is deciding if the user would benefit from a chart, supporting the SQL Agent's response. If it does, then it executes the subworkflow to dynamically generate a chart and append the chart to the response from the SQL Agent. If it doesn't, then the SQL Agent response is directly outputted. The sub-workflow calls OpenAI through the HTTP Request node to retrieve a chart definition. In the "set response" node, the chart definition is added at the end of a quickchart.io URL - the URL to the chart image. It is sent back to the AI Agent. How to use it Use an existing or create a new database. 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 considers a chart would be useful, it will generate a chart in addition to the response from the SQL Agent. Notes The full Quickchart.io specifications have not been integrated, thus there are some possible glitches (e.g., due to the size of the graph, radar graphs are not displayed properly).

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.

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 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 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 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.

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 workflow has multiple functionalities. It starts with a manual trigger, "When clicking 'Execute Workflow'", that activates two separate paths. The first path takes a preset string "Tell me a joke" and processes it through a custom Language Learning Model (LLM) chain node. This node interacts with an OpenAI node for query processing. The second path takes another preset string "What year was Einstein born?" and passes it to an "Agent" node. This agent further interacts with a Chat OpenAI node and a custom Wikipedia node to produce the required information. The workflow uses both built-in and custom nodes, and integrates with OpenAI for both paths. It's built for experimenting with language models, specifically in the context of conversational agents and information retrieval. Note that to use this template, you need to be on n8n version 1.19.4 or later.

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.

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.

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 } }

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

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

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

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.

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

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.