Integrate LangChain Call n8n Workflow Tool in your LLM apps and 422+ apps and services

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.

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 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.

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 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.

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

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.

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.

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.

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.

Purpose of workflow: The purpose of this workflow is to create an AI-powered technical analysis agent capable of analyzing financial charts, specifically for cryptocurrencies like Bitcoin or equity stocks. This agent provides users with insights into market trends, price movements, and technical indicators to assist in making informed trading decisions. How it works: The agent uses the Sonnet model from Anthropic for LLM It integrates with TradingView charts through the chart-img.com API to generate and download financial charts. The agent analyzes the chart using AI vision capabilities, examining candlestick patterns, pricing trends, and technical indicators like Relative Strength Index (RSI) and Directional Movement Index (DMI). It provides a detailed analysis of the chart, including support and resistance levels, market trends, and volume analysis. The agent generates a visual representation of the analysis, displaying candlesticks, volume, RSI, and DMI. Step by step setup: Create free API key from chart-img.com Set the exchange for the ticker (defaults to NYSE) Tutorial

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 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.

Disclaimer: This template only works on self-hosted n8n instances and needs a community node. This workflow is designed to streamline the process of creating professional follow-up emails based on Google Meet recordings. It leverages OpenAI's transcription and summarization capabilities to extract key points, action items, and next steps, then drafts an email in Gmail for easy review and sending. Also, this workflow is designed for non-English Google Meet recordings. If you record Google Meet conferences in English, you can use the native transcription feature and work directly with the transcript file, bypassing most of this workflow. Important : Custom n8n Installation with ffmpeg (Recommended): The workflow is optimized for instances of n8n with ffmpeg installed. This allows for seamless conversion of Google Meet recordings from MP4 to MP3 format, significantly reducing file size and improving processing efficiency. OAuth Configuration Required: For self-hosted n8n instances, ensure OAuth authentication is properly configured for Google Drive and Gmail. This is essential for accessing recordings and creating email drafts securely. Privacy Considerations: Before sending Google Meet recordings to OpenAI for processing, verify compliance with the data protection standards of the country or company you belong to. As this workflow is shared publicly, the creator is not responsible for ensuring compliance with regulations in your region or company. Workflow Overview: Detects when a Google Meet recording is uploaded to a designated Google Drive folder. Processes the recording via one of the following paths (choose the path based on whether you record meetings with subtitles or not): Convert MP4 to MP3: If ffmpeg is available, the recording is converted to MP3 format and processed through OpenAI for transcription. Extract Subtitles: If subtitles are included in the Google Meet recording, they are extracted directly as a text file, bypassing the need for transcription and saving costs. Summarizes the meeting and generates a professional follow-up email draft in Gmail, ready for your review and customization.

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.

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.