Agents & Automations

Use case This workflow enables a Telegram bot that can: - Accept speech input in one of 55 supported languages - Automatically detect the language spoken and translate the speech to another language - Respond back with the translated speech output. This allows users to communicate across language barriers by simply speaking to the bot, which will handle the translation seamlessly. How does it work?Translation In the translation step, the workflow converts the user's speech input to text and detects the language of the input text. If it's English, it will translate to French. If it's French, it will translate to English. To change the default translation languages, you can update the prompt in the AI node. Output In the output step, we provide the translated text output back to the user, and speech output is generated in the translated language. Setup steps 1. Obtain Telegram API Token 2. Start a chat with the BotFather. 3. Enter /newbot and reply with your new bot's display name and username. 4. Copy the bot token and use it in the Telegram node credentials in n8n. 5. Update the Settings node to customize the desired languages. 6. Activate the flow. Full list of supported languages

AI-powered Telegram message analysis with multi-tool notifications (Gmail, Telegram) This workflow triggers on Telegram updates, analyzes messages with an AI Agent using MCP tools, and sends notifications via Gmail and Telegram. Who is this for? This template is for teams, businesses, or individuals using Telegram for communication who need automated, AI-driven insights and notifications. It’s ideal for customer support teams, project managers, or tech enthusiasts wanting to process Telegram messages intelligently and receive alerts via Gmail and Telegram. What problem is this workflow solving? Use case This workflow solves the challenge of manually monitoring Telegram messages by automating message analysis and notifications. For example, a support team can use it to analyze customer queries on Telegram with AI tools (OpenAI, Airbnb, Brave, FireCrawl) and get notified via Gmail and Telegram for quick responses. What this workflow does The workflow: - Triggers on a Telegram update (e.g., a new message) using the Listen for Telegram Updates node. - Processes the message with the Analyze Message with AI node, an AI Agent using MCP tools like OpenAI Chat, Airbnb search, Brave search, and FireCrawl. - Sends notifications via the Send Gmail Notification and Send Telegram Alert nodes, including AI-generated insights. SetupPrerequisites: - Telegram bot token for the trigger and notification nodes. - Gmail API credentials for sending emails. - API keys for OpenAI, Airbnb, Brave, and FireCrawl (used in the AI Agent). Steps: 1. Configure the Listen for Telegram Updates node with your Telegram bot token. 2. Set up the Analyze Message with AI node with your OpenAI API key and other tool credentials. 3. Configure the Send Gmail Notification node with your Gmail credentials. 4. Set up the Send Telegram Alert node with your Telegram bot token. 5. Test by sending a Telegram message to trigger the workflow. Setup takes ~15-30 minutes. Detailed instructions are in sticky notes within the workflow. How to customize this workflow to your needs - Add more AI tools (e.g., sentiment analysis) in the Analyze Message with AI node. - Modify the notification message in the Send Gmail Notification and Send Telegram Alert nodes to include specific AI outputs. - Add nodes for other channels like Slack or SMS after the AI Agent. Disclaimer This workflow uses Community nodes (e.g., Airbnb, Brave, FireCrawl), which are available only in self-hosted n8n instances. Ensure your n8n setup supports Community nodes before using this template.

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: - Store and recall long-term memory from MongoDB - Query vector embeddings stored in Atlas Vector Search - Use these results in your LLM chains and automation logic In this example, we present an ingestion and AI Agent flows that focus on Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites: - MongoDB Atlas project and Cluster - OpenAI Valid API Key for embeddings (can be another provider) - Gemini API Key for the LLM (can be another provider) How it works: There are 2 main flows. One is the ingesting flow: - Gets a document from a webhook and uses MongoDB Vector Atlas to embed the document title and description into the points_of_interest collection. - Embeddings are stored in a field named embedding. - Embeddings used are OpenAI's, but it can be any type of supported embedders. The second flow is an AI Agent node with Chat Memory stored in MongoDB Atlas and a Vector Search node as a tool: - Chat Message Trigger: Chatting with the AI Agent will trigger the conversation stored in the MongoDB Chat Memory node. - When data is necessary, like a location search or details, it will go to the Vector Search tool. Vector Search Tool - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider, make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] }

This n8n workflow provides a ready-to-use API endpoint for extracting structured data from images. It processes an image URL using an AI-powered OCR model and returns the extracted details in a structured JSON format. Use Cases - Document OCR – Extract details from ID cards, invoices, receipts, etc. - Text Extraction from Images – Process screenshots, scanned documents, and photos. - Automated Form Processing – Digitize and capture information from paper forms. - Business Card Data Extraction – Extract names, emails, and phone numbers from business cards. How It Works Send a GET request with an image URL and define the required extraction parameters. The image is converted to base64 for processing. The AI model (Gemini API - Flash Lite) extracts relevant text. The response returns structured JSON data containing only the requested fields. Features ✔️ No-Code API Setup – Easily integrate into any application. ✔️ Customizable Extraction – Modify the request parameters to fit your needs. ✔️ AI-Powered OCR – Uses advanced models for accurate text recognition. ✔️ Automated Processing – Ideal for document processing and digitization. Integration Works with any frontend/backend system that supports API calls. Can be used for workflow automation in CRM, ERP, and document management solutions. Supports further customization based on specific OCR requirements.

This n8n workflow was developed to evaluate and categorize incoming leads based on certain criteria. The workflow is triggered by adding a new row in a Google Sheets document. The workflow uses the OpenAI node to process the lead information. The system query contains detailed qualification rules and the response format. The user message contains the data for the individual lead. The JSON response from the OpenAI node is then processed by the Edit Fields node to extract the response. This response is merged together with the original lead data by the Merge node. Finally, the Google Sheets node updates the original lead entry in the Google Sheets document with the qualification result ("qualified" or "not qualified") in a separate column. This allows for easy tracking and sorting of the qualified leads.

This template helps you perform data mining on a website at regular intervals. First, a screenshot of the URL is taken via HTML/CSS. Then, the screenshot's text is recognized with Google Cloud Vision (OCR) and the price is automatically extracted with a MonkeyLearn machine learning model. Eventually, the result is stored in Google Sheets.

Description: Repurpose a long-form piece of content (article, podcast script, video script) into Facebook and LinkedIn posts. Optional: Have them posted for you as well. This automation uses: Google Sheets, Perplexity AI, ChatGPT.

OpenAI Assistant is a powerful tool, but at the time of writing, it doesn't automatically remember past messages from a conversation. This workflow demonstrates how to get around this by managing the chat history in n8n and passing it to the assistant when required. This makes it possible to use OpenAI Assistant for chatbot use cases. Note: To use this template, you need to be on n8n version 1.28.0 or later.

Welcome to Ozki Your Data Analyst Agent V1. The Ozki Data Analyst Agent is designed to analyze data from Google Sheets. To use it, you'll need to provide the URL of your Google Sheet file. The agent will then process the data and provide you with analysis results, including key performance indicators (KPIs). Configuration: - Configure your credentials on the OpenAI model or select the n8n free OpenAI credits. - Set up your agent memory. Use Simple Memory as default. - Set your credentials to Google Sheets. Log in with the Google Sheet tool. Instructions: Start with a "Hi" to get the instructions. Ozki needs your Google Sheet URL, which you can get from the address bar of your browser when you have the file open. Follow the conversation with Ozki for your data analysis results.

Vector Database as a Big Data Analysis Tool for AI Agents This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant - The first pipeline to upload an image dataset to Qdrant. Anomaly detection tool - Set up meta-variables for anomaly detection in Qdrant. - The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. - The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification - The first pipeline to upload an image dataset to Qdrant. - This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. KNN classification tool This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands). An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding. The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes. Majority voting is done for classes of neighbouring images. A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve. When the loop finally resolves, the identified class is returned to the calling workflow.

How It WorksForm Submission: The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message. Text Classification: The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories: - Request Quote: For quote requests. - Product info: For general product inquiries. - General problem: For issues or problems related to products. - Order: For questions about placed orders. - Other: For any messages that don’t fit the above categories. Email Routing: Based on the classification, the workflow routes the message to the appropriate department via email: - Prod. Dep.: For product-related inquiries. - Quote Dep.: For quote requests. - Gen. Dep.: For general problems. - Order Dep.: For order-related questions. - Other Dep.: For all other inquiries. Each email includes the user's name, email, message, and the classified category. Data Logging: The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including: - User’s name, email, and message. - Submission date and time. - Assigned category. - Email recipient details. AI Model Integration: The OpenAI node provides the AI model (GPT-4) used by the Text Classifier to classify the messages. The model is instructed to classify the text into one of the predefined categories without additional explanations. Set Up StepsConfigure the Form Trigger: Set up the On form submission node to collect user inputs (name, email, and message) and trigger the workflow. Set Up the Text Classifier: Configure the Text Classifier node to use the OpenAI model (GPT-4) for text classification. Define the categories and their descriptions (e.g., "Request Quote", "Product info", etc.). Set the fallback category to "Other" for unclassifiable messages. Configure Email Sending: Set up the Email Send nodes for each department (Prod. Dep., Quote Dep., Gen. Dep., Order Dep., Other Dep.). Configure the email subject, body, and reply-to address using the form data and classification results. Ensure SMTP credentials are correctly configured for sending emails. Set Up Google Sheets Integration: Configure the Google Sheets nodes to append data to the appropriate sheets for each department. Map the form data (name, email, message, date, category, and recipient) to the corresponding columns in the Google Sheets document. Test the Workflow: Submit a test form to ensure the workflow correctly classifies the message, sends the email to the right department, and logs the data in Google Sheets. Verify that the OpenAI model is classifying messages accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling contact form submissions. Key Features - Automated Classification: Uses AI to classify messages into relevant categories, reducing manual effort. - Email Routing: Sends emails to the appropriate department based on the classification. - Data Logging: Logs all form submissions and classification results in Google Sheets for tracking and analysis. - Scalability: Easily adaptable to additional categories or departments by modifying the workflow. This workflow is ideal for eCommerce businesses or customer support teams looking to automate and streamline the handling of contact form submissions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

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

This n8n template demonstrates how you can automate community moderation using human-in-the-loop functionality for Discord. The use-case is for detecting and dealing with spam messages in a predefined and consistent way. Human-in-the-loop allows for a balance between overly aggressive bots and the time and effort from the moderation team. How it works A scheduled trigger is used to scan the most recent messages in a Discord Channel. Messages are tagged via the "Remove Duplicates" node so they don't get processed again in the future. Messages are grouped by user to minimize the number of notifications sent. An AI text classifier node is then used to detect spam in each user's message. When detected, a notification is sent to a moderation channel using the Send-and-wait mode for Discord. This notification comes with an n8n form and a dropdown list of predefined actions to take in dealing with the spam messages. Once sent, the workflow waits until a response is received. Once a moderator selects an action, the workflow continues and carries out a predefined moderation action. How to use Depending on how busy your community is and subject to spammers, you may need to increase the scheduled interval. Add as many or few moderation actions as required. Remember to activate the workflow to get it started. Customizing this template It is possible to cover multiple channels. Add as many as your community needs. Not using Discord? The template can also work in Slack or other services that offer the same bot functionality.

This n8n workflow demonstrates how to automate indexing of images to build an object-based image search. By utilizing a Detr-Resnet-50 Object Classification model, we can identify objects within an image and store these associations in Elasticsearch along with a reference to the image. How it works An image is imported into the workflow via HTTP request node. The image is then sent to Cloudflare's Worker AI API where the service runs the image through the Detr-Resnet-50 object classification model. The API returns the object associations with their positions in the image, labels, and confidence score of the classification. Confidence scores of less than 0.9 are discarded for brevity. The image's URL and its associations are then indexed in an ElasticSearch server ready for searching. Requirements - A Cloudflare account with Workers AI enabled to access the object classification model. - An ElasticSearch instance to store the image URL and related associations. Extending this workflow Further enrich your indexed data with additional attributes or metrics relevant to your users. Use a vector store to provide similarity search over the images.

Sync YouTube Videos with Google Sheets This workflow is the first part of a multi-part automation system designed to perform large-scale YouTube comment sentiment analysis along with a detailed dashboard. It solves the problem of manually tracking new videos across multiple YouTube channels by automatically fetching and organizing video URLs in a Google Sheet, setting the stage for deeper analysis in Part 2. What It Does - Reads Channel IDs from Sheet3 of a connected Google Sheet. - Fetches the latest videos from each Channel ID using the YouTube Data API. - Extracts video URLs and metadata (like title and publish date). - Appends the video data to Sheet2 of the same Google Sheet — this sheet is later used by Part 2 for further processing. Part of a Multi-Step System This is Part 1 of a 2-workflow system: - Part 1 (this workflow) populates a sheet with the latest videos from a list of channels. - Part 2 reads the video URLs from Sheet2, fetches comments for each video, analyzes their sentiment using OpenAI, and stores structured results in Sheet1. Use Cases - Monitor and organize new videos from a list of YouTube channels. - Automate content pipelines for social media teams and analysts. - Build scalable datasets for comment and sentiment analysis. - Perfect for creators, agencies, or data analysts managing multiple YouTube accounts. Why Use This? Manually checking YouTube channels for new content is time-consuming and error-prone. This automation ensures your data stays current and structured — enabling consistent tracking and deeper analysis (especially when paired with Part 2). It brings speed, scale, and automation to your YouTube content operations. How to Customize 1. Modify Trigger Settings - Change the Google Sheet (Sheet 3) channel ID entry to track other channels. - Use a time-based trigger to fetch new videos regularly, ensuring your data stays up to date. 2. Adjust Output Fields - Fetch additional details from YouTube, such as view count, description, or thumbnails. - Add custom columns in Sheet 2 for organizing videos by different criteria, such as: - "Published Date" - "Video Type" - "View Count" - "Video Description" 3. Extend with Integrations - Integrate with other workflows like YouTube Comment Sentiment Analysis (Part 2) for a deeper dive into content analysis. - Use filters to fetch videos by certain tags, keywords, or publish dates. 4. Adjust Sheet Structure - Modify the structure of Sheet 2 to categorize videos based on criteria like: - Channel - Video Status (e.g., "Published," "Scheduled") - Video Type (e.g., "Tutorial," "Review") 5. Schedule Regular Fetching - Set a schedule trigger to fetch videos at regular intervals (e.g., daily or weekly), ensuring new content is automatically added to your sheet. 6. Customize Google Sheet Layout - Change the layout of Sheet 2 to better fit your needs. For example, you can add additional columns for organizing video data.

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document, especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditional OCR because: - It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. - It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. - It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow. A PDF bank statement is imported via Google Drive. For this demo, a mock bank statement includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, we have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable, which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node, which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data), which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLM's ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Customizing the workflow At the time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini, however, you can switch to other multimodal LLMs such as OpenAI GPT or Anthropic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements anytime soon? This template also works for invoices, inventory lists, contracts, legal documents, etc.

This workflow automates the process of gathering LinkedIn advice articles, extracting their content, and generating unique contributions for each article using an AI model. The contributions are then posted to a Slack channel and a NocoDB database for record-keeping. The workflow is triggered weekly to ensure new articles are continuously collected and responded to. Who is this for? This workflow is designed for professionals, marketers, and content creators looking to boost their LinkedIn presence by regularly engaging with LinkedIn advice articles. It’s especially useful for those who want to be seen as a "thought leader" or "top voice" in their niche by contributing relevant and unique advice to trending topics. What problem is this workflow solving? Manually searching for relevant LinkedIn articles, reading through them, and crafting thoughtful contributions can be time-consuming. This workflow solves that by automating the process of finding new articles, extracting key content, and generating AI-powered contributions. It helps users stay consistently active on LinkedIn, contributing value to trending discussions. What this workflow does - Triggers Weekly: The workflow is set to run every Monday at 8:00 AM. - Search Google for LinkedIn Advice Articles: Uses a predefined Google search URL to find the latest LinkedIn advice articles based on the user's area of expertise. - Extract LinkedIn Article Links: A code node extracts all LinkedIn advice article links from the search results. - Retrieve Article Content: For each article link, the workflow retrieves the HTML content and extracts the article title, topics, and existing contributions. - Generate AI-Powered Contributions: The workflow sends the extracted article content to an AI model, which generates unique, helpful advice for each topic within the article. - Post to Slack & NocoDB: The AI-generated contributions, along with the article links, are posted to a designated Slack channel and stored in a NocoDB database for future reference. Setup - Google Search URL: Update the Google search URL with the relevant LinkedIn advice query for your field (e.g., "site:linkedin.com/advice 'marketing automation'"). - Slack Integration: Connect your Slack account and specify the Slack channel where you want the contributions to be posted. - NocoDB Integration: Set up your NocoDB project to store the generated contributions along with the article titles and links. How to customize this workflow - Change Search Terms: Modify the Google search URL to focus on a different LinkedIn topic or expertise area. - Adjust Trigger Frequency: The workflow is set to run weekly, but you can adjust the frequency by changing the schedule trigger. - Enhance Contribution Quality: Customize the AI model's prompt to generate contributions that align with your brand voice or content strategy. Workflow Summary This workflow helps users maintain a consistent presence on LinkedIn by automating the discovery of new advice articles and generating unique contributions using AI. It is ideal for professionals who want to engage with LinkedIn content regularly without spending too much time manually searching and drafting responses.

Automatically update your Google Sheets with new articles using RSS feeds. Scrape content, generate OpenAI completions, and add rows effortlessly.

The workflow starts by listening for messages from Telegram users. The message is then processed, and based on its content, different actions are taken. If it's a regular chat message, the workflow generates a response using the OpenAI API and sends it back to the user. If it's a command to create an image, the workflow generates an image using the OpenAI API and sends the image to the user. If the command is unsupported, an error message is sent. Throughout the workflow, there are additional nodes for displaying notes and simulating typing actions.

Analyze exchange data, market indexes, and community sentiment from CoinMarketCap—powered by AI. This sub-agent provides access to exchange listings, token holdings, metadata, and high-level metrics like the CMC 100 Index and the Fear & Greed Index. It’s designed for use within your larger CoinMarketCap AI Analyst system or as a standalone workflow. This agent can be triggered by a supervisor or manually used with message and sessionId inputs. Supported Tools (5 Total) 🔍 Exchange Map Get CoinMarketCap IDs, names, and slugs for exchanges (used as lookup before deeper queries). 🧾 Exchange Info Metadata including launch date, social links, country, and operational status. 💰 Exchange Assets Token balances, wallet addresses, and total USD value held by a specific exchange. 📈 CoinMarketCap 100 Index Constituents and weights of the CMC 100 Index, updated live. 😱 Fear & Greed Index Market sentiment score updated daily, ranging from Extreme Fear to Extreme Greed. What You Can Do with This Agent 🔹 Map exchanges to retrieve their ID and slug 🔹 Analyze exchange holdings by token and blockchain 🔹 Pull metadata for major CEXs like Binance or Coinbase 🔹 Compare global sentiment using the Fear & Greed Index 🔹 Access index data to understand CMC’s top 100 crypto asset breakdownExample Queries You Can Use ✅ "What is the latest Fear and Greed Index reading?" ✅ "Get a list of all exchanges on CoinMarketCap." ✅ "What tokens are held by Binance?" ✅ "Retrieve metadata for Coinbase." ✅ "Show me the top assets in the CMC 100 Index."Agent Architecture AI Brain: GPT-4o-mini Memory: Window buffer memory using sessionId Tools: 5 API-connected nodes Trigger: External input via message and sessionIdSetup Instructions Get a CoinMarketCap API Key Apply here: https://coinmarketcap.com/api/ Configure n8n Credentials Use HTTP Header Auth to store your CoinMarketCap API key. Optional: Trigger from a Supervisor Connect to a parent agent using Execute Workflow with message and sessionId inputs. Test Sample Prompts “Get all exchanges”, “Fetch CMC index”, “Show Binance token holdings” Sticky Notes Included - Exchange & Community Guide – Explains agent purpose and component connections - Usage & Examples – Walkthrough for sample use cases - Error Handling & Licensing – Includes API error code reference and licensing details ✅ Final Notes This agent is part of a broader CoinMarketCap AI Analyst System. Understand exchange behavior and community sentiment—automated with AI and CoinMarketCap.

Every time a new file is uploaded to Uploadcare, Make will automatically perform a label detection for that file via Google Cloud Vision. Then, the label with the highest confidence score is stored in Google Sheets.