AI agent to chat with Airtable and analyze data

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. 1. Dynamic Data Retrieval: The agent uses user prompts to dynamically query the dataset. 2. Memory Management: It retains context during conversations, allowing users to engage in a more natural dialogue. 3. Search and Filter Capabilities: Users can perform tailored searches with specific parameters or filters to refine their results. Set up steps 1. Separate workflows: - Create additional workflow and move there Workflow 2. 2. Replace credentials: - Replace connections and credentials in all nodes. 3. Start chat: - Ask questions and don't forget to mention required base name.