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Complete Airtable database management with AI agents and Redis storage

DenisDenis
205 views
2/3/2026
AIVision AIWebhookImage AnalysisGoogle Gemini
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What this workflow does

Complete Airtable database management system using MCP (Model Context Protocol) for AI agents. Create bases, tables with complex field types, manage records, and maintain state with Redis storage.

Setup steps

  1. Add your Airtable Personal Access Token to credentials
  2. Configure Redis connection for ID storage
  3. Get your workspace ID from Airtable (starts with wsp...)
  4. Connect to MCP Server Trigger
  5. Configure your AI agent with the provided instructions

Key features

  • Create new Airtable bases and custom tables
  • Support for all field types (date, number, select, etc.)
  • Full CRUD operations on records
  • Rename tables and fields
  • Store base/workspace IDs to avoid repeated requests
  • Generic operations work with ANY Airtable structure

Included operations

  • create_base, create_custom_table, add_field
  • get_table_ids, get_existing_records
  • update_record, rename_table, rename_fields
  • delete_record
  • get/set base_id and workspace_id (Redis storage)

Notes

Check sticky notes in workflow for ID locations and field type requirements.

n8n AI Agent Chat with Redis Memory

This n8n workflow demonstrates a conversational AI agent powered by LangChain, OpenAI, and Redis for persistent chat memory. It allows you to interact with an AI agent that remembers previous conversations, making interactions more natural and context-aware.

What it does

This workflow orchestrates an AI agent to engage in dynamic conversations, retaining context across multiple interactions using Redis.

  1. Listens for Chat Messages: The workflow is triggered by an incoming chat message, acting as the user's input to the AI agent.
  2. Initializes AI Agent: An AI Agent node is set up to process the chat message.
  3. Configures OpenAI Chat Model: The agent utilizes an OpenAI Chat Model for generating responses, leveraging its advanced language understanding capabilities.
  4. Manages Chat Memory with Redis: A Redis Chat Memory node is integrated to store and retrieve past conversation turns, ensuring the AI agent maintains context throughout the dialogue.
  5. Provides MCP Client Tool: The AI agent is equipped with an MCP Client Tool, enabling it to interact with other Model Context Protocol (MCP) compatible services or agents, expanding its capabilities.
  6. Responds via MCP Server: The AI agent's responses are sent back through an MCP Server Trigger, which can then be used to deliver the message back to the user or another system.

Prerequisites/Requirements

To run this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the OpenAI Chat Model.
  • Redis Instance: A Redis server for chat memory persistence. You'll need the connection details (host, port, password if applicable).
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the Workflow:
    • Download the provided JSON file.
    • In your n8n instance, go to "Workflows" and click "New".
    • Click the three dots menu (⋮) and select "Import from JSON".
    • Paste the workflow JSON or upload the file.
  2. Configure Credentials:
    • OpenAI Chat Model: Create an OpenAI credential and provide your API key.
    • Redis Chat Memory: Create a Redis credential and configure it with your Redis server details (host, port, password).
  3. Activate the Workflow: After configuring all necessary credentials, activate the workflow by toggling the "Active" switch in the top right corner.
  4. Interact with the Chat Trigger: Send a message to the "When chat message received" trigger node. This can typically be done via an external chat integration (e.g., Slack, Discord, custom chat UI) configured to send messages to n8n's chat trigger endpoint. The AI agent will then process your message, use its memory, and respond.

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