Query Databricks data and SQL insights via Slack with Gemini AI agent
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 11 nodes and runs on Slack events. Along the way it connects Slack, Google Gemini Chat Model, Redis Chat Memory and HTTP Request.
How it runs
Trigger: On Slack events.
- Set Databricks Config (Edit Fields (Set)): sets 2 fields: target_table, warehouse_id
- Fetch Databricks Schema (HTTP Request): calls an HTTP API (POST)
- Parse Table Schema (Code): runs custom JavaScript code
- SQL Data Analyst Agent (AI Agent): runs an AI agent, with model: Gemini Model; 1 tool (Run Primary SQL Query); memory: Redis Chat Memory
- If: checks a condition and branches into true / false
- Post to Slack Channel (Slack): uses Slack
- Post Error to Slack (Slack): uses Slack
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
- Still check Slack Trigger requires a signing secret on the Slack API credential (When Slack Message Received).
Nodes used (10)
- Slack Trigger
n8n-nodes-base.slackTrigger - Code
n8n-nodes-base.code - Slack
n8n-nodes-base.slack - HTTP Request
n8n-nodes-base.httpRequest - Edit Fields (Set)
n8n-nodes-base.set - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Redis Chat Memory
@n8n/n8n-nodes-langchain.memoryRedisChat - Http Request Tool
n8n-nodes-base.httpRequestTool - If
n8n-nodes-base.if
From the author
Automated Databricks Data Querying & SQL Insights via Slack with AI Agent & Gemini --- Node-by-Node Explanation This workflow is divided into three functional phases: Initialization, AI Processing, and Response Delivery. | Node Name | Category | What it does…