Advanced multi-source AI research with Bright Data, OpenAI, Redis
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 37 nodes and runs when an HTTP POST request hits its webhook URL. Along the way it connects Redis, OpenAI Chat Model, MCP Client and Slack. It also calls api.brightdata.com over HTTP.
How it runs
Trigger: When an HTTP POST request hits its webhook URL.
- Set Variables (Edit Fields (Set)): sets 5 fields: userPrompt, cellReference, outputLanguage…
- Cache Check (Redis): get
- Check Cache Hit (If): checks a condition and branches into true / false
- Return Cached Result (Respond to Webhook): sends the response back to the webhook caller
- Log Cache Hit (Data table): append
- Rate Limit Check (Code): runs custom JavaScript code
- Multi-Step Reasoning Agent (AI Agent): runs an AI agent, with model: GPT-4o (Reasoning); output parser: Reasoning Output Parser
- Split Sub-Queries (Split Out): splits a list into separate items
- Query Optimizer Agent (AI Agent): runs an AI agent, with model: GPT-4o Mini (Optimizer); output parser: Optimizer Output Parser
- Multi-Source Search Agent (AI Agent): runs an AI agent, with model: GPT-4o (Search); 1 tool (Bright Data MCP Tool); output parser: Search Output Parser
…plus 13 more steps. See the full node list below.
Services it uses
HTTP calls to: api.brightdata.com
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.
Nodes used (17)
- Webhook
n8n-nodes-base.webhook - Edit Fields (Set)
n8n-nodes-base.set - Redis
n8n-nodes-base.redis - If
n8n-nodes-base.if - Code
n8n-nodes-base.code - AI Agent
@n8n/n8n-nodes-langchain.agent - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Split Out
n8n-nodes-base.splitOut - MCP Client Tool
@n8n/n8n-nodes-langchain.mcpClientTool - HTTP Request
n8n-nodes-base.httpRequest - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - Aggregate
n8n-nodes-base.aggregate - Respond to Webhook
n8n-nodes-base.respondToWebhook - Slack
n8n-nodes-base.slack - Send Email
n8n-nodes-base.emailSend - Data table
n8n-nodes-base.dataTable
From the author
How it Works This workflow transforms natural language queries into research reports through a five-stage AI pipeline. When triggered via webhook (typically from Google Sheets using the companion google-apps-script.js (GitHub gist), it first checks Redis…