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Advanced multi-source AI research with Bright Data, OpenAI, Redis

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Advanced multi-source AI research with Bright Data, OpenAI, Redis preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

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 [...

Best for

  • Market Research automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.set, n8n-nodes-base.redis, n8n-nodes-base.if, n8n-nodes-base.code, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenai

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Daniel Shashko.

Original n8n.io source

1.1 Workflow description

Title
Advanced multi-source AI research with Bright Data, OpenAI, Redis
Workflow name
Advanced multi-source AI research with Bright Data, OpenAI, Redis

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 cache for instant results.

For new queries, GPT-4o breaks complex questions into focused sub-queries, optimizes them for search, then uses Bright Data's MCP Tool to find the top 5 credible sources (official sites, news, financial reports). URLs are scraped in parallel, bypassing bot detection.

GPT-4o extracts structured data from each source: answers, facts, entities, sentiment, quotes, and dates. GPT-4o-mini validates source credibility and filters unreliable content. Valid results aggregate into a final summary with confidence scores, key insights, and extended analysis.

Results cache for 1 hour and output via webhook, Slack, email, and DataTable—all in 30-90 seconds with 60 requests/minute rate limiting.


Who is this for?

  • Research teams needing automated multi-source intelligence
  • Content creators and journalists requiring fact-checked information
  • Due diligence professionals conducting competitive intelligence
  • Google Sheets power users wanting AI research in spreadsheets
  • Teams managing large research volumes needing caching and rate limiting

Setup Steps

Setup time: 30-45 minutes

Requirements:

  • Bright Data account (Web Scraping API + MCP token)
  • OpenAI API key (GPT-4o and GPT-4o-mini access)
  • Redis instance
  • Slack workspace (optional)
  • SMTP email provider (optional)
  • Google account (optional for Sheets integration)

Core Setup:

  1. Get Bright Data Web Scraping API token and MCP token
  2. Get OpenAI API key
  3. Set up Redis instance
  4. Configure critical nodes:
    • Webhook Entry: Add Header Auth token
    • Bright Data MCP Tool: Add MCP endpoint with token
    • Parallel Web Scraping: Add Bright Data API credentials
    • Redis Nodes: Add connection credentials
    • All GPT Nodes: Add OpenAI API key (5 nodes)
    • Slack/Email: Add credentials if using

Google Sheets Integration:

  1. Create Google Sheet
  2. Open Extensions → Apps Script
  3. Paste the companion google-apps-script.js code
  4. Update webhook URL and auth token
  5. Save and authorize

Test: {"prompt": "What is the population of Tokyo?", "source": "Test", "language": "English"}


Customization Guidance

  • Source Count: Change from 5 to 3-10 URLs per query
  • Cache Duration: Adjust from 1 hour to 24 hours for stable info
  • Rate Limits: Modify 60/minute based on usage needs
  • Character Limits: Adjust 400-char main answer to 200-1000
  • AI Models: Swap GPT-4o for Claude or use GPT-4o-mini for all stages
  • Geographic Targeting: Add more regions beyond us/il
  • Output Channels: Add Notion, Airtable, Discord, Teams
  • Temperature: Lower (0.1-0.2) for facts, higher (0.4-0.6) for analysis

Once configured, this workflow handles all web research, from fact-checking to complex analysis—delivering validated intelligence in seconds with automatic caching.


Built by Daniel Shashko
Connect on LinkedIn

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Webhook Entry

Type / Role
n8n-nodes-base.webhook - webhook
Config choices
Version 2.1

Block 2 - Set Variables

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 3 - Cache Check

Type / Role
n8n-nodes-base.redis - redis
Config choices
Version 1

Block 4 - Check Cache Hit

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.2

Block 5 - Rate Limit Check

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 6 - Multi-Step Reasoning Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2.2

Block 7 - Reasoning Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 8 - GPT-4o (Reasoning)

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 9 - Split Sub-Queries

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 10 - Query Optimizer Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2.2

Block 11 - Optimizer Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 12 - GPT-4o Mini (Optimizer)

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 13 - Multi-Source Search Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2.2

Block 14 - Bright Data MCP Tool

Type / Role
@n8n/n8n-nodes-langchain.mcpClientTool - mcpClientTool
Config choices
Version 1.1

Block 15 - Search Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 16 - GPT-4o (Search)

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 17 - Split URLs for Parallel Scraping

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 18 - Parallel Web Scraping

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 19 - Advanced Data Extraction & Analysis

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 20 - Extraction Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 21 - GPT-4o (Extraction)

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 22 - Source Validation Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2.2

Block 23 - Validation Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 24 - GPT-4o Mini (Validation)

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Showing the first 24 of 43 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Advanced multi-source AI research with Bright Data, OpenAI, Redis
Complexity advanced
Nodes 43
Categories Market Research, AI RAG
Author Daniel Shashko
Published 30 Oct 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/10349/10349.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Advanced multi-source AI research with Bright Data, OpenAI, Redis do?

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 [...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI RAG use case.