Block 1 - Webhook Entry
- Type / Role
- n8n-nodes-base.webhook - webhook
- Config choices
- Version 2.1
This workflow is provided as-is. Please review and test before using in production.
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 [...
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
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Daniel Shashko.
Original n8n.io sourceThis 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.
Setup time: 30-45 minutes
Requirements:
Core Setup:
Google Sheets Integration:
google-apps-script.js codeTest: {"prompt": "What is the population of Tokyo?", "source": "Test", "language": "English"}
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
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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.
Showing the first 24 of 43 workflow blocks. Download the JSON for the full node graph.
| 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 |
Use the JSON export at /data/workflows/10349/10349.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
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.
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 [...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
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.