n8n Research Report Workflow: OpenAI + Wikipedia + Google Sheets

Turn any research topic into a structured report—automatically

This n8n Research Report Workflow accepts a topic via Webhook, uses OpenAI to generate targeted Wikipedia search queries, extracts and cleans matching Wikipedia content, then synthesizes a multi-section research report and saves it to Google Sheets—while notifying Slack and returning the result to the caller.

What this workflow does

• Receives a POST webhook request with a JSON body containing a topic value.
• Validates the topic and uses OpenAI (model noted as gpt-4o-mini) to generate five focused Wikipedia search queries.
• Searches Wikipedia for each query, iterates through top results, and fetches the plaintext extracts for matching pages.
• Cleans and normalizes the extracted Wikipedia content, then merges it into a single research context.
• Generates a structured report with OpenAI, covering sections such as summary, findings, facts, benefits, challenges, trends, outlook, and references.
• Parses output into structured JSON, then appends or updates a row in Google Sheets.
• Posts a completion message to Slack and returns the structured report in the webhook response.

Use cases

• SaaS operators generating quick “research briefs” for product decisions.
• Automation engineers building topic-to-report pipelines for internal teams.
• Content and analyst workflows that need consistent report structure with sources.

Technical details

• Webhook (input: topic; output: structured JSON report)
• OpenAI (query generation + report synthesis; gpt-4o-mini noted)
• Wikipedia extraction (search results + plaintext page extracts)
• Google Sheets (store Topic, Date, Summary, Result—columns must exist)
• Slack notification on completion
• n8n nodes mentioned: if, set, code, merge, slack, webhook