n8n Trustpilot Themes → GitHub Issues with Bright Data & OpenAI

Turn Bright Data Trustpilot complaints into new GitHub issues—automatically

This n8n workflow crawls recent low-star Trustpilot reviews using Bright Data, uses OpenAI to group complaints into actionable themes, then checks your GitHub issues and files new ones when something new appears—plus a Slack digest of what changed.

What this workflow does

• Runs weekly (Mondays) or on manual trigger, then loads your Trustpilot URL, GitHub repo details, and configured filtering thresholds.
• Reads previously filed complaint themes from an n8n Data Table to avoid duplicates.
• Triggers a Bright Data dataset crawl for the Trustpilot review page and polls until the snapshot is ready.
• Downloads the snapshot, keeps only 1–2 star reviews within your time window, and builds a short text brief of the complaints.
• If there are enough complaints, sends the brief to OpenAI to extract up to six complaint themes, including descriptions, severity, and supporting review IDs.
• Fetches open GitHub issues and matches each theme against issue titles/bodies and your stored theme history using shared-word overlap.
• Creates a new GitHub issue for each unmatched theme, then upserts the theme record into the Data Table.
• Posts a Slack digest summarizing new themes and existing matches. If too few complaints exist or the workflow fails, it posts a message explaining why nothing was filed.

Use cases

• For SaaS operators: convert recurring negative feedback from Trustpilot into prioritized GitHub issue tracking.
• For support/engineering teams: continuously detect new complaint themes and route them to the right backlog items.
• For automation engineers: maintain an always-updated “customer complaints → engineering tickets” pipeline using n8n.

Technical details

• Integrations: Bright Data (HTTP Header Auth), OpenAI, GitHub, Slack, n8n Data Table.
• Workflow nodes/types referenced: if, set, code, wait, merge, slack.