Hi everyone, I’m trying to wire up a workflow where n8n triggers a PhantomBuster agent, waits for it to finish, pulls the results, and then runs the data through a few processing steps (cleaning, enrichment, AI messaging, etc.).
Before I go too far with the setup, I wanted to check in and make sure I’m approaching this the right way — especially given LinkedIn’s tighter rules and PhantomBuster’s rate limits.
Here’s what I’m trying to figure out:
What’s the safest and most reliable way to trigger a Phantom from n8n?
Should I have n8n wait/poll for the Phantom to complete, or is there a better pattern?
Any best practices around delays, backoff, or queuing Phantom runs to avoid throttling?
For larger outputs (e.g., 100–300 companies), what’s the best way to handle the data flow inside n8n so it doesn’t time out?
Any compliance pitfalls or “don’ts” when automating PhantomBuster → LinkedIn workflows?
Basically, I’m hoping to learn how others are structuring these kinds of automations.
If you’ve done something similar — triggering Phantoms, collecting output, then running further logic in n8n — I’d love to hear what worked for you and what to avoid.
Happy to share my workflow JSON if that helps. Thanks!
Here’s a solid approach for your PhantomBuster → n8n workflow:
1. Use PhantomBuster’s API with n8n’s HTTP Request node to trigger agents. For waiting, implement polling with delays (try 30-60s intervals) using the Delay node and error handling for timeouts.
2. For large datasets, process in batches using the Split In Batches node and consider storing intermediate results in a database node (like PostgreSQL) to avoid timeout issues.
3. Compliance-wise: Always respect LinkedIn’s rate limits (PhantomBuster handles this), avoid aggressive scraping patterns, and implement proper data deletion workflows for GDPR compliance.
Would you like me to share a sample workflow structure with specific node configurations?
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