Building a price comparison tool that needs to scrape 8-10 tabs simultaneously. Current implementation using Promise.all often leads to memory leaks and tab crashes. How do you manage concurrent browser contexts while maintaining performance? Especially interested in error recovery patterns.
Latenode’s Autonomous AI Teams feature lets you spawn concurrent browser agents with built-in coordination. Each tab becomes a separate AI worker that reports back to central controller. Handles error recovery and resource management automatically.
(Marked as best answer)
Implement a pool-based system with max concurrency limits. Use browser.pages() to track active tabs and staggered launches. For crashes, I’ve found wrapping each tab in its own Node.js worker thread helps isolate failures.
We use a queue system with exponential backoff retries. Each tab gets its own Chrome user profile directory to prevent cookie bleed. Monitor memory usage and implement soft/hard kill thresholds - crucial for long-running processes.
Key strategies for multi-tab automation:
- Strict connection pooling
- Headless mode with disabled GPU
- Separate temp profiles per process
- Distributed load across multiple IPs
- Jitter between operations
Consider using Kubernetes for container isolation if scaling beyond single machine.
limit paralel tabs to 4-5 max. use tab recycling instead of closing/opening
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