Multi-Agent Flow
Combine MrScraper's AI agents to create powerful end-to-end scraping workflows for large-scale data extraction.
MrScraper's Multi-Agent Flow allows you to combine multiple AI scraper agents into a seamless workflow for extracting comprehensive data from entire websites. By chaining together the Map Agent, Listing Agent, and General Agent, you can build scalable scraping systems that handle everything from URL discovery to detailed product data extraction.
Tip
Perfect for large-scale e-commerce scraping, marketplace data collection, and comprehensive website data extraction.
Available Multi-Agent Workflows
MrScraper supports two primary multi-agent workflows depending on your starting point:
| Workflow | Starting Point | Use Case |
|---|---|---|
| Map → Listing → General | Single seed URL (homepage, domain root) | When you only have the website's main URL and want to extract all product details from the entire site |
| Listing → General | Specific listing page URL(s) | When you already know which category/listing pages to scrape and want detailed product information |
Comparison: When to Use Each Workflow
| Criteria | Map → Listing → General | Listing → General |
|---|---|---|
| Starting Point | Single seed URL only | Known listing page URLs |
| Use Case | Full website scraping | Targeted category scraping |
| Discovery | Automatic URL discovery | Manual URL input |
| Execution Time | Longer (3 steps) | Faster (2 steps) |
| Data Coverage | Complete site coverage | Specific sections only |
| Best For | New site exploration | Recurring scraping jobs |
Limitations
Warning!
MrScraper provides the API infrastructure only. You are responsible for:
- Orchestrating the workflow: Building the logic to chain agents together
- Managing the data pipeline: Handling data between agent calls
- Error handling: Implementing retry logic and failure recovery
- Rate limiting: Controlling request frequency to avoid blocks
- Data storage: Saving and organizing extracted data
- Monitoring: Tracking scraping progress and success rates
MrScraper does not provide:
- Pre-built workflow automation
- Scheduled scraping jobs
- Automatic data pipelines
- Built-in data storage solutions
Tips and Best Practices
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Follow these best practices for successful multi-agent workflows:
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Start Small, Then Scale - Test with a single URL before processing thousands, validate data quality from each agent before moving to the next step, and monitor API usage to stay within your plan limits.
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Implement Robust Error Handling - Set up retry logic with exponential backoff, log failed URLs separately for review, handle timeouts gracefully, and implement maximum retry limits to prevent infinite loops.
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Filter URLs Intelligently - Include relevant patterns like
/product/,/item/,/details/, exclude non-data pages like/account/,/login/,/cart/, and remove static assets like.jpg,.png,.css,.jsfiles. -
Batch Your Requests - Process URLs in batches of 10-50 at a time, add delays between batches to respect rate limits, save results after each batch to prevent data loss, and adjust batch size based on success rates.
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Track Progress and Resume Capability - Save progress regularly every 10-20 items, store the last processed URL, keep a list of failed URLs for retry, and implement checkpoint system to resume interrupted workflows.
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Use Appropriate Modes - Start with Cheap Mode for testing and validation, switch to Super Mode when encountering bot protection, consistent blocking issues, or for critical production workflows where failure is costly.
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Monitor and Optimize Costs - Track API calls per agent type, calculate estimated costs before running large workflows, review usage regularly, and balance cost vs. success rate based on your data value.